how to make data analysis in research

• Privacy Policy

Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Data Verification – Process, Types and Examples

Significance of the Study – Examples and Writing...

Research Gap – Types, Examples and How to...

Research Methodology – Types, Examples and...

Phenomenology – Methods, Examples and Guide

Histogram – Types, Examples and Making Guide

A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

• Defining the question
• Collecting the data
• Cleaning the data
• Analyzing the data
• Sharing your results
• Embracing failure

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

• Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
• Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
• Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
• Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:  What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial .  We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

• Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
• Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
• Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
• Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
• Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
• Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

• These are the top 9 data analytics tools
• 10 great places to find free datasets for your next project
• How to build a data analytics portfolio

Data Analysis Techniques in Research – Methods, Tools & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

For Latest Tech Related Information, Join Our Official Free Telegram Group : PW Skills Telegram Group

Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

• Top Best Big Data Analytics Classes 2024

Many websites and institutions provide online remote big data analytics classes to help you learn and also earn certifications for…

• 10 Best Companies For Data Analysis Internships 2024

This article will help you provide the top 10 best companies for a Data Analysis Internship which will not only…

• Best 5 Unique Strategies to Use Artificial Intelligence Data Analytics

There are many AI tools and technologies available nowadays that can help make our artificial intelligence data analytics effective. With…

Related Articles

• 5 Challenges of Data Analytics Every Data Analyst Faces!
• BI & Analytics: What’s The Difference?
• Best Data Analyst Class Online
• The Best Data And Analytics Courses For Beginners
• AI and Data Analytics: Tools, Uses, Importance, Salary, and more!
• What is OLAP (Online Analytical Processing)?
• Data Science in Sports Analytics: Importance, Uses, Examples

Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
• Qualitative Analysis Tools
• Mixed Methods Analysis
• Geospatial Analysis
• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

• Next: Planning >>
• Last Updated: Jun 25, 2024 10:23 AM
• URL: https://guides.library.georgetown.edu/data-analysis

• AI Templates
• Get a demo Sign up for free Log in Log in

The Beginner’s Guide to Analyzing Data

15 Minute Read

Data comes in many forms, which makes the challenge of analyzing data unique to each dataset and its purpose. There isn’t a “one size fits all” approach for analyzing data. Knowing how to analyze, interpret, and apply findings to your data analysis can be the difference between successful and accurate insights… and leaving information on the table, still hidden in raw data but just out of reach.

By delving into data analysis techniques, we can begin to build a toolkit to reliably call upon to find meaning in data no matter the occasion or objective.

In this beginner’s guide, we’ll tackle the basics of data analysis for beginners and answer some of the most common questions people have, like how to analyze data and ways to apply data in research.

 Let’s start with the fundamentals.

What Is Data Analysis?

Data analysis is the process of gathering, organizing, and interpreting information. This can be anything from open-ended survey responses, transcripts , or notes. Data analysis goes a step beyond simply putting information in one place and organizing it. An analysis process helps you create models to visualize the information, find patterns, see tension, draw stronger conclusions, and even forecast potential outcomes.

All data analysis starts with “raw data.” This is unfiltered, uncategorized information. It can be something a person wrote, feedback they provided, or comments made in a remote user interview.

Data analysis helps you highlight the most relevant details, find similarities among data clusters (groups) and even break it down into different categories based on classifications using tags. 

How Data Is Analyzed in Research

There are numerous ways to analyze data, but the easiest method is to use a flexible platform that centralizes all of the steps. With greater insight and statistical evidence, you minimize risks that inherently accompany the decision-making process. Rather than hope you’ve gotten the best information, you can weigh both sides of a hypothesis and make the best call based on factual conclusions.

Research platforms like Notably help you import and analyze data in both structured and visual ways. With an open and spatial canvas, shifting data visually gives you the context of your entire dataset to see connections and identify relationships you may not have considered without a bird’s eye view of your data.

Before starting with the determined process for analyzing data, let’s review the most common types of data analysis, which are:

• Qualitative data analysis
• Quantitative data analysis
• Evaluative data analysis

What Is Quantitative Data Analysis?

Quantitative data deals with numbers, so it reflects any information pertaining to figures. This could be calculating the average age of a group, looking at grades and GPAs, discerning averages, and so on.

Quantitative data analysis is strictly mathematical, but its actual numbers can be derived from factual data. For example, let’s imagine you wanted to identify key differences between a target group. Rather than measure each criteria individually, you could assign a number to each factor, e.g. language, location and gender could each be given their own number.

U.S. visitors would fall under “1” while someone from Europe may fall under “2.” These numbers allow you to easily group information without losing any of the important meaning behind them. Rather than dilute information into statistics, you use number-based operations to draw more concrete, clear conclusions about your audience.

What Is Qualitative Data Analysis?

Qualitative data spans beyond numbers to look more closely at experiences, emotions, and human characteristics. It allows you to delve deeper into a research question, explore the implications of your hypothesis, and even form new hypotheses based on the data you gather.

Notably features for data analysis are rooted in qualitative data collection; by drawing key points from video transcripts or notes, you can unpack all sorts on inquiries through interview analysis , user testing, and more.

Let’s look at an example that differentiates quantitative vs. qualitative data.

Quantitative data: A company sent out an email to 500 subscribers and 150 readers opened the email. This represents a 30% open rate out of their mailing list.

Qualitative data: In an open-response survey, participants revealed that they often did not have time to read long emails during their usual active hours. Some reported that they felt emails often pertained to irrelevant information, or they found the subject headings misleading.

While quantitative data can capture immediate facts based on numbers, qualitative data gives a deeper level of understanding into a person’s experience. You learn, through their own narrative, what their core challenges are and what problems they’re facing. It allows researchers to incorporate feelings and wants into analysis without becoming too subjective.

Evaluative Data Analysis

Evaluative analysis, or evaluative research , helps businesses determine whether their end goals have been met and if a product or application is delivering its desired results. It’s a fundamental aspect of UX, where the end-user’s experience ultimately determines an effort’s success.

At Notably, we allow our curiosity to guide us, and evaluative research helps us build new questions after we’ve completed a project’s lifecycle. Evaluate analysis is integral to innovation and ongoing improvement. It’s what helps companies separate the distance between themselves and their consumers to tap into core needs and deliver more effective solutions in the future.

Rather than asking “Did this work?”, evaluative data helps you answer questions that reach deeper, like “Did this work as well as we intended, and did it create new and unexpected challenges that we can grow from?”

Why Is Rigorous Data Analysis Important?

Proper data analysis ensures you only get the most relevant information from your audience. It helps remove your ego and bias from the equation to look more closely at the real experience behind human users.

While many misconstrue data analysis as a cold, detached process, in reality, it ensures that people, their wants, needs, or ethics, are always put first in research.

How Can You Make Business Decisions From Data Analysis?

Once you’ve answered, “How do you analyze data?”, it’s time to go a step further and ask, “What can I do with what I’ve gathered?”

Data collection and analysis is just one part of the picture. Analysis is really a tool that propels companies forward through educated and personalized insight. Using data analysis, businesses can run hypothetical scenarios, test hypotheses, and lower risks by taking the most educated path toward their goals.

Moreover, data allows you to prioritize objectives and refine strategies to reflect user needs. It can make the shift from business-centric to customer-centric more practical without sacrificing the bottom line.

Create your own AI-powered templates for better, faster research synthesis. Discover new customer insights from data instantly.

The top 10 things Notably shipped in 2023 and themes for 2024.

Meet Posty: Your AI Research Assistant for Automatic Analysis

Introducing Notably + Miro Integration: 3 Tips to Analyze Miro Boards with AI in Notably

Give your research synthesis superpowers..

Try Teams for 7 days

Free for 1 project

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

When You Have Something Important to Say, You want to Shout it From the Rooftops

Jun 28, 2024

The Item I Failed to Leave Behind — Tuesday CX Thoughts

Jun 25, 2024

Feedback Loop: What It Is, Types & How It Works?

Jun 21, 2024

QuestionPro Thrive: A Space to Visualize & Share the Future of Technology

Jun 18, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.

Data & Finance for Work & Life

Data Analysis for Qualitative Research: 6 Step Guide

Data analysis for qualitative research is not intuitive. This is because qualitative data stands in opposition to traditional data analysis methodologies: while data analysis is concerned with quantities, qualitative data is by definition unquantified . But there is an easy, methodical approach that anyone can take use to get reliable results when performing data analysis for qualitative research. The process consists of 6 steps that I’ll break down in this article:

• Perform interviews(if necessary )
• Gather all documents and transcribe any non-paper records
• Decide whether to either code analytical data, analyze word frequencies, or both
• Decide what interpretive angle you want to take: content analysis , narrative analysis, discourse analysis, framework analysis, and/or grounded theory
• Compile your data in a spreadsheet using document saving techniques (windows and mac)
• Identify trends in words, themes, metaphors, natural patterns, and more

To complete these steps, you will need:

• Microsoft word
• Microsoft excel
• Internet access

You can get the free Intro to Data Analysis eBook to cover the fundamentals and ensure strong progression in all your data endeavors.

What is qualitative research?

Qualitative research is not the same as quantitative research. In short, qualitative research is the interpretation of non-numeric data. It usually aims at drawing conclusions that explain why a phenomenon occurs, rather than that one does occur. Here’s a great quote from a nursing magazine about quantitative vs qualitative research:

“A traditional quantitative study… uses a predetermined (and auditable) set of steps to confirm or refute [a] hypothesis. “In contrast, qualitative research often takes the position that an interpretive understanding is only possible by way of uncovering or deconstructing the meanings of a phenomenon. Thus, a distinction between explaining how something operates (explanation) and why it operates in the manner that it does (interpretation) may be [an] effective way to distinguish quantitative from qualitative analytic processes involved in any particular study.” (bold added) (( EBN ))

Learn to Interpret Your Qualitative Data

This article explain what data analysis is and how to do it. To learn how to interpret the results, visualize, and write an insightful report, sign up for our handbook below.

Step 1a: Data collection methods and techniques in qualitative research: interviews and focus groups

Step 1 is collecting the data that you will need for the analysis. If you are not performing any interviews or focus groups to gather data, then you can skip this step. It’s for people who need to go into the field and collect raw information as part of their qualitative analysis.

Since the whole point of an interview and of qualitative analysis in general is to understand a research question better, you should start by making sure you have a specific, refined research question . Whether you’re a researcher by trade or a data analyst working on one-time project, you must know specifically what you want to understand in order to get results.

