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Education Standards

Radford university.

Learning Domain: Social Work

Standard: Basic Research Methodology

Lesson 10: Sampling in Qualitative Research

Lesson 11: qualitative measurement & rigor, lesson 12: qualitative design & data gathering, lesson 1: introduction to research, lesson 2: getting started with your research project, lesson 3: critical information literacy, lesson 4: paradigm, theory, and causality, lesson 5: research questions, lesson 6: ethics, lesson 7: measurement in quantitative research, lesson 8: sampling in quantitative research, lesson 9: quantitative research designs, powerpoint slides: sowk 621.01: research i: basic research methodology.

PowerPoint Slides: SOWK 621.01: Research I: Basic Research Methodology

The twelve lessons for SOWK 621.01: Research I: Basic Research Methodology as previously taught by Dr. Matthew DeCarlo at Radford University. Dr. DeCarlo and his team developed a complete package of materials that includes a textbook, ancillary materials, and a student workbook as part of a VIVA Open Course Grant.

The PowerPoint slides associated with the twelve lessons of the course, SOWK 621.01: Research I: Basic Research Methodology, as previously taught by Dr. Matthew DeCarlo at Radford University. 

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Research Method

Home » Variables in Research – Definition, Types and Examples

Variables in Research – Definition, Types and Examples

Table of Contents

Variables in Research

Variables in Research

Definition:

In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest.

Types of Variables in Research

Types of Variables in Research are as follows:

Independent Variable

This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.

Dependent Variable

This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.

Confounding Variable

This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.

Mediating Variable

This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.

Moderator Variable

This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.

Control Variable

This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.

Continuous Variable

This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.

Categorical Variable

This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

Discrete Variable

This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.

Dummy Variable

This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.

Extraneous Variable

This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.

Latent Variable

This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.

Moderator-mediator Variable

This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.

Variables Analysis Methods

There are different methods to analyze variables in research, including:

  • Descriptive statistics: This involves analyzing and summarizing data using measures such as mean, median, mode, range, standard deviation, and frequency distribution. Descriptive statistics are useful for understanding the basic characteristics of a data set.
  • Inferential statistics : This involves making inferences about a population based on sample data. Inferential statistics use techniques such as hypothesis testing, confidence intervals, and regression analysis to draw conclusions from data.
  • Correlation analysis: This involves examining the relationship between two or more variables. Correlation analysis can determine the strength and direction of the relationship between variables, and can be used to make predictions about future outcomes.
  • Regression analysis: This involves examining the relationship between an independent variable and a dependent variable. Regression analysis can be used to predict the value of the dependent variable based on the value of the independent variable, and can also determine the significance of the relationship between the two variables.
  • Factor analysis: This involves identifying patterns and relationships among a large number of variables. Factor analysis can be used to reduce the complexity of a data set and identify underlying factors or dimensions.
  • Cluster analysis: This involves grouping data into clusters based on similarities between variables. Cluster analysis can be used to identify patterns or segments within a data set, and can be useful for market segmentation or customer profiling.
  • Multivariate analysis : This involves analyzing multiple variables simultaneously. Multivariate analysis can be used to understand complex relationships between variables, and can be useful in fields such as social science, finance, and marketing.

Examples of Variables

  • Age : This is a continuous variable that represents the age of an individual in years.
  • Gender : This is a categorical variable that represents the biological sex of an individual and can take on values such as male and female.
  • Education level: This is a categorical variable that represents the level of education completed by an individual and can take on values such as high school, college, and graduate school.
  • Income : This is a continuous variable that represents the amount of money earned by an individual in a year.
  • Weight : This is a continuous variable that represents the weight of an individual in kilograms or pounds.
  • Ethnicity : This is a categorical variable that represents the ethnic background of an individual and can take on values such as Hispanic, African American, and Asian.
  • Time spent on social media : This is a continuous variable that represents the amount of time an individual spends on social media in minutes or hours per day.
  • Marital status: This is a categorical variable that represents the marital status of an individual and can take on values such as married, divorced, and single.
  • Blood pressure : This is a continuous variable that represents the force of blood against the walls of arteries in millimeters of mercury.
  • Job satisfaction : This is a continuous variable that represents an individual’s level of satisfaction with their job and can be measured using a Likert scale.

