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• Types of Variables in Research | Definitions & Examples

Types of Variables in Research | Definitions & Examples

Published on 19 September 2022 by Rebecca Bevans . Revised on 28 November 2022.

In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts.
• Categorical data represents groupings.

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variable can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables.

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is colour-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms ‘dependent’ and ‘independent’ don’t apply, because you are not trying to establish a cause-and-effect relationship.

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases, you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e., the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variable are listed below.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

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Variables in Research | Types, Definiton & Examples

Introduction

What is a variable, what are the 5 types of variables in research, other variables in research.

Variables are fundamental components of research that allow for the measurement and analysis of data. They can be defined as characteristics or properties that can take on different values. In research design , understanding the types of variables and their roles is crucial for developing hypotheses , designing methods , and interpreting results .

This article outlines the the types of variables in research, including their definitions and examples, to provide a clear understanding of their use and significance in research studies. By categorizing variables into distinct groups based on their roles in research, their types of data, and their relationships with other variables, researchers can more effectively structure their studies and achieve more accurate conclusions.

A variable represents any characteristic, number, or quantity that can be measured or quantified. The term encompasses anything that can vary or change, ranging from simple concepts like age and height to more complex ones like satisfaction levels or economic status. Variables are essential in research as they are the foundational elements that researchers manipulate, measure, or control to gain insights into relationships, causes, and effects within their studies. They enable the framing of research questions, the formulation of hypotheses, and the interpretation of results.

Variables can be categorized based on their role in the study (such as independent and dependent variables ), the type of data they represent (quantitative or categorical), and their relationship to other variables (like confounding or control variables). Understanding what constitutes a variable and the various variable types available is a critical step in designing robust and meaningful research.

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Variables are crucial components in research, serving as the foundation for data collection , analysis , and interpretation . They are attributes or characteristics that can vary among subjects or over time, and understanding their types is essential for any study. Variables can be broadly classified into five main types, each with its distinct characteristics and roles within research.

This classification helps researchers in designing their studies, choosing appropriate measurement techniques, and analyzing their results accurately. The five types of variables include independent variables, dependent variables, categorical variables, continuous variables, and confounding variables. These categories not only facilitate a clearer understanding of the data but also guide the formulation of hypotheses and research methodologies.

Independent variables

Independent variables are foundational to the structure of research, serving as the factors or conditions that researchers manipulate or vary to observe their effects on dependent variables. These variables are considered "independent" because their variation does not depend on other variables within the study. Instead, they are the cause or stimulus that directly influences the outcomes being measured. For example, in an experiment to assess the effectiveness of a new teaching method on student performance, the teaching method applied (traditional vs. innovative) would be the independent variable.

The selection of an independent variable is a critical step in research design, as it directly correlates with the study's objective to determine causality or association. Researchers must clearly define and control these variables to ensure that observed changes in the dependent variable can be attributed to variations in the independent variable, thereby affirming the reliability of the results. In experimental research, the independent variable is what differentiates the control group from the experimental group, thereby setting the stage for meaningful comparison and analysis.

Dependent variables

Dependent variables are the outcomes or effects that researchers aim to explore and understand in their studies. These variables are called "dependent" because their values depend on the changes or variations of the independent variables.

Essentially, they are the responses or results that are measured to assess the impact of the independent variable's manipulation. For instance, in a study investigating the effect of exercise on weight loss, the amount of weight lost would be considered the dependent variable, as it depends on the exercise regimen (the independent variable).

The identification and measurement of the dependent variable are crucial for testing the hypothesis and drawing conclusions from the research. It allows researchers to quantify the effect of the independent variable , providing evidence for causal relationships or associations. In experimental settings, the dependent variable is what is being tested and measured across different groups or conditions, enabling researchers to assess the efficacy or impact of the independent variable's variation.

To ensure accuracy and reliability, the dependent variable must be defined clearly and measured consistently across all participants or observations. This consistency helps in reducing measurement errors and increases the validity of the research findings. By carefully analyzing the dependent variables, researchers can derive meaningful insights from their studies, contributing to the broader knowledge in their field.

Categorical variables

Categorical variables, also known as qualitative variables, represent types or categories that are used to group observations. These variables divide data into distinct groups or categories that lack a numerical value but hold significant meaning in research. Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis.

Categorical variables can be further classified into two subtypes: nominal and ordinal. Nominal variables are categories without any inherent order or ranking among them, such as blood type or ethnicity. Ordinal variables, on the other hand, imply a sort of ranking or order among the categories, like levels of satisfaction (high, medium, low) or education level (high school, bachelor's, master's, doctorate).

Understanding and identifying categorical variables is crucial in research as it influences the choice of statistical analysis methods. Since these variables represent categories without numerical significance, researchers employ specific statistical tests designed for a nominal or ordinal variable to draw meaningful conclusions. Properly classifying and analyzing categorical variables allow for the exploration of relationships between different groups within the study, shedding light on patterns and trends that might not be evident with numerical data alone.

Continuous variables

Continuous variables are quantitative variables that can take an infinite number of values within a given range. These variables are measured along a continuum and can represent very precise measurements. Examples of continuous variables include height, weight, temperature, and time. Because they can assume any value within a range, continuous variables allow for detailed analysis and a high degree of accuracy in research findings.

The ability to measure continuous variables at very fine scales makes them invaluable for many types of research, particularly in the natural and social sciences. For instance, in a study examining the effect of temperature on plant growth, temperature would be considered a continuous variable since it can vary across a wide spectrum and be measured to several decimal places.

When dealing with continuous variables, researchers often use methods incorporating a particular statistical test to accommodate a wide range of data points and the potential for infinite divisibility. This includes various forms of regression analysis, correlation, and other techniques suited for modeling and analyzing nuanced relationships between variables. The precision of continuous variables enhances the researcher's ability to detect patterns, trends, and causal relationships within the data, contributing to more robust and detailed conclusions.

Confounding variables

Confounding variables are those that can cause a false association between the independent and dependent variables, potentially leading to incorrect conclusions about the relationship being studied. These are extraneous variables that were not considered in the study design but can influence both the supposed cause and effect, creating a misleading correlation.

Identifying and controlling for a confounding variable is crucial in research to ensure the validity of the findings. This can be achieved through various methods, including randomization, stratification, and statistical control. Randomization helps to evenly distribute confounding variables across study groups, reducing their potential impact. Stratification involves analyzing the data within strata or layers that share common characteristics of the confounder. Statistical control allows researchers to adjust for the effects of confounders in the analysis phase.

Properly addressing confounding variables strengthens the credibility of research outcomes by clarifying the direct relationship between the dependent and independent variables, thus providing more accurate and reliable results.

Beyond the primary categories of variables commonly discussed in research methodology , there exists a diverse range of other variables that play significant roles in the design and analysis of studies. Below is an overview of some of these variables, highlighting their definitions and roles within research studies:

• Discrete variables : A discrete variable is a quantitative variable that represents quantitative data , such as the number of children in a family or the number of cars in a parking lot. Discrete variables can only take on specific values.
• Categorical variables : A categorical variable categorizes subjects or items into groups that do not have a natural numerical order. Categorical data includes nominal variables, like country of origin, and ordinal variables, such as education level.
• Predictor variables : Often used in statistical models, a predictor variable is used to forecast or predict the outcomes of other variables, not necessarily with a causal implication.
• Outcome variables : These variables represent the results or outcomes that researchers aim to explain or predict through their studies. An outcome variable is central to understanding the effects of predictor variables.
• Latent variables : Not directly observable, latent variables are inferred from other, directly measured variables. Examples include psychological constructs like intelligence or socioeconomic status.
• Composite variables : Created by combining multiple variables, composite variables can measure a concept more reliably or simplify the analysis. An example would be a composite happiness index derived from several survey questions .
• Preceding variables : These variables come before other variables in time or sequence, potentially influencing subsequent outcomes. A preceding variable is crucial in longitudinal studies to determine causality or sequences of events.

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Types of Variables – A Comprehensive Guide

Published by Carmen Troy at August 14th, 2021 , Revised On October 26, 2023

A variable is any qualitative or quantitative characteristic that can change and have more than one value, such as age, height, weight, gender, etc.

Before conducting research, it’s essential to know what needs to be measured or analysed and choose a suitable statistical test to present your study’s findings. 

In most cases, you can do it by identifying the key issues/variables related to your research’s main topic.

Example:  If you want to test whether the hybridisation of plants harms the health of people. You can use the key variables like agricultural techniques, type of soil, environmental factors, types of pesticides used, the process of hybridisation, type of yield obtained after hybridisation, type of yield without hybridisation, etc.

Variables are broadly categorised into:

• Independent variables
• Dependent variable
• Control variable

Independent Vs. Dependent Vs. Control Variable

The research includes finding ways:

• To change the independent variables.
• To prevent the controlled variables from changing.
• To measure the dependent variables.

Note:  The term dependent and independent is not applicable in  correlational research  as this is not a  controlled experiment.  A researcher doesn’t have control over the variables. The association and between two or more variables are measured. If one variable affects another one, then it’s called the predictor variable and outcome variable.

Example:  Correlation between investment (predictor variable) and profit (outcome variable)

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Types of Variables Based on the Types of Data

A data is referred to as the information and statistics gathered for analysis of a research topic. Data is broadly divided into two categories, such as:

Quantitative/Numerical data  is associated with the aspects of measurement, quantity, and extent. 

Categorial data  is associated with groupings.

A qualitative variable consists of qualitative data, and a quantitative variable consists of a quantitative variable.

Quantitative Variable

The quantitative variable is associated with measurement, quantity, and extent, like how many . It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population.

Example:  Find out the weight of students of the fifth standard studying in government schools.

The quantitative variable can be further categorised into continuous and discrete.

Categorial Variable

The categorical variable includes measurements that vary in categories such as names but not in terms of rank or degree. It means one level of a categorical variable cannot be considered better or greater than another level. 

Example: Gender, brands, colors, zip codes

The categorical variable is further categorised into three types:

Note:  Sometimes, an ordinal variable also acts as a quantitative variable. Ordinal data has an order, but the intervals between scale points may be uneven.

Example: Numbers on a rating scale represent the reviews’ rank or range from below average to above average. However, it also represents a quantitative variable showing how many stars and how much rating is given.

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Other Types of Variables

It’s important to understand the difference between dependent and independent variables and know whether they are quantitative or categorical to choose the appropriate statistical test.

There are many other types of variables to help you differentiate and understand them.

Also, read a comprehensive guide written about inductive and deductive reasoning .

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

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Types of Variable

All experiments examine some kind of variable(s). A variable is not only something that we measure, but also something that we can manipulate and something we can control for. To understand the characteristics of variables and how we use them in research, this guide is divided into three main sections. First, we illustrate the role of dependent and independent variables. Second, we discuss the difference between experimental and non-experimental research. Finally, we explain how variables can be characterised as either categorical or continuous.

Dependent and Independent Variables

An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable.

Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons: (1) some students spend more time revising for their test; and (2) some students are naturally more intelligent than others. As such, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. The dependent and independent variables for the study are:

Dependent Variable: Test Mark (measured from 0 to 100)

Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score)

The dependent variable is simply that, a variable that is dependent on an independent variable(s). For example, in our case the test mark that a student achieves is dependent on revision time and intelligence. Whilst revision time and intelligence (the independent variables) may (or may not) cause a change in the test mark (the dependent variable), the reverse is implausible; in other words, whilst the number of hours a student spends revising and the higher a student's IQ score may (or may not) change the test mark that a student achieves, a change in a student's test mark has no bearing on whether a student revises more or is more intelligent (this simply doesn't make sense).

Therefore, the aim of the tutor's investigation is to examine whether these independent variables - revision time and IQ - result in a change in the dependent variable, the students' test scores. However, it is also worth noting that whilst this is the main aim of the experiment, the tutor may also be interested to know if the independent variables - revision time and IQ - are also connected in some way.

In the section on experimental and non-experimental research that follows, we find out a little more about the nature of independent and dependent variables.

