what is a single report of a quantitative research study

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10.1 Overview of Single-Subject Research

Learning objectives.

Explain what single-subject research is, including how it differs from other types of psychological research.
Explain what case studies are, including some of their strengths and weaknesses.
Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this book is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some areas of psychology.

Before continuing, it is important to distinguish single-subject research from two other approaches, both of which involve studying in detail a small number of participants. One is qualitative research, which focuses on understanding people’s subjective experience by collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

It is also important to distinguish single-subject research from case studies. A case study is a detailed description of an individual, which can include both qualitative and quantitative analyses. (Case studies that include only qualitative analyses can be considered a type of qualitative research.) The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 10.5 “The Case of “Anna O.”” ) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920), who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat. Case studies can be useful for suggesting new research questions and for illustrating general principles. They can also help researchers understand rare phenomena, such as the effects of damage to a specific part of the human brain. As a general rule, however, case studies cannot substitute for carefully designed group or single-subject research studies. One reason is that case studies usually do not allow researchers to determine whether specific events are causally related, or even related at all. For example, if a patient is described in a case study as having been sexually abused as a child and then as having developed an eating disorder as a teenager, there is no way to determine whether these two events had anything to do with each other. A second reason is that an individual case can always be unusual in some way and therefore be unrepresentative of people more generally. Thus case studies have serious problems with both internal and external validity.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961). (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst (p. 9).

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return.

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.2

“Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis.

Wikimedia Commons – public domain.

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behavior of each one. But why take this approach instead of the group approach? There are several important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behavior of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behavior of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behavior of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behavior of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

A second assumption of single-subject research is that it is important to discover causal relationships through the manipulation of an independent variable, the careful measurement of a dependent variable, and the control of extraneous variables. For this reason, single-subject research is often considered a type of experimental research with good internal validity. Recall, for example, that Hall and his colleagues measured their dependent variable (studying) many times—first under a no-treatment control condition, then under a treatment condition (positive teacher attention), and then again under the control condition. Because there was a clear increase in studying when the treatment was introduced, a decrease when it was removed, and an increase when it was reintroduced, there is little doubt that the treatment was the cause of the improvement.

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976). The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often chaotic elementary school classrooms.

Who Uses Single-Subject Research?

Single-subject research has been around as long as the field of psychology itself. In the late 1800s, one of psychology’s founders, Wilhelm Wundt, studied sensation and consciousness by focusing intensively on each of a small number of research participants. Herman Ebbinghaus’s research on memory and Ivan Pavlov’s research on classical conditioning are other early examples, both of which are still described in almost every introductory psychology textbook.

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938). He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis (Baer, Wolf, & Risley, 1968). Applied behavior analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis .

Although most contemporary single-subject research is conducted from the behavioral perspective, it can in principle be used to address questions framed in terms of any theoretical perspective. For example, a studying technique based on cognitive principles of learning and memory could be evaluated by testing it on individual high school students using the single-subject approach. The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioral, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement (Kazdin, 1982).

Key Takeaways

Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology.
Single-subject studies must be distinguished from case studies, in which an individual case is described in detail. Case studies can be useful for generating new research questions, for studying rare phenomena, and for illustrating general principles. However, they cannot substitute for carefully controlled experimental or correlational studies because they are low in internal and external validity.
Single-subject research has been around since the beginning of the field of psychology. Today it is most strongly associated with the behavioral theoretical perspective, but it can in principle be used to study behavior from any perspective.
Practice: Find and read a published article in psychology that reports new single-subject research. (A good source of articles published in the Journal of Applied Behavior Analysis can be found at http://seab.envmed.rochester.edu/jaba/jabaMostPop-2011.html .) Write a short summary of the study.

Practice: Find and read a published case study in psychology. (Use case study as a key term in a PsycINFO search.) Then do the following:

Describe one problem related to internal validity.
Describe one problem related to external validity.
Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis , 1 , 91–97.

Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton.

Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press.

Skinner, B. F. (1938). The behavior of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts.

Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology , 3 , 1–14.

Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis, 11 , 203–214.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

Description : To provide a detailed and accurate description of a particular phenomenon or population.
Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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10.1 Overview of Single-Subject Research

Learning objectives.

Explain what single-subject research is, including how it differs from other types of psychological research.
Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this textbook is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some more applied areas of psychology.

Before continuing, it is important to distinguish single-subject research from case studies and other more qualitative approaches that involve studying in detail a small number of participants. As described in Chapter 6, case studies involve an in-depth analysis and description of an individual, which is typically primarily qualitative in nature. More broadly speaking, qualitative research focuses on understanding people’s subjective experience by observing behavior and collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

Assumptions of Single-Subject Research

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976) [1] . The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often-chaotic elementary school classrooms.

Who Uses Single-Subject Research?

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938) [2] . He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis (Baer, Wolf, & Risley, 1968) [3] . Applied behavior analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis .

Key Takeaways

Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology.
Single-subject studies must be distinguished from qualitative research on a single person or small number of individuals. Unlike more qualitative research, single-subject research focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.
Single-subject research has been around since the beginning of the field of psychology. Today it is most strongly associated with the behavioral theoretical perspective, but it can in principle be used to study behavior from any perspective.
Practice: Find and read a published article in psychology that reports new single-subject research. (An archive of articles published in the Journal of Applied Behavior Analysis can be found at http://www.ncbi.nlm.nih.gov/pmc/journals/309/ ) Write a short summary of the study.
Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis, 11 , 203–214. ↵
Skinner, B. F. (1938). T he behavior of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts. ↵
Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1 , 91–97. ↵
Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press. ↵

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What is Quantitative Research? Definition, Methods, Types, and Examples

what is a single report of a quantitative research study

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns. 

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.

Here are two quantitative research examples:

Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).
A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:

Theory : Define the problem area or area of interest and create a research question.
Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.
Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
Data collection : This process could be extensive based on your research objective and sample size.
Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.
Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.

Quantitative research characteristics 4

Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .
Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.
Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.
Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.
Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.
Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.

The following data collection methods are commonly used in primary quantitative research :

Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.
Interviews : These are commonly telephonic or face-to-face.
Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .
Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:

Cross-tabulation —Uses a tabular format to draw inferences among collected data
MaxDiff analysis —Gauges the preferences of the respondents
TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business
Gap analysis —Identify gaps in attaining the desired results
SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization
Text analysis —Used for interpreting unstructured data

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.

The main sources of secondary data are:

The Internet
Government and non-government sources
Public libraries
Educational institutions
Commercial information sources such as newspapers, journals, radio, TV

When to use quantitative research 6

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:

recommend a final course of action
find whether a consensus exists regarding a particular subject
generalize results to a larger population
determine a cause-and-effect relationship between variables
describe characteristics of specific groups of people
test hypotheses and examine specific relationships
identify and establish size of market segments

A research case study to understand when to use quantitative research 7

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?

Method: The researchers obtained quantitative data from three sources:

Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.
Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.
Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.

Results of quantitative research : The following observations were made based on quantitative data analysis:

The move to the new design did not result in a significant change in the proportion of time spent on different activities.
Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.
A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.
Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.
Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.

Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.
This type of research uses numeric data so analysis is relatively easier .
In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.
The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.
Higher levels of control can be applied to the research so the chances of bias can be reduced.
Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research.

Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.
Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.
Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.
Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.
Large sample sizes are needed for more accurate and generalizable analysis .
Quantitative research cannot be used to address complex issues.

Frequently asked questions on  quantitative research  

Q:  What is the difference between quantitative research and qualitative research? 1

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.


Purpose and design
Research question
Sample size	Large	Small
Data
Data collection method	Experiments, controlled observations, questionnaires and surveys with a rating scale or close-ended questions. The methods can be experimental, quasi-experimental, descriptive, or correlational.	Semi-structured interviews/surveys with open-ended questions, document study/literature reviews, focus groups, case study research, ethnography
Data analysis

Q:  What is the difference between reliability and validity? 8,9  

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.

The following table gives the key differences between reliability and validity.


Importance	Refers to the consistency of a measure	Refers to the accuracy of a measure
Ease of achieving	Easier, yields results faster	Involves more analysis, more difficult to achieve
Assessment method	By examining the consistency of outcomes over time, between various observers, and within the test	By comparing the accuracy of the results with accepted theories and other measurements of the same idea
Relationship	Unreliable measurements typically cannot be valid	Valid measurements are also reliable
Types	Test-retest reliability, internal consistency, inter-rater reliability	Content validity, criterion validity, face validity, construct validity

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.

References

Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research
Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/
The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/
What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/
Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research
Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/
Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/
McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html
Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/
Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr

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Writing Quantitative Research Studies

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Ankur Singh 2 ,
Adyya Gupta 3 &
Karen G. Peres 4

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Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to maintain the credibility and integrity of quantitative studies is outlined. Commonly used terminologies in empirical studies are defined and discussed with suitable examples. Key elements that build consistency between different sections (background, methods, results, and the discussion) of a research study using quantitative methods in a journal article are explicated. Specifically, recommended standard guidelines for randomized controlled trials and observational studies for reporting and discussion of findings from quantitative studies are elaborated. Key aspects of methodology that include describing the study population, sampling strategy, data collection methods, measurements/variables, and statistical analysis which informs the quality of a study from the reviewer’s perspective are described. Effective use of references in the methods section to strengthen the rationale behind specific statistical techniques and choice of measures has been highlighted with examples. Identifying ways in which data can be most succinctly and effectively summarized in tables and graphs according to their suitability and purpose of information is also detailed in this chapter. Strategies to present and discuss the quantitative findings in a structured discussion section are also provided. Overall, the chapter provides the readers with a comprehensive set of tools to identify key strategies to be considered when reporting quantitative research.

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Quantitative Research

Case Study 3: Application of Quantitative Methodology

Bhaumik S, Arora M, Singh A, Sargent JD. Impact of entertainment media smoking on adolescent smoking behaviours. Cochrane Database Syst Rev. 2015;6:1–12. https://doi.org/10.1002/14651858.CD011720 .

Article Google Scholar

Dickersin K, Manheimer E, Wieland S, Robinson KA, Lefebvre C, McDonald S. Development of the Cochrane Collaboration’s CENTRAL register of controlled clinical trials. Eval Health Prof. 2002;25(1):38–64.

Google Scholar

Docherty M, Smith R. The case for structuring the discussion of scientific papers: much the same as that for structuring abstracts. Br Med J. 1999;318(7193):1224–5.

Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37–48.

Horton R. The rhetoric of research. Br Med J. 1995;310(6985):985–7.

Kool B, Ziersch A, Robinson P, Wolfenden L, Lowe JB. The ‘Seven deadly sins’ of rejected papers. Aust N Z J Public Health. 2016;40(1):3–4.

Mannocci A, Saulle R, Colamesta V, D’Aguanno S, Giraldi G, Maffongelli E, et al. What is the impact of reporting guidelines on public health journals in Europe? The case of STROBE, CONSORT and PRISMA. J Public Health. 2015;37(4):737–40.

Rothwell PM. External validity of randomised controlled trials: “to whom do the results of this trial apply?”. Lancet. 2005;365(9453):82–93.

Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. PLoS Med. 2010;7(3):e1000251.

Szklo M. Quality of scientific articles. Rev Saude Publica. 2006;40 Spec no:30–5.

Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4(10):e297.

Weiss NS, Koepsell TD, Psaty BM. Generalizability of the results of randomized trials. Arch Intern Med. 2008;168(2):133–5.

Singh A, Gupta A, Peres MA, Watt RG, Tsakos G, Mathur MR. Association between tooth loss and hypertension among a primarily rural middle aged and older Indian adult population. J Public Health Dent. 2016;76:198–205.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
Sample size: Quantitative research is conducted on a significant sample size representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

Primary quantitative research methods
Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection.

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
Multiple samples can be analyzed and compared using a cross-sectional survey research method.
Multiple variables can be evaluated using this type of survey research.
The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
In situations where the sequence of events is highly essential, longitudinal surveys are used.
Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

The relationship between stress and depression.
The equation between fame and money.
The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

Traditional research methods are more effective than modern techniques.
Systematic teaching schedules help children who struggle to cope with the course.
It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Neag School of Education

Educational Research Basics by Del Siegle

Single subject research.