Applications of Variables

Variables are used in many different applications across various fields. Here are some examples:

  • Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
  • Business and marketing: Variables are used in business and marketing to understand customer behavior and to make decisions about product development and marketing strategies. For example, businesses may study variables such as consumer preferences, spending habits, and market trends to identify opportunities for growth.
  • Healthcare : Variables are used in healthcare to monitor patient health and to make treatment decisions. For example, doctors may use variables such as blood pressure, heart rate, and cholesterol levels to diagnose and treat cardiovascular disease.
  • Education : Variables are used in education to measure student performance and to evaluate the effectiveness of teaching strategies. For example, teachers may use variables such as test scores, attendance, and class participation to assess student learning.
  • Social sciences : Variables are used in social sciences to study human behavior and to understand the factors that influence social interactions. For example, sociologists may study variables such as income, education level, and family structure to examine patterns of social inequality.

Purpose of Variables

Variables serve several purposes in research, including:

  • To provide a way of measuring and quantifying concepts: Variables help researchers measure and quantify abstract concepts such as attitudes, behaviors, and perceptions. By assigning numerical values to these concepts, researchers can analyze and compare data to draw meaningful conclusions.
  • To help explain relationships between different factors: Variables help researchers identify and explain relationships between different factors. By analyzing how changes in one variable affect another variable, researchers can gain insight into the complex interplay between different factors.
  • To make predictions about future outcomes : Variables help researchers make predictions about future outcomes based on past observations. By analyzing patterns and relationships between different variables, researchers can make informed predictions about how different factors may affect future outcomes.
  • To test hypotheses: Variables help researchers test hypotheses and theories. By collecting and analyzing data on different variables, researchers can test whether their predictions are accurate and whether their hypotheses are supported by the evidence.

Characteristics of Variables

Characteristics of Variables are as follows:

  • Measurement : Variables can be measured using different scales, such as nominal, ordinal, interval, or ratio scales. The scale used to measure a variable can affect the type of statistical analysis that can be applied.
  • Range : Variables have a range of values that they can take on. The range can be finite, such as the number of students in a class, or infinite, such as the range of possible values for a continuous variable like temperature.
  • Variability : Variables can have different levels of variability, which refers to the degree to which the values of the variable differ from each other. Highly variable variables have a wide range of values, while low variability variables have values that are more similar to each other.
  • Validity and reliability : Variables should be both valid and reliable to ensure accurate and consistent measurement. Validity refers to the extent to which a variable measures what it is intended to measure, while reliability refers to the consistency of the measurement over time.
  • Directionality: Some variables have directionality, meaning that the relationship between the variables is not symmetrical. For example, in a study of the relationship between smoking and lung cancer, smoking is the independent variable and lung cancer is the dependent variable.

Advantages of Variables

Here are some of the advantages of using variables in research:

  • Control : Variables allow researchers to control the effects of external factors that could influence the outcome of the study. By manipulating and controlling variables, researchers can isolate the effects of specific factors and measure their impact on the outcome.
  • Replicability : Variables make it possible for other researchers to replicate the study and test its findings. By defining and measuring variables consistently, other researchers can conduct similar studies to validate the original findings.
  • Accuracy : Variables make it possible to measure phenomena accurately and objectively. By defining and measuring variables precisely, researchers can reduce bias and increase the accuracy of their findings.
  • Generalizability : Variables allow researchers to generalize their findings to larger populations. By selecting variables that are representative of the population, researchers can draw conclusions that are applicable to a broader range of individuals.
  • Clarity : Variables help researchers to communicate their findings more clearly and effectively. By defining and categorizing variables, researchers can organize and present their findings in a way that is easily understandable to others.