Experimental and Non-Experimental Research

• Experimental research : In experimental research, the aim is to manipulate an independent variable(s) and then examine the effect that this change has on a dependent variable(s). Since it is possible to manipulate the independent variable(s), experimental research has the advantage of enabling a researcher to identify a cause and effect between variables. For example, take our example of 100 students completing a maths exam where the dependent variable was the exam mark (measured from 0 to 100), and the independent variables were revision time (measured in hours) and intelligence (measured using IQ score). Here, it would be possible to use an experimental design and manipulate the revision time of the students. The tutor could divide the students into two groups, each made up of 50 students. In "group one", the tutor could ask the students not to do any revision. Alternately, "group two" could be asked to do 20 hours of revision in the two weeks prior to the test. The tutor could then compare the marks that the students achieved.
• Non-experimental research : In non-experimental research, the researcher does not manipulate the independent variable(s). This is not to say that it is impossible to do so, but it will either be impractical or unethical to do so. For example, a researcher may be interested in the effect of illegal, recreational drug use (the independent variable(s)) on certain types of behaviour (the dependent variable(s)). However, whilst possible, it would be unethical to ask individuals to take illegal drugs in order to study what effect this had on certain behaviours. As such, a researcher could ask both drug and non-drug users to complete a questionnaire that had been constructed to indicate the extent to which they exhibited certain behaviours. Whilst it is not possible to identify the cause and effect between the variables, we can still examine the association or relationship between them. In addition to understanding the difference between dependent and independent variables, and experimental and non-experimental research, it is also important to understand the different characteristics amongst variables. This is discussed next.

Categorical and Continuous Variables

Categorical variables are also known as discrete or qualitative variables. Categorical variables can be further categorized as either nominal , ordinal or dichotomous .

• Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the USA by state. In this case there will be many more levels of the nominal variable (50 in fact).
• Dichotomous variables are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female". This is an example of a dichotomous variable (and also a nominal variable). Another example might be if we asked a person if they owned a mobile phone. Here, we may categorise mobile phone ownership as either "Yes" or "No". In the real estate agent example, if type of property had been classified as either residential or commercial then "type of property" would be a dichotomous variable.
• Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. So if you asked someone if they liked the policies of the Democratic Party and they could answer either "Not very much", "They are OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories, namely "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the levels, we cannot place a "value" to them; we cannot say that "They are OK" is twice as positive as "Not very much" for example.

Continuous variables are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables.

• Interval variables are variables for which their central characteristic is that they can be measured along a continuum and they have a numerical value (for example, temperature measured in degrees Celsius or Fahrenheit). So the difference between 20°C and 30°C is the same as 30°C to 40°C. However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable.
• Ratio variables are interval variables, but with the added condition that 0 (zero) of the measurement indicates that there is none of that variable. So, temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0°C does not mean there is no temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often called absolute zero) indicates that there is no temperature whatsoever. Other examples of ratio variables include height, mass, distance and many more. The name "ratio" reflects the fact that you can use the ratio of measurements. So, for example, a distance of ten metres is twice the distance of 5 metres.

Ambiguities in classifying a type of variable

In some cases, the measurement scale for data is ordinal, but the variable is treated as continuous. For example, a Likert scale that contains five values - strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree - is ordinal. However, where a Likert scale contains seven or more value - strongly agree, moderately agree, agree, neither agree nor disagree, disagree, moderately disagree, and strongly disagree - the underlying scale is sometimes treated as continuous (although where you should do this is a cause of great dispute).

It is worth noting that how we categorise variables is somewhat of a choice. Whilst we categorised gender as a dichotomous variable (you are either male or female), social scientists may disagree with this, arguing that gender is a more complex variable involving more than two distinctions, but also including measurement levels like genderqueer, intersex and transgender. At the same time, some researchers would argue that a Likert scale, even with seven values, should never be treated as a continuous variable.

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Types of Variables

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CO-4: Distinguish among different measurement scales, choose the appropriate descriptive and inferential statistical methods based on these distinctions, and interpret the results.

CO-7: Use statistical software to analyze public health data.

Classifying Types of Variables

Learning objectives.

LO 4.1: Determine the type (categorical or quantitative) of a given variable.

LO 4.2: Classify a given variable as nominal, ordinal, discrete, or continuous.

Types of Variables (3 Parts; 13:25 total time)

Variables can be broadly classified into one of two types :

• Quantitative
• Categorical

Below we define these two main types of variables and provide further sub-classifications for each type.

Categorical variables take category or label values, and place an individual into one of several groups .

Categorical variables are often further classified as either:

• Nominal, when there is no natural ordering among the categories .

Common examples would be gender, eye color, or ethnicity.

• Ordinal , when there is a natural order among the categories , such as, ranking scales or letter grades.

However, ordinal variables are still categorical and do not provide precise measurements.

Differences are not precisely meaningful, for example, if one student scores an A and another a B on an assignment, we cannot say precisely the difference in their scores, only that an A is larger than a B.

Quantitative variables take numerical values, and represent some kind of measurement .

Quantitative variables are often further classified as either:

• Discrete , when the variable takes on a countable number of values.

Most often these variables indeed represent some kind of count such as the number of prescriptions an individual takes daily.

• Continuous , when the variable can take on any value in some range of values .

Our precision in measuring these variables is often limited by our instruments.

Units should be provided.

Common examples would be height (inches), weight (pounds), or time to recovery (days).

One special variable type occurs when a variable has only two possible values.

A variable is said to be Binary or Dichotomous , when there are only two possible levels.

These variables can usually be phrased in a “yes/no” question. Whether nor not someone is a smoker is an example of a binary variable.

Currently we are primarily concerned with classifying variables as either categorical or quantitative.

Sometimes, however, we will need to consider further and sub-classify these variables as defined above.

These concepts will be discussed and reviewed as needed but here is a quick practice on sub-classifying categorical and quantitative variables.

Did I Get This?

Example: medical records.

Let’s revisit the dataset showing medical records for a sample of patients

In our example of medical records, there are several variables of each type:

• Age, Weight, and Height are quantitative variables.
• Race, Gender, and Smoking are categorical variables.
• Notice that the values of the categorical variable Smoking have been coded as the numbers 0 or 1.

It is quite common to code the values of a categorical variable as numbers, but you should remember that these are just codes.

They have no arithmetic meaning (i.e., it does not make sense to add, subtract, multiply, divide, or compare the magnitude of such values).

Usually, if such a coding is used, all categorical variables will be coded and we will tend to do this type of coding for datasets in this course.

• Sometimes, quantitative variables are divided into groups for analysis, in such a situation, although the original variable was quantitative, the variable analyzed is categorical.

A common example is to provide information about an individual’s Body Mass Index by stating whether the individual is underweight, normal, overweight, or obese.

This categorized BMI is an example of an ordinal categorical variable.

• Categorical variables are sometimes called qualitative variables, but in this course we’ll use the term “categorical.”

Software Activity

LO 7.1: View a dataset in EXCEL, text editor, or other spreadsheet or statistical software.

Learn By Doing:

Exploring a Dataset using Software

Why Does the Type of Variable Matter?

The types of variables you are analyzing directly relate to the available descriptive and inferential statistical methods .

It is important to:

• assess how you will measure the effect of interest and
• know how this determines the statistical methods you can use.

As we proceed in this course, we will continually emphasize the types of variables that are appropriate for each method we discuss .

For example:

To compare the number of polio cases in the two treatment arms of the Salk Polio vaccine trial, you could use

• Fisher’s Exact Test
• Chi-Square Test

To compare blood pressures in a clinical trial evaluating two blood pressure-lowering medications, you could use

• Two-sample t-Test
• Wilcoxon Rank-Sum Test

(Optional) Great Resource: : UCLA Institute for Digital Research and Education – What statistical analysis should I use?

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Neag School of Education

Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative.  QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language:   Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable:  Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement  that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis:  There is no relationship between height and weight. Alternative Hypothesis:   There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better.  Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis:   Boys do not like reading more than girls. Alternative Hypothesis:   Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis:   There is no difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis:   There is a difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

Variables: Definition, Examples, Types of Variables in Research

What is a Variable?

Within the context of a research investigation, concepts are generally referred to as variables. A variable is, as the name applies, something that varies.

Examples of Variable

These are all examples of variables because each of these properties varies or differs from one individual to another.

• income and expenses,
• family size,
• country of birth,
• capital expenditure,
• class grades,
• blood pressure readings,
• preoperative anxiety levels,
• eye color, and
• vehicle type.

What is Variable in Research?

A variable is any property, characteristic, number, or quantity that increases or decreases over time or can take on different values (as opposed to constants, such as n , that do not vary) in different situations.

When conducting research, experiments often manipulate variables. For example, an experimenter might compare the effectiveness of four types of fertilizers.

In this case, the variable is the ‘type of fertilizers.’ A social scientist may examine the possible effect of early marriage on divorce. Her early marriage is variable.

A business researcher may find it useful to include the dividend in determining the share prices . Here, the dividend is the variable.

Effectiveness, divorce, and share prices are variables because they also vary due to manipulating fertilizers, early marriage, and dividends.

11 Types of Variables in Research

Qualitative variables.

An important distinction between variables is the qualitative and quantitative variables.

Qualitative variables are those that express a qualitative attribute, such as hair color, religion, race, gender, social status, method of payment, and so on. The values of a qualitative variable do not imply a meaningful numerical ordering.

The value of the variable ‘religion’ (Muslim, Hindu.., etc..) differs qualitatively; no ordering of religion is implied. Qualitative variables are sometimes referred to as categorical variables.

For example, the variable sex has two distinct categories: ‘male’ and ‘female.’ Since the values of this variable are expressed in categories, we refer to this as a categorical variable.

Similarly, the place of residence may be categorized as urban and rural and thus is a categorical variable.

Categorical variables may again be described as nominal and ordinal.

Ordinal variables can be logically ordered or ranked higher or lower than another but do not necessarily establish a numeric difference between each category, such as examination grades (A+, A, B+, etc., and clothing size (Extra large, large, medium, small).

Nominal variables are those that can neither be ranked nor logically ordered, such as religion, sex, etc.

A qualitative variable is a characteristic that is not capable of being measured but can be categorized as possessing or not possessing some characteristics.

Quantitative Variables

Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person’s age.

Age can take on different values because a person can be 20 years old, 35 years old, and so on. Likewise, family size is a quantitative variable because a family might be comprised of one, two, or three members, and so on.

Each of these properties or characteristics referred to above varies or differs from one individual to another. Note that these variables are expressed in numbers, for which we call quantitative or sometimes numeric variables.

A quantitative variable is one for which the resulting observations are numeric and thus possess a natural ordering or ranking.

Discrete and Continuous Variables

Quantitative variables are again of two types: discrete and continuous.

Variables such as some children in a household or the number of defective items in a box are discrete variables since the possible scores are discrete on the scale.

For example, a household could have three or five children, but not 4.52 children.

Other variables, such as ‘time required to complete an MCQ test’ and ‘waiting time in a queue in front of a bank counter,’ are continuous variables.

The time required in the above examples is a continuous variable, which could be, for example, 1.65 minutes or 1.6584795214 minutes.

Of course, the practicalities of measurement preclude most measured variables from being continuous.

Discrete Variable

A discrete variable, restricted to certain values, usually (but not necessarily) consists of whole numbers, such as the family size and a number of defective items in a box. They are often the results of enumeration or counting.

A few more examples are;

• The number of accidents in the twelve months.
• The number of mobile cards sold in a store within seven days.
• The number of patients admitted to a hospital over a specified period.
• The number of new branches of a bank opened annually during 2001- 2007.
• The number of weekly visits made by health personnel in the last 12 months.

Continuous Variable

A continuous variable may take on an infinite number of intermediate values along a specified interval. Examples are:

• The sugar level in the human body;
• Blood pressure reading;
• Temperature;
• Height or weight of the human body;
• Rate of bank interest;
• Internal rate of return (IRR),
• Earning ratio (ER);
• Current ratio (CR)

No matter how close two observations might be, if the instrument of measurement is precise enough, a third observation can be found, falling between the first two.

A continuous variable generally results from measurement and can assume countless values in the specified range.

Dependent Variables and Independent Variable

In many research settings, two specific classes of variables need to be distinguished from one another: independent variable and dependent variable.

Many research studies aim to reveal and understand the causes of underlying phenomena or problems with the ultimate goal of establishing a causal relationship between them.

Look at the following statements:

• Low intake of food causes underweight.
• Smoking enhances the risk of lung cancer.
• Level of education influences job satisfaction.
• Advertisement helps in sales promotion.
• The drug causes improvement of health problems.
• Nursing intervention causes more rapid recovery.
• Previous job experiences determine the initial salary.
• Blueberries slow down aging.
• The dividend per share determines share prices.

In each of the above queries, we have two independent and dependent variables. In the first example, ‘low intake of food’ is believed to have caused the ‘problem of being underweight.’

It is thus the so-called independent variable. Underweight is the dependent variable because we believe this ‘problem’ (the problem of being underweight) has been caused by ‘the low intake of food’ (the factor).