“ Single subject research (also known as single case experiments) is popular in the fields of special education and counseling. This research design is useful when the researcher is attempting to change the behavior of an individual or a small group of individuals and wishes to document that change. Unlike true experiments where the researcher randomly assigns participants to a control and treatment group, in single subject research the participant serves as both the control and treatment group. The researcher uses line graphs to show the effects of a particular intervention or treatment. An important factor of single subject research is that only one variable is changed at a time. Single subject research designs are “weak when it comes to external validity….Studies involving single-subject designs that show a particular treatment to be effective in changing behavior must rely on replication–across individuals rather than groups–if such results are be found worthy of generalization” (Fraenkel & Wallen, 2006, p. 318).

Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day for several days. In the example below, the target student was disruptive seven times on the first day, six times on the second day, and seven times on the third day. Note how the sequence of time is depicted on the x-axis (horizontal axis) and the dependent variable (outcome variable) is depicted on the y-axis (vertical axis).

Once a baseline of behavior has been established (when a consistent pattern emerges with at least three data points), the intervention begins. The researcher continues to plot the frequency of behavior while implementing the intervention of praise.

In this example, we can see that the frequency of disruptions decreased once praise began. The design in this example is known as an A-B design. The baseline period is referred to as A and the intervention period is identified as B.

Another design is the A-B-A design. An A-B-A design (also known as a reversal design) involves discontinuing the intervention and returning to a nontreatment condition.

Sometimes an individual’s behavior is so severe that the researcher cannot wait to establish a baseline and must begin with an intervention. In this case, a B-A-B design is used. The intervention is implemented immediately (before establishing a baseline). This is followed by a measurement without the intervention and then a repeat of the intervention.

Multiple-Baseline Design

Sometimes, a researcher may be interested in addressing several issues for one student or a single issue for several students. In this case, a multiple-baseline design is used.

“In a multiple baseline across subjects design, the researcher introduces the intervention to different persons at different times. The significance of this is that if a behavior changes only after the intervention is presented, and this behavior change is seen successively in each subject’s data, the effects can more likely be credited to the intervention itself as opposed to other variables. Multiple-baseline designs do not require the intervention to be withdrawn. Instead, each subject’s own data are compared between intervention and nonintervention behaviors, resulting in each subject acting as his or her own control (Kazdin, 1982). An added benefit of this design, and all single-case designs, is the immediacy of the data. Instead of waiting until postintervention to take measures on the behavior, single-case research prescribes continuous data collection and visual monitoring of that data displayed graphically, allowing for immediate instructional decision-making. Students, therefore, do not linger in an intervention that is not working for them, making the graphic display of single-case research combined with differentiated instruction responsive to the needs of students.” (Geisler, Hessler, Gardner, & Lovelace, 2009)

Regardless of the research design, the line graphs used to illustrate the data contain a set of common elements.

Generally, in single subject research we count the number of times something occurs in a given time period and see if it occurs more or less often in that time period after implementing an intervention. For example, we might measure how many baskets someone makes while shooting for 2 minutes. We would repeat that at least three times to get our baseline. Next, we would test some intervention. We might play music while shooting, give encouragement while shooting, or video the person while shooting to see if our intervention influenced the number of shots made. After the 3 baseline measurements (3 sets of 2 minute shooting), we would measure several more times (sets of 2 minute shooting) after the intervention and plot the time points (number of baskets made in 2 minutes for each of the measured time points). This works well for behaviors that are distinct and can be counted.

Sometimes behaviors come and go over time (such as being off task in a classroom or not listening during a coaching session). The way we can record these is to select a period of time (say 5 minutes) and mark down every 10 seconds whether our participant is on task. We make a minimum of three sets of 5 minute observations for a baseline, implement an intervention, and then make more sets of 5 minute observations with the intervention in place. We use this method rather than counting how many times someone is off task because one could continually be off task and that would only be a count of 1 since the person was continually off task. Someone who might be off task twice for 15 second would be off task twice for a score of 2. However, the second person is certainly not off task twice as much as the first person. Therefore, recording whether the person is off task at 10-second intervals gives a more accurate picture. The person continually off task would have a score of 30 (off task at every second interval for 5 minutes) and the person off task twice for a short time would have a score of 2 (off task only during 2 of the 10 second interval measures.

I also have additional information about how to record single-subject research data .

I hope this helps you better understand single subject research.

I have created a PowerPoint on Single Subject Research , which also available below as a video.

I have also created instructions for creating single-subject research design graphs with Excel .

Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education (6th ed.). Boston, MA: McGraw Hill.

Geisler, J. L., Hessler, T., Gardner, R., III, & Lovelace, T. S. (2009). Differentiated writing interventions for high-achieving urban African American elementary students. Journal of Advanced Academics, 20, 214–247.

Del Siegle, Ph.D. University of Connecticut [email protected] www.delsiegle.info

Revised 02/02/2024

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20 16. Reporting quantitative results

Chapter outline.

Reporting quantitative results (8 minute read time)

Content warning: Brief discussion of violence against women.

16.1 Reporting quantitative results

Learning objectives.

Learners will be able to…

Execute a quantitative research report using key elements for accuracy and openness

So you’ve completed your quantitative analyses and are ready to report your results. We’re going to spend some time talking about what matters in quantitative research reports, but the very first thing to understand is this: openness with your data and analyses is key. You should never hide what you did to get to a particular conclusion and, if someone wanted to and could ethically access your data, they should be able to replicate more or less exactly what you did. While your quantitative report won’t have every single step you took to get to your conclusion, it should have plenty of detail so someone can get the picture.

Below, I’m going to take you through the key elements of a quantitative research report. This overview is pretty general and conceptual, and it will be helpful for you to look at existing scholarly articles that deal with quantitative research (like ones in your literature review) to see the structure applied. Also keep in mind that your instructor may want the sections broken out slightly differently; nonetheless, the content I outline below should be in your research report.

Introduction and literature review

These are what you’re working on building with your research proposal this semester. They should be included as part of your research report so that readers have enough information to evaluate your research for themselves. What’s here should be very similar to the introduction and literature review from your research proposal, where you described the literature relevant to the study you wanted to do. With your results in hand, though, you may find that you have to add information to the literature you wrote previously to help orient the reader of the report to important topics needed to understand the results of your study.

In this section, you should explicitly lay out your study design – for instance, if it was experimental, be specific about the type of experimental design. Discuss the type of sampling that you used, if that’s applicable to your project. You should also go into a general description of your data, including the time period, any exclusions you made from the original data set and the source – i.e., did you collect it yourself or was it secondary data? Next, talk about the specific statistical methods you used, like t- tests, Chi-square tests, or regression analyses. For descriptive statistics, you can be relatively general – you don’t need to say “I looked at means and medians,” for instance. You need to provide enough information here that someone could replicate what you did.

In this section, you should also discuss how you operationalized your variables. What did you mean when you asked about educational attainment – did you ask for a grade number, or did you ask them to pick a range that you turned into a category? This is key information for readers to understand your research. Remember when you were looking for ways to operationalize your variables? Be the kind of author who provides enough information on operationalization so people can actually understand what they did.

You’re going to run lots of different analyses to settle on what finally makes sense to get a result – positive or negative – for your study. For this section, you’re going to provide tables with descriptions of your sample, including, but not limited to, sample size, frequencies of sample characteristics like race and gender, levels of measurement, appropriate measures of central tendency, standard deviations and variances. Here you will also want to focus on the analyses you used to actually draw whatever conclusion you settled on, both descriptive and inferential (i.e., bivariate or multivariate).

The actual statistics you report depend entirely on the kind of statistical analysis you do. For instance, if you’re reporting on a logistic regression, it’s going to look a little different than reporting on an ANOVA. In the previous chapter, we provided links to open textbooks that detail how to conduct quantitative data analysis. You should look at these resources and consult with your research professor to help you determine what is expected in a report about the particular statistical method you used.

The important thing to remember here – as we mentioned above – is that you need to be totally transparent about your results, even and especially if they don’t support your hypothesis. There is value in a disproved hypothesis, too – you now know something about how the state of the world is not .

In this section, you’re going to connect your statistical results back to your hypothesis and discuss whether your results support your hypothesis or not. You are also going to talk about what the results mean for the larger field of study of which your research is a part, the implications of your findings if you’re evaluating some kind of intervention, and how your research relates to what is already out there in this field. When your research doesn’t pan out the way you expect, if you’re able to make some educated guesses as to why this might be (supported by literature if possible, but practice wisdom works too), share those as well.

Let’s take a minute to talk about what happens when your findings disprove your hypothesis or actually indicate something negative about the group you are studying. The discussion section is where you can contextualize “negative” findings. For example, say you conducted a study that indicated that a certain group is more likely to commit violent crime. Here, you have an opportunity to talk about why this might be the case outside of their membership in that group, and how membership in that group does not automatically mean someone will commit a violent crime. You can present mitigating factors, like a history of personal and community trauma. It’s extremely important to provide this relevant context so that your results are more difficult to use against a group you are studying in a way that doesn’t reflect your actual findings.

Limitations

In this section, you’re going to critique your own study. What are the advantages, disadvantages, and trade-offs of what you did to define and analyze your variables? Some questions you might consider include: What limits the study’s applicability to the population at large? Were there trade-offs you had to make between rigor and available data? Did the statistical analyses you used mean that you could only get certain types of results? What would have made the study more widely applicable or more useful for a certain group? You should be thinking about this throughout the analysis process so you can properly contextualize your results.

In this section, you may also consider discussing any threats to internal validity that you identified and whether you think you can generalize your research. Finally, if you used any measurement tools that haven’t been validated yet, discuss how this could have affected your results.

Significance and conclusions

Finally, you want to use the conclusions section to bring it full circle for your reader – why did this research matter? Talk about how it contributed to knowledge around the topic and how might it be used to further practice. Identify and discuss ethical implications of your findings for social workers and social work research. Finally, make sure to talk about the next steps for you, other researchers, or policy-makers based on your research findings.

Key Takeaways

Your quantitative research report should provide the reader with transparent, replicable methods and put your research into the context of existing literature, real-world practice and social work ethics.
Think about the research project you are building now. What could a negative finding be, and how might you provide your reader with context to ensure that you are not harming your study population?

The process of determining how to measure a construct that cannot be directly observed

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

Graduate research methods in social work Copyright © 2020 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Perspect Clin Res
v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

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Classification of research study designs

Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Conflicts of interest.

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Chapter Four: Quantitative Methods (Part 1)

Once you have chosen a topic to investigate, you need to decide which type of method is best to study it. This is one of the most important choices you will make on your research journey. Understanding the value of each of the methods described in this textbook to answer different questions allows you to be able to plan your own studies with more confidence, critique the studies others have done, and provide advice to your colleagues and friends on what type of research they should do to answer questions they have. After briefly reviewing quantitative research assumptions, this chapter is organized in three parts or sections. These parts can also be used as a checklist when working through the steps of your study. Specifically, part 1 focuses on planning a quantitative study (collecting data), part two explains the steps involved in doing a quantitative study, and part three discusses how to make sense of your results (organizing and analyzing data).

Chapter One: Introduction
Chapter Two: Understanding the distinctions among research methods
Chapter Three: Ethical research, writing, and creative work
Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
Chapter Five: Qualitative Methods (Part 1)
Chapter Five: Qualitative Data (Part 2)
Chapter Six: Critical / Rhetorical Methods (Part 1)
Chapter Six: Critical / Rhetorical Methods (Part 2)
Chapter Seven: Presenting Your Results

Quantitative Worldview Assumptions: A Review

In chapter 2, you were introduced to the unique assumptions quantitative research holds about knowledge and how it is created, or what the authors referred to in chapter one as "epistemology." Understanding these assumptions can help you better determine whether you need to use quantitative methods for a particular research study in which you are interested.

Quantitative researchers believe there is an objective reality, which can be measured. "Objective" here means that the researcher is not relying on their own perceptions of an event. S/he is attempting to gather "facts" which may be separate from people's feeling or perceptions about the facts. These facts are often conceptualized as "causes" and "effects." When you ask research questions or pose hypotheses with words in them such as "cause," "effect," "difference between," and "predicts," you are operating under assumptions consistent with quantitative methods. The overall goal of quantitative research is to develop generalizations that enable the researcher to better predict, explain, and understand some phenomenon.