Disadvantages of Variables

Here are some of the main disadvantages of using variables in research:

  • Simplification : Variables may oversimplify the complexity of real-world phenomena. By breaking down a phenomenon into variables, researchers may lose important information and context, which can affect the accuracy and generalizability of their findings.
  • Measurement error : Variables rely on accurate and precise measurement, and measurement error can affect the reliability and validity of research findings. The use of subjective or poorly defined variables can also introduce measurement error into the study.
  • Confounding variables : Confounding variables are factors that are not measured but that affect the relationship between the variables of interest. If confounding variables are not accounted for, they can distort or obscure the relationship between the variables of interest.
  • Limited scope: Variables are defined by the researcher, and the scope of the study is therefore limited by the researcher’s choice of variables. This can lead to a narrow focus that overlooks important aspects of the phenomenon being studied.
  • Ethical concerns: The selection and measurement of variables may raise ethical concerns, especially in studies involving human subjects. For example, using variables that are related to sensitive topics, such as race or sexuality, may raise concerns about privacy and discrimination.

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Research Variables

Feb 17, 2014

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Research Variables. Research Definitions. An experiment is a process in which an investigator devises two or more different experiences (treatments) for subjects or participants. Involves a control group and one or more treatment groups. Research Variables.

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Presentation Transcript

Research Definitions • An experiment is a process in which an investigator devises two or more different experiences (treatments) for subjects or participants. • Involves a control group and one or more treatment groups

Research Variables • Independent Variable (IV): the controlled variable in a study; hypothesized to have an effect on the dependent variable • In a true experiment, this is an experimental (manipulated) variable. • In a quasi-experiment, this is a subject variable (not subject to manipulation). Experimental Subject

Research Variables • Regardless of type, there will always be two or more levels of the independent variable. • Variable: a characteristic or phenomenon that may take on different values; variables must vary! • The levels of the independent variable can be: • Independent of one another: between-subjects design • Dependent on one another: within-subjects design

Research Variables • Dependent Variable (DV): an outcome of interest that is observed and measured by the researcher; hypothesized to be affected by the independent variable

Defining Research Variables • Operational Definition: a definition of a variable in terms of the operations used to manipulate it or measure it • Must be precise enough that anyone reading a review of your research could replicate your experiment exactly.

Problem Variables • Extraneous (Nuisance) Variables: uncontrolled variables which can affect the experimental outcome • Extraneous variables become confounding variables when their values change systematically along with the independent variable in an experiment.

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introduction to research methodology

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10 Types of Variables in Research | Examples | PPT

In any research study, there are variables. Variables are any characteristics that can be measured or observed. There are many great uses for these variables, but it is important to know what they are!

In this article, you will learn the basics of variables and will give you a clear picture of the types of variables that exist in the social sciences and statistics and how they can be used.

What is a Variable?

In research, variables are the factors that are manipulated to measure their effects on an outcome variable.

Example of Variable

3- For example, if a person’s skin color is the variable in an experiment, its value can range from brown to pale to white, from individual to individual.

10 Types of Variables

Independent variable, example of independent variable, dependent variable.

The dependent variable is a variable that represents the experiment’s outcome. The variable that is measured in order to determine the effect of an independent variable. The dependent variable is the variable being measured.

Example of Dependent Variable

Mediating variable, example of mediating variable.

For example, if income is the independent variable and longevity is the dependent variable, the researcher could postulate that access to quality healthcare is the mediating variable that connects income and longevity.

Moderating Variable

Control variables.

Control variables are those that remain constant throughout the experiment. Variables that are held constant in order to isolate the effect of a given independent variable.

Example of Control Variables

Developing conceptual framework with variables.

Organization learning is IV that is independent variable and innovation is DV dependent Variable.

Lets add moderator between organizational learning and innovation. Take leadership as a moderator as it will strengthen the relationship of IV and DV.

The larger firms need to be more innovative as compared to the smaller firms. So, these variables, by default have impact on firm’s innovation. So, we should control these variables in order to see whether IV, mediator and moderator have an effect on the dependent variable.