Good research questions are specific enough to guide action but open enough to leave room for insight and growth. Examples of good research questions include:

• Good : To what degree does living in a city impact the quality of a person’s life? (open-ended, complex)
• Bad : Does living in a city impact the quality of a person’s life? (closed, simple)

Once you understand the research question, you need to develop a list of interview questions. These questions should likewise be open-ended and provide liberty of expression to the responder. They should support the research question in an active way without prejudicing the response. Examples of good interview questions include:

• Good : Tell me what it’s like to live in a city versus in the country. (open, not leading)
• Bad : Don’t you prefer the city to the country because there are more people? (closed, leading)

Some additional helpful tips include:

• Begin each interview with a neutral question to get the person relaxed
• Limit each question to a single idea
• If you don’t understand, ask for clarity
• Do not pass any judgements
• Do not spend more than 15m on an interview, lest the quality of responses drop

Focus groups

The alternative to interviews is focus groups. Focus groups are a great way for you to get an idea for how people communicate their opinions in a group setting, rather than a one-on-one setting as in interviews.

In short, focus groups are gatherings of small groups of people from representative backgrounds who receive instruction, or “facilitation,” from a focus group leader. Typically, the leader will ask questions to stimulate conversation, reformulate questions to bring the discussion back to focus, and prevent the discussion from turning sour or giving way to bad faith.

Focus group questions should be open-ended like their interview neighbors, and they should stimulate some degree of disagreement. Disagreement often leads to valuable information about differing opinions, as people tend to say what they mean if contradicted.

However, focus group leaders must be careful not to let disagreements escalate, as anger can make people lie to be hurtful or simply to win an argument. And lies are not helpful in data analysis for qualitative research.

Step 1b: Tools for qualitative data collection

When it comes to data analysis for qualitative analysis, the tools you use to collect data should align to some degree with the tools you will use to analyze the data.

As mentioned in the intro, you will be focusing on analysis techniques that only require the traditional Microsoft suite programs: Microsoft Excel and Microsoft Word . At the same time, you can source supplementary tools from various websites, like Text Analyzer and WordCounter.

In short, the tools for qualitative data collection that you need are Excel and Word , as well as web-based free tools like Text Analyzer and WordCounter . These online tools are helpful in the quantitative part of your qualitative research.

Step 2: Gather all documents & transcribe non-written docs

Once you have your interviews and/or focus group transcripts, it’s time to decide if you need other documentation. If you do, you’ll need to gather it all into one place first, then develop a strategy for how to transcribe any non-written documents.

When do you need documentation other than interviews and focus groups? Two situations usually call for documentation. First , if you have little funding , then you can’t afford to run expensive interviews and focus groups.

Second , social science researchers typically focus on documents since their research questions are less concerned with subject-oriented data, while hard science and business researchers typically focus on interviews and focus groups because they want to know what people think, and they want to know today.

Non-written records

Other factors at play include the type of research, the field, and specific research goal. For those who need documentation and to describe non-written records, there are some steps to follow:

• Put all hard copy source documents into a sealed binder (I use plastic paper holders with elastic seals ).
• If you are sourcing directly from printed books or journals, then you will need to digitalize them by scanning them and making them text readable by the computer. To do so, turn all PDFs into Word documents using online tools such as PDF to Word Converter . This process is never full-proof, and it may be a source of error in the data collection, but it’s part of the process.
• If you are sourcing online documents, try as often as possible to get computer-readable PDF documents that you can easily copy/paste or convert. Locked PDFs are essentially a lost cause .
• Transcribe any audio files into written documents. There are free online tools available to help with this, such as 360converter . If you run a test through the system, you’ll see that the output is not 100%. The best way to use this tool is as a first draft generator. You can then correct and complete it with old fashioned, direct transcription.

Step 3: Decide on the type of qualitative research

Before step 3 you should have collected your data, transcribed it all into written-word documents, and compiled it in one place. Now comes the interesting part. You need to decide what you want to get out of your research by choosing an analytic angle, or type of qualitative research.

The available types of qualitative research are as follows. Each of them takes a unique angle that you must choose to get what information you want from the analysis . In addition, each of them has a different impact on the data analysis for qualitative research (coding vs word frequency) that we use.

Content analysis

Narrative analysis, discourse analysis.

• Framework analysis, and/or

Grounded theory

From a high level, content, narrative, and discourse analysis are actionable independent tactics, whereas framework analysis and grounded theory are ways of honing and applying the first three.

• Definition : Content analysis is identify and labelling themes of any kind within a text.
• Focus : Identifying any kind of pattern in written text, transcribed audio, or transcribed video. This could be thematic, word repetition, idea repetition. Most often, the patterns we find are idea that make up an argument.
• Goal : To simplify, standardize, and quickly reference ideas from any given text. Content analysis is a way to pull the main ideas from huge documents for comparison. In this way, it’s more a means to an end.
• Pros : The huge advantage of doing content analysis is that you can quickly process huge amounts of texts using simple coding and word frequency techniques we will look at below. To use a metaphore, it is to qualitative analysis documents what Spark notes are to books.
• Cons : The downside to content analysis is that it’s quite general. If you have a very specific, narrative research question, then tracing “any and all ideas” will not be very helpful to you.
• Definition : Narrative analysis is the reformulation and simplification of interview answers or documentation into small narrative components to identify story-like patterns.
• Focus : Understanding the text based on its narrative components as opposed to themes or other qualities.
• Goal : To reference the text from an angle closer to the nature of texts in order to obtain further insights.
• Pros : Narrative analysis is very useful for getting perspective on a topic in which you’re extremely limited. It can be easy to get tunnel vision when you’re digging for themes and ideas from a reason-centric perspective. Turning to a narrative approach will help you stay grounded. More importantly, it helps reveal different kinds of trends.
• Cons : Narrative analysis adds another layer of subjectivity to the instinctive nature of qualitative research. Many see it as too dependent on the researcher to hold any critical value.
• Definition : Discourse analysis is the textual analysis of naturally occurring speech. Any oral expression must be transcribed before undergoing legitimate discourse analysis.
• Focus : Understanding ideas and themes through language communicated orally rather than pre-processed on paper.
• Goal : To obtain insights from an angle outside the traditional content analysis on text.
• Pros : Provides a considerable advantage in some areas of study in order to understand how people communicate an idea, versus the idea itself. For example, discourse analysis is important in political campaigning. People rarely vote for the candidate who most closely corresponds to his/her beliefs, but rather for the person they like the most.
• Cons : As with narrative analysis, discourse analysis is more subjective in nature than content analysis, which focuses on ideas and patterns. Some do not consider it rigorous enough to be considered a legitimate subset of qualitative analysis, but these people are few.

Framework analysis

• Definition : Framework analysis is a kind of qualitative analysis that includes 5 ordered steps: coding, indexing, charting, mapping, and interpreting . In most ways, framework analysis is a synonym for qualitative analysis — the same thing. The significant difference is the importance it places on the perspective used in the analysis.
• Focus : Understanding patterns in themes and ideas.
• Goal : Creating one specific framework for looking at a text.
• Pros : Framework analysis is helpful when the researcher clearly understands what he/she wants from the project, as it’s a limitation approach. Since each of its step has defined parameters, framework analysis is very useful for teamwork.
• Cons : It can lead to tunnel vision.
• Definition : The use of content, narrative, and discourse analysis to examine a single case, in the hopes that discoveries from that case will lead to a foundational theory used to examine other like cases.
• Focus : A vast approach using multiple techniques in order to establish patterns.
• Goal : To develop a foundational theory.
• Pros : When successful, grounded theories can revolutionize entire fields of study.
• Cons : It’s very difficult to establish ground theories, and there’s an enormous amount of risk involved.

Step 4: Coding, word frequency, or both

Coding in data analysis for qualitative research is the process of writing 2-5 word codes that summarize at least 1 paragraphs of text (not writing computer code). This allows researchers to keep track of and analyze those codes. On the other hand, word frequency is the process of counting the presence and orientation of words within a text, which makes it the quantitative element in qualitative data analysis.

Video example of coding for data analysis in qualitative research

In short, coding in the context of data analysis for qualitative research follows 2 steps (video below):

• Reading through the text one time
• Adding 2-5 word summaries each time a significant theme or idea appears

Let’s look at a brief example of how to code for qualitative research in this video:

Click here for a link to the source text. 1

Example of word frequency processing

And word frequency is the process of finding a specific word or identifying the most common words through 3 steps:

• Decide if you want to find 1 word or identify the most common ones
• Use word’s “Replace” function to find a word or phrase
• Use Text Analyzer to find the most common terms

Here’s another look at word frequency processing and how you to do it. Let’s look at the same example above, but from a quantitative perspective.

Imagine we are already familiar with melanoma and KITs , and we want to analyze the text based on these keywords. One thing we can do is look for these words using the Replace function in word

• Locate the search bar
• Click replace
• Type in the word
• See the total results

Here’s a brief video example:

Another option is to use an online Text Analyzer. This methodology won’t help us find a specific word, but it will help us discover the top performing phrases and words. All you need to do it put in a link to a target page or paste a text. I pasted the abstract from our source text, and what turns up is as expected. Here’s a picture:

Step 5: Compile your data in a spreadsheet

After you have some coded data in the word document, you need to get it into excel for analysis. This process requires saving the word doc as an .htm extension, which makes it a website. Once you have the website, it’s as simple as opening that page, scrolling to the bottom, and copying/pasting the comments, or codes, into an excel document.

You will need to wrangle the data slightly in order to make it readable in excel. I’ve made a video to explain this process and places it below.

Step 6: Identify trends & analyze!

There are literally thousands of different ways to analyze qualitative data, and in most situations, the best technique depends on the information you want to get out of the research.

Nevertheless, there are a few go-to techniques. The most important of this is occurrences . In this short video, we finish the example from above by counting the number of times our codes appear. In this way, it’s very similar to word frequency (discussed above).

A few other options include:

• Ranking each code on a set of relevant criteria and clustering
• Pure cluster analysis
• Causal analysis

We cover different types of analysis like this on the website, so be sure to check out other articles on the home page .