Because of trying to prove cause-effect relationships that can be generalized to the population at large, the research process and related procedures are very important for quantitative methods. Research should be consistently and objectively conducted, without bias or error, in order to be considered to be valid (accurate) and reliable (consistent). Perhaps this emphasis on accurate and standardized methods is because the roots of quantitative research are in the natural and physical sciences, both of which have at their base the need to prove hypotheses and theories in order to better understand the world in which we live. When a person goes to a doctor and is prescribed some medicine to treat an illness, that person is glad such research has been done to know what the effects of taking this medicine is on others' bodies, so s/he can trust the doctor's judgment and take the medicines.

As covered in chapters 1 and 2, the questions you are asking should lead you to a certain research method choice. Students sometimes want to avoid doing quantitative research because of fear of math/statistics, but if their questions call for that type of research, they should forge ahead and use it anyway. If a student really wants to understand what the causes or effects are for a particular phenomenon, they need to do quantitative research. If a student is interested in what sorts of things might predict a person's behavior, they need to do quantitative research. If they want to confirm the finding of another researcher, most likely they will need to do quantitative research. If a student wishes to generalize beyond their participant sample to a larger population, they need to be conducting quantitative research.

So, ultimately, your choice of methods really depends on what your research goal is. What do you really want to find out? Do you want to compare two or more groups, look for relationships between certain variables, predict how someone will act or react, or confirm some findings from another study? If so, you want to use quantitative methods.

A topic such as self-esteem can be studied in many ways. Listed below are some example RQs about self-esteem. Which of the following research questions should be answered with quantitative methods?

Is there a difference between men's and women's level of self- esteem?
How do college-aged women describe their ups and downs with self-esteem?
How has "self-esteem" been constructed in popular self-help books over time?
Is there a relationship between self-esteem levels and communication apprehension?

What are the advantages of approaching a topic like self-esteem using quantitative methods? What are the disadvantages?

For more information, see the following website: Analyse This!!! Learning to analyse quantitative data

Answers: 1 & 4

Quantitative Methods Part One: Planning Your Study

Planning your study is one of the most important steps in the research process when doing quantitative research. As seen in the diagram below, it involves choosing a topic, writing research questions/hypotheses, and designing your study. Each of these topics will be covered in detail in this section of the chapter.

Topic Choice

Decide on topic.

How do you go about choosing a topic for a research project? One of the best ways to do this is to research something about which you would like to know more. Your communication professors will probably also want you to select something that is related to communication and things you are learning about in other communication classes.

When the authors of this textbook select research topics to study, they choose things that pique their interest for a variety of reasons, sometimes personal and sometimes because they see a need for more research in a particular area. For example, April Chatham-Carpenter studies adoption return trips to China because she has two adopted daughters from China and because there is very little research on this topic for Chinese adoptees and their families; she studied home vs. public schooling because her sister home schools, and at the time she started the study very few researchers had considered the social network implications for home schoolers (cf. http://www.uni.edu/chatham/homeschool.html ).

When you are asked in this class and other classes to select a topic to research, think about topics that you have wondered about, that affect you personally, or that know have gaps in the research. Then start writing down questions you would like to know about this topic. These questions will help you decide whether the goal of your study is to understand something better, explain causes and effects of something, gather the perspectives of others on a topic, or look at how language constructs a certain view of reality.

Review Previous Research

In quantitative research, you do not rely on your conclusions to emerge from the data you collect. Rather, you start out looking for certain things based on what the past research has found. This is consistent with what was called in chapter 2 as a deductive approach (Keyton, 2011), which also leads a quantitative researcher to develop a research question or research problem from reviewing a body of literature, with the previous research framing the study that is being done. So, reviewing previous research done on your topic is an important part of the planning of your study. As seen in chapter 3 and the Appendix, to do an adequate literature review, you need to identify portions of your topic that could have been researched in the past. To do that, you select key terms of concepts related to your topic.

Some people use concept maps to help them identify useful search terms for a literature review. For example, see the following website: Concept Mapping: How to Start Your Term Paper Research .

Narrow Topic to Researchable Area

Once you have selected your topic area and reviewed relevant literature related to your topic, you need to narrow your topic to something that can be researched practically and that will take the research on this topic further. You don't want your research topic to be so broad or large that you are unable to research it. Plus, you want to explain some phenomenon better than has been done before, adding to the literature and theory on a topic. You may want to test out what someone else has found, replicating their study, and therefore building to the body of knowledge already created.

To see how a literature review can be helpful in narrowing your topic, see the following sources. Narrowing or Broadening Your Research Topic and How to Conduct a Literature Review in Social Science

Research Questions & Hypotheses

Write Your Research Questions (RQs) and/or Hypotheses (Hs)

Once you have narrowed your topic based on what you learned from doing your review of literature, you need to formalize your topic area into one or more research questions or hypotheses. If the area you are researching is a relatively new area, and no existing literature or theory can lead you to predict what you might find, then you should write a research question. Take a topic related to social media, for example, which is a relatively new area of study. You might write a research question that asks:

"Is there a difference between how 1st year and 4th year college students use Facebook to communicate with their friends?"

If, however, you are testing out something you think you might find based on the findings of a large amount of previous literature or a well-developed theory, you can write a hypothesis. Researchers often distinguish between null and alternative hypotheses. The alternative hypothesis is what you are trying to test or prove is true, while the null hypothesis assumes that the alternative hypothesis is not true. For example, if the use of Facebook had been studied a great deal, and there were theories that had been developed on the use of it, then you might develop an alternative hypothesis, such as: "First-year students spend more time on using Facebook to communicate with their friends than fourth-year students do." Your null hypothesis, on the other hand, would be: "First-year students do not spend any more time using Facebook to communication with their friends than fourth-year students do." Researchers, however, only state the alternative hypothesis in their studies, and actually call it "hypothesis" rather than "alternative hypothesis."

Process of Writing a Research Question/Hypothesis.

Once you have decided to write a research question (RQ) or hypothesis (H) for your topic, you should go through the following steps to create your RQ or H.

Name the concepts from your overall research topic that you are interested in studying.

RQs and Hs have variables, or concepts that you are interested in studying. Variables can take on different values. For example, in the RQ above, there are at least two variables – year in college and use of Facebook (FB) to communicate. Both of them have a variety of levels within them.

When you look at the concepts you identified, are there any concepts which seem to be related to each other? For example, in our RQ, we are interested in knowing if there is a difference between first-year students and fourth-year students in their use of FB, meaning that we believe there is some connection between our two variables.

Decide what type of a relationship you would like to study between the variables. Do you think one causes the other? Does a difference in one create a difference in the other? As the value of one changes, does the value of the other change?

Identify which one of these concepts is the independent (or predictor) variable, or the concept that is perceived to be the cause of change in the other variable? Which one is the dependent (criterion) variable, or the one that is affected by changes in the independent variable? In the above example RQ, year in school is the independent variable, and amount of time spent on Facebook communicating with friends is the dependent variable. The amount of time spent on Facebook depends on a person's year in school.

If you're still confused about independent and dependent variables, check out the following site: Independent & Dependent Variables .

Express the relationship between the concepts as a single sentence – in either a hypothesis or a research question.

For example, "is there a difference between international and American students on their perceptions of the basic communication course," where cultural background and perceptions of the course are your two variables. Cultural background would be the independent variable, and perceptions of the course would be your dependent variable. More examples of RQs and Hs are provided in the next section.

APPLICATION: Try the above steps with your topic now. Check with your instructor to see if s/he would like you to send your topic and RQ/H to him/her via e-mail.

Types of Research Questions/Hypotheses

Once you have written your RQ/H, you need to determine what type of research question or hypothesis it is. This will help you later decide what types of statistics you will need to run to answer your question or test your hypothesis. There are three possible types of questions you might ask, and two possible types of hypotheses. The first type of question cannot be written as a hypothesis, but the second and third types can.

Descriptive Question.

The first type of question is a descriptive question. If you have only one variable or concept you are studying, OR if you are not interested in how the variables you are studying are connected or related to each other, then your question is most likely a descriptive question.

This type of question is the closest to looking like a qualitative question, and often starts with a "what" or "how" or "why" or "to what extent" type of wording. What makes it different from a qualitative research question is that the question will be answered using numbers rather than qualitative analysis. Some examples of a descriptive question, using the topic of social media, include the following.

"To what extent are college-aged students using Facebook to communicate with their friends?"

"Why do college-aged students use Facebook to communicate with their friends?"

Notice that neither of these questions has a clear independent or dependent variable, as there is no clear cause or effect being assumed by the question. The question is merely descriptive in nature. It can be answered by summarizing the numbers obtained for each category, such as by providing percentages, averages, or just the raw totals for each type of strategy or organization. This is true also of the following research questions found in a study of online public relations strategies:

"What online public relations strategies are organizations implementing to combat phishing" (Baker, Baker, & Tedesco, 2007, p. 330), and

"Which organizations are doing most and least, according to recommendations from anti- phishing advocacy recommendations, to combat phishing" (Baker, Baker, & Tedesco, 2007, p. 330)

The researchers in this study reported statistics in their results or findings section, making it clearly a quantitative study, but without an independent or dependent variable; therefore, these research questions illustrate the first type of RQ, the descriptive question.

Difference Question/Hypothesis.

The second type of question is a question/hypothesis of difference, and will often have the word "difference" as part of the question. The very first research question in this section, asking if there is a difference between 1st year and 4th year college students' use of Facebook, is an example of this type of question. In this type of question, the independent variable is some type of grouping or categories, such as age. Another example of a question of difference is one April asked in her research on home schooling: "Is there a difference between home vs. public schoolers on the size of their social networks?" In this example, the independent variable is home vs. public schooling (a group being compared), and the dependent variable is size of social networks. Hypotheses can also be difference hypotheses, as the following example on the same topic illustrates: "Public schoolers have a larger social network than home schoolers do."

Relationship/Association Question/Hypothesis.

The third type of question is a relationship/association question or hypothesis, and will often have the word "relate" or "relationship" in it, as the following example does: "There is a relationship between number of television ads for a political candidate and how successful that political candidate is in getting elected." Here the independent (or predictor) variable is number of TV ads, and the dependent (or criterion) variable is the success at getting elected. In this type of question, there is no grouping being compared, but rather the independent variable is continuous (ranges from zero to a certain number) in nature. This type of question can be worded as either a hypothesis or as a research question, as stated earlier.

Test out your knowledge of the above information, by answering the following questions about the RQ/H listed below. (Remember, for a descriptive question there are no clear independent & dependent variables.)

What is the independent variable (IV)?
What is the dependent variable (DV)?
What type of research question/hypothesis is it? (descriptive, difference, relationship/association)
"Is there a difference on relational satisfaction between those who met their current partner through online dating and those who met their current partner face-to-face?"
"How do Fortune 500 firms use focus groups to market new products?"
"There is a relationship between age and amount of time spent online using social media."

Answers: RQ1 is a difference question, with type of dating being the IV and relational satisfaction being the DV. RQ2 is a descriptive question with no IV or DV. RQ3 is a relationship hypothesis with age as the IV and amount of time spent online as the DV.

Design Your Study

The third step in planning your research project, after you have decided on your topic/goal and written your research questions/hypotheses, is to design your study which means to decide how to proceed in gathering data to answer your research question or to test your hypothesis. This step includes six things to do. [NOTE: The terms used in this section will be defined as they are used.]

Decide type of study design: Experimental, quasi-experimental, non-experimental.
Decide kind of data to collect: Survey/interview, observation, already existing data.
Operationalize variables into measurable concepts.
Determine type of sample: Probability or non-probability.
Decide how you will collect your data: face-to-face, via e-mail, an online survey, library research, etc.
Pilot test your methods.

Types of Study Designs

With quantitative research being rooted in the scientific method, traditional research is structured in an experimental fashion. This is especially true in the natural sciences, where they try to prove causes and effects on topics such as successful treatments for cancer. For example, the University of Iowa Hospitals and Clinics regularly conduct clinical trials to test for the effectiveness of certain treatments for medical conditions ( University of Iowa Hospitals & Clinics: Clinical Trials ). They use human participants to conduct such research, regularly recruiting volunteers. However, in communication, true experiments with treatments the researcher controls are less necessary and thus less common. It is important for the researcher to understand which type of study s/he wishes to do, in order to accurately communicate his/her methods to the public when describing the study.