Quantitative Variable

Example of quantitative variable, discrete variable, example of discrete variable.

1- As an example, consider the money in your pocket or the funds in your savings account.

Continuous Variable

Example of continuous variable, qualitative variables.

Qualitative variables, often known as categorical variables, are non-numerical values or categories. You can realistically count any numerical variables.

Example of Qualitative Variables

Binary variable.

A binary variable is a variable that can take on only two values, usually 0 and 1. In research, binary variables are often used to represent the presence or absence of something,

Example of Binary Variable

1- To know whether a person has a disease (0 = no, 1 = yes).

 Nominal Variable

Nominal variables are sometimes called categorical variables. Nominal variables are usually coded with numbers, but the numbers do not have any mathematical meaning. In other words, the order of the numbers does not matter.

Ordinal Variable

In research, an ordinal variable is a variable that is used to rank items. They are the groups that are arranged in a particular order. Ordinal variables are often used in surveys.

Example of Ordinal Variable

Extraneous variable.

Extraneous variables are factors that affect the dependent variable but were not originally considered by the researcher while designing the experiment. These unexpected variables can alter the outcomes of a study or how a researcher perceives the results.

Example of Extraneous Variable

Latent variable.

A latent variable is a variable that is not directly observed but is instead inferred from other variables that are observed.

Example of Latent Variable

Confounding variable.

A variable in your experiment that conceals the true influence of another variable. This can occur when another variable is strongly related to a variable of interest but is not controlled in your experiment.

Example of Confounding Variable

1- Suppose there is an association between smoking and lung cancer. If age is a confounder, then this means that older people are more likely to get lung cancer, but they are also more likely to smoke. Therefore, the true association between smoking and lung cancer may be underestimated if age is not taken into account.

Composite Variable

Example of composite variable.

A composite variable could be created by combining the variables “height” and “weight” to create the variable “BMI.”

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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

Type of variable Definition Example
Independent Variable (Stimulus) It is the variable that influences other variables.
Dependent variable (Response) The dependent variable is the outcome of the influence of the independent variable. You want to identify “How refined carbohydrates affect the health of human beings?”

: refined carbohydrates

: the health of human beings

You can manipulate the consumption of refined carbs in your human participants and measure how those levels of consuming processed carbohydrates influence human health.

Control Variables
Control variables are variables that are not changed and kept constant throughout the experiment.

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.

Types of 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.

Type of variable Definition Example
Continuous Variable A continuous variable is a quantitative variable that can take a value between two specific values.
Discrete Variable A discrete variable is a quantitative variable whose attributes are separated from each other.  Literacy rate, gender, and nationality.

Scale: Nominal and ordinal.

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:

Type of variable Definition Example
Dichotomous (Binary) Variable This is the categorical variable with two possible results (Yes/No) Alcoholic (Yes/No)
Nominal Variable Nominal Variable can take the value that is not organised in terms of groups, degree, or rank.
Ordinal Variable Ordinal Variable can take the value that can be logically ordered or ranked.

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 .

Type of variable Definition Example
Confounding variables The confounding variable is a hidden variable that produces an association between two unrelated variables because the hidden variable affects both of them. There is an association between water consumption and cold drink sales.

The confounding variable could be the   and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat.

Latent Variable These are the variables that cannot be observed or measured directly. Self-confidence and motivation cannot be measured directly. Still, they can be interpreted through other variables such as habits, achievements, perception, and lifestyle.
Composite variables
A composite variable is a combination of multiple variables. It is used to measure multidimensional aspects that are difficult to observe.
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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 Variables in Research & Statistics | Examples

Types of Variables in Research & Statistics | Examples

Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.

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 .

If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .

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, other interesting articles, 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 variables 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 .

Discrete vs continuous variables
Type of variable What does the data represent? Examples
Discrete variables (aka integer variables) Counts of individual items or values.
Continuous variables (aka ratio variables) Measurements of continuous or non-finite values.

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 .