How to analyze qualitative data from an interview

To analyze qualitative data from an interview , follow the same 6 steps for quantitative data analysis:

• Perform the interviews
• Transcribe the interviews onto paper
• Decide whether to either code analytical data (open, axial, selective), analyze word frequencies, or both
• Compile your data in a spreadsheet using document saving techniques (for windows and mac)
• Source text [ ↩ ]

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

File available immediately.

Notice: JavaScript is required for this content.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Prevent plagiarism. Run a free check.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Other students also liked, writing strong research questions | criteria & examples.

• What Is a Research Design | Types, Guide & Examples
• Data Collection | Definition, Methods & Examples

More interesting articles

• Between-Subjects Design | Examples, Pros, & Cons
• Cluster Sampling | A Simple Step-by-Step Guide with Examples
• Confounding Variables | Definition, Examples & Controls
• Construct Validity | Definition, Types, & Examples
• Content Analysis | Guide, Methods & Examples
• Control Groups and Treatment Groups | Uses & Examples
• Control Variables | What Are They & Why Do They Matter?
• Correlation vs. Causation | Difference, Designs & Examples
• Correlational Research | When & How to Use
• Critical Discourse Analysis | Definition, Guide & Examples
• Cross-Sectional Study | Definition, Uses & Examples
• Descriptive Research | Definition, Types, Methods & Examples
• Ethical Considerations in Research | Types & Examples
• Explanatory and Response Variables | Definitions & Examples
• Explanatory Research | Definition, Guide, & Examples
• Exploratory Research | Definition, Guide, & Examples
• External Validity | Definition, Types, Threats & Examples
• Extraneous Variables | Examples, Types & Controls
• Guide to Experimental Design | Overview, Steps, & Examples
• How Do You Incorporate an Interview into a Dissertation? | Tips
• How to Do Thematic Analysis | Step-by-Step Guide & Examples
• How to Write a Literature Review | Guide, Examples, & Templates
• How to Write a Strong Hypothesis | Steps & Examples
• Inclusion and Exclusion Criteria | Examples & Definition
• Independent vs. Dependent Variables | Definition & Examples
• Inductive Reasoning | Types, Examples, Explanation
• Inductive vs. Deductive Research Approach | Steps & Examples
• Internal Validity in Research | Definition, Threats, & Examples
• Internal vs. External Validity | Understanding Differences & Threats
• Longitudinal Study | Definition, Approaches & Examples
• Mediator vs. Moderator Variables | Differences & Examples
• Mixed Methods Research | Definition, Guide & Examples
• Multistage Sampling | Introductory Guide & Examples
• Naturalistic Observation | Definition, Guide & Examples
• Operationalization | A Guide with Examples, Pros & Cons
• Population vs. Sample | Definitions, Differences & Examples
• Primary Research | Definition, Types, & Examples
• Qualitative vs. Quantitative Research | Differences, Examples & Methods
• Quasi-Experimental Design | Definition, Types & Examples
• Questionnaire Design | Methods, Question Types & Examples
• Random Assignment in Experiments | Introduction & Examples
• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

What is your plagiarism score?

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

6 How to Analyze Data in a Primary Research Study

Melody Denny and Lindsay Clark

This chapter introduces students to the idea of working with primary research data grounded in qualitative inquiry, closed-and open-ended methods, and research ethics (Driscoll; Mackey and Gass; Morse; Scott and Garner). [1] We know this can seem intimidating to students, so we will walk them through the process of analyzing primary research, using information from public datasets including the Pew Research Center. Using sample data on teen social media use, we share our processes for analyzing sample data to demonstrate different approaches for analyzing primary research data (Charmaz; Creswell; Merriam and Tisdale; Saldaña). We also include links to additional public data sets, chapter discussion prompts, and sample activities for students to apply these strategies.

At this point in your education, you are familiar with what is known as secondary research or what many students think of as library research. Secondary research makes use of sources most often found in the library or, these days, online (books, journal articles, magazines, and many others). There’s another kind of research that you may or may not be familiar with: primary research. The Purdue OWL defines primary research as “any type of research you collect yourself” and lists examples as interviews, observations, and surveys (“What is Primary Research”).

Primary research is typically divided into two main types—quantitative and qualitative research. These two methods (or a mix of these) are used by many fields of study, so providing a singular definition for these is a bit tricky. Sheard explains that “quantitative research…deals with data that are numerical or that can be converted into numbers. The basic methods used to investigate numerical data are called ‘statistics’” (429). Guest, et al. explain that qualitative research is “information that is difficult to obtain through more quantitatively-oriented methods of data collection” and is used more “to answer the whys and hows of human behavior, opinion, and experience” (1).

This chapter focuses on qualitative methods that explore peoples’ behaviors, interpretations, and opinions. Rather than being only a reader and reporter of research, primary research allows you to be creators of research. Primary research provides opportunities to collect information based on your specific research questions and generate new knowledge from those questions to share with others. Generally, primary research tends to follow these steps:

• Develop a research question. Secondary research often uses this as a starting point as well. With primary research, however, rather than using library research to answer your research question, you’ll also collect data yourself to answer the question you developed. Data, in this case, is the information you collect yourself through methods such as interviews, surveys, and observations.
• Decide on a research method. According to Scott and Garner, “A research method is a recognized way of collecting or producing [primary data], such as a survey, interview, or content analysis of documents” (8). In other words, the method is how you obtain the data.
• Collect data. Merriam and Tisdale clarify what it means to collect data: “data collection is about asking, watching, and reviewing” (105-106). Primary research might include asking questions via surveys or interviews, watching or observing interactions or events, and examining documents or other texts.
• Analyze data. Once data is collected, it must then be analyzed. “Data analysis is the process of making sense out of the data… Basically, data analysis is the process used to answer your research question(s)” (Merriam and Tisdale 202). It’s worth noting that many researchers collect data and analyze at the same time, so while these may seem like different steps in the process, they actually overlap.
• Report findings. Once the researcher has spent time understanding and interpreting the data, they are then ready to write about their research, often called “findings.” You may also see this referred to as “results.”

While the entire research process is discussed, this chapter focuses on the analysis stage of the process (step 4). Depending on where you are in the research process, you may need to spend more time on step 1, 2, or 3 and review Driscoll’s “Introduction to Primary Research” (Volume 2 of Writing Spaces ).

Primary research can seem daunting, and some students might think that they can’t do primary research, that this type of research is for professionals and scholars, but that’s simply not true. It’s true that primary research data can be difficult to collect and even more difficult to analyze, but the findings are typically very revealing. This chapter and the examples included break down this research process and demonstrate how general curiosity can lead to exciting chances to learn and share information that is relevant and interesting. The goal of this chapter is to provide you with some information about data analysis and walk you through some activities to prepare you for your own data analysis. The next section discusses analyzing data from closed-ended methods and open-ended methods.

Data from Primary Research

As stated above, this chapter doesn’t focus on methods, but before moving on to analysis, it’s important to clarify a few things related to methods as they are directly connected to analyzing data. As a quick reminder, a research method is how researchers collect their data such as surveys, interviews, or textual analysis. No matter which method used, researchers need to think about the types of questions to ask for answering their overall research question. Generally, there are two types of questions to consider: closed-ended and open-ended. The next section provides examples of the data you might receive from asking closed-ended and open-ended questions and options for analyzing and presenting that data.

Data from Closed-Ended Methods

The data that is generated by closed-ended questions on methods such as surveys and polls is often easier to organize. Because the way respondents could answer those questions is limited to specific answers (Yes/No, numbered scales, multiple choice), the data can be analyzed by each question or by looking at the responses individually or as a whole. Though there are several approaches to analyzing the data that comes from closed-ended questions, this section will introduce you to a few different ways to make sense of this kind of data.

Closed-ended questions are those that have limited answers, like multiple choice or check-all-that-apply questions. These questions mean that respondents can provide only the answers given or they may select an “other” option. An example of a closed-ended question could be “Do you use YouTube? Yes, No, Sometimes.” Closed-ended questions have their perks because they (mostly) keep participants from misinterpreting the question or providing unhelpful responses. They also make data analysis a bit easier.

If you were to ask the “Yes, No, Sometimes” question about YouTube to 20 of your closest friends, you may get responses like Yes = 18, No = 1, and Sometimes = 1. But, if you were to ask a more detailed question like “Which of the following social media platforms do you use?” and provide respondents with a check-all-that-apply option, like “Facebook, YouTube, Twitter, Instagram, Snapchat, Reddit, and Tumblr,” you would get a very different set of data. This data might look like Facebook = 17, YouTube = 18, Twitter = 12, Instagram = 20, Snapchat = 15, Reddit = 8, and Tumblr = 3. The big takeaway here is that how you ask the question determines the type of data you collect.

Analyzing Closed-Ended Data

Now that you have data, it’s time to think about analyzing and presenting that data. Luckily, the Pew Research Center conducted a similar study that can be used as an example. The Pew Research Center is a “nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research” (“About Pew Research Center”). The information provided below comes from their public dataset “Teens, Social Media, and Technology, 2018” (Anderson and Jiang). This example is used to show how you might analyze this type of data once collected and what that data might look like. “Teens, Social Media, and Technology 2018” reported responses to questions related to which online platforms teens use and which they use most often. In figure 1 below, Pew researchers show the final product of their analysis of the data:

Pew analyzed their data and organized the findings by percentages to show what they discovered. They had 743 teens who responded to these questions, so presenting their findings in percentages helps readers better “see” the data overall (rather than saying YouTube = 631 and Instagram = 535). However, results can be represented in different ways. When the Pew researchers were deciding how to present their data, they could have reported the frequency, or the number of people who said they used YouTube, Instagram, and Snapchat.

In the scenario of polling 20 of your closest friends, you, too, would need to decide how to present your data: Facebook = 17, YouTube = 18, Twitter = 12, Instagram = 20, Snapchat = 15, Reddit = 8, and Tumblr = 3. In your case, you might want to present the frequency (number) of responses rather than the percentages of responses like Pew did. You could choose a bar graph like Pew or maybe a simple table to show your data.

Looking again at the Pew data, researchers could use this data to generate further insights or questions about user preferences. For example, one could highlight the fact that 85% of respondents reported using YouTube the most, while only 7% reported using Reddit. Why is that? What conclusions might you be able to make based on these data? Does the data make you wonder if any additional questions might be explored? If you want to learn more about your respondents’ opinions or preference, you might need to ask open-ended questions.