There are three possible types of studies you may choose to do, when embarking on quantitative research: (a) True experiments, (b) quasi-experiments, and (c) non-experiments.

For more information to read on these types of designs, take a look at the following website and related links in it: Types of Designs .

The following flowchart should help you distinguish between the three types of study designs described below.

True Experiments.

The first two types of study designs use difference questions/hypotheses, as the independent variable for true and quasi-experiments is nominal or categorical (based on categories or groupings), as you have groups that are being compared. As seen in the flowchart above, what distinguishes a true experiment from the other two designs is a concept called "random assignment." Random assignment means that the researcher controls to which group the participants are assigned. April's study of home vs. public schooling was NOT a true experiment, because she could not control which participants were home schooled and which ones were public schooled, and instead relied on already existing groups.

An example of a true experiment reported in a communication journal is a study investigating the effects of using interest-based contemporary examples in a lecture on the history of public relations, in which the researchers had the following two hypotheses: "Lectures utilizing interest- based examples should result in more interested participants" and "Lectures utilizing interest- based examples should result in participants with higher scores on subsequent tests of cognitive recall" (Weber, Corrigan, Fornash, & Neupauer, 2003, p. 118). In this study, the 122 college student participants were randomly assigned by the researchers to one of two lecture video viewing groups: a video lecture with traditional examples and a video with contemporary examples. (To see the results of the study, look it up using your school's library databases).

A second example of a true experiment in communication is a study of the effects of viewing either a dramatic narrative television show vs. a nonnarrative television show about the consequences of an unexpected teen pregnancy. The researchers randomly assigned their 367 undergraduate participants to view one of the two types of shows.

Moyer-Gusé, E., & Nabi, R. L. (2010). Explaining the effects of narrative in an entertainment television program: Overcoming resistance to persuasion. Human Communication Research, 36 , 26-52.

A third example of a true experiment done in the field of communication can be found in the following study.

Jensen, J. D. (2008). Scientific uncertainty in news coverage of cancer research: Effects of hedging on scientists' and journalists' credibility. Human Communication Research, 34, 347-369.

In this study, Jakob Jensen had three independent variables. He randomly assigned his 601 participants to 1 of 20 possible conditions, between his three independent variables, which were (a) a hedged vs. not hedged message, (b) the source of the hedging message (research attributed to primary vs. unaffiliated scientists), and (c) specific news story employed (of which he had five randomly selected news stories about cancer research to choose from). Although this study was pretty complex, it does illustrate the true experiment in our field since the participants were randomly assigned to read a particular news story, with certain characteristics.

Quasi-Experiments.

If the researcher is not able to randomly assign participants to one of the treatment groups (or independent variable), but the participants already belong to one of them (e.g., age; home vs. public schooling), then the design is called a quasi-experiment. Here you still have an independent variable with groups, but the participants already belong to a group before the study starts, and the researcher has no control over which group they belong to.

An example of a hypothesis found in a communication study is the following: "Individuals high in trait aggression will enjoy violent content more than nonviolent content, whereas those low in trait aggression will enjoy violent content less than nonviolent content" (Weaver & Wilson, 2009, p. 448). In this study, the researchers could not assign the participants to a high or low trait aggression group since this is a personality characteristic, so this is a quasi-experiment. It does not have any random assignment of participants to the independent variable groups. Read their study, if you would like to, at the following location.

Weaver, A. J., & Wilson, B. J. (2009). The role of graphic and sanitized violence in the enjoyment of television dramas. Human Communication Research, 35 (3), 442-463.

Benoit and Hansen (2004) did not choose to randomly assign participants to groups either, in their study of a national presidential election survey, in which they were looking at differences between debate and non-debate viewers, in terms of several dependent variables, such as which candidate viewers supported. If you are interested in discovering the results of this study, take a look at the following article.

Benoit, W. L., & Hansen, G. J. (2004). Presidential debate watching, issue knowledge, character evaluation, and vote choice. Human Communication Research, 30 (1), 121-144.

Non-Experiments.

The third type of design is the non-experiment. Non-experiments are sometimes called survey designs, because their primary way of collecting data is through surveys. This is not enough to distinguish them from true experiments and quasi-experiments, however, as both of those types of designs may use surveys as well.

What makes a study a non-experiment is that the independent variable is not a grouping or categorical variable. Researchers observe or survey participants in order to describe them as they naturally exist without any experimental intervention. Researchers do not give treatments or observe the effects of a potential natural grouping variable such as age. Descriptive and relationship/association questions are most often used in non-experiments.

Some examples of this type of commonly used design for communication researchers include the following studies.

Serota, Levine, and Boster (2010) used a national survey of 1,000 adults to determine the prevalence of lying in America (see Human Communication Research, 36 , pp. 2-25).
Nabi (2009) surveyed 170 young adults on their perceptions of reality television on cosmetic surgery effects, looking at several things: for example, does viewing cosmetic surgery makeover programs relate to body satisfaction (p. 6), finding no significant relationship between those two variables (see Human Communication Research, 35 , pp. 1-27).
Derlega, Winstead, Mathews, and Braitman (2008) collected stories from 238 college students on reasons why they would disclose or not disclose personal information within close relationships (see Communication Research Reports, 25 , pp. 115-130). They coded the participants' answers into categories so they could count how often specific reasons were mentioned, using a method called content analysis , to answer the following research questions:

RQ1: What are research participants' attributions for the disclosure and nondisclosure of highly personal information?

RQ2: Do attributions reflect concerns about rewards and costs of disclosure or the tension between openness with another and privacy?

RQ3: How often are particular attributions for disclosure/nondisclosure used in various types of relationships? (p. 117)

All of these non-experimental studies have in common no researcher manipulation of an independent variable or even having an independent variable that has natural groups that are being compared.

Identify which design discussed above should be used for each of the following research questions.

Is there a difference between generations on how much they use MySpace?
Is there a relationship between age when a person first started using Facebook and the amount of time they currently spend on Facebook daily?
Is there a difference between potential customers' perceptions of an organization who are shown an organization's Facebook page and those who are not shown an organization's Facebook page?

[HINT: Try to identify the independent and dependent variable in each question above first, before determining what type of design you would use. Also, try to determine what type of question it is – descriptive, difference, or relationship/association.]

Answers: 1. Quasi-experiment 2. Non-experiment 3. True Experiment

Data Collection Methods

Once you decide the type of quantitative research design you will be using, you will need to determine which of the following types of data you will collect: (a) survey data, (b) observational data, and/or (c) already existing data, as in library research.

Using the survey data collection method means you will talk to people or survey them about their behaviors, attitudes, perceptions, and demographic characteristics (e.g., biological sex, socio-economic status, race). This type of data usually consists of a series of questions related to the concepts you want to study (i.e., your independent and dependent variables). Both of April's studies on home schooling and on taking adopted children on a return trip back to China used survey data.

On a survey, you can have both closed-ended and open-ended questions. Closed-ended questions, can be written in a variety of forms. Some of the most common response options include the following.

Likert responses – for example: for the following statement, ______ do you strongly agree agree neutral disagree strongly disagree

Semantic differential – for example: does the following ______ make you Happy ..................................... Sad

Yes-no answers for example: I use social media daily. Yes / No.

One site to check out for possible response options is http://www.360degreefeedback.net/media/ResponseScales.pdf .

Researchers often follow up some of their closed-ended questions with an "other" category, in which they ask their participants to "please specify," their response if none of the ones provided are applicable. They may also ask open-ended questions on "why" a participant chose a particular answer or ask participants for more information about a particular topic. If the researcher wants to use the open-ended question responses as part of his/her quantitative study, the answers are usually coded into categories and counted, in terms of the frequency of a certain answer, using a method called content analysis , which will be discussed when we talk about already-existing artifacts as a source of data.

Surveys can be done face-to-face, by telephone, mail, or online. Each of these methods has its own advantages and disadvantages, primarily in the form of the cost in time and money to do the survey. For example, if you want to survey many people, then online survey tools such as surveygizmo.com and surveymonkey.com are very efficient, but not everyone has access to taking a survey on the computer, so you may not get an adequate sample of the population by doing so. Plus you have to decide how you will recruit people to take your online survey, which can be challenging. There are trade-offs with every method.

For more information on things to consider when selecting your survey method, check out the following website:

Selecting the Survey Method .

There are also many good sources for developing a good survey, such as the following websites. Constructing the Survey Survey Methods Designing Surveys

Observation.

A second type of data collection method is observation . In this data collection method, you make observations of the phenomenon you are studying and then code your observations, so that you can count what you are studying. This type of data collection method is often called interaction analysis, if you collect data by observing people's behavior. For example, if you want to study the phenomenon of mall-walking, you could go to a mall and count characteristics of mall-walkers. A researcher in the area of health communication could study the occurrence of humor in an operating room, for example, by coding and counting the use of humor in such a setting.

One extended research study using observational data collection methods, which is cited often in interpersonal communication classes, is John Gottman's research, which started out in what is now called "The Love Lab." In this lab, researchers observe interactions between couples, including physiological symptoms, using coders who look for certain items found to predict relationship problems and success.

Take a look at the YouTube video about "The Love Lab" at the following site to learn more about the potential of using observation in collecting data for a research study: The "Love" Lab .

Already-Existing Artifacts.

The third method of quantitative data collection is the use of already-existing artifacts . With this method, you choose certain artifacts (e.g., newspaper or magazine articles; television programs; webpages) and code their content, resulting in a count of whatever you are studying. With this data collection method, researchers most often use what is called quantitative content analysis . Basically, the researcher counts frequencies of something that occurs in an artifact of study, such as the frequency of times something is mentioned on a webpage. Content analysis can also be used in qualitative research, where a researcher identifies and creates text-based themes but does not do a count of the occurrences of these themes. Content analysis can also be used to take open-ended questions from a survey method, and identify countable themes within the questions.

Content analysis is a very common method used in media studies, given researchers are interested in studying already-existing media artifacts. There are many good sources to illustrate how to do content analysis such as are seen in the box below.

See the following sources for more information on content analysis. Writing Guide: Content Analysis A Flowchart for the Typical Process of Content Analysis Research What is Content Analysis?

With content analysis and any method that you use to code something into categories, one key concept you need to remember is inter-coder or inter-rater reliability , in which there are multiple coders (at least two) trained to code the observations into categories. This check on coding is important because you need to check to make sure that the way you are coding your observations on the open-ended answers is the same way that others would code a particular item. To establish this kind of inter-coder or inter-rater reliability, researchers prepare codebooks (to train their coders on how to code the materials) and coding forms for their coders to use.

To see some examples of actual codebooks used in research, see the following website: Human Coding--Sample Materials .

There are also online inter-coder reliability calculators some researchers use, such as the following: ReCal: reliability calculation for the masses .

Regardless of which method of data collection you choose, you need to decide even more specifically how you will measure the variables in your study, which leads us to the next planning step in the design of a study.

Operationalization of Variables into Measurable Concepts

When you look at your research question/s and/or hypotheses, you should know already what your independent and dependent variables are. Both of these need to be measured in some way. We call that way of measuring operationalizing a variable. One way to think of it is writing a step by step recipe for how you plan to obtain data on this topic. How you choose to operationalize your variable (or write the recipe) is one all-important decision you have to make, which will make or break your study. In quantitative research, you have to measure your variables in a valid (accurate) and reliable (consistent) manner, which we discuss in this section. You also need to determine the level of measurement you will use for your variables, which will help you later decide what statistical tests you need to run to answer your research question/s or test your hypotheses. We will start with the last topic first.

Level of Measurement

Level of measurement has to do with whether you measure your variables using categories or groupings OR whether you measure your variables using a continuous level of measurement (range of numbers). The level of measurement that is considered to be categorical in nature is called nominal, while the levels of measurement considered to be continuous in nature are ordinal, interval, and ratio. The only ones you really need to know are nominal, ordinal, and interval/ratio.

Nominal variables are categories that do not have meaningful numbers attached to them but are broader categories, such as male and female, home schooled and public schooled, Caucasian and African-American. Ordinal variables do have numbers attached to them, in that the numbers are in a certain order, but there are not equal intervals between the numbers (e.g., such as when you rank a group of 5 items from most to least preferred, where 3 might be highly preferred, and 2 hated). Interval/ratio variables have equal intervals between the numbers (e.g., weight, age).