Binary vs nominal vs ordinal variables
Type of variable What does the data represent? Examples
Binary variables (aka dichotomous variables) Yes or no outcomes.
Nominal variables Groups with no rank or order between them.
Ordinal variables Groups that are ranked in a specific order. *

*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 color-coded according to the type of variable: nominal , continuous , ordinal , and binary .

Example data sheet showing types of variables in a plant salt tolerance experiment

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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.

Independent vs dependent vs control variables
Type of variable Definition Example (salt tolerance experiment)
Independent variables (aka treatment variables) Variables you manipulate in order to affect the outcome of an experiment. The amount of salt added to each plant’s water.
Dependent variables (aka ) Variables that represent the outcome of the experiment. Any measurement of plant health and growth: in this case, plant height and wilting.
Control variables Variables that are held constant throughout the experiment. The temperature and light in the room the plants are kept in, and the volume of water given to each plant.

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.

Example of a data sheet showing dependent and independent variables for a plant salt tolerance experiment.

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 ( causation ).

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 variables are listed below.

Type of variable Definition Example (salt tolerance experiment)
A variable that hides the true effect of another variable in your experiment. This can happen when another variable is closely related to a variable you are interested in, but you haven’t controlled it in your experiment. Be careful with these, because confounding variables run a high risk of introducing a variety of to your work, particularly . Pot size and soil type might affect plant survival as much or more than salt additions. In an experiment you would control these potential confounders by holding them constant.
Latent variables A variable that can’t be directly measured, but that you represent via a proxy. Salt tolerance in plants cannot be measured directly, but can be inferred from measurements of plant health in our salt-addition experiment.
Composite variables A variable that is made by combining multiple variables in an experiment. These variables are created when you analyze data, not when you measure it. The three plant health variables could be combined into a single plant-health score to make it easier to present your findings.

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

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic

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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 .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

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).

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Research Methodology Lecture No : 10 (Measurement of Variables/Scales)

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Research Methodology Lecture No : 10 (Measurement of Variables/Scales)

Research Methodology Chapter 8 & 9.

variables in research methodology ppt

Developing a Questionnaire

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Conceptualization and Measurement

variables in research methodology ppt

Chapter 12 MeasurementScales McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.

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Scale Types.

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Question Development. Steps in the Measurement Process The researcher must determine which level is appropriate for the data that will contribute to management.

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Scaling. Why do we assign numbers to characteristics of objects? What is scaling. What is a nominal scale and what is it used for in Marketing Nominal.

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McGraw-Hill/Irwin Business Research Methods, 10eCopyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 12 MeasurementScales.

variables in research methodology ppt

CH. 9 MEASUREMENT: SCALING, RELIABILITY, VALIDITY

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Comparative Scaling. Some Key Concepts Measurement –Assigning numbers or other symbols to characteristics of objects being measured, according to predetermined.

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Some Approaches to Measuring Hypothetical Constructs (e.g. Attitudes) Following are approaches that have been used to measure psychological constructs:

variables in research methodology ppt

Scaling, Reliability and Validity

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Measurement in Survey Research Developing Questionnaire Items with Respect to Content and Analysis.

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A survey is only as good as the questions it asks

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MEASUREMENT. Measurement “If you can’t measure it, you can’t manage it.” Bob Donath, Consultant.

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Research Methods in MIS

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Scaling and Attitude Measurement in Travel and Hospitality Research Research Methodologies CHAPTER 11.

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variables in research methodology ppt

(Business Research Methods)

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[Updated 2023] Top 20 PowerPoint Templates to Devise a Systematic Research Methodology

[Updated 2023] Top 20 PowerPoint Templates to Devise a Systematic Research Methodology

Kritika Saini

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Developing a systematic research methodology is essential for conducting effective investigations. It ensures clarity, rigor, validity, replicability, ethical integrity, and efficiency in the research process. It serves as a roadmap that guides researchers through the study, enabling them to generate reliable findings and contribute to the advancement of knowledge in their respective fields.