Data from Open-Ended Methods

Whereas closed-ended questions limit how respondents might answer, open-ended questions do not limit respondents’ answers and allow them to answer more freely. An example of an open-ended question, to build off the question above, could be “Why do you use social media? Explain.” This type of question gives respondents more space to fully explain their responses. Open-ended questions can make the data varied because each respondent may answer differently. These questions, which can provide fruitful responses, can also mean unexpected responses or responses that don’t help to answer the overall research question, which can sometimes make data analysis challenging.

In that same Pew Research Center data, respondents were likely limited in how they were able to answer by selecting social media platforms from a list. Pew also shares selected data (Appendix A), and based on these data, it can be assumed they also asked open-ended questions, something about the positive or negative effects of social media platforms. Because their research method included both closed-ended questions about which platforms teens use as well as open-ended questions that invited their thoughts about social media, Pew researchers were able to learn more about these participants’ thoughts and perceptions. To give us, the readers, a clearer idea of how they justified their presentation of the data, Pew offers 15 sample excerpts from those open-ended questions. They explain that these excerpts are what the researchers believe are representative of the larger data set. We explain below how we might analyze those excerpts.

Analyzing Open-Ended Data

As Driscoll reminds us, ethical considerations impact all stages of the research process, and researchers should act ethically throughout the entire research process. You already know a little something about research ethics. For example, you know that ethical writers cite sources used in research papers by giving credit to the person who created that information. When creating primary sources, you have a few different ethical considerations for analyzing data, which will be discussed below.

To demonstrate how to analyze data from open-ended methods, we explain how we (Melody and Lindsay) analyzed the 15 excerpts from the Pew data using open coding. Open coding means analyzing the data without any predetermined categories or themes; researchers are just seeing what emerges or seems significant (Charmaz). Creswell suggests four specific steps when coding qualitative data, though he also stresses that these steps are iterative, meaning that researchers may need to revisit a step anywhere throughout the process. We use these four steps to explain our analysis process, including how we ethically coded the data, interpreted what the coding process revealed, and worked together to identify and explain categories we saw in the data.

Step 1: Organizing and Preparing the Data

The first part of the analysis stage is organizing the data before examining it. When organizing data, researchers must be careful to work with primary data ethically because that data often represents actual peoples’ information and opinions. Therefore, researchers need to carefully organize the data in such a way as to not identify their participants or reveal who they are. This is a key component to The Belmont Report , guidelines published in 1979 meant to guide researchers and help protect participants. Using pseudonyms or assigning numbers or codes (in place of names) to the data is a recommended ethical step to maintain participants’ confidentiality in a study. Anonymizing data, or removing names, has the additional effect of eliminating researcher bias, which can occur when researchers are so familiar with their own data and participants that the researchers may begin to think they already know the answers or see connections prior to analysis (Driscoll). By assigning pseudonyms, researchers can also ensure that they take an objective look at each participant’s answers without being persuaded by participant identity.

The first part of coding is to make notations while reading through the data (Merriam and Tisdale). At this point, researchers are open to many possibilities regarding their data. This is also where researchers begin to construct categories. Offering a simple example to illustrate this decision-making process, Merriam and Tisdale ask us to imagine sorting and categorizing two hundred grocery store items (204). Some items could be sorted into more than one category; for example, ice cream could be categorized as “frozen” or as “dessert.” How you decide to sort that item depends on your research question and what you want to learn.

For this step, we, Melody and Lindsay, each created a separate document that included the 15 excerpts. Melody created a table for the quotes, leaving a column for her coding notes, and Lindsay added spaces between the excerpts for her notes. For our practice analysis, we analyzed the data independently, and then shared what we did to compare, verify, and refine our analysis. This brings a second, objective view to the analysis, reduces the effect of researcher bias, and ensures that your analysis can be verified and supported by the data. To support your analysis, you need to demonstrate how you developed the opinions and conclusions you have about your data. After all, when researchers share their analyses, readers often won’t see all of the raw data, so they need to be able to trust the analysis process.

Step 2: Reading through All the Data

Creswell suggests getting a general sense of the data to understand its overall meaning. As you start reading through your data, you might begin to recognize trends, patterns, or recurring features that give you ideas about how to both analyze and later present the data. When we read through the interview excerpts of these 15 participants’ opinions of social media, we both realized that there were two major types of comments: positive and negative. This might be similar to categorizing the items in the grocery store (mentioned above) into fresh/frozen foods and non-perishable items.

To better organize the data for further analysis, Melody marked each positive comment with a plus sign and each negative comment with a minus sign. Lindsay color-coded the comments (red for negative, indicated by boldface type below; green for positive, indicated by grey type below) and then organized them on the page by type. This approach is in line with Merriam and Tisdale’s explanation of coding: “assigning some sort of shorthand designation to various aspects of your data so that you can easily retrieve specific pieces of the data. The designations can be single words, letters, numbers, phrases, colors, or combinations of these” (199). While we took different approaches, as shown the two sections below, both allowed us to visually recognize the major sections of the data:

Lindsay’s Coding Round 1, which shows her color coding indicated by boldface type

“[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) “It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15) “[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17) “It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15) “I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14) “Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14) “We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

Melody’s Coding Round 1, showing her use of plus and minus signs to classify the comments as positive or negative, respectively

+ “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) – “It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15) – “[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17) + “It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15) + “I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14) – “Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14) + “We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

Step 3: Doing Detailed Coding Analysis of the Data

It’s important to mention that Creswell dedicates pages of description on coding data because there are various ways of approaching detailed analysis. To code our data, we added a descriptive word or phrase that “symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute” to a portion of data (Saldaña 3). From the grocery store example above, that could mean looking at the category of frozen foods and dividing them into entrees, side dishes, desserts, appetizers, etc. We both coded for topics or what the teens were generally talking about in their responses. For example, one excerpt reads “Social media allows us to communicate freely and see what everyone else is doing. It gives us a voice that can reach many people.” To code that piece of data, researchers might assign words like communication, voice, or connection to explain what the data is describing.

In this way, we created the codes from what the data said, describing what we read in those excerpts. Notice in the section below that, even though we coded independently, we described these pieces of data in similar ways using bolded keywords:

Melody’s Coding Round 2, with key words added to summarize the meanings of the different quotes

– “Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13) bullying – “It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15) fake + “Because a lot of things created or made can spread joy.” (Boy, age 17) reaching people + “I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15) connection + “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) reaching people

Lindsay’s Coding Round 2, with key words added in capital letters to summarize the meanings of the quotations

“Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13) OPPORTUNITIES TO COMMUNICATE NEGATIVELY/MORE EASILY “It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15) FAKE, NOT REALITY “Because a lot of things created or made can spread joy.” (Boy, age 17) SPREAD JOY “I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15) INTERACTION, LESS LONELY “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) COMMUNICATE, VOICE

Though there are methods that allow for researchers to use predetermined codes (like from previous studies), “the traditional approach…is to allow the codes to emerge during the data analysis” (Creswell 187).

Step 4: Using the Codes to Create a Description Using Categories, Themes, Settings, or People

Our individual coding happened in phases, as we developed keywords and descriptions that could then be defined and relabeled into concise coding categories (Saldaña 11). We shared our work from Steps 1-3 to further define categories and determine which themes were most prominent in the data. A few times, we interpreted something differently and had to discuss and come to an agreement about which category was best.

In our process, one excerpt comment was interpreted as negative by one of us and positive by the other. Together we discussed and confirmed which comments were positive or negative and identified themes that seemed to appear more than once, such as positive feelings towards the interactional element of social media use and the negative impact of social media use on social skills. When two coders compare their results, this allows for qualitative validity, which means “the researcher checks for the accuracy of the findings” (Creswell 190). This could also be referred to as intercoder reliability (Lavrakas). For intercoder reliability, researchers sometimes calculate how often they agree in a percentage. Like many other aspects of primary research, there is no consensus on how best to establish or calculate intercoder reliability, but generally speaking, it’s a good idea to have someone else check your work and ensure you are ethically analyzing and reporting your data.

Interpreting Coded Data

Once we agreed on the common categories and themes in this dataset, we worked together on the final analysis phase of interpreting the data, asking “what does it mean?” Data interpretation includes “trying to give sense to the data by creatively producing insights about it” (Gibson and Brown 6). Though we acknowledge that this sample of only 15 excerpts is small, and it might be difficult to make claims about teens and social media from just this data, we can share a few insights we had as part of this practice activity.

Overall, we could report the frequency counts and percentages that came from our analysis. For example, we counted 8 positive comments and 7 negative comments about social media. Presented differently, those 8 positive comments represent 53% of the responses, so slightly over half. If we focus on just the positive comments, we are able to identify two common themes among those 8 responses: Interaction and Expression. People who felt positively about social media use identified the ability to connect with people and voice their feelings and opinions as the main reasons. When analyzing only the 7 negative responses, we identified themes of Bullying and Social Skills as recurring reasons people are critical of social media use among teens. Identifying these topics and themes in the data allows us to begin thinking about what we can learn and share with others about this data.

How we represent what we have learned from our data can demonstrate our ethical approach to data analysis. In short, we only want to make claims we can support, and we want to make those claims ethically, being careful to not exaggerate or be misleading.

To better understand a few common ethical dilemmas regarding the presentation of data, think about this example: A few years ago, Lindsay taught a class that had only four students. On her course evaluations, those four students rated the class experience as “Excellent.” If she reports that 100% of her students answered “Excellent,” is she being truthful? Yes. Do you see any potential ethical considerations here? If she said that 4/4 gave that rating, does that change how her data might be perceived by others? While Lindsay could show the raw data to support her claims, important contextual information could be missing if she just says 100%. Perhaps others would assume this was a regular class of 20-30 students, which would make that claim seem more meaningful and impressive than it might be.

Another word for this is cherry picking. Cherry picking refers to making conclusions based on thin (or not enough) data or focusing on data that’s not necessarily representative of the larger dataset (Morse). For example, if Lindsay reported the comment that one of her students made about this being the “best class ever,” she would be telling the truth but really only focusing on the reported opinion of 25% of the class (1 out of 4). Ideally, researchers want to make claims about the data based on ideas that are prominent, trending, or repeated. Less prominent pieces of data, like the opinion of that one student, are known as outliers, or data that seem to “be atypical of the rest of the dataset” (Mackey and Gass 257). Focusing on those less-representative portions might misrepresent or overshadow the aspects of the data that are prominent or meaningful, which could create ethical problems for your study. With these ethical considerations in mind, the last step of conducting primary research would be to write about the analysis and interpretation to share your process with others.