For more information about these levels of measurement, check out one of the following websites. Levels of Measurement Measurement Scales in Social Science Research What is the difference between ordinal, interval and ratio variables? Why should I care?

Validity and Reliability

When developing a scale/measure or survey, you need to be concerned about validity and reliability. Readers of quantitative research expect to see researchers justify their research measures using these two terms in the methods section of an article or paper.

Validity. Validity is the extent to which your scale/measure or survey adequately reflects the full meaning of the concept you are measuring. Does it measure what you say it measures? For example, if researchers wanted to develop a scale to measure "servant leadership," the researchers would have to determine what dimensions of servant leadership they wanted to measure, and then create items which would be valid or accurate measures of these dimensions. If they included items related to a different type of leadership, those items would not be a valid measure of servant leadership. When doing so, the researchers are trying to prove their measure has internal validity. Researchers may also be interested in external validity, but that has to do with how generalizable their study is to a larger population (a topic related to sampling, which we will consider in the next section), and has less to do with the validity of the instrument itself.

There are several types of validity you may read about, including face validity, content validity, criterion-related validity, and construct validity. To learn more about these types of validity, read the information at the following link: Validity .

To improve the validity of an instrument, researchers need to fully understand the concept they are trying to measure. This means they know the academic literature surrounding that concept well and write several survey questions on each dimension measured, to make sure the full idea of the concept is being measured. For example, Page and Wong (n.d.) identified four dimensions of servant leadership: character, people-orientation, task-orientation, and process-orientation ( A Conceptual Framework for Measuring Servant-Leadership ). All of these dimensions (and any others identified by other researchers) would need multiple survey items developed if a researcher wanted to create a new scale on servant leadership.

Before you create a new survey, it can be useful to see if one already exists with established validity and reliability. Such measures can be found by seeing what other respected studies have used to measure a concept and then doing a library search to find the scale/measure itself (sometimes found in the reference area of a library in books like those listed below).

Reliability . Reliability is the second criterion you will need to address if you choose to develop your own scale or measure. Reliability is concerned with whether a measurement is consistent and reproducible. If you have ever wondered why, when taking a survey, that a question is asked more than once or very similar questions are asked multiple times, it is because the researchers one concerned with proving their study has reliability. Are you, for example, answering all of the similar questions similarly? If so, the measure/scale may have good reliability or consistency over time.

Researchers can use a variety of ways to show their measure/scale is reliable. See the following websites for explanations of some of these ways, which include methods such as the test-retest method, the split-half method, and inter-coder/rater reliability. Types of Reliability Reliability

To understand the relationship between validity and reliability, a nice visual provided below is explained at the following website (Trochim, 2006, para. 2). Reliability & Validity

Self-Quiz/Discussion:

Take a look at one of the surveys found at the following poll reporting sites on a topic which interests you. Critique one of these surveys, using what you have learned about creating surveys so far.

http://www.pewinternet.org/ http://pewresearch.org/ http://www.gallup.com/Home.aspx http://www.kff.org/

One of the things you might have critiqued in the previous self-quiz/discussion may have had less to do with the actual survey itself, but rather with how the researchers got their participants or sample. How participants are recruited is just as important to doing a good study as how valid and reliable a survey is.

Imagine that in the article you chose for the last "self-quiz/discussion" you read the following quote from the Pew Research Center's Internet and American Life Project: "One in three teens sends more than 100 text messages a day, or 3000 texts a month" (Lenhart, 2010, para.5). How would you know whether you could trust this finding to be true? Would you compare it to what you know about texting from your own and your friends' experiences? Would you want to know what types of questions people were asked to determine this statistic, or whether the survey the statistic is based on is valid and reliable? Would you want to know what type of people were surveyed for the study? As a critical consumer of research, you should ask all of these types of questions, rather than just accepting such a statement as undisputable fact. For example, if only people shopping at an Apple Store were surveyed, the results might be skewed high.

In particular, related to the topic of this section, you should ask about the sampling method the researchers did. Often, the researchers will provide information related to the sample, stating how many participants were surveyed (in this case 800 teens, aged 12-17, who were a nationally representative sample of the population) and how much the "margin of error" is (in this case +/- 3.8%). Why do they state such things? It is because they know the importance of a sample in making the case for their findings being legitimate and credible. Margin of error is how much we are confident that our findings represent the population at large. The larger the margin of error, the less likely it is that the poll or survey is accurate. Margin of error assumes a 95% confidence level that what we found from our study represents the population at large.

For more information on margin of error, see one of the following websites. Answers.com Margin of Error Stats.org Margin of Error Americanresearchgroup.com Margin of Error [this last site is a margin of error calculator, which shows that margin of error is directly tied to the size of your sample, in relationship to the size of the population, two concepts we will talk about in the next few paragraphs]

In particular, this section focused on sampling will talk about the following topics: (a) the difference between a population vs. a sample; (b) concepts of error and bias, or "it's all about significance"; (c) probability vs. non-probability sampling; and (d) sample size issues.

Population vs. Sample

When doing quantitative studies, such as the study of cell phone usage among teens, you are never able to survey the entire population of teenagers, so you survey a portion of the population. If you study every member of a population, then you are conducting a census such as the United States Government does every 10 years. When, however, this is not possible (because you do not have the money the U.S. government has!), you attempt to get as good a sample as possible.

Characteristics of a population are summarized in numerical form, and technically these numbers are called parameters . However, numbers which summarize the characteristics of a sample are called statistics .

Error and Bias

If a sample is not done well, then you may not have confidence in how the study's results can be generalized to the population from which the sample was taken. Your confidence level is often stated as the margin of error of the survey. As noted earlier, a study's margin of error refers to the degree to which a sample differs from the total population you are studying. In the Pew survey, they had a margin of error of +/- 3.8%. So, for example, when the Pew survey said 33% of teens send more than 100 texts a day, the margin of error means they were 95% sure that 29.2% - 36.8% of teens send this many texts a day.

Margin of error is tied to sampling error , which is how much difference there is between your sample's results and what would have been obtained if you had surveyed the whole population. Sample error is linked to a very important concept for quantitative researchers, which is the notion of significance . Here, significance does not refer to whether some finding is morally or practically significant, it refers to whether a finding is statistically significant, meaning the findings are not due to chance but actually represent something that is found in the population. Statistical significance is about how much you, as the researcher, are willing to risk saying you found something important and be wrong.

For the difference between statistical significance and practical significance, see the following YouTube video: Statistical and Practical Significance .

Scientists set certain arbitrary standards based on the probability they could be wrong in reporting their findings. These are called significance levels and are commonly reported in the literature as p <.05 or p <.01 or some other probability (or p ) level.

If an article says a statistical test reported that p < .05 , it simply means that they are most likely correct in what they are saying, but there is a 5% chance they could be wrong and not find the same results in the population. If p < .01, then there would be only a 1% chance they were wrong and would not find the same results in the population. The lower the probability level, the more certain the results.

When researchers are wrong, or make that kind of decision error, it often implies that either (a) their sample was biased and was not representative of the true population in some way, or (b) that something they did in collecting the data biased the results. There are actually two kinds of sampling error talked about in quantitative research: Type I and Type II error. Type 1 error is what happens when you think you found something statistically significant and claim there is a significant difference or relationship, when there really is not in the actual population. So there is something about your sample that made you find something that is not in the actual population. (Type I error is the same as the probability level, or .05, if using the traditional p-level accepted by most researchers.) Type II error happens when you don't find a statistically significant difference or relationship, yet there actually is one in the population at large, so once again, your sample is not representative of the population.

For more information on these two types of error, check out the following websites. Hypothesis Testing: Type I Error, Type II Error Type I and Type II Errors - Making Mistakes in the Justice System

Researchers want to select a sample that is representative of the population in order to reduce the likelihood of having a sample that is biased. There are two types of bias particularly troublesome for researchers, in terms of sampling error. The first type is selection bias , in which each person in the population does not have an equal chance to be chosen for the sample, which happens frequently in communication studies, because we often rely on convenience samples (whoever we can get to complete our surveys). The second type of bias is response bias , in which those who volunteer for a study have different characteristics than those who did not volunteer for the study, another common challenge for communication researchers. Volunteers for a study may very well be different from persons who choose not to volunteer for a study, so that you have a biased sample by relying just on volunteers, which is not representative of the population from which you are trying to sample.

Probability vs. Non-Probability Sampling

One of the best ways to lower your sampling error and reduce the possibility of bias is to do probability or random sampling. This means that every person in the population has an equal chance of being selected to be in your sample. Another way of looking at this is to attempt to get a representative sample, so that the characteristics of your sample closely approximate those of the population. A sample needs to contain essentially the same variations that exist in the population, if possible, especially on the variables or elements that are most important to you (e.g., age, biological sex, race, level of education, socio-economic class).

There are many different ways to draw a probability/random sample from the population. Some of the most common are a simple random sample , where you use a random numbers table or random number generator to select your sample from the population.

There are several examples of random number generators available online. See the following example of an online random number generator: http://www.randomizer.org/ .

A systematic random sample takes every n-th number from the population, depending on how many people you would like to have in your sample. A stratified random sample does random sampling within groups, and a multi-stage or cluster sample is used when there are multiple groups within a large area and a large population, and the researcher does random sampling in stages.

If you are interested in understanding more about these types of probability/random samples, take a look at the following website: Probability Sampling .

However, many times communication researchers use whoever they can find to participate in their study, such as college students in their classes since these people are easily accessible. Many of the studies in interpersonal communication and relationship development, for example, used this type of sample. This is called a convenience sample. In doing so, they are using a non- probability or non-random sample. In these types of samples, each member of the population does not have an equal opportunity to be selected. For example, if you decide to ask your facebook friends to participate in an online survey you created about how college students in the U.S. use cell phones to text, you are using a non-random type of sample. You are unable to randomly sample the whole population in the U.S. of college students who text, so you attempt to find participants more conveniently. Some common non-random or non-probability samples are:

accidental/convenience samples, such as the facebook example illustrates
quota samples, in which you do convenience samples within subgroups of the population, such as biological sex, looking for a certain number of participants in each group being compared
snowball or network sampling, where you ask current participants to send your survey onto their friends.

For more information on non-probability sampling, see the following website: Nonprobability Sampling .

Researchers, such as communication scholars, often use these types of samples because of the nature of their research. Most research designs used in communication are not true experiments, such as would be required in the medical field where they are trying to prove some cause-effect relationship to cure or alleviate symptoms of a disease. Most communication scholars recognize that human behavior in communication situations is much less predictable, so they do not adhere to the strictest possible worldview related to quantitative methods and are less concerned with having to use probability sampling.

They do recognize, however, that with either probability or non-probability sampling, there is still the possibility of bias and error, although much less with probability sampling. That is why all quantitative researchers, regardless of field, will report statistical significance levels if they are interested in generalizing from their sample to the population at large, to let the readers of their work know how confident they are in their results.

Size of Sample

The larger the sample, the more likely the sample is going to be representative of the population. If there is a lot of variability in the population (e.g., lots of different ethnic groups in the population), a researcher will need a larger sample. If you are interested in detecting small possible differences (e.g., in a close political race), you need a larger sample. However, the bigger your population, the less you have to increase the size of your sample in order to have an adequate sample, as is illustrated by an example sample size calculator such as can be found at http://www.raosoft.com/samplesize.html .

Using the example sample size calculator, see how you might determine how large of a sample you might need in order to study how college students in the U.S. use texting on their cell phones. You would have to first determine approximately how many college students are in the U.S. According to ANEKI, there are a little over 14,000,000 college students in the U.S. ( Countries with the Most University Students ). When inputting that figure into the sample size calculator below (using no commas for the population size), you would need a sample size of approximately 385 students. If the population size was 20,000, you would need a sample of 377 students. If the population was only 2,000, you would need a sample of 323. For a population of 500, you would need a sample of 218.

It is not enough, however, to just have an adequate or large sample. If there is bias in the sampling, you can have a very bad large sample, one that also does not represent the population at large. So, having an unbiased sample is even more important than having a large sample.

So, what do you do, if you cannot reasonably conduct a probability or random sample? You run statistics which report significance levels, and you report the limitations of your sample in the discussion section of your paper/article.