Similarly, smoking, dividend, and advertisement are all independent variables, and lung cancer, job satisfaction, and sales are dependent variables.

In general, an independent variable is manipulated by the experimenter or researcher, and its effects on the dependent variable are measured.

Independent Variable

The variable that is used to describe or measure the factor that is assumed to cause or at least to influence the problem or outcome is called an independent variable.

The definition implies that the experimenter uses the independent variable to describe or explain its influence or effect of it on the dependent variable.

Variability in the dependent variable is presumed to depend on variability in the independent variable.

Depending on the context, an independent variable is sometimes called a predictor variable, regressor, controlled variable, manipulated variable, explanatory variable, exposure variable (as used in reliability theory), risk factor (as used in medical statistics), feature (as used in machine learning and pattern recognition) or input variable.

The explanatory variable is preferred by some authors over the independent variable when the quantities treated as independent variables may not be statistically independent or independently manipulable by the researcher.

If the independent variable is referred to as an explanatory variable, then the term response variable is preferred by some authors for the dependent variable.

Dependent Variable

The variable used to describe or measure the problem or outcome under study is called a dependent variable.

In a causal relationship, the cause is the independent variable, and the effect is the dependent variable. If we hypothesize that smoking causes lung cancer, ‘smoking’ is the independent variable and cancer the dependent variable.

A business researcher may find it useful to include the dividend in determining the share prices. Here dividend is the independent variable, while the share price is the dependent variable.

The dependent variable usually is the variable the researcher is interested in understanding, explaining, or predicting.

In lung cancer research, the carcinoma is of real interest to the researcher, not smoking behavior per se. The independent variable is the presumed cause of, antecedent to, or influence on the dependent variable.

Depending on the context, a dependent variable is sometimes called a response variable, regressand, predicted variable, measured variable, explained variable, experimental variable, responding variable, outcome variable, output variable, or label.

An explained variable is preferred by some authors over the dependent variable when the quantities treated as dependent variables may not be statistically dependent.

If the dependent variable is referred to as an explained variable, then the term predictor variable is preferred by some authors for the independent variable.

Levels of an Independent Variable

If an experimenter compares an experimental treatment with a control treatment, then the independent variable (a type of treatment) has two levels: experimental and control.

If an experiment were to compare five types of diets, then the independent variables (types of diet) would have five levels.

In general, the number of levels of an independent variable is the number of experimental conditions.

Background Variable

In almost every study, we collect information such as age, sex, educational attainment, socioeconomic status, marital status, religion, place of birth, and the like. These variables are referred to as background variables.

These variables are often related to many independent variables, so they indirectly influence the problem. Hence they are called background variables.

The background variables should be measured if they are important to the study. However, we should try to keep the number of background variables as few as possible in the interest of the economy.

Moderating Variable

In any statement of relationships of variables, it is normally hypothesized that in some way, the independent variable ’causes’ the dependent variable to occur.

In simple relationships, all other variables are extraneous and are ignored.

In actual study situations, such a simple one-to-one relationship needs to be revised to take other variables into account to explain the relationship better.

This emphasizes the need to consider a second independent variable that is expected to have a significant contributory or contingent effect on the originally stated dependent-independent relationship.

Such a variable is termed a moderating variable.

Suppose you are studying the impact of field-based and classroom-based training on the work performance of health and family planning workers. You consider the type of training as the independent variable.

If you are focusing on the relationship between the age of the trainees and work performance, you might use ‘type of training’ as a moderating variable.

Extraneous Variable

Most studies concern the identification of a single independent variable and measuring its effect on the dependent variable.

But still, several variables might conceivably affect our hypothesized independent-dependent variable relationship, thereby distorting the study. These variables are referred to as extraneous variables.

Extraneous variables are not necessarily part of the study. They exert a confounding effect on the dependent-independent relationship and thus need to be eliminated or controlled for.

An example may illustrate the concept of extraneous variables. Suppose we are interested in examining the relationship between the work status of mothers and breastfeeding duration.

It is not unreasonable in this instance to presume that the level of education of mothers as it influences work status might have an impact on breastfeeding duration too.

Education is treated here as an extraneous variable. In any attempt to eliminate or control the effect of this variable, we may consider this variable a confounding variable.

An appropriate way of dealing with confounding variables is to follow the stratification procedure, which involves a separate analysis of the different levels of lies in confounding variables.

For this purpose, one can construct two cross­tables for illiterate mothers and the other for literate mothers.

Suppose we find a similar association between work status and duration of breast­feeding in both the groups of mothers. In that case, we conclude that mothers’ educational level is not a confounding variable.

Intervening Variable

Often an apparent relationship between two variables is caused by a third variable.

For example, variables X and Y may be highly correlated, but only because X causes the third variable, Z, which in turn causes Y. In this case, Z is the intervening variable.

An intervening variable theoretically affects the observed phenomena but cannot be seen, measured, or manipulated directly; its effects can only be inferred from the effects of the independent and moderating variables on the observed phenomena.

We might view motivation or counseling as the intervening variable in the work-status and breastfeeding relationship.

Thus, motive, job satisfaction, responsibility, behavior, and justice are some of the examples of intervening variables.

Suppressor Variable

In many cases, we have good reasons to believe that the variables of interest have a relationship, but our data fail to establish any such relationship. Some hidden factors may suppress the true relationship between the two original variables.

Such a factor is referred to as a suppressor variable because it suppresses the relationship between the other two variables.

The suppressor variable suppresses the relationship by being positively correlated with one of the variables in the relationship and negatively correlated with the other. The true relationship between the two variables will reappear when the suppressor variable is controlled for.

Thus, for example, low age may pull education up but income down. In contrast, a high age may pull income up but education down, effectively canceling the relationship between education and income unless age is controlled for.

4 Relationships Between Variables

In dealing with relationships between variables in research, we observe a variety of dimensions in these relationships.

Positive and Negative Relationship

Symmetrical relationship, causal relationship, linear and non-linear relationship.

Two or more variables may have a positive, negative, or no relationship. In the case of two variables, a positive relationship is one in which both variables vary in the same direction.

However, they are said to have a negative relationship when they vary in opposite directions.

When a change in the other variable does not accompany the change or movement of one variable, we say that the variables in question are unrelated.

For example, if an increase in wage rate accompanies one’s job experience, the relationship between job experience and the wage rate is positive.

If an increase in an individual’s education level decreases his desire for additional children, the relationship is negative or inverse.

If the level of education does not have any bearing on the desire, we say that the variables’ desire for additional children and ‘education’ are unrelated.

Strength of Relationship

Once it has been established that two variables are related, we want to ascertain how strongly they are related.

A common statistic to measure the strength of a relationship is the so-called correlation coefficient symbolized by r. r is a unit-free measure, lying between -1 and +1 inclusive, with zero signifying no linear relationship.

As far as the prediction of one variable from the knowledge of the other variable is concerned, a value of r= +1 means a 100% accuracy in predicting a positive relationship between the two variables, and a value of r = -1 means a 100% accuracy in predicting a negative relationship between the two variables.

So far, we have discussed only symmetrical relationships in which a change in the other variable accompanies a change in either variable.

This relationship does not indicate which variable is the independent variable and which variable is the dependent variable.

In other words, you can label either of the variables as the independent variable.

Such a relationship is a symmetrical  relationship. In an asymmetrical relationship, a change in variable X (say) is accompanied by a change in variable Y, but not vice versa.

The amount of rainfall, for example, will increase productivity, but productivity will not affect the rainfall. This is an asymmetrical relationship.

Similarly, the relationship between smoking and lung cancer would be asymmetrical because smoking could cause cancer, but lung cancer could not cause smoking.

Indicating a relationship between two variables does not automatically ensure that changes in one variable cause changes in another.

It is, however, very difficult to establish the existence of causality between variables. While no one can ever be certain that variable A causes variable B , one can gather some evidence that increases our belief that A leads to B.

In an attempt to do so, we seek the following evidence:

• Is there a relationship between A and B?  When such evidence exists, it indicates a possible causal link between the variables.
• Is the relationship asymmetrical so that a change in A results in B but not vice-versa? In other words, does A occur before B? If we find that B occurs before A, we can have little confidence that A causes.
• Does a change in A result in a change in B regardless of the actions of other factors? Or, is it possible to eliminate other possible causes of B? Can one determine that C, D, and E (say) do not co-vary with B in a way that suggests possible causal connections?

A linear relationship is a straight-line relationship between two variables, where the variables vary at the same rate regardless of whether the values are low, high, or intermediate.

This is in contrast with the non-linear (or curvilinear) relationships, where the rate at which one variable changes in value may differ for different values of the second variable.

Whether a variable is linearly related to the other variable or not can simply be ascertained by plotting the K values against X values.

If the values, when plotted, appear to lie on a straight line, the existence of a linear relationship between X and Y is suggested.

Height and weight almost always have an approximately linear relationship, while age and fertility rates have a non-linear relationship.

Frequently Asked Questions about Variable

What is a variable within the context of a research investigation.

A variable, within the context of a research investigation, refers to concepts that vary. It can be any property, characteristic, number, or quantity that can increase or decrease over time or take on different values.

How is a variable used in research?

In research, a variable is any property or characteristic that can take on different values. Experiments often manipulate variables to compare outcomes. For instance, an experimenter might compare the effectiveness of different types of fertilizers, where the variable is the ‘type of fertilizers.’

What distinguishes qualitative variables from quantitative variables?

Qualitative variables express a qualitative attribute, such as hair color or religion, and do not imply a meaningful numerical ordering. Quantitative variables, on the other hand, are measured in terms of numbers, like a person’s age or family size.

How do discrete and continuous variables differ in terms of quantitative variables?

Discrete variables are restricted to certain values, often whole numbers, resulting from enumeration or counting, like the number of children in a household. Continuous variables can take on an infinite number of intermediate values along a specified interval, such as the time required to complete a test.

What are the roles of independent and dependent variables in research?

In research, the independent variable is manipulated by the researcher to observe its effects on the dependent variable. The independent variable is the presumed cause or influence, while the dependent variable is the outcome or effect that is being measured.

What is a background variable in a study?

Background variables are information collected in a study, such as age, sex, or educational attainment. These variables are often related to many independent variables and indirectly influence the main problem or outcome, hence they are termed background variables.

How does a suppressor variable affect the relationship between two other variables?

A suppressor variable can suppress or hide the true relationship between two other variables. It does this by being positively correlated with one of the variables and negatively correlated with the other. When the suppressor variable is controlled for, the true relationship between the two original variables can be observed.

Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable.

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

Need a helping hand?

What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Types of Variables in Psychology Research

Examples of Independent and Dependent Variables

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

 James Lacy, MLS, is a fact-checker and researcher.

Dependent and Independent Variables

• Intervening Variables
• Extraneous Variables
• Controlled Variables
• Confounding Variables
• Operationalizing Variables

Frequently Asked Questions

Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Variables in psychology play a critical role in the research process. By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships.

The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about psychological phenomena.

This article discusses different types of variables that are used in psychology research. It also covers how to operationalize these variables when conducting experiments.

Students often report problems with identifying the independent and dependent variables in an experiment. While this task can become more difficult as the complexity of an experiment increases, in a psychology experiment:

• The independent variable is the variable that is manipulated by the experimenter. An example of an independent variable in psychology: In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.
• The dependent variable is the variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

So how do you differentiate between the independent and dependent variables? Start by asking yourself what the experimenter is manipulating. The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.

Intervening Variables in Psychology

Intervening variables, also sometimes called intermediate or mediator variables, are factors that play a role in the relationship between two other variables. In the previous example, sleep problems in university students are often influenced by factors such as stress. As a result, stress might be an intervening variable that plays a role in how much sleep people get, which may then influence how well they perform on exams.

Extraneous Variables in Psychology

Independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

For example, in our previous example of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so any impact can be controlled for.

There are two basic types of extraneous variables:

• Participant variables : These extraneous variables are related to the individual characteristics of each study participant that may impact how they respond. These factors can include background differences, mood, anxiety, intelligence, awareness, and other characteristics that are unique to each person.
• Situational variables : These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

Other extraneous variables include the following:

• Demand characteristics : Clues in the environment that suggest how a participant should behave
• Experimenter effects : When a researcher unintentionally suggests clues for how a participant should behave

Controlled Variables in Psychology

In many cases, extraneous variables are controlled for by the experimenter. A controlled variable is one that is held constant throughout an experiment.

In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors don't interfere with the results. Holding these variables constant is important for an experiment because it allows researchers to be sure that all other variables remain the same across all conditions.  