This chapter has introduced you to ethically analyzing data within the primary research tradition by focusing on close-ended and open-ended data. We’ve provided you with examples of how data might be analyzed, interpreted, and presented to help you understand the process of making sense of your data. This is just one way to approach data analysis, but no matter your research method, having a systematic approach is recommended. Data analysis is a key component in the overall primary research process, and we hope that you are now excited and curious to participate in a primary research project.

Works Cited

“About Pew Research Center.” Pew Research Center, 2020. www.pewresearch.org/about/ . Accessed 28 Dec 2020. Anderson, Monica, and Jingjing Jiang.

“Teens, Social Media & Technology 2018.” Pew Research Center, May 2018, www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/ .

The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research, Office for Human Research Protections, www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html . 18 Apr. 1979.

Charmaz, Kathy. “Grounded Theory.” Approaches to Qualitative Research: A Reader on Theory and Practice , edited by Sharlene Nagy Hesse-Biber and Patricia Leavy, Oxford UP, 2004, pp. 496-521.

Corpus of Contemporary American English (COCA) . (n.d.). Retrieved April 11, 2021, from https://www.english-corpora.org/coca/

Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 3rd edition, Sage, 2009.

Data.gov . (2020). Retrieved April 11, 2021, from https://www.data.gov/

Driscoll, Dana Lynn. “Introduction to Primary Research: Observations, Surveys, and Interviews.” Writing Spaces: Readings on Writing , Volume 2, Parlor Press, 2011, pp. 153-174.

Explore Census Data . (n.d.). United States Census Bureau. Retrieved April 11, 2021, from https://data.census.gov/cedsci/

Gibson, William J., and Andrew Brown. Working with Qualitative Data . London, Sage, 2009.

Google Trends. (n.d.). Retrieved April 11, 2021, from https://trends.google.com/trends/explore

Guest, Greg, et al. Collecting Qualitative Data: A Field Manual for Applied Research . Sage, 2013.

HealthData.gov . (n.d.). Retrieved April 11, 2021, from https://healthdata.gov/

Lavrakas, Paul J. Encyclopedia of Survey Research Methods . Sage, 2008.

Mackey, Allison, and Sue M. Gass. Second Language Research: Methodology and Design . Lawrence Erlbaum Associates, 2005.

Merriam, Sharan B., and Elizabeth J. Tisdell. Qualitative Research: A Guide to Design and Implementation , John Wiley & Sons, Incorporated, 2015. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/unco/detail.action?docID=2089475 .

Michigan Corpus of Academic Spoken English. (n.d.). Retrieved April 11, 2021, from https://quod.lib.umich.edu/cgi/c/corpus/corpus?c=micase;page=simple

Morse, Janice. M. “‘Cherry Picking’: Writing from Thin Data.” Qualitative Health Research , vol. 20, no. 1, 2009, p. 3.

Pew Research Center . (2021). Retrieved April 11, 2021, from https://www.pewresearch.org/

Saldaña, Johnny. The Coding Manual for Qualitative Researchers , 2nd edition, Sage, 2013.

Scott, Greg, and Roberta Garner. Doing Qualitative Research: Designs, Methods, and Techniques , 1st edition, Pearson, 2012.

Sheard, Judithe. “Quantitative Data Analysis.” Research Methods Information, Systems, and Contexts , edited by Kirsty Williamson and Graeme Johanson, Elsevier, 2018, pp. 429-452.

Teens and Social Media , Google Trends, trends.google.com/trends/explore?-date=all&q=teens%20and%20social%20media . Accessed 15 Jul. 2020.

“What is Primary Research and How Do I Get Started?” The Writing Lab and OWL at Purdue and Purdue U , 2020. owl.purdue.edu/owl . Accessed 21 Dec. 2020.

Zhao, Alice. “How Text Messages Change from Dating to Marriage.” Huffington Post , 21 Oct. 2014, www.huffpost.com .

“My mom had to get a ride to the library to get what I have in my hand all the time. She reminds me of that a lot.” (Girl, age 14)

“Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13)

“It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15)

“Because a lot of things created or made can spread joy.” (Boy, age 17)

“I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15)

“[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15)

“It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15)

“[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17)

“It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15)

“I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14)

“Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14)

“We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

“It has given many kids my age an outlet to express their opinions and emotions, and connect with people who feel the same way.” (Girl, age 15)

“People can say whatever they want with anonymity and I think that has a negative impact.” (Boy, age 15)

“It has a negative impact on social (in-person) interactions.” (Boy, age 17)

Teacher Resources for How to Analyze Data in a Primary Research Study

Overview and teaching strategies.

This chapter is intended as an overview of analyzing qualitative research data and was written as a follow-up piece to Dana Lynn Driscoll’s “Introduction to Primary Research: Observations, Surveys, and Interviews” in Volume 2 of this collection. This chapter could work well for leading students through their own data analysis of a primary research project or for introducing students to the idea of primary research by using outside data sources, those in the chapter and provided in the activities below, or data you have access to.

From our experiences, students usually have limited experience with primary research methods outside of conducting a small survey for other courses, like sociology. We have found that few of our students have been formally introduced to primary research and analysis. Therefore, this chapter strives to briefly introduce students to primary research while focusing on analysis. We’ve presented analysis by categorizing data as open-ended and closed-ended without getting into too many details about qualitative versus quantitative. Our students tend to produce data collection tools with a mix of these types of questions, so we feel it’s important to cover the analysis of both.

In this chapter, we bring students real examples of primary data and lead them through analysis by showing examples. Any of these exercises and the activities below may be easily supplemented with additional outside data. One way that teachers can bring in outside data is through the use of public datasets.

Public Data Sets

There are many public data sets that teachers can use to acquaint their students with analyzing data. Be aware that some of these datasets are for experienced researchers and provide the data in CSV files or include metadata, all of which is probably too advanced for most of our students. But if you are comfortable converting this data, it could be valuable for a data analysis activity.

• In the chapter, we pulled from Pew Research, and their website contains many free and downloadable data sets (Pew Research Center).
• The site Data.gov provides searchable datasets, but you can also explore their data by clicking on “data” and seeing what kinds of reports they offer.
• The U.S. Census Bureau offers some datasets as well (Explore Census Data): Much of this data is presented in reports, but teachers could pull information from reports and have students analyze the data and compare their results to those in the report, much like we did with the Pew Research data in the chapter.
• Similarly, HealthData.gov offers research-based reports packed with data for students to analyze.
• In one of the activities below, we used Google Trends to look at searches over a period of time. There are some interesting data and visuals provided on the homepage to help students get started.
• If you’re looking for something a bit more academic, the Michigan Corpus of Academic Spoken English is a great database of transcripts from academic interactions and situations.
• Similarly, the Corpus of Contemporary American English allows users to search for words or word strings to see their frequency and in which genre and when these occur.

Before moving on to student activities, we’d like to offer one additional suggestion for teachers to consider.

Class Google Form

One thing that Melody does at the beginning of almost all of her research-based writing courses is ask students to complete a Google Form at the beginning of the semester. Sometimes, these forms are about their experiences with research. Other times, they revolve around a class topic (recently, she’s been interested in Generation Z or iGeneration and has asked students questions related to that). Then, when it’s time to start thinking about primary research, she uses that Google Form to help students understand more about the primary research process. Here are some ways that teachers can employ the data gathered from Google Form given to students.

• Ask students to look at the questions asked on the survey and deduce the overall research question.
• • Ask students to look at the types of questions asked (open- and closed-ended) and consider why they were constructed that way.
• Ask students to evaluate the wording of the questions asked.
• Ask students to examine the results of a few (or more) or the questions on the survey. This can be done in groups with each group looking at 1-3 questions, depending on the size of your Google Form.
• Ask students to think about how they might present that data in visual form. Yes, Google provides some visuals, but you can give them the raw data and see what they come up with.
• Ask students to come up with 1-3 major takeaways based on all the data.

This exercise allows students to work with real data and data that’s directly related to them and their classmates. It’s also completely within ethical boundaries because it’s data collected in the classroom, for educational purposes, and it stays within the classroom.

Below we offer some guiding questions to help move students through the chapter and the activities as well as some additional activities.

Discussion Questions

• In the opening of this chapter, we introduced you to primary research , or “any type of research you collect yourself” (“What is Primary Research”). Have you completed primary research before? How did you decide on your research method, based on your research question? If you have not worked on primary research before, brainstorm a potential research question for a topic you want to know more about. Discuss what research method you might use, including closed- or open-ended methods and why.
• Looking at the chart from the Pew Research dataset, “Teens, Social Media, and Technology 2018,” would you agree that the distributions among online platforms remain similar, or have trends changed?
• What do you make of the “none of the above” category on the Pew table? Do you think teens are using online platforms that aren’t listed, or do you think those respondents don’t use any online platforms?

• When analyzing data from open-ended questions, which step seems most challenging to you? Explain.

Activity #1: TurnItIn and Infographics

Infographics can be a great way to help you see and understand data, while also giving you a way to think about presenting your own data. Multiple infographics are available on TurnItIn, downloadable for free, that provide information about plagiarism.

Figure 3, titled “The Plagiarism Spectrum,” provides you with the “severity” and “frequency” based on survey findings of nearly 900 high school and college instructors from around the world. TurnItIn encourages educators to print this infographic and hang in their classroom:

This infographic provides some great data analysis examples: specific categories with definitions (and visual representation of their categories), frequency counts with bar graphs, and color gradient bars to show higher vs. lower numbers.

• Write a summary of how this infographic presents data.
• How do you think they analyzed the data based on this visual?

Activity #2: How Text Messages Change from Dating to Marriage

In Alice Zhao’s Huffington Post piece, she analyzes text messages that she collected during her relationship with her boyfriend, turned fiancé, turned husband to answer the question of how text messages (or communication) change over the course of a relationship. While Zhao offers some insight into her data, she also provides readers with some really cool graphics that you can use to practice your analysis skills.

These first graphics are word clouds. In figure 4, Zhao put her textual data into a program that creates these images based on the most frequently occurring words. Word clouds are another option for analyzing your data. If you have a lot of textual data and want to know what participants said the most, placing your data into a word cloud program is an easy way to “see” the data in a new way. This is usually one of the first steps of analysis, and additional analysis is almost always needed.