Pilot Testing Methods

Now that we have talked about the different elements of your study design, you should try out your methods by doing a pilot test of some kind. This means that you try out your procedures with someone to try to catch any mistakes in your design before you start collecting data from actual participants in your study. This will save you time and money in the long run, along with unneeded angst over mistakes you made in your design during data collection. There are several ways you might do this.

You might ask an expert who knows about this topic (such as a faculty member) to try out your experiment or survey and provide feedback on what they think of your design. You might ask some participants who are like your potential sample to take your survey or be a part of your pilot test; then you could ask them which parts were confusing or needed revising. You might have potential participants explain to you what they think your questions mean, to see if they are interpreting them like you intended, or if you need to make some questions clearer.

The main thing is that you do not just assume your methods will work or are the best type of methods to use until you try them out with someone. As you write up your study, in your methods section of your paper, you can then talk about what you did to change your study based on the pilot study you did.

Institutional Review Board (IRB) Approval

The last step of your planning takes place when you take the necessary steps to get your study approved by your institution's review board. As you read in chapter 3, this step is important if you are planning on using the data or results from your study beyond just the requirements for your class project. See chapter 3 for more information on the procedures involved in this step.

Conclusion: Study Design Planning

Once you have decided what topic you want to study, you plan your study. Part 1 of this chapter has covered the following steps you need to follow in this planning process:

decide what type of study you will do (i.e., experimental, quasi-experimental, non- experimental);
decide on what data collection method you will use (i.e., survey, observation, or already existing data);
operationalize your variables into measureable concepts;
determine what type of sample you will use (probability or non-probability);
pilot test your methods; and
get IRB approval.

At that point, you are ready to commence collecting your data, which is the topic of the next section in this chapter.

Research Writing and Analysis

NVivo Group and Study Sessions
SPSS This link opens in a new window
Statistical Analysis Group sessions
Using Qualtrics
Dissertation and Data Analysis Group Sessions
Defense Schedule - Commons Calendar This link opens in a new window
Research Process Flow Chart
Research Alignment Chapter 1 This link opens in a new window
Step 1: Seek Out Evidence
Step 2: Explain
Step 3: The Big Picture
Step 4: Own It
Step 5: Illustrate
Annotated Bibliography
Literature Review This link opens in a new window
Systematic Reviews & Meta-Analyses
How to Synthesize and Analyze
Synthesis and Analysis Practice
Synthesis and Analysis Group Sessions
Problem Statement
Purpose Statement
Conceptual Framework
Theoretical Framework
Locating Theoretical and Conceptual Frameworks This link opens in a new window
Quantitative Research Questions
Qualitative Research Questions
Trustworthiness of Qualitative Data
Analysis and Coding Example- Qualitative Data
Thematic Data Analysis in Qualitative Design
Dissertation to Journal Article This link opens in a new window
International Journal of Online Graduate Education (IJOGE) This link opens in a new window
Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Map of the world with hands holding a pen.

A Case study is:

An in-depth research design that primarily uses a qualitative methodology but sometimes includes quantitative methodology.
Used to examine an identifiable problem confirmed through research.
Used to investigate an individual, group of people, organization, or event.
Used to mostly answer "how" and "why" questions.

What are the different types of case studies?


Descriptive	This type of case study allows the researcher to:	How has the implementation and use of the instructional coaching intervention for elementary teachers impacted students’ attitudes toward reading?
Explanatory	This type of case study allows the researcher to:	Why do differences exist when implementing the same online reading curriculum in three elementary classrooms?
Exploratory	This type of case study allows the researcher to:	What are potential barriers to student’s reading success when middle school teachers implement the Ready Reader curriculum online?
Multiple Case Studies or Collective Case Study	This type of case study allows the researcher to:	How are individual school districts addressing student engagement in an online classroom?
Intrinsic	This type of case study allows the researcher to:	How does a student’s familial background influence a teacher’s ability to provide meaningful instruction?
Instrumental	This type of case study allows the researcher to:	How a rural school district’s integration of a reward system maximized student engagement?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types.

Who are your case study participants?


	This type of study is implemented to understand an individual by developing a detailed explanation of the individual’s lived experiences or perceptions.
	This type of study is implemented to explore a particular group of people’s perceptions.
	This type of study is implemented to explore the perspectives of people who work for or had interaction with a specific organization or company.
	This type of study is implemented to explore participant’s perceptions of an event.

What is triangulation ?

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

Man holding his hand out to show five fingers.

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Methodology

What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall research objectives and approach
Whether you’ll rely on primary research or secondary research
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach	Quantitative approach
	and describe frequencies, averages, and correlations about relationships between variables

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

How much time do you have to collect data and write up the research?
Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and quasi-experimental designs allow you to test cause-and-effect relationships
Descriptive and correlational designs allow you to measure variables and describe relationships between them.

Type of design	Purpose and characteristics
Experimental	relationships effect on a
Quasi-experimental	)
Correlational
Descriptive

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design	Purpose and characteristics


Grounded theory
Phenomenology

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling	Non-probability sampling

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Questionnaires	Interviews
	)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field	Examples of data collection methods
Media & communication	Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology	Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education	Using tests or assignments to collect data on knowledge and skills
Physical sciences	Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability	Validity
	) )

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)
The central tendency of the data (e.g., the mean to describe the average score)
The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

Make estimates about the population based on your sample data.
Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

Approach	Characteristics
Thematic analysis
Discourse analysis

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

Your research questions and/or hypotheses
Your overall approach (e.g., qualitative or quantitative )
The type of design you’re using (e.g., a survey , experiment , or case study )
Your data collection methods (e.g., questionnaires , observations)
Your data collection procedures (e.g., operationalization , timing and data management)
Your data analysis methods (e.g., statistical tests or thematic analysis )

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Quantitative Research Study: Definition, Approaches, Methods & Examples

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Quantitative research is a type of scientific study that involves the collection and analysis of numerical data. It uses mathematical and statistical techniques to identify patterns in large datasets. Analysis of numbers allows researchers to make predictions about future trends or outcomes. Quantitative methods include surveys, experiments, field studies, structured interviews, standardized assessments and questionnaires.

In this article, we will focus on what a quantitative study is and its main methods. Prepare to go through:

Key characteristics
Main types and approaches
Steps of conducting a quantitative study .

Our paper writers also provided the best quantitative research methods and examples to showcase the benefits of this approach.

What Is Quantitative Research: Definition

Before jumping into a detailed discussion on how to launch quantitative research, let’s outline a definition of this type of study.

Quantitative research involves analyzing numerical data to uncover patterns and statistical information, which can be used to test hypotheses and respond to research questions . Quantitative methods often include statistical analysis, surveys, and experiments to generate measurable data and make accurate predictions.

Quantitative research studies are usually applied to fields such as social science, economics, marketing, biology, etc. It is also commonly used for descriptive , correlational , or experimental studies .

Next, we will delve deeper into specific methods to define which one can best fit your academic work. However, before you start analysis and data collection, you need to be clear with the study purpose and research questions you will try to answer in your work.

>> Read more: Difference Between Qualitative and Quantitative Research

Characteristics of Quantitative Research

First, let’s define quantitative research characteristics to ensure that you choose the right type of data for your study. A deep understanding of key traits is the guarantee that you won’t make a mistake when conducting your own study.

Quantitative data is measurable. There are variables that can be easily counted and applied to statistical formulas. In other words, this is numeric data.
You can apply structured tools for your quantitative research. Those tools are surveys, polls, and questionnaires – structured forms you can use to collect the information.
The sample size should be sufficient. To get accurate results, you need to collect data from a significant portion of the target market.Obtaining only 10 survey responses, for example, would not yield any meaningful insights.
Your data can be represented in tables, graphs, or charts. As quantitative methods of data collection are focused on numbers, you should utilize visual aids to structure those numbers clearly for analysis. Such representation can provide valuable insights into patterns, trends, and relationships between independent and dependent variables.

If you need a thesis helper online , check our expert services. Our team of professional writers and editors can provide valuable guidance and support in all stages of research and writing.

Quantitative Research Examples

Quite often, it is challenging to apply all the knowledge about this type of research to your specific field of study. However, we want to share examples of quantitative research that illustrate that it can be used for any purpose.

Quantitative research example 1

Example of quantitative research 2

Types of Quantitative Research

There are a few different types of quantitative research that can be used for various studies. Let’s overview each type of quantitative research to understand in what circumstances and for what goals you can use each of them.

We would focus on 4 main types of quantitative approaches in data collection and analysis:

Descriptive study Descriptive research is used to measure variables for understanding the situation. It does not involve any manipulation of variables. In other words, descriptive studies focus on defining key statistical measures, without testing for specific data insights.
Correlation Correlational study is used in the quantitative research process when you need to measure a relationship between two variables and understand how one variable (for example, the age of respondents) is related to positive answers in the customer survey. Ensure you understand the difference between correlation and causation while making this kind of research.
Causal-comparative research Causal-comparative research is a type of non-experimental study that aims to investigate the causes behind differences in behavior among multiple groups. It is one of the commonly used types of quantitative studies for investigating causal relationships between variables.
Experimental research. Experimental research involves manipulating independent variables to observe how they affect dependent variables under controlled conditions. Put simply, the researcher will need to alter the situation to measure various outcomes that may occur.

Primary Quantitative Research Methods

When discussing types of quantitative research methods, we also need to define primary and secondary methods. Primary methods are used to collect data directly from the source, such as through surveys , experiments , or systematic observations .

Below we will explain each of these methods in detail.

1. Survey Research

Surveys are a widely used quantitative research methodology across various disciplines and fields of study. They are organized both online or offline to gather data from different audiences. In recent years, online surveys have become increasingly popular, replacing traditional methods such as phone or in-person questioning

Surveys can be applied to achieve various study aims, such as understanding attitudes, behaviors, opinions, preferences, or demographic characteristics.

We would like to define 2 main quantitative survey methods:

Cross-sectional surveys Cross sectional survey that analyzes data across a sample population at a specific point in time. It means you may send this survey to various different groups of people, but you will need a one-time point you are researching for your study. It is a common method in such fields like economy, epidemiology, or medicine.
Longitudinal surveys In longitudinal research , you will measure the same group of people, but the data should be collected repeatedly over time. This method requires repeated measurements at regular intervals, such as days, months, or even years, to track changes in dependent variables over time.

Survey quantitative research method example

2. Experiment

Experimentation is one of the quantitative approaches to research that assumes testing various theories to prove or disprove them, or to identify their limitations. This is another powerful quantitative research method that is often used in psychology, biology, physics, and sociology, among others.

Experimentation is a systematic quantitative research approach to testing hypotheses and understanding the causal relationships between variables. Researchers manipulate independent variables while holding all other variables constant to observe changes in their dependent variable. By comparing the outcomes of the experimental group to those of the control group , researchers can determine if intervention was effective.

There are two main types of experiments:

Laboratory experiments A laboratory experiment is conducted in a controlled environment, such as a laboratory or research center, where researchers have complete control over the variables they manipulate. Such experiments are carefully designed to ensure that all resulting data is carefully analyzed in a lab report .
Field experiments Field experiment is a quantitative methodology conducted in real-world settings, such as schools, businesses, or communities, where researchers have less control over variables.

Example of experiment method

>> Read more: How to Design an Experiment

3. Systematic Observation

One of the most reliable types of quantitative research methodologies is systematic observation. It requires researchers to observe specific situations, behavior, or case and collect numeric data based on predefined forms. Those forms are based on the theoretical framework for a specific quantitative study. Usually, this method involves one or more observers and can be applied to different events or behavioral observations.

Systematic observation relies on accurate coding and the proper recording of data onto the structured forms used in the study. This quantitative research method is commonly employed in fields such as sociology, medicine, education, and psychology, and requires precise numeric data to be collected. Although observations can be documented through video or audio recordings, researchers using systematic observation focus specifically on measuring specific variables of interest.

Observation: quantitative research design example

Quantitative Methods for Data Collection

When we discuss various analytical techniques, we have already mentioned some types of quantitative data collection methods . However, let’s go deeper and identify key methods to gather numeric data. We will speak about sampling methods , surveys , and polls . Also, our experts prepared examples of quantitative methods for data collection to help students and researchers work accurately. You also should consider using specific tools for working with this type of data and what is the most important to understand your study purpose.

1. Sampling Method

Let’s imagine you are conducting research about teens and their usage of social media. There is no way you can send a survey, and they analyze data from all teens in the world. However, you will need to choose a reliable number of teens and then create quantitative research designs for this group.