Using controlled variables means that when changes occur, the researchers can be sure that these changes are due to the manipulation of the independent variable and not caused by changes in other variables.

It is important to also note that a controlled variable is not the same thing as a control group . The control group in a study is the group of participants who do not receive the treatment or change in the independent variable.

All other variables between the control group and experimental group are held constant (i.e., they are controlled). The dependent variable being measured is then compared between the control group and experimental group to see what changes occurred because of the treatment.

Confounding Variables in Psychology

If a variable cannot be controlled for, it becomes what is known as a confounding variabl e. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Operationalizing Variables in Psychology

An operational definition describes how the variables are measured and defined in the study. Before conducting a psychology experiment , it is essential to create firm operational definitions for both the independent variable and dependent variables.

For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is "Students who are sleep deprived will score significantly lower on a test," then we would have a few different concepts to define:

• Students : First, what do we mean by "students?" In our example, let’s define students as participants enrolled in an introductory university-level psychology course.
• Sleep deprivation : Next, we need to operationally define the "sleep deprivation" variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test.
• Test variable : Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.

Once all the variables are operationalized, we're ready to conduct the experiment.

Variables play an important part in psychology research. Manipulating an independent variable and measuring the dependent variable allows researchers to determine if there is a cause-and-effect relationship between them.

A Word From Verywell

Understanding the different types of variables used in psychology research is important if you want to conduct your own psychology experiments. It is also helpful for people who want to better understand what the results of psychology research really mean and become more informed consumers of psychology information .

Independent and dependent variables are used in experimental research. Unlike some other types of research (such as correlational studies ), experiments allow researchers to evaluate cause-and-effect relationships between two variables.

Researchers can use statistical analyses to determine the strength of a relationship between two variables in an experiment. Two of the most common ways to do this are to calculate a p-value or a correlation. The p-value indicates if the results are statistically significant while the correlation can indicate the strength of the relationship.

In an experiment on how sugar affects short-term memory, sugar intake would be the independent variable and scores on a short-term memory task would be the independent variable.

In an experiment looking at how caffeine intake affects test anxiety, the amount of caffeine consumed before a test would be the independent variable and scores on a test anxiety assessment would be the dependent variable.

Just as with other types of research, the independent variable in a cognitive psychology study would be the variable that the researchers manipulate. The specific independent variable would vary depending on the specific study, but it might be focused on some aspect of thinking, memory, attention, language, or decision-making.

American Psychological Association. Operational definition . APA Dictionary of Psychology.

American Psychological Association. Mediator . APA Dictionary of Psychology.

Altun I, Cınar N, Dede C. The contributing factors to poor sleep experiences in according to the university students: A cross-sectional study .  J Res Med Sci . 2012;17(6):557-561. PMID:23626634

Skelly AC, Dettori JR, Brodt ED. Assessing bias: The importance of considering confounding .  Evid Based Spine Care J . 2012;3(1):9-12. doi:10.1055/s-0031-1298595

• Evans, AN & Rooney, BJ. Methods in Psychological Research. Thousand Oaks, CA: SAGE Publications; 2014.
• Kantowitz, BH, Roediger, HL, & Elmes, DG. Experimental Psychology. Stamfort, CT: Cengage Learning; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Types of Variables, Descriptive Statistics, and Sample Size

Feroze kaliyadan.

Department of Dermatology, King Faisal University, Al Hofuf, Saudi Arabia

Vinay Kulkarni

1 Department of Dermatology, Prayas Amrita Clinic, Pune, Maharashtra, India

This short “snippet” covers three important aspects related to statistics – the concept of variables , the importance, and practical aspects related to descriptive statistics and issues related to sampling – types of sampling and sample size estimation.

What is a variable?[ 1 , 2 ] To put it in very simple terms, a variable is an entity whose value varies. A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same. The importance of variables is that they help in operationalization of concepts for data collection. For example, if you want to do an experiment based on the severity of urticaria, one option would be to measure the severity using a scale to grade severity of itching. This becomes an operational variable. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance. Variables can be classified into various ways as discussed below.

Quantitative vs qualitative

A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type)

A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks).

Quantitative variables can be either discrete or continuous

Discrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria).

Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test. If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. The mid-way value would be 11.5 min which makes sense. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense.

Under the umbrella of qualitative variables, you can have nominal/categorical variables and ordinal variables

Nominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis).

Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching).

Dependent and independent variables

In the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study. For example, in a clinical trial on psoriasis, the PASI (psoriasis area severity index) would possibly be one dependent variable. The independent variable (sometime also called explanatory variable) is something which is not affected by the experiment itself but which can be manipulated to affect the dependent variable. Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment.

Descriptive Statistics

Statistics can be broadly divided into descriptive statistics and inferential statistics.[ 3 , 4 ] Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary. When we describe the population using tools such as frequency distribution tables, percentages, and other measures of central tendency like the mean, for example, we are talking about descriptive statistics. When we use a specific statistical test (e.g., Mann–Whitney U-test) to compare the mean scores and express it in terms of statistical significance, we are talking about inferential statistics. Descriptive statistics can help in summarizing data in the form of simple quantitative measures such as percentages or means or in the form of visual summaries such as histograms and box plots.

Descriptive statistics can be used to describe a single variable (univariate analysis) or more than one variable (bivariate/multivariate analysis). In the case of more than one variable, descriptive statistics can help summarize relationships between variables using tools such as scatter plots.

Descriptive statistics can be broadly put under two categories:

• Sorting/grouping and illustration/visual displays
• Summary statistics.

Sorting and grouping

Sorting and grouping is most commonly done using frequency distribution tables. For continuous variables, it is generally better to use groups in the frequency table. Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”).

Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description.

Suppose the weight in kilograms of a group of 10 patients is as follows:

56, 34, 48, 43, 87, 78, 54, 62, 61, 59

The “stem” records the value of the “ten's” place (or higher) and the “leaf” records the value in the “one's” place [ Table 1 ].

Stem and leaf plot

Illustration/visual display of data

The most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). Composite bar charts can be used to compare variables. For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1 .

Composite bar chart

A pie chart helps show how a total quantity is divided among its constituent variables. Scatter diagrams can be used to illustrate the relationship between two variables. For example, global scores given for improvement in a condition like acne by the patient and the doctor [ Figure 2 ].

Scatter diagram

Summary statistics

The main tools used for summary statistics are broadly grouped into measures of central tendency (such as mean, median, and mode) and measures of dispersion or variation (such as range, standard deviation, and variance).

Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order:

30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86

Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data.

The mean is basically the sum of all the values divided by the total number. In this case, we get a value of 45.

The problem is that some extreme values (outliers), like “'86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50 th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8 th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median.

The mode is the most common data point. In our example, this would be 38. The mode as in our case may not necessarily be in the center of the distribution.

The median is the best measure of central tendency from among the mean, median, and mode. In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3 , a right skewed distribution is seen (direction of skew is based on the tail); data values' distribution is longer on the right-hand (positive) side than on the left-hand side. The mean is typically greater than the median in such cases.

Location of mode, median, and mean

Measures of dispersion

The range gives the spread between the lowest and highest values. In our previous example, this will be 86-30 = 56.

A more valuable measure is the interquartile range. A quartile is one of the values which break the distribution into four equal parts. The 25 th percentile is the data point which divides the group between the first one-fourth and the last three-fourth of the data. The first one-fourth will form the first quartile. The 75 th percentile is the data point which divides the distribution into a first three-fourth and last one-fourth (the last one-fourth being the fourth quartile). The range between the 25 th percentile and 75 th percentile is called the interquartile range.

Variance is also a measure of dispersion. The larger the variance, the further the individual units are from the mean. Let us consider the same example we used for calculating the mean. The mean was 45.

For the first value (30), the deviation from the mean will be 15; for the last value (86), the deviation will be 41. Similarly we can calculate the deviations for all values in a sample. Adding these deviations and averaging will give a clue to the total dispersion, but the problem is that since the deviations are a mix of negative and positive values, the final total becomes zero. To calculate the variance, this problem is overcome by adding squares of the deviations. So variance would be the sum of squares of the variation divided by the total number in the population (for a sample we use “n − 1”). To get a more realistic value of the average dispersion, we take the square root of the variance, which is called the “standard deviation.”

The box plot

The box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ].

The concept of skewness and kurtosis

Skewness is a measure of the symmetry of distribution. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. When this is not the case, it is said to be skewed. Kurtosis is a representation of outliers. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. There are formulas to calculate both skewness and kurtosis [Figures ​ [Figures5 5 – 8 ].

Positive skew

High kurtosis (positive kurtosis – also called leptokurtic)

Negative skew

Low kurtosis (negative kurtosis – also called “Platykurtic”)

Sample Size

In an ideal study, we should be able to include all units of a particular population under study, something that is referred to as a census.[ 5 , 6 ] This would remove the chances of sampling error (difference between the outcome characteristics in a random sample when compared with the true population values – something that is virtually unavoidable when you take a random sample). However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error.

We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. Examples of probability sampling include methods such as simple random sampling (each member in a population has an equal chance of being selected), stratified random sampling (in nonhomogeneous populations, the population is divided into subgroups – followed be random sampling in each subgroup), systematic (sampling is based on a systematic technique – e.g., every third person is selected for a survey), and cluster sampling (similar to stratified sampling except that the clusters here are preexisting clusters unlike stratified sampling where the researcher decides on the stratification criteria), whereas nonprobability sampling, where every unit in the population does not have an equal chance of inclusion into the sample, includes methods such as convenience sampling (e.g., sample selected based on ease of access) and purposive sampling (where only people who meet specific criteria are included in the sample).

An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis. A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources.

We will first go through the sample size calculation for a hypothesis-based design (like a randomized control trial).

The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance (standard deviation for quantitative data), and expected proportions in the case of qualitative data. This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size). It is also important to know whether the data follow a normal distribution or not.

Two essential aspects we must understand are the concept of Type I and Type II errors. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error. When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test. While there are no absolute rules, the minimal levels accepted are 0.05 for α (corresponding to a significance level of 5%) and 0.20 for β (corresponding to a minimum recommended power of “1 − 0.20,” or 80%).

Effect size and minimal clinically relevant difference

For a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant (for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.). While we will not go into details of the formula for sample size calculation, some important points are as follows:

In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size. What this means in effect is that reducing the effect size will lead to an increase in the required sample size.

Reducing the level of significance (alpha) or increasing power (1-β) will lead to an increase in the calculated sample size.

An increase in variance of the outcome leads to an increase in the calculated sample size.

A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. This includes an idea about total population size (this generally does not make a major difference when population size is above 20,000, so in situations where population size is not known we can assume a population of 20,000 or more). The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates.

Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size. In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this.

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Stats and R

Variable types and examples, different types of variables for different types of statistical analysis, big picture, from continuous to discrete, from quantitative to qualitative, misleading data encoding, introduction.

If you happen to work with datasets frequently, you probably know that each row of your dataset represents a different experimental unit (also called observation ) and each column represents a different characteristic (called variable ):

If you do some research on the weight and height of 100 students of your university, for example, you will most likely have a dataset containing 100 rows and 3 columns:

• one for the student’s ID (could be anonymized or not),
• one for the weight,
• and one for the height.

These three columns represent three characteristics of the 100 students. They are called variables .

In this article, we are going to focus on variables, and in particular on the different types of variable that exist in statistics. (To learn about the different data types in R, read “ Data types in R ”.)

First, one may wonder why we are interested in defining the types of our variables of interest.

The reason why we often class variables into different types is because not all statistical analyses can be performed on all variable types. For instance, it is impossible to compute the mean of the variable “hair color” as you cannot sum brown and blond hair.

On the other hand, finding the mode of a continuous variable does not really make any sense because most of the time there will not be two exact same values, so there will be no mode. And even in the case there is a mode, there will be very few observations with this value. As an example, try finding the mode of the height of the students in your class. If you are lucky, a couple of students will have the same size. However, most of the time, every student will have a different size (especially if heights have been measured in millimeters) and thus there will be no mode. To see what kind of analysis is possible on each type of variable, see more details in the articles “ Descriptive statistics by hand ” and “ Descriptive statistics in R ”.

Similarly, some statistical tests can only be performed on certain type of variables. For example, the Pearson correlation is usually computed on two quantitative variables, while a Chi-square test of independence is done with two qualitative variables, and a Student t-test or ANOVA requires a mix of one quantitative and one qualitative variable.

In statistics, variables are classified into 4 different types:

We present each type together with examples in the following sections.