• What do you notice about the texts from 2008 to 2014?
• What do you notice between her texts (me) and his texts (him)?

Zhao also provided this graphic (figure 5), a comparative look at what she saw as the most frequently occurring words from the word clouds. This could be another step in your data analysis procedure: zooming in on a few key aspects and digging a bit deeper.

• What do you make of this data? Why might the word “hey” occur more frequently in the dating time frame and the word “ok” occur more frequently in the married time frame?

As part of her research, Zhao also looked at the time of day text messages were sent, shown below in figure 6:

Here, Zhao looked at messages sent a month after their first date, a month after their engagement, and a month after their wedding.

• She offers her own interpretation in her piece in figure 6, but what do you think of this?
• Also make note of this graphic. It’s a great way to look at the data another way. If your data may be time sensitive, this type of graphic may help you better analyze and understand your data.
• This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) and is subject to the Writing Spaces Terms of Use. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ , email [email protected] , or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. To view the Writing Spaces Terms of Use, visit http://writingspaces.org/terms-of-use . ↵

How to Analyze Data in a Primary Research Study Copyright © 2021 by Melody Denny and Lindsay Clark is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

Share This Book

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Data Analysis

Methodology chapter of your dissertation should include discussions about the methods of data analysis. You have to explain in a brief manner how you are going to analyze the primary data you will collect employing the methods explained in this chapter.

There are differences between qualitative data analysis and quantitative data analysis . In qualitative researches using interviews, focus groups, experiments etc. data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives.

Data analysis for quantitative studies, on the other hand, involves critical analysis and interpretation of figures and numbers, and attempts to find rationale behind the emergence of main findings. Comparisons of primary research findings to the findings of the literature review are critically important for both types of studies – qualitative and quantitative.

Data analysis methods in the absence of primary data collection can involve discussing common patterns, as well as, controversies within secondary data directly related to the research area.

John Dudovskiy

Data Analysis in Research

Ai generator.

Data analysis in research involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is a crucial step that enables researchers to identify patterns, relationships, and trends within the data, transforming raw information into valuable insights. Through methods such as descriptive statistics, inferential statistics, and qualitative analysis, researchers can interpret their findings, draw conclusions, and support decision-making processes. An effective data analysis plan and robust methodology ensure the accuracy and reliability of research outcomes, ultimately contributing to the advancement of knowledge across various fields.

What is Data Analysis in Research?

Data analysis in research involves using statistical and logical techniques to describe, summarize, and compare collected data. This includes inspecting, cleaning, transforming, and modeling data to find useful information and support decision-making. Quantitative data provides measurable insights, and a solid research design ensures accuracy and reliability. This process helps validate hypotheses, identify patterns, and make informed conclusions, making it a crucial step in the scientific method.

Examples of Data analysis in Research

• Survey Analysis : Researchers collect survey responses from a sample population to gauge opinions, behaviors, or characteristics. Using descriptive statistics, they summarize the data through means, medians, and modes, and then inferential statistics to generalize findings to a larger population.
• Experimental Analysis : In scientific experiments, researchers manipulate one or more variables to observe the effect on a dependent variable. Data is analyzed using methods such as ANOVA or regression analysis to determine if changes in the independent variable(s) significantly affect the dependent variable.
• Content Analysis : Qualitative research often involves analyzing textual data, such as interview transcripts or open-ended survey responses. Researchers code the data to identify recurring themes, patterns, and categories, providing a deeper understanding of the subject matter.
• Correlation Studies : Researchers explore the relationship between two or more variables using correlation coefficients. For example, a study might examine the correlation between hours of study and academic performance to identify if there is a significant positive relationship.
• Longitudinal Analysis : This type of analysis involves collecting data from the same subjects over a period of time. Researchers analyze this data to observe changes and developments, such as studying the long-term effects of a specific educational intervention on student achievement.
• Meta-Analysis : By combining data from multiple studies, researchers perform a meta-analysis to increase the overall sample size and enhance the reliability of findings. This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention.

Data analysis in Qualitative Research

Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings. Here are some key steps and methods used in qualitative data analysis:

• Coding : Researchers categorize the data by assigning labels or codes to specific segments of the text. These codes represent themes or concepts relevant to the research question.
• Thematic Analysis : This method involves identifying and analyzing patterns or themes within the data. Researchers review coded data to find recurring topics and construct a coherent narrative around these themes.
• Content Analysis : A systematic approach to categorize verbal or behavioral data to classify, summarize, and tabulate the data. This method often involves counting the frequency of specific words or phrases.
• Narrative Analysis : Researchers focus on the stories and experiences shared by participants, analyzing the structure, content, and context of the narratives to understand how individuals make sense of their experiences.
• Grounded Theory : This method involves generating a theory based on the data collected. Researchers collect and analyze data simultaneously, continually refining and adjusting their theoretical framework as new data emerges.
• Discourse Analysis : Examining language use and communication patterns within the data, researchers analyze how language constructs social realities and power relationships.
• Case Study Analysis : An in-depth analysis of a single case or multiple cases, exploring the complexities and unique aspects of each case to gain a deeper understanding of the phenomenon under study.

Data analysis in Quantitative Research

Data analysis in quantitative research involves the systematic application of statistical techniques to numerical data to identify patterns, relationships, and trends. Here are some common methods used in quantitative data analysis:

• Descriptive Statistics : This includes measures such as mean, median, mode, standard deviation, and range, which summarize and describe the main features of a data set.
• Inferential Statistics : Techniques like t-tests, chi-square tests, and ANOVA (Analysis of Variance) are used to make inferences or generalizations about a population based on a sample.
• Regression Analysis : This method examines the relationship between dependent and independent variables. Simple linear regression analyzes the relationship between two variables, while multiple regression examines the relationship between one dependent variable and several independent variables.
• Correlation Analysis : Researchers use correlation coefficients to measure the strength and direction of the relationship between two variables.
• Factor Analysis : This technique is used to identify underlying relationships between variables by grouping them into factors based on their correlations.
• Cluster Analysis : A method used to group a set of objects or cases into clusters, where objects in the same cluster are more similar to each other than to those in other clusters.
• Hypothesis Testing : This involves testing an assumption or hypothesis about a population parameter. Common tests include z-tests, t-tests, and chi-square tests, which help determine if there is enough evidence to reject the null hypothesis.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
• Multivariate Analysis : Techniques like MANOVA (Multivariate Analysis of Variance) and PCA (Principal Component Analysis) are used to analyze data that involves multiple variables to understand their effect and relationships.
• Structural Equation Modeling (SEM) : A multivariate statistical analysis technique that is used to analyze structural relationships. This method is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between measured variables and latent constructs.

Data analysis in Research Methodology

Data analysis in research methodology involves the process of systematically applying statistical and logical techniques to describe, condense, recap, and evaluate data. Here are the key components and methods involved:

• Data Preparation : This step includes collecting, cleaning, and organizing raw data. Researchers ensure data quality by handling missing values, removing duplicates, and correcting errors.
• Descriptive Analysis : Researchers use descriptive statistics to summarize the basic features of the data. This includes measures such as mean, median, mode, standard deviation, and graphical representations like histograms and pie charts.
• Inferential Analysis : This involves using statistical tests to make inferences about the population from which the sample was drawn. Common techniques include t-tests, chi-square tests, ANOVA, and regression analysis.
• Qualitative Data Analysis : For non-numeric data, researchers employ methods like coding, thematic analysis, content analysis, narrative analysis, and discourse analysis to identify patterns and themes.
• Quantitative Data Analysis : For numeric data, researchers apply statistical methods such as correlation, regression, factor analysis, cluster analysis, and time series analysis to identify relationships and trends.
• Hypothesis Testing : Researchers test hypotheses using statistical methods to determine whether there is enough evidence to reject the null hypothesis. This involves calculating p-values and confidence intervals.
• Data Interpretation : This step involves interpreting the results of the data analysis. Researchers draw conclusions based on the statistical findings and relate them back to the research questions and objectives.
• Validation and Reliability : Ensuring the validity and reliability of the analysis is crucial. Researchers check for consistency in the results and use methods like cross-validation and reliability testing to confirm their findings.
• Visualization : Effective data visualization techniques, such as charts, graphs, and plots, are used to present the data in a clear and understandable manner, aiding in the interpretation and communication of results.
• Reporting : The final step involves reporting the results in a structured format, often including an introduction, methodology, results, discussion, and conclusion. This report should clearly convey the findings and their implications for the research question.

Types of Data analysis in Research

• Purpose : To summarize and describe the main features of a dataset.
• Methods : Mean, median, mode, standard deviation, frequency distributions, and graphical representations like histograms and pie charts.
• Example : Calculating the average test scores of students in a class.
• Purpose : To make inferences or generalizations about a population based on a sample.
• Methods : T-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and confidence intervals.
• Example : Testing whether a new teaching method significantly affects student performance compared to a traditional method.
• Purpose : To analyze data sets to find patterns, anomalies, and test hypotheses.
• Methods : Visualization techniques like box plots, scatter plots, and heat maps; summary statistics.
• Example : Visualizing the relationship between hours of study and exam scores using a scatter plot.
• Purpose : To make predictions about future outcomes based on historical data.
• Methods : Regression analysis, machine learning algorithms (e.g., decision trees, neural networks), and time series analysis.
• Example : Predicting student graduation rates based on their academic performance and demographic data.
• Purpose : To provide recommendations for decision-making based on data analysis.
• Methods : Optimization algorithms, simulation, and decision analysis.
• Example : Suggesting the best course of action for improving student retention rates based on various predictive factors.
• Purpose : To identify and understand cause-and-effect relationships.
• Methods : Controlled experiments, regression analysis, path analysis, and structural equation modeling (SEM).
• Example : Determining the impact of a specific intervention, like a new curriculum, on student learning outcomes.
• Purpose : To understand the specific mechanisms through which variables affect one another.
• Methods : Detailed modeling and simulation, often used in scientific research to understand biological or physical processes.
• Example : Studying how a specific drug interacts with biological pathways to affect patient health.

How to write Data analysis in Research

Data analysis is crucial for interpreting collected data and drawing meaningful conclusions. Follow these steps to write an effective data analysis section in your research.

1. Prepare Your Data

Ensure your data is clean and organized:

• Remove duplicates and irrelevant data.
• Check for errors and correct them.
• Categorize data if necessary.