There are two main sampling methods we are going to discuss – probability and non-probability sampling .

Probability Sampling

This quantitative method of data collection can be applied to cases when you need to analyze a specific group of people. For instance, you need to learn what the chance is that a city will vote for a chosen politician. It means you need to understand the age and gender percentages in a city and choose people for the survey based on this information. If your city has 34% of women age 55+, using a quantitative approach, you need to have the same percentage in your sampling.

There are 4 types of probability sampling:

Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling .

Non-Probability Sampling

These quantitative data collection methods consider that the choice of samples depends on a researcher's experience and knowledge. In other words, not everyone can be selected for this data collection procedure - not everyone has an equal probability of being a part of your study.

Quantitative researchers use five models for non-probability data gathering:

Convenience sampling: the only reason to choose study participants is their proximity to researchers.
Quota sampling: scientists use their knowledge and experience to form a quota.
Consecutive sampling: similar to the conventional method but can be applied to the same situation during some period of time.
Snowball sampling: researchers ask their target audience whom they can recommend for the same study.
Judgmental samplings: it is usually chosen based on the researcher's skills.

The next and quite popular method to collect data is the quantitative survey method. The design of your question will depend on the theory you are using for your study. For instance, If you are looking at how customer awareness about product features influence their engagement with a brand, you will apply relationship communication theory. In other words, you can’t put any questions you want into the survey.

You can conduct surveys for your quantitative research using the following ways:

Social media
Survey on your website
Offline surveys, and other methods.

As quantitative studies are focused on numeric data, you need to use a likable scale for answers and not open questions.

Polls are a commonly used quantitative research method, particularly in election and exit polls. Conducting quantitative research for the election means you can ask simple questions with multiple choices. For instance, you may offer a few demographic questions (e.g., age, employment),as well as questions about voting behavior (e.g., candidate they voted for).

Applying quantitative design for polls, researchers need to ensure that the answers can be analyzed with statistical formulas. That is why the questions for the polls are often quite simple and comprise up to 5 questions. However, in some cases, polls may include even less queries.

A quantitative research study can use benchmarks, brushfire, and tracking polls.

Data Analysis Methods

After you collect the results of your polls, samplings, or surveys, you need to analyze quantitative data. As we are discussing primary data, researchers have raw information that can be analyzed and later interpreted in different ways.

Based on quantitative research approaches, we identify 2 key methods for the analysis of numeric data:

Descriptive statistics Descriptive statistics allows us to get average data on questions or measure variability. It helps to overview the data with statistical evaluation. Applying descriptive statistics, you can count the average mean or standard deviation.
Inferential statistics Inferential statistics helps to design predictions and understand the relations between variables. You can run a T-test to measure the relation between two variables. Likewise, you may arrange a Pearson correlation test and measure how one variable depends on another one.

Using these statistical instruments, scientists can go deeper into result discussion and test hypotheses.

Secondary Quantitative Research Methods

Secondary methods of quantitative research are based on the analysis of existing data – the information already gathered by someone or presented in other papers. In this case, we do not need to collect data. Instead, scientists conduct their own quantitative research applying statistical analysis methods and formulas to gain new insights from existing data.

There are 5 most commonly used types of secondary quantitative research methods:

Data from open online sources This is probably one of the most frequently used resources for quantitative study is the internet. A lot of companies and government institutions share the data on their own work, like the number of mobile users or a number of people using state health insurance.
Official data from government and non-government organizations Some data can exist in official reports but are not published online. In this case, you can ask for data that can be shared without breaking privacy protection laws. You may need to make an official request for the information you want to use for your study.
Public libraries You may think that no one uses public libraries. However, this is where you can find old studies conducted by someone else. The library also has a dataset for the papers that can be used for your own study.
Educational institutions A lot of educational institutions are also conducting research. While you can find the analytics published in open sources, a data set can be shared with you after the request.
Commercial sources These sources typically include information from private research firms or companies that collect and analyze data on specific industries, markets, or consumer behavior. Researchers can access this data through websites, reports, or journals, or by requesting access directly from the companies themselves.

How to Conduct Quantitative Research?

If you are working in the academic field or going to get a master's or Ph.D. degree, you definitely will need to conduct various types of studies to write a dissertation . Let’s look at the common ways to conduct quantitative research. Make sure you keep these important considerations in mind:

Determine the type of research you need to conduct. Will you be testing a hypothesis? If so, you will likely need to analyze numerical data.
Identify the appropriate sample size for your study. Do you need a large sample size to obtain reliability of outcomes, or will a smaller sample size suffice?
Be clear about your research goals. It's important to define your research objectives and ensure that your study design aligns with these goals.
Simplify your research questions. If your questions are clear and concise, it will be easier to determine the appropriate type of analysis needed to answer them.

Adhering to these recommendations ensures that research is targeted and generates valuable findings.

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Advantages of Quantitative Research

Before choosing this analytical type for your work, you need to be aware of the advantages of quantitative research methods.

Here are the pros of using quantitative research methodologies for the research:

Time efficiency Gathering and analyzing numerical data usually takes less time than collecting and analyzing non-numerical data.
Reliable data Working with numbers allows for precise statistical analysis, resulting in more reliable results.
Objectivity The absence of personal comments or interpretation in quantitative data collection reduces the possibility of bias in the results.
Scientific approach The quantitative method is considered one of the most scientific research methods, which helps to establish credibility and believability of the results.
Verifiability The results can be easily checked and verified by repeating the formula or analysis, ensuring the accuracy of the data.

Disadvantages of Quantitative Research

It may look like working with quantitative research can bring only pros to your study. However, there are a few cons you need to be aware of before starting your data collection. How can methodology in quantitative research become a disadvantage for your study?

Risk of bias We mentioned that there is no way you will put your emotions into statistical formulas. But researcher experience and personal feelings can be used to form samplings. Even the daytime for data collection can influence the final results.
Narrow focus It is possible that you can be so focused on numbers that you miss the bigger picture. Anytime you are running the numeric study, you need to look at your questions broadly. You may also need some qualitative methods to answer your research questions.
Complexity For people who are not very good at math and statistics, it can be problematic to identify what type of numbers they need and what test should be conducted to get results.

Bottom Line on Quantitative Research

In the few paragraphs, we tried to guide you through key principles of numeric research and answer the question of what is a quantitative study and how to conduct it correctly. We identified critical approaches in collecting data for this type of analysis and outlined limitations you need to have in mind running this study.

You also can find the best quantitative methods examples that will definitely help you with your own study. Try your best to launch a valuable and reliable study using all the knowledge on how to work with numbers!

FAQ About Quantitative Research Studies & Methods

1. what is the purpose of quantitative research.

The primary purpose of quantitative research is to test the hypothesis you may have in your study. This is one of the most frequently used types of data analysis, but before start working with numbers, you need to be clear with your goal. For example, for answering research questions, you may need only qualitative data.

2. What is a quantitative research method?

Quantitative research methods are types of data collection and analysis that focus on numeric information. In other words, this is the research when you work with numbers instead of words. You may need to apply some statistical formulas to those numbers to get results, while in a qualitative study, you will deal with content analysis mostly.

3. When is quantitative research used?

You may need to conduct quantitative research in case you are going to test the hypothesis by running statistical formulas. In most cases, you understand what type of research you need to conduct when you are clear with the study's aims and purpose. After you define hypotheses or questions, you may focus on the methodology that will help you get results.

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Overview of Single-Subject Research

Learning Objectives

Explain what single-subject research is, including how it differs from other types of psychological research.
Explain what case studies are, including some of their strengths and weaknesses.
Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behaviour of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behaviour primarily in terms of group means, standard deviations, and so on. The majority of this textbook is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some areas of psychology.

Before continuing, it is important to distinguish single-subject research from two other approaches, both of which involve studying in detail a small number of participants. One is qualitative research, which focuses on understanding people’s subjective experience by collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behaviour through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

It is also important to distinguish single-subject research from case studies. A case study is a detailed description of an individual, which can include both qualitative and quantitative analyses. (Case studies that include only qualitative analyses can be considered a type of qualitative research.) The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 10.5 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [1] , who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat. Case studies can be useful for suggesting new research questions and for illustrating general principles. They can also help researchers understand rare phenomena, such as the effects of damage to a specific part of the human brain. As a general rule, however, case studies cannot substitute for carefully designed group or single-subject research studies. One reason is that case studies usually do not allow researchers to determine whether specific events are causally related, or even related at all. For example, if a patient is described in a case study as having been sexually abused as a child and then as having developed an eating disorder as a teenager, there is no way to determine whether these two events had anything to do with each other. A second reason is that an individual case can always be unusual in some way and therefore be unrepresentative of people more generally. Thus case studies have serious problems with both internal and external validity.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [2] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

A woman in a floor-length dress with long sleeves. She holds a long white stick.

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behaviour of each one. But why take this approach instead of the group approach? There are several important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behaviour of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behaviour of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behaviour of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behaviour of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviours and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976) [3] . The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behaviour that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often-chaotic elementary school classrooms.

Who Uses Single-Subject Research?

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938) [4] . He and other researchers then used it to describe how rewards, punishments, and other external factors affect behaviour over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behaviour —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behaviour . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behaviour analysis (Baer, Wolf, & Risley, 1968) [5] . Applied behaviour analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behaviour, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behaviour Analysis .

Although most contemporary single-subject research is conducted from the behavioural perspective, it can in principle be used to address questions framed in terms of any theoretical perspective. For example, a studying technique based on cognitive principles of learning and memory could be evaluated by testing it on individual high school students using the single-subject approach. The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioural, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement (Kazdin, 1982) [6] .

Key Takeaways

Single-subject research—which involves testing a small number of participants and focusing intensively on the behaviour of each individual—is an important alternative to group research in psychology.
Single-subject studies must be distinguished from case studies, in which an individual case is described in detail. Case studies can be useful for generating new research questions, for studying rare phenomena, and for illustrating general principles. However, they cannot substitute for carefully controlled experimental or correlational studies because they are low in internal and external validity.
Single-subject research has been around since the beginning of the field of psychology. Today it is most strongly associated with the behavioural theoretical perspective, but it can in principle be used to study behaviour from any perspective.
Practice: Find and read a published article in psychology that reports new single-subject research. ( An archive of articles published in the Journal of Applied Behaviour Analysis can be found at http://www.ncbi.nlm.nih.gov/pmc/journals/309/) Write a short summary of the study.
Describe one problem related to internal validity.
Describe one problem related to external validity.
Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.

Media Attributions

Pappenheim 1882 by unknown is in the Public Domain .
Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton. ↵
Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behaviour analysis is finding its heart. Journal of Applied Behaviour Analysis, 11 , 203–214. ↵
Skinner, B. F. (1938). T he behaviour of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts. ↵
Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behaviour analysis. Journal of Applied Behaviour Analysis, 1 , 91–97. ↵
Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press. ↵

A type of quantitative research that involves studying the behaviour of each small number of participants in detail.

The study of large numbers of participants and examining their behaviour primarily in terms of group means, standard deviations, and so on.

A detailed description of an individual, which can include both qualitative and quantitative analyses.

The study of strong and consistent effects that can be implemented reliably in the real-world contexts in which they occur.

Laboratory methods that rely on single-subject research; based upon B. F. Skinner’s philosophy of behaviourism which posits that everything organisms do is behaviour.

Starting in the 1960s, researchers began using single-subject techniques to conduct applied research with human subjects.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human. Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things. However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms.

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
The research aims for objectivity (i.e., without bias) and is separated from the data.
The design of the study is determined before it begins.
For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
Useful for testing and validating already constructed theories.
Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

Mixed methods research
Designing qualitative research
Methods of data collection and analysis
Introduction to quantitative and qualitative research
Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
Qualitative research in health care: Analysing qualitative data
Qualitative data analysis: the framework approach
Using the framework method for the analysis of
Qualitative data in multi-disciplinary health research
Content Analysis
Grounded Theory
Thematic Analysis

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Quantitative Risk Analysis

Report generator.

Research is a systematic process of addressing an issue or inquiry. It can be divided into qualitative and quantitative investigations. Mainly descriptive, the conclusions from the former are derived from interviews and the researchers’ experience. On the other hand, quantitative research is about getting answers from numbers and measurements. It requires researchers to use analysis tools like statistics and formulas to report their findings.