Quantitative

A quantitative variable is a variable that reflects a notion of magnitude , that is, if the values it can take are numbers . A quantitative variable represents thus a measure and is numerical.

Quantitative variables are divided into two types: discrete and continuous . The difference is explained in the following two sections.

Quantitative discrete variables are variables for which the values it can take are countable and have a finite number of possibilities . The values are often (but not always) integers. Here are some examples of discrete variables:

• Number of children per family
• Number of students in a class
• Number of citizens of a country

Even if it would take a long time to count the citizens of a large country, it is still technically doable. Moreover, for all examples, the number of possibilities is finite . Whatever the number of children in a family, it will never be 3.58 or 7.912 so the number of possibilities is a finite number and thus countable.

On the other hand, quantitative continuous variables are variables for which the values are not countable and have an infinite number of possibilities . For example:

For simplicity, we usually referred to years, kilograms (or pounds) and centimeters (or feet and inches) for age, weight and height respectively. However, a 28-year-old man could actually be 28 years, 7 months, 16 days, 3 hours, 4 minutes, 5 seconds, 31 milliseconds, 9 nanoseconds old.

For all measurements, we usually stop at a standard level of granularity, but nothing (except our measurement tools) prevents us from going deeper, leading to an infinite number of potential values . The fact that the values can take an infinite number of possibilities makes it uncountable.

Qualitative

In opposition to quantitative variables, qualitative variables (also referred as categorical variables or factors in R) are variables that are not numerical and which values fit into categories .

In other words, a qualitative variable is a variable which takes as its values modalities, categories or even levels, in contrast to quantitative variables which measure a quantity on each individual.

Qualitative variables are divided into two types: nominal and ordinal .

A qualitative nominal variable is a qualitative variable where no ordering is possible or implied in the levels.

For example, the variable gender is nominal because there is no order in the levels (no matter how many levels you consider for the gender—only two with female/male, or more than two with female/male/ungendered/others, levels are un ordered). Eye color is another example of a nominal variable because there is no order among blue, brown or green eyes.

A nominal variable can have:

• two levels (e.g., do you smoke? Yes/No, or are you pregnant? Yes/No), or
• a large number of levels (what is your college major? Each major is a level in that case).

Note that a qualitative variable with exactly 2 levels is also referred as a binary or dichotomous variable.

On the other hand, a qualitative ordinal variable is a qualitative variable with an order implied in the levels . For instance, if the severity of road accidents has been measured on a scale such as light, moderate and fatal accidents, this variable is a qualitative ordinal variable because there is a clear order in the levels.

Another good example is health, which can take values such as poor, reasonable, good, or excellent. Again, there is a clear order in these levels so health is in this case a qualitative ordinal variable.

Variable transformations

There are two main variable transformations:

• From a continuous to a discrete variable
• From a quantitative to a qualitative variable

Let’s say we are interested in babies’ ages. The data collected is the age of the babies, so a quantitative continuous variable. However, we may work with only the number of weeks since birth and thus transforming the age into a discrete variable. The variable age remains a quantitative continuous variable but the variable we are working on (i.e., the number of weeks since birth) can be seen as a quantitative discrete variable.

Let’s say we are interested in the Body Mass Index (BMI). For this, a researcher collects data on height and weight of individuals and computes the BMI. The BMI is a quantitative continuous variable but the researcher may want to turn it into a qualitative variable by categorizing individuals below a certain threshold as underweighted, above a certain threshold as overweighted and the rest as normal weighted. The raw BMI is a quantitative continuous variable but the categorization of the BMI makes the transformed variable a qualitative (ordinal) variable, where the levels are in this case underweighted < normal < overweighted.

Same goes for age when age is transformed to a qualitative ordinal variable with levels such as minors, adults and seniors. It is also often the case (especially in surveys) that the variable salary (quantitative continuous) is transformed into a qualitative ordinal variable with different range of salaries (e.g., < 1000€, 1000 - 2000€, > 2000€).

Additional notes

Last but not least, in datasets it is very often the case that numbers are used for qualitative variables. For instance, a researcher may assign the number “1” to women and the number “2” to men (or “0” to the answer “No” and “1” to the answer “Yes”). Despite the numerical classification, the variable gender is still a qualitative variable and not a discrete variable as it may look. The numerical classification is only used to facilitate data collection and data management. It is indeed easier to write the number “1” or “2” instead of “women” or “men”, and thus less prone to encoding errors.

The same goes for the identification of each observation. Suppose you collected information on 100 students. You may use their student’s ID to identify them in the dataset (so that you can trace them back). Most of the time, students’ ID (or ID in general) are encoded as numeric values. At first sight, it may thus look like a quantitative variable (because it goes from 1 to 100 for example). However, ID is clearly not a quantitative variable because it actually corresponds to an anonymized version of the student’s first and last name. If you think about it, it would make no sense to compute the mean or median on the IDs, as it does not represent a numerical measurement (but rather just an easier way to identify students than with their names).

If you face this kind of setup, do not forget to transform your variable into the right type before performing any statistical analyses. Usually, a basic descriptive analysis (and knowledge about the variables which have been measured) prior to the main statistical analyses is enough to check that all variable types are correct.

Thanks for reading.

I hope this article helped you to understand the different types of variable. If you would like to learn more about the different data types in R, read the article “ Data types in R ”.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

Related articles

• Pearson, Spearman and Kendall correlation coefficients by hand
• How to: one-way ANOVA by hand
• One-sample Wilcoxon test in R
• What statistical test should I do?
• Hypothesis test by hand

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The Different Types Of Variables Used In Research And Statistics

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Scientists and statisticians conduct experiments on a regular basis. Scientists use these experiments to identify cause and effect, while Statisticians use variables to represent the unknown or varied data in their experiments.

Determining which variables to use is vital to the experiment. Also, choosing the right variables will lead to clearer analyses and more accurate results.

What Is a Variable?

A variable is something you can control, manipulate, or measure when conducting research or experiments. They are characteristics, numbers, or quantities and can represent specific items, people, places, or an idea. The variables may be referred to as data items.

The variables in an experiment will vary depending on the desired outcome. All scientific experiments and statistical studies will analyze a variable.

They are referred to as variables because the values can vary. A variable’s value can change within a single experiment. Whether there is a change between the groups being studied or the value changes over time, it may not necessarily be a constant.

Designing Experiments

As noted, all scientific experiments and statistical studies will control, manipulate, or measure a variable. In fact, the experiments are usually designed to determine the effect one variable has on another variable, cause, and effect.

When designing experiments, it is extremely important to choose the right variables. Choosing incorrectly can skew the results and derail the experiment or study completely. Choosing right can help an experiment or study run much more smoothly and produce more accurate results.

It is not just the specific variable within the experiment that needs to be determined, but the variable type as well. Knowing the variable type will allow you to interpret the results of the experiment or study.

It should be noted, though, that categorizing variables is a little subjective. Scientists and statisticians have some wiggle room when they categorize their experiment variables.

Generally, you will need to know what data the variable represents and what part of the experiment the variable represents in order to determine the variable type.

Independent, Dependent, and Control Variables

Typically, there will be an independent variable, dependent variable, and control variable in every experiment or study conducted.

Independent variables. Independent variables are the variables in your experiment that are being manipulated. They are referred to as independent variables due to the fact that their value is independent of other variables, which means that the other variables cannot change the independent variable.

Dependent variables. Dependent variables are the variables in your experiment that rely on other variables and can be changed or manipulated by the other variables being measured.

Control variables. Control variables are the variables in your experiment that are constant. They do not change over the course of the experiment or study and will have no direct effect on the other variables being measured.

Qualitative Versus Quantitative Variables

Every single variable you include in your experiment will need to be categorized as either a qualitative variable or a quantitative variable.

Qualitative variables. Also referred to as categorical variables, qualitative variables are any variables that hold no numerical value. They are nominal labels. For example, eye color would be a qualitative variable. The data being recorded is not a number but a color.

These variables don’t necessarily measure, but they describe a characteristic of the data set. They can be broken down further as either ordinal variables or nominal variables (see below for definitions).

Quantitative variables. Quantitative variables, or numeric variables, are the variables in your experiment that hold a numerical value. Unsurprisingly, they will represent a measurable quantity and will be recorded as a number.

These variables will measure “how many” or “how much” of the data being collected. These can be broken down further as either continuous variables or discrete variables (see below for definitions).

30 Other Variable Types Used in Experiments

This is by no means a comprehensive list, as the list of all variable types would be difficult to document in one place.

Below are many of the common and some less common variable types used in scientific experiments and statistical studies. Included is a brief overview of what that variable type measures.

Active variable. An active variable is a variable that can be manipulated by those running the experiment.

Antecedent variable. Antecedent variables come before the independent and dependent variables. With “antecedent” meaning “preceding in time or order,” this is not surprising.

Attribute variable. An attribute variable also called a passive variable, is not manipulated during the experiment. It may be a fixed variable or simply a variable that is not manipulated for one experiment but could be for another.

Binary variable. Binary variables only have two values. Typically, this will be represented as a zero or one but can be yes/no or another two-value combination.

Categorical variable. Categorical variables are variables that can be divided into larger buckets or categories. Shoe brands, for instance, could include Nike, Reebok, or Adidas.

Composite variable. This variable type is a bit different from others. A composite variable is made up of two or more other variables. The individual variables that make up the composite variable will be closely related either conceptually or statistically.

Confounding variable. Confounding variables are not good. They can affect both independent and dependent variables and invalidate results. Sometimes referred to as a lurking variable, these variables are considered “extra” and were not accounted for during the designing phase.

Continuous variable. Continuous variables have an infinite number of values between the highest point and lowest point. Distance is a continuous variable.

Covariate variable. A covariate variable can affect the dependent variable in addition to the independent variable. It will not be of interest in the results of the experiment, though.

Criterion variable. This is a statistical variable only. It is another name for the dependent variable.

Dichotomous variable. This is another name for a binary variable. Dichotomous variables will have two values only.

Discrete variable. Discrete variables are the opposite of continuous variables. Where continuous variables have an infinite number of possible values, discrete variables have a finite number.

Endogenous variable. Endogenous variables are dependent on other variables and are used only in statistical studies, in econometrics specifically. The value of these variables is determined by the model.

Exogenous variable. An exogenous variable is the opposite of an endogenous variable. The value of this type of variable is determined outside of the model and will have an impact on other variables within the model.

Explanatory variable. This is a commonly used name for the independent variable or the variable that is being manipulated by those running the experiment.

Grouping variable. A grouping variable is used to sort, or split up, the data set into groups or categories.

Interval variable. Interval variables show the meaningful difference between the two values.

Intervening variable. Intervening variables, or mediator variables, explains the cause, connection, or relationship between two other variables being measured.

Manifest variable. A manifest variable is a variable that can be directly observed or measured within the experiment.

Moderating variable. A moderating variable can affect the relationship between the independent variable and dependent variable. It can either strengthen, diminish, or negate the relationship.

Nominal variable. This is another way of saying categorical value. Nominal values will have two or more categories.

Observed variable. Observed variables are variables that are being measured during the experiment.

Ordinal variable. Ordinal variables are similar to categorical or nominal variables but have a clear ordering of categories. Examples such as High to low and like to dislike would both be ordinal variables.

Polychotomous variable. Polychotomous variables have more than two possible categories or values. These can be either nominal or ordinal.

Ranked variable. Ranked variables are ordinal variables. The researcher may not know the exact value, but they will know the order in which the data points should fall.

Ratio variable. Ratio variables are similar to interval variables but have a clear definition of zero.

Responding variable. Responding variables are the effect or outcome of the experiment. Similar to dependent variables, responding variables will “respond” to changes being made in the experiment.

Scale variable. A scale variable is a variable that has a numeric value that can be ordered with a meaningful metric. It will be the amount or number of something.

Study variable. Often referred to as a research variable, a study variable is any variable used that has some kind of cause and effect relationship.

Test variable. A test variable also referred to as the dependent variable, is a variable that represents the outcome of the experiment.

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Chris Kolmar is a co-founder of Zippia and the editor-in-chief of the Zippia career advice blog. He has hired over 50 people in his career, been hired five times, and wants to help you land your next job. His research has been featured on the New York Times, Thrillist, VOX, The Atlantic, and a host of local news. More recently, he's been quoted on USA Today, BusinessInsider, and CNBC.

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Variables are an important part of science projects and experiments. What is a variable? Basically, a variable is any factor that can be controlled, changed, or measured in an experiment. Scientific experiments have several types of variables. The independent and dependent variables are the ones usually plotted on a chart or graph, but there are other types of variables you may encounter.