2. Choose the Right Analysis Method

Select a method that fits your data type and research question:

• Quantitative Data : Use statistical analysis such as t-tests, ANOVA, regression analysis.
• Qualitative Data : Use thematic analysis, content analysis, or narrative analysis.

3. Describe Your Analytical Techniques

Clearly explain the methods you used:

• Software and Tools : Mention any software (e.g., SPSS, NVivo) used.
• Statistical Tests : Detail the statistical tests applied, such as chi-square tests or correlation analysis.
• Qualitative Techniques : Describe coding and theme identification processes.

4. Present Your Findings

Organize your findings logically:

• Use Tables and Figures : Display data in tables, graphs, and charts for clarity.
• Summarize Key Results : Highlight the most significant findings.
• Include Relevant Statistics : Report p-values, confidence intervals, means, and standard deviations.

5. Interpret the Results

Explain what your findings mean in the context of your research:

• Compare with Hypotheses : State whether the results support your hypotheses.
• Relate to Literature : Compare your results with previous studies.
• Discuss Implications : Explain the significance of your findings.

6. Discuss Limitations

Acknowledge any limitations in your data or analysis:

• Sample Size : Note if the sample size was small.
• Biases : Mention any potential biases in data collection.
• External Factors : Discuss any factors that might have influenced the results.

7. Conclude with a Summary

Wrap up your data analysis section:

• Restate Key Findings : Briefly summarize the main results.
• Future Research : Suggest areas for further investigation.

Importance of Data analysis in Research

Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance:

• Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable. By using statistical techniques, researchers can minimize errors and biases, ensuring that the results are dependable.
• Facilitates Informed Decision-Making Through data analysis, researchers can make informed decisions based on empirical evidence. This is crucial in fields like healthcare, business, and social sciences, where decisions impact policies, strategies, and outcomes.
• Identifies Trends and Patterns Analyzing data helps researchers uncover trends and patterns that might not be immediately visible. These insights can lead to new hypotheses and areas of study, advancing knowledge in the field.
• Supports Hypothesis Testing Data analysis is vital for testing hypotheses. Researchers can use statistical methods to determine whether their hypotheses are supported or refuted, which is essential for validating theories and advancing scientific understanding.
• Provides a Basis for Predictions By analyzing current and historical data, researchers can develop models that predict future outcomes. This predictive capability is valuable in numerous fields, including economics, climate science, and public health.

FAQ’s

What is the difference between qualitative and quantitative data analysis.

Qualitative analysis focuses on non-numerical data to understand concepts, while quantitative analysis deals with numerical data to identify patterns and relationships.

What is descriptive statistics?

Descriptive statistics summarize and describe the features of a data set, including measures like mean, median, mode, and standard deviation.

What is inferential statistics?

Inferential statistics use sample data to make generalizations about a larger population, often through hypothesis testing and confidence intervals.

What is regression analysis?

Regression analysis examines the relationship between dependent and independent variables, helping to predict outcomes and understand variable impacts.

What is the role of software in data analysis?

Software like SPSS, R, and Excel facilitate data analysis by providing tools for statistical calculations, visualization, and data management.

What are data visualization techniques?

Data visualization techniques include charts, graphs, and maps, which help in presenting data insights clearly and effectively.

What is data cleaning?

Data cleaning involves removing errors, inconsistencies, and missing values from a data set to ensure accuracy and reliability in analysis.

What is the significance of sample size in data analysis?

Sample size affects the accuracy and generalizability of results; larger samples generally provide more reliable insights.

How does correlation differ from causation?

Correlation indicates a relationship between variables, while causation implies one variable directly affects the other.

What are the ethical considerations in data analysis?

Ethical considerations include ensuring data privacy, obtaining informed consent, and avoiding data manipulation or misrepresentation.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Economic Research

• Data library

Market Summary: April 2024

• Visualize the data
• Download the data
• 2024 Housing Market Forecast

Latest research

• Featured Posted in Featured Articles on Mar 21, 2024 The Best Time to Sell in 2024: The Week of April 14-20
• Posted in Affordability on Jun 28, 2024 Realtor.com Survey Finds 33% of ‘Sandwich Generation’ Say their Circumstance has Helped then Buy a Home
• Posted in Housing Finance on Jun 27, 2024 Mortgage Rate Drops 0.01 Percentage Point to 6.86%

See more research

Sign up for updates

Join our mailing list to receive the latest data and research.

Featured Realtor.com ® Reports

Check out our in-depth reports that take a look at current economic and housing market conditions.

View all reports

Build your real estate data set and download

Our Real Estate Data Library is based on the most comprehensive and accurate database of MLS-listed for-sale homes in the industry.

Download data

Please attribute to Realtor.com ® Economic Research when quoting members of our team and/or using our housing data, research, and insights. Please include a link to our data library .

Data dictionary

Home sellers on the fence waiting for that perfect moment shouldn’t wait too long, because the…

Roughly one in six Americans falls into the ‘Sandwich Generation’, meaning they are caring for their…

The Freddie Mac rate for a 30-year mortgage dropped 0.01 percentage points to 6.86% this week.

Pending home sales fell again in May, dipping 2.1% month-over-month as mortgage rates hovered around 7%.…

Our research team releases weekly housing trends reports looking at inventory metrics like the number of…

New home sales fell 11.3% in May, dropping from April’s upwardly revised level to a seasonally…

The April S&P CoreLogic Case-Shiller Index showed that home price momentum slipped even as the index…

Existing-home sales eased in May, slipping 0.7% to a pace of 4.11 million and trailing the…

The Freddie Mac rate for a 30-year mortgage decreased by 0.08 percentage points to 6.87% this…

New construction activity slowed in May, with the rate of housing permits, starts and completions falling…

In celebration of 80 years of GI Bill benefits (formally known as the Servicemen’s Readjustment Act…

For media inquiries, please contact our comms team: [email protected] For questions or guidance on exploring the data: [email protected]

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Research: Using AI at Work Makes Us Lonelier and Less Healthy

• David De Cremer
• Joel Koopman

Employees who use AI as a core part of their jobs report feeling more isolated, drinking more, and sleeping less than employees who don’t.

The promise of AI is alluring — optimized productivity, lightning-fast data analysis, and freedom from mundane tasks — and both companies and workers alike are fascinated (and more than a little dumbfounded) by how these tools allow them to do more and better work faster than ever before. Yet in fervor to keep pace with competitors and reap the efficiency gains associated with deploying AI, many organizations have lost sight of their most important asset: the humans whose jobs are being fragmented into tasks that are increasingly becoming automated. Across four studies, employees who use it as a core part of their jobs reported feeling lonelier, drinking more, and suffering from insomnia more than employees who don’t.

Imagine this: Jia, a marketing analyst, arrives at work, logs into her computer, and is greeted by an AI assistant that has already sorted through her emails, prioritized her tasks for the day, and generated first drafts of reports that used to take hours to write. Jia (like everyone who has spent time working with these tools) marvels at how much time she can save by using AI. Inspired by the efficiency-enhancing effects of AI, Jia feels that she can be so much more productive than before. As a result, she gets focused on completing as many tasks as possible in conjunction with her AI assistant.

• David De Cremer is a professor of management and technology at Northeastern University and the Dunton Family Dean of its D’Amore-McKim School of Business. His website is daviddecremer.com .
• JK Joel Koopman is the TJ Barlow Professor of Business Administration at the Mays Business School of Texas A&M University. His research interests include prosocial behavior, organizational justice, motivational processes, and research methodology. He has won multiple awards from Academy of Management’s HR Division (Early Career Achievement Award and David P. Lepak Service Award) along with the 2022 SIOP Distinguished Early Career Contributions award, and currently serves on the Leadership Committee for the HR Division of the Academy of Management .

Partner Center

Future-proof your business and equip your employees with the most in-demand skills

Explore our programs and take the first step toward transforming your teams

Stay informed on the latest workforce trends

Read our latest research and access practical guides for leaders

Access expert insights from live and on-demand events

Unlock business value from AI

How to build AI-enabled teams

How to build a future-proof workforce

Preparing for the AI revolution

Five statistics every leader should know

The case for apprenticeships in 2024

We have one thing on our minds. Your success.

Set the foundation for a successful career

Supercharge your career by acquiring in-demand data and tech skills

Explore our diverse range of programs tailored to your professional growth

The Multiverse Community is a powerful network of apprentices and alumni who support each other to achieve their goals

Visit our support page for general information, FAQs, and contact options to help you get the support you need

Announcing AI Jumpstart: A new AI learning module for apprentices

What are on the job training programs and how to find them

How to get a job in tech with no experience

We're providing equitable access to economic opportunity, for everyone

It’s time for our workforces to reflect our society. Let’s make it happen

Together we’ll change what’s possible in education and work, through the power of professional apprenticeships

The latest news and blog from Multiverse

Find out more about our Learning Model - MAGE, and exciting research happening across our Learning Science Team

Multiverse’s Chief Revenue Officer - Alex Varel

Evolving Multiverse’s Sales Team

Celebrating Women in Tech at Multiverse

Individuals

.css-v0jgcu{position:absolute;top:0;left:0;} .css-19sk4h4{position:relative;} Business data analytics: Definition and how to get started

By Team Multiverse

• Arrow Right Streamline Icon: https://streamlinehq.com Real-world applications of business data analytics
• Arrow Right Streamline Icon: https://streamlinehq.com The role of a Business Data Analyst
• Arrow Right Streamline Icon: https://streamlinehq.com Essential skills for Business Data Analysts
• Arrow Right Streamline Icon: https://streamlinehq.com Steps to becoming a Business Data Analyst
• Arrow Right Streamline Icon: https://streamlinehq.com Take the next step in your data analytics journey with Multiverse

Business data analytics is a cornerstone of modern decision making and innovation.

Companies use the insights they gain from business analytics to create data-driven strategies. These approaches can improve customer satisfaction, operational efficiency, and profitability. Retailers, for example, can use data analytics to predict which products will sell fastest and optimize its supply chain management.

What is business data analytics?

Business data analytics uses software and statistical techniques to interpret data and gain meaningful insights. This process allows organizations to understand their operations better and improve performance. Business analytics also assists with strategic planning and risk management.

Say, for example, a national restaurant brand wants to update its menu. Data analytics allows the company to interpret customer reviews and sales trends to determine which meals and ingredients perform best. Based on these insights, the restaurant can tailor its menu to satisfy customers and boost sales.