9+ Quantitative Research Report Templates

In reporting quantitative investigations, researchers analyze and condense numerical findings into graphs, charts, tables, and other illustrative representations of data for easier reading consumption. However, quantitative data aren’t usable without statistics. Researchers use statistical analysis to describe patterns and relationships of numbers and how they relate to the assumptions used in the research. Because this domain of research is less susceptible to biases, the findings are generally seen as credible and reliable information. The following are sample templates of where quantitative research can be utilized.

1. Business Research Report Template

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Business research reports contain relevant information on how to successfully operate your enterprise. Surveys, a common and practical research method, can show data about an issue that affects your company. The report can include a means on how to address the problem. It can also show the shifting of market demand at certain times of the year. When used to its full potential, business research reports can give you a window on how to steer your business in the right direction. Make changes a little more predictable and ride the trends to your favor with this report template!

2. Market Research Report Template

Market research keeps your business grounded on what your customer base wants. It is vital to keep an ear out for how you can keep your clients coming. The interpretation of the data collected will guide business decisions. How you present and analyze the data will influence the conclusions that you would make. This Market Research Report Template lets you communicate your findings clearly. It allocates space for the research objectives, methodology, results, and conclusion. There are diagrams included that you can use to illustrate the highlights of your investigation. Download this template now!

3. Scientific Report Template

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Scientific reports document a search for an answer to a specific question. Most reports has a format that follows a methodical presentation of the findings. They include an abstract or summary of the study and a review of related literature. The research methodology must be detailed enough that when the investigation is replicated, the results will be similar. In general, the conclusions in scientific reports would have to be backed by quantifiable data, ensuring the objectivity of the study. Create a systematic report of your scientific undertaking with this sample template. Get yours now!

4. Lab Report Template

Lab reports allow professors to assess the understanding of each student about the experiment or observation performed. In practice, you have to explain the events that unfolded in the activity using concepts and scientific theories that you have learned during lectures. The next time you will make your lab report, you can use this report template! It features an editable cover page and subheadings for the essential elements of a lab report, such as the introduction, procedures, results, discussion, and conclusion. Impress your professors with a detailed and thorough report!

5. Chemistry Lab Report Template

It can be hard to imagine how chemical reactions occur when you mix reagents or how endpoints can be observed during titration. Laboratory activities supplement learning because you can see scientific theories and chemical equations in action. There would be times that you will be tasked to identify an unknown substance through the different chemical tests you have learned previously. You can elaborate on your procedure in your lab report and why you did or didn’t arrive at expected results. Let this downloadable template help make your chemistry class easier!

6. Physics Lab Report Template

Physics is the study of the fundamentals of how the universe moves. The lectures can get too conceptual for students; that’s why physics laboratory experiments are designed to aid in learning. You can present the results of the experiment in tables and graphs that effectively encapsulates the idea that you are trying to convey. Math is the language of physics. Therefore, your interpretation of the experiment is as good as your presentation of your quantitative data. Show that you understood the lab activity with this lab report template!

7. Scientific Report Template

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Without statistical analysis, you cannot conclude that treatment A is better than treatment B just because one bar is taller than the other. You have to show that A is significantly higher than B. Data analysis and interpretation carry weight in your report. Quantitative research is mainly objective because its conclusions are from the statistical treatment of the gathered numbers. Show in your report how you analyzed your data and the techniques you used to either support or reject an assumption. Take inspiration from how this sample organized a report into an understandable research document. Download now!

8. Restaurant SWOT Analysis Template

There are different data analysis techniques that you, as a researcher, can employ your investigation. Particularly in market research, one of these methods is SWOT analysis . This method is useful in gaining insights on the strengths, weaknesses, and areas of improvement of the business. Surveys are effective research tool because they can yield quantitative information. They can be constructed with rating scales or closed questions that follow a dichotomous yes/no format. The result can be used to improve your business. Integrate this market research method for your business. Use this template now!

9. Free Research Report Cover Page Template

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Talking about numbers and statistics doesn’t have to be bland, not with this creative Free Research Report Cover Page Template! Even though the content of your research is important, that doesn’t mean you can’t make a good impression with a neat title cover. You can include this page in your report during submission. Your company logo can be printed on top your research title to show whom is the study intended for. The dates when the research was conducted or when it will be submitted can be written below when need be. Download this free cover page now!

10. Outline for Quantitative Research Paper Sample

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There are many advantages to and uses of quantitative investigation. If you find this research approach to be ideal for your research question yet difficult to work on, you are not alone. The author of this embedded document, Paiva, recognized this challenge for new researchers. Therefore, he suggested an outline that you can follow in writing your research report. Each heading includes a short instruction on what to add in the section and how to write it. Get your copy of this handy sample now and start writing better research reports!

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Generate a report on the impact of technology in the classroom on student learning outcomes

Prepare a report analyzing the trends in student participation in sports and arts programs over the last five years at your school.

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Q. How do I know if my research article is a qualitative or a quantitative research study report?

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Quantitative Studies

May be referred to as experimental research
Reduces data to numerical findings that are often expressed in charts and graphs
Groups certain features together and counts them
Deductive logic
Often tools - surveys, questionnaires, etc. - are used to compile data
Researcher is removed from the subject of investigation to remain neutral/objective

Qualitative Studies

Descriptive
Inductive logic
Often involve interviews or observation to compile data
Researcher is more often immersed in the subject of investigation, and more likely to be subjective

The study in question: the author is a member of the program being studied; the data comes from observation and is highly descriptive: A qualitative study.

See the entries for Quantitative and Qualitative Research in the Encyclopedia of Research Design for more information.

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Thankyou for the answer it was helpful by mikachi on May 15, 2017

Towards routine proteome profiling of FFPE tissue: Insights from a 1,200 case pan-cancer study

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Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. Here, we did so by analyzing 1,220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4,000 proteins and discovering that current software fails to process such large data sets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community by way of a web resource. It enables a range of analysis including quantitative comparisons of proteins between patients or cohorts or the discovery of protein fingerprints representing the tissue of origin, or proteins enriched in certain cancer entities.

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10.1 Overview of Single-Subject Research
Key Takeaways. Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology. Single-subject studies must be distinguished from case studies, in which an individual case is described in detail.
PDF Quantitative Research Methods
Quantitative . Research Methods. T. his chapter focuses on research designs commonly used when conducting . quantitative research studies. The general purpose of quantitative research is to investigate a particular topic or activity through the measurement of variables in quantifiable terms. Quantitative approaches to conducting educational ...
A Practical Guide to Writing Quantitative and Qualitative Research
Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes.2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ...
What Is Quantitative Research?
Quantitative research methods. You can use quantitative research methods for descriptive, correlational or experimental research. In descriptive research, you simply seek an overall summary of your study variables.; In correlational research, you investigate relationships between your study variables.; In experimental research, you systematically examine whether there is a cause-and-effect ...
Quantitative Research
Here are some key characteristics of quantitative research: Numerical data: Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
10.1 Overview of Single-Subject Research
Key Takeaways. Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology. Single-subject studies must be distinguished from qualitative research on a single person or small number of individuals.
What is Quantitative Research? Definition, Methods, Types, and Examples
Quantitative research is the process of collecting and analyzing numerical data to describe, predict, or control variables of interest. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations. The purpose of quantitative research is to test a predefined ...
Guidelines for Reporting Quantitative Methods and Results in Primary
Guidelines for Reporting Quantitative Methods and Results in Primary Research. Adequate reporting of quantitative research about language learning involves careful consideration of the logic, rationale, and actions underlying both study designs and the ways in which data are analyzed. These guidelines, commissioned and vetted by the board of ...
Writing Quantitative Research Studies
Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to ...
Quantitative Research: What It Is, Practices & Methods
Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It's used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.
What is Quantitative Research? Definition, Examples, Key Advantages
Quantitative research is a type of research that focuses on collecting and analyzing numerical data to answer research questions. There are two main methods used to conduct quantitative research: 1. Primary Method. There are several methods of primary quantitative research, each with its own strengths and limitations.
Single Subject Research
Single subject research designs are "weak when it comes to external validity….Studies involving single-subject designs that show a particular treatment to be effective in changing behavior must rely on replication-across individuals rather than groups-if such results are be found worthy of generalization" (Fraenkel & Wallen, 2006, p ...
16. Reporting quantitative results
Execute a quantitative research report using key elements for accuracy and openness. So you've completed your quantitative analyses and are ready to report your results. We're going to spend some time talking about what matters in quantitative research reports, but the very first thing to understand is this: openness with your data and ...
Study designs: Part 1
The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on "study designs," we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.
Chapter Four: Quantitative Methods (Part 1)
These parts can also be used as a checklist when working through the steps of your study. Specifically, part 1 focuses on planning a quantitative study (collecting data), part two explains the steps involved in doing a quantitative study, and part three discusses how to make sense of your results (organizing and analyzing data). Research Methods.
LibGuides: Research Writing and Analysis: Case Study
A Case study is: An in-depth research design that primarily uses a qualitative methodology but sometimes includes quantitative methodology. Used to examine an identifiable problem confirmed through research. Used to investigate an individual, group of people, organization, or event. Used to mostly answer "how" and "why" questions.
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
What Is Quantitative Research Study: Methods & Examples
As quantitative studies are focused on numeric data, you need to use a likable scale for answers and not open questions. 3. Polls. Polls are a commonly used quantitative research method, particularly in election and exit polls. Conducting quantitative research for the election means you can ask simple questions with multiple choices.
Overview of Single-Subject Research
Key Takeaways. Single-subject research—which involves testing a small number of participants and focusing intensively on the behaviour of each individual—is an important alternative to group research in psychology. Single-subject studies must be distinguished from case studies, in which an individual case is described in detail.
PDF Evidence Pyramid
Meta-Analysis: A process of using quantitative methods to summarize the results from multiple studies, obtained and critically reviewed using a rigorous process (to minimize bias) for identifying, appraising, and synthesizing studies to answer a specific question and draw conclusions about the data gathered. The purpose of this process is to
Qualitative vs Quantitative Research: What's the Difference?
Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings. Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.
Quantitative Research Design
Quantitative research design is a systematic approach used to investigate phenomena by collecting and analyzing numerical data. It involves the use of structured tools such as surveys, experiments, and statistical analysis to quantify variables and identify patterns, relationships, and cause-and-effect dynamics.
Quantitative Research Report
9+ Quantitative Research Report Templates. In reporting quantitative investigations, researchers analyze and condense numerical findings into graphs, charts, tables, and other illustrative representations of data for easier reading consumption. However, quantitative data aren't usable without statistics.
How do I know if my research article is a qualitative or a quantitative
Quantitative Studies. May be referred to as experimental research. Reduces data to numerical findings that are often expressed in charts and graphs. Groups certain features together and counts them. Deductive logic. Often tools - surveys, questionnaires, etc. - are used to compile data. Researcher is removed from the subject of investigation to ...
Towards routine proteome profiling of FFPE tissue: Insights from a
Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. Here, we did so by analyzing 1,220 tumors from six cancer ...

10.1 Overview of Single-Subject Research

What Is Single-Subject Research?

The Case of “Anna O.”

Who Uses Single-Subject Research?

Key Takeaways

Quantitative Research – Methods, Types and Analysis

Quantitative Research

Quantitative Research Methods

Descriptive Research Design

Correlational Research Design

Quasi-experimental Research Design

Experimental Research Design

Survey Research

Quantitative Research Analysis Methods

Statistical Analysis

Regression Analysis

Factor Analysis

Structural Equation Modeling

Time Series Analysis

Multilevel Modeling

Applications of Quantitative Research

Characteristics of Quantitative Research

Examples of Quantitative Research

How to Conduct Quantitative Research

When to use Quantitative Research

Purpose of Quantitative Research

Advantages of Quantitative Research

Limitations of Quantitative Research

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Muhammad Hassan

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What is Quantitative Research? Definition, Methods, Types, and Examples

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Quantitative research characteristics 4

Quantitative research methods 5

Primary quantitative research method:

Secondary quantitative research methods :

When to use quantitative research 6

A research case study to understand when to use quantitative research 7

Advantages of quantitative research 1,2

Disadvantages of quantitative research 1,2

Frequently asked questions on quantitative research

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