Types of Variables

• Independent Variable: The independent variable is the one condition that you change in an experiment. Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature.
• Dependent Variable: The dependent variable is the variable that you measure or observe. The dependent variable gets its name because it is the factor that is dependent on the state of the independent variable . Example: In the experiment measuring the effect of temperature on solubility, solubility would be the dependent variable.
• Controlled Variable: A controlled variable or constant variable is a variable that does not change during an experiment. Example : In the experiment measuring the effect of temperature on solubility, controlled variable could include the source of water used in the experiment, the size and type of containers used to mix chemicals, and the amount of mixing time allowed for each solution.
• Extraneous Variables: Extraneous variables are "extra" variables that may influence the outcome of an experiment but aren't taken into account during measurement. Ideally, these variables won't impact the final conclusion drawn by the experiment, but they may introduce error into scientific results. If you are aware of any extraneous variables, you should enter them in your lab notebook . Examples of extraneous variables include accidents, factors you either can't control or can't measure, and factors you consider unimportant. Every experiment has extraneous variables. Example : You are conducting an experiment to see which paper airplane design flies longest. You may consider the color of the paper to be an extraneous variable. You note in your lab book that different colors of papers were used. Ideally, this variable does not affect your outcome.

Using Variables in Science Experiment

In a science experiment , only one variable is changed at a time (the independent variable) to test how this changes the dependent variable. The researcher may measure other factors that either remain constant or change during the course of the experiment but are not believed to affect its outcome. These are controlled variables. Any other factors that might be changed if someone else conducted the experiment but seemed unimportant should also be noted. Also, any accidents that occur should be recorded. These are extraneous variables.

Variables and Attributes

In science, when a variable is studied, its attribute is recorded. A variable is a characteristic, while an attribute is its state. For example, if eye color is the variable, its attribute might be green, brown, or blue. If height is the variable, its attribute might be 5 m, 2.5 cm, or 1.22 km.

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Home » Quantitative Variable – Definition, Types and Examples

Quantitative Variable – Definition, Types and Examples

Table of Contents

Quantitative Variable

Definition:

Quantitative variable is a type of variable in statistics that measures a numerical quantity or amount. It is a variable that can be measured on a numeric or quantitative scale, where each value has a specific numerical meaning.

Types of Quantitative Variables

There are two types of quantitative variables:

Discrete Variable

A discrete variable is a variable that can take only specific values. It cannot take on any value between two specific points. Examples of discrete variables include the number of children in a family, the number of students in a class, and the number of cars in a parking lot.

Continuous Variable

A continuous variable is a variable that can take on any value within a certain range. It can be measured on a continuous scale, and there are no gaps or interruptions between the values. Examples of continuous variables include age, weight, height, and temperature. Continuous variables are often measured using instruments that provide a high level of precision, such as a scale or a thermometer.

Quantitative Variable Measuring Scales

There are four types of quantitative variable measuring scales:

Nominal Scale

A nominal scale is a scale in which data are classified into mutually exclusive categories with no inherent order or ranking. Examples of nominal scale variables include gender, race, and occupation.

Ordinal Scale

An ordinal scale is a scale in which data are classified into categories that can be ranked or ordered. However, the intervals between the categories may not be equal. Examples of ordinal scale variables include education level (high school, college, graduate school), and socioeconomic status (low, middle, high).

Interval Scale

An interval scale is a scale in which data are classified into categories with equal intervals between them, but there is no true zero point. Examples of interval scale variables include temperature (measured in Celsius or Fahrenheit) and IQ scores.

Ratio Scale

A ratio scale is a scale in which data are classified into categories with equal intervals between them and a true zero point. Examples of ratio scale variables include weight, height, and income. With ratio scales, we can make meaningful comparisons between the numbers, and we can multiply or divide them to create meaningful ratios. For example, if someone’s income is twice that of someone else, we can say that their income ratio is 2:1.

Quantitative Variable Examples

Here are some examples of quantitative variables:

• Age : Age is a quantitative variable that can be measured on a continuous scale. It can be measured in years, months, or days.
• Income : Income is a quantitative variable that can be measured on a continuous scale. It can be measured in dollars, pounds, euros, or any other currency.
• Height : Height is a quantitative variable that can be measured on a continuous scale. It can be measured in inches, centimeters, or any other unit of measurement.
• Weight : Weight is a quantitative variable that can be measured on a continuous scale. It can be measured in pounds, kilograms, or any other unit of measurement.
• Test Scores : Test scores are quantitative variables that can be measured on a continuous scale. They can be measured as a percentage, a fraction, or a raw score.
• Number of Siblings : Number of siblings is a quantitative variable that can be measured on a discrete scale. It can take on specific values such as 0, 1, 2, 3, etc.
• Time to Complete a Task : Time to complete a task is a quantitative variable that can be measured on a continuous scale. It can be measured in seconds, minutes, or hours.
• Distance Traveled : Distance traveled is a quantitative variable that can be measured on a continuous scale. It can be measured in miles, kilometers, or any other unit of measurement.

Applications of Quantitative Variable

Quantitative variables have numerous applications in a wide range of fields, including:

• Social Sciences: In social sciences such as sociology, psychology, and economics, quantitative variables are used to measure and analyze social and economic phenomena such as income inequality, poverty rates, and education levels.
• Health Sciences: In health sciences, quantitative variables are used to measure and analyze health-related phenomena such as body mass index, blood pressure, and cholesterol levels.
• Physical Sciences: In physical sciences such as physics, chemistry, and engineering, quantitative variables are used to measure and analyze physical phenomena such as velocity, temperature, and mass.
• Business: In business, quantitative variables are used to measure and analyze financial and economic phenomena such as sales figures, profits, and market trends.
• Education: In education, quantitative variables are used to measure and analyze student achievement, learning outcomes, and teacher effectiveness.
• Environmental Science : In environmental science, quantitative variables are used to measure and analyze environmental phenomena such as pollution levels, climate change, and natural resource depletion.

When to use Quantitative Variable

Quantitative variables are used when we need to measure or analyze a numerical quantity, and the data can be expressed on a scale. Here are some situations where quantitative variables are appropriate:

• When we need to measure a physical characteristic: Quantitative variables are often used to measure physical characteristics such as height, weight, and blood pressure.
• When we need to analyze economic or financial data: Quantitative variables are often used in finance and economics to analyze financial and economic data such as income, expenditures, and market trends.
• When we need to analyze social phenomena: Quantitative variables are often used in social sciences such as sociology and psychology to analyze social phenomena such as educational attainment, poverty rates, and crime statistics.
• When we need to make precise comparisons: Quantitative variables provide a precise way to make comparisons between different groups or individuals.
• When we need to conduct statistical analysis: Quantitative variables are often used in statistical analysis to test hypotheses and make inferences about populations based on samples.

Purpose of Quantitative Variable

The purpose of a quantitative variable is to provide a numerical measurement of a phenomenon or attribute. It allows us to obtain precise and accurate data that can be analyzed and interpreted using statistical methods. Quantitative variables are used to:

• Measure and describe a phenomenon : Quantitative variables allow us to describe a phenomenon or attribute using numerical measurements. For example, height, weight, and age are all quantitative variables that allow us to describe physical characteristics of individuals.
• Test hypotheses and make inferences : Quantitative variables are often used in statistical analysis to test hypotheses and make inferences about populations based on samples. This allows us to draw conclusions about a larger group based on a smaller sample.
• Compare and contrast: Quantitative variables provide a structured and precise way to make comparisons and draw conclusions about differences and similarities between individuals, groups, or populations.
• Monitor changes over time : Quantitative variables can be used to monitor changes in a phenomenon over time, such as changes in economic indicators or health outcomes.

Characteristics of Quantitative Variable

The main characteristics of quantitative variables are:

• Numerical measurement: Quantitative variables are measured using numerical values, which can be expressed on a continuous or discrete scale.
• Precise and objective: Quantitative variables are typically more precise and objective than qualitative variables, as they can be measured using standardized instruments and methods.
• Statistical analysis: Quantitative variables are often used in statistical analysis, allowing for testing of hypotheses, making inferences, and drawing conclusions based on data.
• Different levels of measurement: Quantitative variables can be measured at different levels, including nominal, ordinal, interval, and ratio scales, which provide different levels of precision and allow for different types of statistical analysis.
• Continuous or discrete : Quantitative variables can be either continuous or discrete. Continuous variables can take on any value within a range, while discrete variables can only take on certain values.
• Mean and standard deviation : Quantitative variables are often described using summary statistics such as mean and standard deviation, which provide information about the central tendency and spread of the data.

Advantages of Quantitative Variable

• Precise measurements : Quantitative variables provide precise and measurable data, as they are measured using numerical values. This helps to reduce errors and make accurate conclusions.
• Statistical analysis: Quantitative variables are easier to analyze statistically, as numerical data can be easily graphed, compared, and manipulated.
• Easy to compare: Quantitative variables can be compared more easily than qualitative variables, as they are based on numerical values that can be ordered and compared.
• More objective : Quantitative variables are generally considered to be more objective than qualitative variables, as they are based on numerical data rather than subjective opinions or observations.
• Useful in modeling and prediction: Quantitative variables are often used in modeling and prediction, as they can be used to make mathematical models and projections based on numerical data.
• Can be used in scientific research: Quantitative variables are commonly used in scientific research, as they can provide precise and objective data that can be used to make empirical conclusions.

Limitation of Quantitative Variable

• Limited understanding of context: Quantitative variables may provide precise numerical measurements, but they often do not provide a full understanding of the context in which the data was collected. This can lead to misinterpretation or incomplete analysis of the data.
• May not capture qualitative aspects : Quantitative variables may not capture the full range of qualitative aspects of a phenomenon or attribute. For example, a quantitative variable such as income may not fully capture the quality of life of an individual or a community.
• May not capture unique experiences: Quantitative variables may not capture unique experiences or perspectives of individuals or groups. For example, a quantitative variable such as satisfaction with a product may not capture the unique experiences or perspectives of individual consumers.
• Potential for measurement error: Quantitative variables can be affected by measurement error, which can be introduced through faulty instruments or human error. This can lead to inaccurate or incomplete data.
• Limited to measurable phenomena: Quantitative variables are limited to phenomena that can be measured and expressed in numerical form. This means that some phenomena may not be fully captured by quantitative variables.

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• Open access
• Published: 26 May 2024

Utilization of reproductive health services among undergraduate regular class students in Assosa University, BGRS: a comparison among three varieties of multilevel binary logistic regression models

• Molalign Gualu Gobena   ORCID: orcid.org/0000-0002-5281-9806 1 &
• Yihenew Mitiku Alemu 2  

BMC Health Services Research volume  24 , Article number:  664 ( 2024 ) Cite this article

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Metrics details

Introduction

Reproductive health service (RHS) helps for people to have a delighted and safe sex through their life journey. It enables especially for women to go safely through pregnancy and childbirth and provide couples with the best chance of having a healthy infant. Therefore, this study aimed to identify the significant determinants of RHS utilization among undergraduate regular class students in Assosa University by using advanced methodology.

We used cross-sectional study design to collect RHS data from 362 students in Assosa University from 5 to 16, may 2021. These students were selected using stratified random sampling technique. We also used cross-tabulation to summarize the extents of RHS utilization across all predictors in terms of percentage and three varieties of multilevel binary logistic regression model to model the determinants of RHS.

42.27% of undergraduate regular class students in Assosa University utilize at least one type of RHS during their time at Assosa University whereas, 57.73% of undergraduate regular class students in this University are not utilized it. Among three varieties of multilevel binary logistic regression models, the random slopes two-level model was selected as a best fitted model for the datasets. At 5% level of significance, awareness about RHS, gender, preference of service fees and student’s monthly average income were significant predictor variables in this model. In addition, the covariates; age, gender and preference of service fees have a significant random effects on utilization of RHS across all colleges/school.

Students who; preferred service fee as usual rate, have awareness about RHS, are females and have high monthly average income were more likely to utilize RHS. RHS utilization among undergraduate regular students in Assosa University is likely to increase more effectively with interventions that address these factors.

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Reproductive health service (RHS) helps for people to have a delighted and safe sex through their life journey. Every men and women have the right to be informed about reproductive health and the right of access to appropriate reproductive health services. This will enable especially for women to go safely through pregnancy and childbirth and provide couples with the best chance of having a healthy infant [ 1 ]. Reproductive health service is defined as the constellation of methods, techniques and services that contribute to reproductive health and wellbeing by preventing and solving reproductive health problems [ 2 , 3 ]. It includes education service, improving antenatal, delivery, postpartum and newborn care; providing high-quality services for family planning, including infertility services; eliminating unsafe abortion; combating sexually transmitted infections, including HIV, reproductive tract infections, cervical cancer and other gynecological morbidities; and promoting sexual health [ 1 , 4 , 5 ].