Understanding business data analysis

You may have already started to master some of the components of business data analytics. This process involves a few basic steps:

• Ask a question - Start with a specific question or concern you want to address with data. For instance, you could explore customer trends to discover why your business's sales have declined.
• Data collection - Identify relevant data sources and gather information. You could survey customers or use data mining techniques to harvest social media posts.
• Data cleaning and processing - Organize the raw data into a usable format. This often involves transforming data by loading it into an environment optimized for analysis. You’ll also fill in missing data, remove inconsistencies, and correct errors.
• Data analysis - Apply statistical techniques and software to reveal patterns and correlations in the data set.
• Data visualization - Use software to transform the data into easy-to-understand graphics. These visualizations may include charts, graphs, and maps.
• Data interpretation - Study the results to extract meaningful insights. For instance, you might determine your target audience’s interests have changed, leading to a sales dip.
• Communicate findings - Share your results with decision makers and recommend the next steps. In this scenario, you might advise developing new services that better align with your consumer base.

Real-world applications of business data analytics

Business data analytics has many practical applications across industries. Here are three case studies that illustrate the value and versatility of this approach.

Tracking Machine Health in Manufacturing

The company installs advanced telematics software in its construction machinery. The software collects data about different aspects of machine behavior, including fault codes, fuel consumption, and idle time. This data gets streamed through the cloud to John Deere’s Machine Health Center in Iowa.

Local dealers use this data to diagnose machine problems remotely instead of traveling to construction sites or farms. They can select the necessary parts and repair tools to bring to the service appointment, saving time and reducing trips. Additionally, John Deere uses this information to identify and fix potential manufacturing errors.

These applications allow John Deere to improve its performance over time and provide more efficient service.

Improving patient care in healthcare

Data analytics allows Stanford Medicine Children’s Health (opens new window) to understand and improve the patient experience.

The organization uses evaluation forms to collect data about patients’ experiences during their hospital stays. Analysts use AI tools to synthesize the information and reveal patterns, such as complaints about staff responsiveness and wait times.

According to Chief Analytics Officer Brendan Watkins, the organization places these insights “directly into the hands of the folks who can make a difference [and], make systemic change with this data.” These stakeholders include healthcare providers who can use the information to deliver better patient care.

Boosting productivity in retail

The grocery chain Kroger (opens new window) has developed two data-driven applications to improve employee productivity.

First, the company created a task management application for Night Crew Managers. This application displays each store’s inventory and merchandise deliveries in real time. It also uses data analytics to optimize employee to-do lists to help them restock stores efficiently.

Additionally, Kroger uses a store management application to streamline store audits. This tool also automatically recommends tasks for employees as they prepare for audits.

Both applications help Kroger associates adapt to changing store conditions and improve the customer experience.

The role of a Business Data Analyst

A Business Data Analyst uses data to solve business problems and identify growth opportunities. They also support decision makers by offering recommendations based on their findings.

The day-to-day responsibilities of these professionals vary by role but typically include these tasks:

• Collect data from a wide range of sources, such as customer feedback forms, financial records, or in-product data from a software application
• Develop databases to organize information
• Process raw data to prepare it for analysis
• Build and train machine learning (ML) models to analyze enormous data sets
• Design predictive models to forecast potential outcomes
• Create data visualizations
• Deliver presentations about their findings
• Collaborate with colleagues in marketing, sales, and other departments
• Learn about the latest advancements and trends in business data analytics

Business Data Analysts wield significant influence in their organizations. Leaders rely on their expertise for a broad range of business decisions, such as:

• Choosing marketing and sales tactics
• Deciding whether to invest in a new venture
• Managing financial resources
• Selecting prototypes to develop into new products
• Scheduling manufacturing equipment for maintenance and replacement

Because Business Data Analysts deliver considerable value, they often earn lucrative salaries . According to Glassdoor, the pay range for this career is $97,000 to $153,000, with an average salary of $121,000.

Essential skills for Business Data Analysts

You’ll need the right technical and soft skills to thrive in a business data analytics role. If you’re interested in this career path, focus on developing these foundational abilities.

Technical skills

Business Data Analysts rely heavily on technology to interpret data. After all, you wouldn’t get very far if you had to analyze a spreadsheet with thousands of data points by hand. These technical skills will help you manage and process data effectively:

• Structured Query Language (SQL) - This language allows you to organize, manipulate, and search structured databases.
• Programming languages - Use R for exploratory data analysis and data visualization. Python enables you to automate tasks, clean data, and build Machine Learning (ML)ML algorithms.
• Statistical analysis - Understand how to use statistical methods to interpret data. For example, descriptive analytics evaluates historical data to understand events and patterns. Prescriptive analytics uses past and present data to recommend future actions.
• Artificial intelligence (AI) and ML - Companies increasingly rely on AI and ML to analyze data and predict future trends. Study foundational ML concepts like clustering algorithms, decision trees, and linear regression. You should also know how to use ML frameworks and libraries like PyTorch and TensorFlow.
• Data visualization - Transform data into accessible and visually appealing graphics. Popular data visualization platforms include Microsoft Power BI, Tableau, and Zoho Analytics.
• Reporting - Use business intelligence tools like Qlikview and Sisense to create interactive dashboards and reports for stakeholders.

Soft skills

Data Analysts need strong interpersonal skills to excel in the workplace, including:

• Adaptability - Business analytics evolves quickly, so prepare to embrace new approaches and tools.
• Collaboration - Share ideas and responsibilities with team members from different backgrounds and departments.
• Communication - Express your ideas clearly in conversations, presentations, and written reports. You should also learn to translate complex technical concepts for lay audiences.
• Critical thinking - Evaluate the accuracy of data, identify potential biases in the results, and assess potential recommendations for feasibility.
• Negotiation - Collaborate with multiple stakeholders to develop solutions that meet everyone’s needs.
• Problem-solving - Learn how to approach problems from different angles and devise novel solutions.

Steps to becoming a Business Data Analyst

There’s no universal blueprint to becoming a Business Data Analyst. You can use many resources and strategies to gain the knowledge and skills required for this career. Here are a few common pathways.

Earn a college degree

A college education is a traditional — but not required — educational pathway for Business Analysts. Many colleges and universities offer degrees in business analysis, data science, mathematics, and other relevant fields. 

Enrolling in a business data analytics program offers several benefits. A structured curriculum gives you a solid foundation in data management, statistical analysis, and other necessary skills. You’ll also receive feedback and guidance from faculty.

But a college education has a few drawbacks. First, a four-year degree requires a significant investment of time and money. Undergraduate students pay an average of $36,436 per year (opens new window) for tuition, books, and other expenses. You’ll also need to dedicate extensive time to studying and attending classes. People with full-time jobs, families, and other obligations may struggle to balance their responsibilities with a college education.

Many colleges also provide limited hands-on experience. A business data analytics major may learn foundational theories but not be able to apply these concepts in the real world. As a result, they may lack the experience and portfolio needed to land a position.

Obtain relevant certifications

Certifications enable you to develop your skills and showcase your abilities to potential employers. Here are a few relevant credentials that could help you prepare for data analytics roles:

• Entry Certificate in Business Analytics (ECBA) - The International Institute of Business Analysis (opens new window) offers this certificate for aspiring and entry-level data professionals. The certification demonstrates foundational competencies in business analysis planning, elicitation and collaboration, and other areas.
• Professional in Business Analysis (PMI-PBA) - The Project Management Institute (opens new window) designed this certification for Business Analysts who use data to support projects.
• Certified Foundation Level Business Analyst - The International Qualification Board for Business Analysis (opens new window) offers this foundational certification. It demonstrates proficiency in business modeling and creating business solutions.

Certifications cost much less than the average four-year degree and typically take less than a year to earn. They can accelerate your professional development and prove your commitment to the field to potential employers.

If you’re an established professional, you may already have many skills needed to succeed in business analytics. But everyone has areas for improvement. Thankfully, upskilling can fill any gaps in your knowledge — making you more productive at work and better prepared to advance in your career.

In fact, according to Gartner, 75% of employees who participate in upskilling programs agree it contributes to career progression.

Multiverse’s Applied Analytics Accelerator is one of the most effective ways to level up your skills. This cost-free six-month program allows you to immerse yourself in the field of business analytics while working for your current employer.

The apprenticeship includes six modules that teach you how to make data driven decisions and improve business processes. You’ll also learn data analysis and visualization skills you can immediately apply in your role. This fusion of structured learning and hands-on experience will help you kickstart or grow your career in business analysis.

Gain hands-on experience

Developing practical experience strengthens your skills and gives you a competitive advantage in the job market. Look for opportunities to apply your skills with real data sets.

For example, you could volunteer to analyze customer data for your current employer and recommend ways to improve marketing initiatives. You could also help clients solve business problems as a freelancer or consultant.

As you create projects, assemble them into a digital portfolio. Include a detailed description of each project and highlight their measurable outcomes. Potential employers can review your portfolio to gauge your experience level and skills.

If you’re looking for hand-on projects, Multiverse’s Applied Analytics Accelerator equips you to upskill your data chops while staying in your current role.

Take the next step in your data analytics journey with Multiverse

As a Business Analyst, you play a critical role in business decision making and strategic planning. Your insights can help companies develop cutting-edge innovations, improve customer experiences, reduce costs, and more.

Prepare for a career in this in-demand field with a Multiverse apprenticeship. Apprentices get paid to upskill and gain hands-on experience with real business analytics projects. They also receive one-on-one coaching tailored to their personal and professional goals.

Tell us about yourself by completing our quick application (opens new window) , and the Multiverse team will get in touch with the next steps.

Team Multiverse

Related posts

Top Data Analytics Courses for 2024

Elevate your career with the right data analytics course. Discover how to choose the best course for you, the skills you'll gain, and the doors it will open in the tech industry.

20 February 2024

What is upskilling? Get paid to advance your career

Learn how to leverage upskilling to master new skills and tools and expand your professional capabilities. Uplevel your career with Multiverse today.

21 November 2023

Upskilling vs reskilling—what’s the difference?

Transition smoothly into new industries or roles by reskilling or upskilling. Learn the differences between the two and how to get started at no cost.

5 December 2023

File(s) under embargo

until file(s) become available

Empirical Analysis of Google’s Privacy-Preserving Targeted Advertising

Degree grantor, degree level, degree name, committee member, thesis type, usage metrics.