The disproportionate number of young people who experience life-threatening reproductive health complications each year is a worrying global trend. As compared to other age groups, this issue is especially concerning, according to a study by [ 6 ]. These issues highlight the need for better access to reproductive healthcare services and education because they seriously jeopardize young people’s wellbeing.

In 2016, Ethiopia’s RHS utilization rate in four types of RHS were 37.5% in health facility deliveries, 37.2% in skilled delivery assistance, 36.3% in 4 + antenatal visits and 20.4% in use of modern contraception, which are highly lower than the East and Central African rate [ 7 ]. It’s crucial to remember, though, that Ethiopia has seen a steady rise in the use of RHS in recent years. For instance, in 2019 the utilization rate in four types of RHS were 48% in health facility deliveries, 50% in skilled delivery assistance, 43% in 4 + antenatal visits and 41% in use of modern contraception, thus the rise in just four years is noteworthy [ 7 , 8 , 9 ]. In Ethiopia, over the period of 12 years (2005–2016), the rates of RHS utilization in four RHS were significant. Moreover, over this period the percentage of change in health facility deliveries, skilled delivery assistance, 4 + antenatal visits and use of modern contraception are 25.2%, 24.5%, 19% and 9.5% respectively [ 7 ]. RHS have become more widely used in Ethiopia as a result of a number of factors, including improved access to care, especially in rural areas, decreased costs, and increased government investment. Raising awareness of the availability and significance of reproductive health services has also contributed to this advancement. Even with the advancements, there are still large regional differences in the availability and use of reproductive health services in Ethiopia [ 10 , 11 , 12 ]. More studies at various settings should be done to guarantee that every young people have access to the high-quality RHS they require, regardless of their geography, wealth, or social standing [ 3 , 13 , 14 , 15 ]. But, still some studies regarding the utilization of RHS and its determinants were conducted in different part of Ethiopia [ 15 , 16 , 17 , 18 ].

The official school age interval of tertiary education in Ethiopia is 19–23 [ 19 ]. This indicates that young and youth people make up the great majority of university students. University students in Ethiopia may be more likely to become unintentionally pregnant, contract sexually transmitted diseases (STDs) like HIV/AIDS, have unsafe abortions, and experience limited sexual and reproductive health education and resources due to factors specific to this age group [ 20 ].

Ethiopia has seen a notable surge in university enrollment, with a rise in students from 447,693 in 2010/11 to 593,571 in 2013/14 [ 19 ]. Due to this quick growth, a sizable fraction of students are in the “youth” and “young” age range. This age group is recognized to be more vulnerable to risks related to sexual and reproductive health (SRH) because of things like insufficient experience and education. Although data indicates an increase in the number of students [ 19 ], research indicates that universities have not kept up with the demand for access to and knowledge about critical Reproductive Health Services (RHS) [ 21 ]. Empirical techniques have frequently been used in prior research on RHS utilization to look into potential influencing factors [ 19 ]. Nevertheless, by using a more sophisticated methodology, this study seeks to close this gap. Additionally, the adjustment to university life brings with it special opportunities as well as difficulties that may affect students’ requirements for sexual and reproductive health [ 21 ].

However, knowledge of the factors that influence reproductive health decisions can help university health provider, policymakers, and educators create interventions that are tailored to the needs of undergraduate regular class students. Hence, this study aimed to identify the significant determinant of RHS utilization among undergraduate regular class students in Assosa University.

Study setting and design

Assosa University is situated in BGRS capital city, Assosa town. It is situated 660 km from Addis Ababa, 96 km east of the Ethiopian-Sudanese border, and 210 km south of the Grand Ethiopian Renaissance Dam. Its founding goals were to produce highly skilled and committed workforce, carry out research, and perform community service. Currently, the university is running both undergraduate and graduate program in seven colleges and one school.

In this study, we have used cross-sectional study design to collect RHS data from students in Assosa University from 5 to 16, may 2021. The data were collected using self-administered questionnaire (it is attached as a supplementary file). The questionnaire was developed by our team. We followed a rigorous process to ensure its reliability and validity. This included reviewing relevant academic research on RHS utilization among university students in Ethiopia [ 5 , 14 , 22 , 23 ], consulting with experts in the field of sexual and reproductive health, and pre-testing the questionnaire with a small group of students at Assosa University.

To minimize errors and bias in data recording and manipulation, we implemented key procedures like pilot testing the questionnaire (reducing measurement error), training data collectors (minimizing interviewer bias), double data entry (mitigating transcription errors), and range checks (identifying potential recording errors).

Utilization of RHS is the outcome variable in this study. It has two possible responses which are coded as 1 if students utilize it otherwise coded as 0. Independent variables in this study are age (its coded value; 0 = 18–20, 1 = 21–23, 2 = > 23), awareness about RHS (its coded value; 1 = yes, 0 = no), parents’ occupation (its coded value; 0 = formal employee, 1 = farmer, 2 = casual laborer, 3 = self-employee), gender (its coded value; 0 = male, 1 = female), students’ monthly average income (its coded value; 0 = < 250 ETB, 1 = 251–500 ETB, 2 = 501-1,000 ETB, 3 = 1,001–1,500 ETB, 4 = > 1,500 ETB), place of residence (its coded value; 0 = urban, 1 = rural), preference of service fees for RHS (its coded value; 0 = At usual rate, 1 = with discount, 2 = free of charge), parents’ monthly average income (its coded value; 0 = < 2,500 ETB, 1 = 2,501-4,000 ETB, 2 = 4,001–5,000 ETB, 3 = 5,001–10,000 ETB, 4 = > 10,001 ETB) and religion (its coded value; 0 = Orthodox, 1 = Muslim, 2 = Protestant, 3 = Catholic, 4 = Other). We have considered students to be RHS users if they utilize at least one type of RHS during their time at Assosa University; if not, we have considered them to be non-users.

Sampling design

We can assume that the students in Assosa University are varying in utilizing reproductive health service from college/school to college/school which implies heterogeneity. Therefore, one of appropriate sampling technique for heterogeneous population is stratified sampling [ 24 ]. Hence, in this study we use stratified random sampling. With stratified sampling, the population is divided into homogeneous, mutually exclusive groups called strata, and then independent samples are selected from each stratum [ 25 ]. Any of the sample design can be used to sample with in strata, from the simpler method such as Simple Random Sampling (SRS) or Systematic Sampling (SYS) to the more complex methods such as Population Proportion to Size (PPS), cluster, multi-stage or multi-phase sampling [ 24 ]. The sample in this study is stratified. Students were stratified by college/school, which yielded 8 sampling strata. Samples of students were selected independently in each stratum (college/school). A total of 362 students were selected with probability proportional to the college/school size and with independent selection in each sampling stratum (college/school) with the sample allocation given in Table  1 . The college/school size is the number of students in each college/school as determined in the record of Assosa University registrar office. The students with in each college/school serve as the sampling unit for the selection of students. The data collectors distribute the questionnaire only for those pre-selected students. No replacement or change of the pre-selected students will not allow in the implementing stages to prevent bias. All students who are usual members of the selected college/school were eligible for this study. By pivot survey, \(p=0.5,q =0.5, \alpha =5\% ,d=5\%\) , we can determine the required number of students in each college/school using the following formula.

If \(\frac{{n}_{0}}{N}<5\%\) , then we will use \(n={n}_{0}\) otherwise we will use \(n=\frac{{n}_{0}}{1+\frac{{n}_{0}}{N}}\) ,

where, \({n}_{0}\) =is initial sample size (number of students) in each college/school, P is proportion of student who is utilizing reproductive health service, q is proportion of students who is not utilizing reproductive health service, d=margin of error, \(\alpha\) =level of significance, n is the selected number of students in each college/school, N is the number of students in Assosa University. Accordingly, by proportional allocation we will get sample size as it illustrated in the following table. \({n}_{h}=\frac{{N}_{h}}{N}\) where, \({n}_{h}\) =the selected number of students in each college/school, \({N}_{h}\) is the number of students in each college/school.

Here, \({n}_{0}=\frac{{1.96}^{2} \times 0.5\times 0.5}{{0.05}^{2}}=384.16\)

hence \(\frac{{n}_{0}}{N}=\frac{384.16}{6298}=0.061>5\%\) ,

which implies \(n=\frac{{n}_{0}}{1+\frac{{n}_{0}}{N}}=n=\frac{384.16}{1+0.061}=362\)

Here also, N= \({N}_{1}\) + \({N}_{2}\) + \({N}_{3}\) + \({N}_{4}\) + \({N}_{5}\) + \({N}_{6}\) + \({N}_{7}\) + \({N}_{8}\) =644+2453+892+482+584+450+573+220=6298 and n= \({n}_{1}\) + \({n}_{2}\) + \({n}_{3}\) + \({n}_{4}\) + \({n}_{5}\) + \({n}_{6}\) + \({n}_{7}\) + \({n}_{8}\) = 37+141+51+28+33+26+33+13=362.

Methods of data analysis

We used both descriptive and inferential methods of data analysis to analysis the collected data. First, we used cross-tabulation to provide the descriptive statistics. In this analysis, the extents of RHS utilization across all predictors in terms of percentage were summarized. Second, we used a multilevel version of binary logistic regression model (i.e., multilevel binary logistic regression model) to model the determinants of RHS. This model helps to identify both fixed effect and random effects. Consequently, the true effect of predictors across all colleges/school and response variation at different college/school were clearly indicated.

Multilevel modeling for stratified data

Data sets in this study have a hierarchical structure. For example, students are nested within their respective college/school. This is clearly reflected in our used sampling design which is called stratified random sampling. Even if this sampling technique provides optimum sample size, in multilevel structure case it leads to dependency among observations within strata (e.g., college/school). One of the preliminary assumptions of using conventional regression model is the validation of independent assumption among observations. In other words, this model no longer applied for data with hierarchical nature. Failing to account for the above assumptions during the analysis phase and using the empirical regression model have an impact on the precision of parameter estimates and their standard errors. This is also having a direct implication on their significant effects. To avoid the confusion in latter description of this hierarchical data structure, we assumed students as “lower level (first), college/school as “higher level (second)” and strata as “group/cluster”. In the phenomena of hierarchical data, variables may be defined at different hierarchical level and this cannot be hosted under empirical regression model. These all problems addressed by the virtue of multilevel modeling. Modeling the effect of individual level factors, group level factor and their collective impacts on the dependent variable of interest is called multilevel modeling.

In our used dataset, grouping students based on their colleges/school may have a contextual impact on the outcome of interest (e.g., Utilization of RHS) which leads to have a correlated response of students within college/school. Moreover, even when the measurements of the characteristics of the selected students are the same, the outcomes of two randomly selected students from the same college/school may be more similar than the outcomes of two randomly selected students from different college/school. These issue handled by the use of Multilevel modeling [ 26 , 27 , 28 ]. Hence, multilevel binary logistic regression model can be used to simulate the relationship between a dichotomous response variable and a group of independent variables while taking the data’s nested structure into account [ 29 ].

Model building and selection process

The overall aim in this section is to progressively build a more complex model that captures the hierarchical structure of the data and identifies significant factors influencing RHS utilization among students at Assosa University. Hence, we have undergone three processes to come up with the final best fitted model for the dataset in this study. First, we made population proportion test among college/school to know whether there is a significance difference between college/school or not. This is helpful because it helps to identify whether the data needs multilevel data analysis or single level data analysis. The test result, however, assured to use multilevel data analysis (refer Population Proportion Heterogeneity ( PPH) Test from result section). Second, we ran random effect univariate model for each individual covariates to know whether they have a significant (p-value < 0.10) random effects across college/school or not. Moreover, it is a precondition to fit a random slopes two-level model for the dataset. Third, we analyzed the data by considering three variety of multilevel binary logistic regression model (e.g., null two-level model, random intercepts two-level model and random slopes two-level model) and then we used their AIC score to select the best fitted model for the dataset. The detail of the above all multilevel models described in [ 26 , 27 , 29 ].

In this section, we have presented both descriptive and inferential statistics outputs. Entirely, the analysis was done using R-4.3.2 statistical software.

Descriptive statistics

The result shows that 42.27% of undergraduate regular students utilize at least one type of RHS during their time at Assosa University whereas, 57.73% of undergraduate regular students are not utilized it.

Undergraduate regular class students utilize at least one type of RHS during their time at Assosa University at the minimum age of 18 years and at the maximum age of 35 years. The percentage of undergraduate regular class students of Assosa University who were utilized at least one type of RHS during their time at Assosa University were higher for those who having awareness about RHS (39%) than those who have not (3.3%). Accordingly, the interpretation for other predictors can be provided (Table  2 ).

We were used chi-square test of association to see the linear association between each independent variables and dependent variable. With this connection, the p-value for each linear association test from the above Table  2 indicate that except the variable place of residence (P-value = 0.700 > 0.05) all variables that we have considered in this study have a linear association with utilization of RHS.

Population Proportion Heterogeneity Test (PPH Test)

We used a chi-squared test to verify that whether this hierarchical data nature calls for a multilevel data analysis or not. The chi-squared test for proportion of utilizing RHS heterogeneity across colleges/school provides X-squared = 32.502, df = 7, p-value = 3.277e-05 which is significant at 5% level of significant. Therefore, this implies that the proportion of utilizing RHS among undergraduate regular class students is differed on at least between two colleges/schools. This is also an implication to use a multilevel data analysis for this dataset.

Test for random effects of covariates across groups

We used likelihood ratio test to verify whether each covariate have a significant random effects on utilization of RHS across groups (college/school) or not at 10% level of significance. Then after, we included those significant variables into the random component of a two-level random slope model to fit the dataset (Table  3 ). Model 3 were built with the consideration of this notion (Table  4 ). The test used to compare two nested models, the random intercept (reduced model) versus the random slope model (i.e., full model). The full model possesses the random effect of covariates, while reduced model doesn’t possess the random effect of covariates. This test is an implicit test for random effects of covariates across groups. For example, if the test result rejects the null hypothesis, it means that the random slope model is a better fit of the data. In other ways, it suggests that the covariate varies across colleges/schools.

The test results shows that the covariates; age, gender and preference of service fees for RHS have a significant random effects across colleges/schools (P-value < 0.10). Specifically, it means that the considered model permits the difference in age, gender and preference of service fees for RHS among undergraduate class students within colleges/schools to differ across colleges/schools (Table  1 ). Therefore, we have considered these variables to fit a two-level random slope models from which our conclusion is based (Table  3 ).

Results from three varieties of multilevel binary logistic regression model

In this analysis, we have fitted three variety of multilevel binary logistic regression model namely; null two-level model (model 1), random intercepts two-level model (model 2) and random slopes two-level model (model 3). At 5% level of significance, the intercepts in model 2 and 3 are significant while it is insignificant in model 1. The insignificancy of intercepts in model 1 implies that the overall probability of utilizing RHS among undergraduate regular class students in Assosa University is not significantly differ from 50%. This means that we have no supportive evidence for the uniformity prevalence of RHS utilization among undergraduate regular class students across all colleges/schools.

The intercept in model 2 is negative (-5.6381). This reveals that the overall probability of utilizing RHS among undergraduate regular class students in Assosa University is less than 50%.

This indicates that only small proportions of students in all colleges/schools are expected to utilize RHS on average. In other way, the significant intercept in this model clearly indicates that grouping students based on colleges/schools related with a significance difference in the overall likelihood of utilizing RHS across all colleges/schools. But, significant intercept in model 3 can be treated in two ways. The first way is similarly interpreted as model 2 i.e. across all colleges/schools, on average, the overall likelihood of RHS utilization among undergraduate regular class students in Assosa University is statistically significance. The second way imply that the effect of each covariate in the likelihood of utilizing RHS across colleges/schools is different. Moreover, grouping students based on colleges/schools is linked to a significant variation in the relationship between the covariates and utilization of RHS across colleges/schools as well as the overall probability of utilizing RHS. The negative intercept in this model have similar interpretation as negative intercept in model 2.

In general, the insignificancy of intercept in model 1 but significant of it in model 2 and 3 can be viewed as the variation in utilization of RHS across all colleges/schools is masked when the grouping factor (colleges/schools) is the only component in the model. Hence, the inclusion of the random intercepts and consideration of random slope makes it possible to identify group-level variations that the null two-level model was unable to show (Table  4 ).

At 5% level of significance, the intercept, awareness, gender, preference of service fees (with free of charge) and student’s monthly average income (251–500 ETB) were significant predictor variables both in model 2 and 3. In model 2, in addition to the above covariates, parent’s occupation (causal laborer) and preference of service fees (with discount) were significant. Among all considered models in this study the AIC score for model 3 (370.8) is smallest. This means that relatively this model best fitted the dataset. So that future descriptions are based on this model.

The odds ratio for awareness of RHS is exp(3.2565) = 25.99. This means that, in a comparison of two students with different awareness of RHS but the same values on the remaining eight covariates and college/school average risk (i.e., the value of the random effect), the student with awareness of RHS has 2.12 times higher odds of utilizing RHS than the student with no awareness of RHS. Put another way, the odds of utilizing RHS for student who is aware of RHS is exp(3.2565) = 25.99 times higher than the odds of utilizing RHS for the student who is not aware of RHS when comparing two subjects within the same college/school who differ in their awareness of RHS but who share identical values of the remaining eight covariates.

Similarly, the odds of utilizing RHS for female student is exp(2.8215) = 16.8 times higher than the odds of utilizing RHS for male student when comparing two students within the same college/school who differ in their gender but who share identical values of the remaining eight covariates. Again, the odds ratio for free of charge (category of preference of service fees) is exp(-1.5117) = 0.22. This means that, for student whose preference of RHS fees is free of charge, the odds of utilizing RHS are 0.22 times lower than for student whose preference is at usual rate when comparing two students within the same college/school who differ in their preference of RHS fees but who share identical values of the remaining eight covariates. In other words, student whose preference of RHS fees is free of charge is 78% (1-0.22 = 0.78) less likely to utilize RHS compared to student whose preference is at usual rate controlling the values on the remaining eight covariates and college/school average risk. exp(2.2592) = 9.58 is also the odds ratio of 251–500 ETB (one of the categories of student’s average monthly income). This is also reveals that, in a comparison of two students with different average monthly income but the same values on the remaining eight covariates and the value of the random effect, the student with an average income of 251–500 ETB has 9.58 times higher odds of utilizing RHS than the student with an average income less than 251 ETB.

The correlation between slopes of; age and gender, age and preference of service fees (with its both categories), and gender and preference of service fees (with its both categories) is negative. This implies that the slopes of each above paired covariates tend to vary in opposite directions across colleges/schools. Stated differently, colleges/schools exhibiting a greater slope for one predictor variable (e.g., age) typically exhibit a lower slope for the other (e.g., gender). Specifically, it means that if the effect of age on utilization of RHS is positive in any particular college/school, then the effect of gender on utilization of RHS will be negative on this particular college/school. The correlation between the random intercept and random slopes of each covariate (i.e., age, gender and preference of service fees (with its both categories)) is positive. This implies that the slopes on each of these covariates tend to be steeper for students with higher intercepts. The standard deviation of the random slope on gender, preference of service fees (both with discount & free of charge) and random intercept are 2.3424, 2.6259, 1.5087 and 2.0665 respectively. These figures imply that slopes on these covariates and the intercepts show higher variability across different colleges or students, revealing the present of heterogeneity in the dataset. In other words, it means that; on average there is different level of RHS utilization across colleges/schools or on students due to more dispersed intercepts and the relationship between the each of these covariates and utilization of RHS differs across different colleges/schools or students due to presence of more dispersed slopes on these covariates (Table  4 ).

The findings from this study emphasize the crucial of using a two-level random slope model to model the determinant of RHS utilization among Assosa University regular class students. In line with previous study, this study revealed that the covariates; student’s awareness about RHS, gender, preference of service fees and student’s monthly average income were significant predictor variables of RHS utilization [ 8 , 30 , 31 , 32 ]. In this study findings, students who; preferred service fee as usual rate, have awareness about RHS, are females and have high monthly average income were more likely to utilize RHS. This finding also consistent with findings of [ 22 , 33 , 34 ].

The implication of these findings can be expressed by two ways. First, further research in the future should be conducted by figuring out the precise mechanisms through which the study’s factors affect the use of RHS, recognizing the justifications for some students’ preference to pay RHS at a rate comparable to the standard rate and creating efficient interventions to boost RHS use in the students who stand to gain the most from them. Second, public health policy and practice to be improved by; providing intervention mechanism that was directed towards students who are unaware of RHS, male students, and students from lower-income families; carried out campaigns to raise awareness of students regarding RHS and offered RHS at reasonable costs.

Contradicted to the present study findings, female students were less likely to utilize RHS than male students in [ 10 , 23 , 35 ] study findings. The difference may be due to the presence of differed cultural settings of these study area from the present study. Moreover, these studies were carried out in a particular cultural setting where male students are more inclined to look for RHS.

The findings from this study also inconsistent from study findings [ 36 , 37 ] in which they found that compared to students with higher income levels, those with lower income levels were more likely to use RHS. This difference may due to the fact that these studies were conducted in rural settings. Students with low average income from this area have an access of services with subsidies or a community-based initiative which in turn helps them to afford RHS cost. Indirectly, this findings support the findings of present study in the way that students with high monthly average income are more likely to have access to RHS and to be afford of the cost for these services. According to [ 30 , 31 , 32 , 38 ] study findings, students who were aware of RHS were more likely than those who were not to make use of these services. This is also another consistent result from the present study finding. This is because, those students who have awareness about RHS may be more likely to look for and use them. In contrary, those who haven’t might not be aware that these services are available or might not know how to use them. In general, this highlights the significance of thorough education programs on reproductive health that support accurate information and favorable attitudes toward reproductive health services.

In this study, students who; preferred service fee as usual rate, have awareness about RHS, are females and have high monthly average income were more likely to utilize RHS. RHS utilization among undergraduate regular students in Assosa University is likely to increase more effectively with interventions that address these factors.

Data availability

The data have been released upon request of the corresponding author.

Abbreviations

Reproductive health service

Sexual Transmitted Disease

human immunodeficiency virus

Acquired Immune Deficiency Syndrome

Simple Random Sampling

Systematic Sampling

Population Proportion to Size

Ethiopian Birr

Population Proportion Heterogeneity Test

Akaike Information Criterion

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Acknowledgements

We are grateful to Assosa University for its appreciated funding of this study. We also appreciate the support of the data collection team in getting the data needed for this study.

This study was supported by ASU/F/1521/20 from Assosa University.

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Gobena, M., Alemu, Y. Utilization of reproductive health services among undergraduate regular class students in Assosa University, BGRS: a comparison among three varieties of multilevel binary logistic regression models. BMC Health Serv Res 24 , 664 (2024). https://doi.org/10.1186/s12913-024-11123-8

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DOI : https://doi.org/10.1186/s12913-024-11123-8

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Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy †.

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a Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an, China E-mail: [email protected] , [email protected]

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c College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an, China

Driven by the “double carbon” strategy, petroleum coke short-term demand is growing rapidly as a negative electrode material for artificial graphite. The analysis of petroleum coke physicochemical properties has always been an important part of its research, encompassing significant indicators such as ash content, volatile matter and calorific value. A strategy based on laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics is proposed to realize the rapid and accurate quantification of the above properties. LIBS spectra of 46 petroleum coke samples were collected, and an original random forest (RF) calibration model was constructed by optimizing the pretreatment parameters. The RF calibration model was further optimized based on variable importance measures (VIM) and variable importance in projection (VIP) methods. After variable selection, the elemental spectral lines related to ash content, volatile matter and calorific value modeling were screened out, thus initially exploring the correlation between these properties and elements. Under the optimized spectral pretreatment method, VI threshold and model parameters, the mean relative error (MRE P ) of the prediction set of ash content, volatile matter and calorific value were 0.0881, 0.0527 and 0.006, the root mean square error (RMSE P ) of the prediction set of ash content, volatile matter and calorific value were 0.0471%, 0.6178% and 0.2697 MJ kg −1 , respectively, and the determination coefficient ( R P 2 ) of the prediction set was 0.9187, 0.9820 and 0.9510, respectively. The combination of LIBS technology and chemometric methods can provide powerful technical means for the analysis and evaluation of the physicochemical properties of petroleum coke.

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Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy

S. Hu, J. Ding, Y. Dong, T. Zhang, H. Tang and H. Li, RSC Adv. , 2024,  14 , 16358 DOI: 10.1039/D4RA02873B

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