Research Methodology Templates to Conduct Rigorous and Reliable Research

By following a well-structured approach, you can enhance the efficiency of your research and produce meaningful results. Therefore, SlideTeam brings you a collection of content-ready and custom-made PPT templates to help you save time by providing pre-designed structures and frameworks for research methodologies. You can customize these templates to fit your specific projects, eliminating the need to create a methodology from scratch. 

This time-saving aspect allows you to focus more on the actual research process. Secondly, these ready-made templates provide you with consistency and standardization in methodologies. They ensure that essential elements are included and organized in a logical manner, making it easier for readers and reviewers to understand and evaluate the research. They also serve as a helpful guide, ensuring that researchers cover all necessary components and follow best practices. They provide a clear and structured format for learning about research methodologies and help researchers develop a systematic approach to their work. Overall, research methodology templates streamline the process, enhance consistency, and serve as educational resources for researchers at various levels of expertise.

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FAQs on Research Methodology

What are the four types of research methodology.

The four types of research methodology commonly used in academic and scientific studies are:

Descriptive Research: This type aims to describe and document the characteristics, behavior, and phenomena of a particular subject or population. It focuses on gathering information and providing an accurate portrayal of the research topic.

Experimental Research: This approach involves the manipulation and control of variables to establish cause-and-effect relationships. It often includes the use of control groups and random assignment to test hypotheses and draw conclusions.

Correlational Research: This methodology examines the statistical relationship between two or more variables without direct manipulation. It aims to identify patterns and associations between variables to understand their degree of relationship.

Qualitative Research: This approach focuses on exploring and understanding the subjective experiences, perspectives, and meanings attributed by individuals or groups. It involves methods such as interviews, observations, and analysis of textual or visual data to uncover insights and interpretations.

What are the 3 main methodological types of research?

The three main methodological types of research are:

Quantitative Research: This approach involves the collection and analysis of numerical data to uncover patterns, relationships, and statistical trends. It focuses on objective measurements, often utilizing surveys, experiments, and statistical analysis to quantify and generalize findings.

Qualitative Research: This methodology aims to understand the subjective experiences, meanings, and social contexts associated with a research topic. It relies on non-numerical data, such as interviews, observations, and textual analysis, to explore in-depth perspectives, motivations, and behavior.

Mixed-Methods Research: This type of research integrates both quantitative and qualitative approaches, combining the strengths of both methodologies. It involves collecting and analyzing both numerical and non-numerical data to gain a comprehensive understanding of the research problem. Mixed-methods research can provide a more nuanced picture by capturing both statistical trends and rich contextual information.

What are the 7 basic research methods?

There are several research methods commonly used in academic and scientific studies. While the specific categorization may vary, here are seven basic research methods:

Experimental Research: Involves controlled manipulation of variables to establish cause-and-effect relationships.

Survey Research: Utilizes questionnaires or interviews to collect data from a sample population to gather insights and opinions.

Observational Research: Involves systematic observation of subjects in their natural environment to gather qualitative or quantitative data.

Case Study Research: In-depth analysis of a particular individual, group, or phenomenon to gain insights and generate detailed descriptions.

Correlational Research: Examines the statistical relationship between variables to identify patterns and associations.

Qualitative Research: Focuses on understanding subjective experiences, meanings, and social contexts through interviews, observations, and textual analysis.

Action Research: Involves collaboration between researchers and participants to address real-world problems and generate practical solutions.

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  1. PPT

    variables in research methodology ppt

  2. 10 Types of Variables in Research

    variables in research methodology ppt

  3. PPT

    variables in research methodology ppt

  4. Types Of Variables In Research Ppt

    variables in research methodology ppt

  5. Quantitative Research Methods PowerPoint Template

    variables in research methodology ppt

  6. Quantitative Research Methods PowerPoint Template

    variables in research methodology ppt

VIDEO

  1. RESEARCH METHODOLOGY (PRESENTATION)

  2. identifying variables in research

  3. Research Methodology Ppt PowerPoint Presentation Summary Graphics Pictures

  4. Practical Research 2 Quarter 1 Module 3: Kinds of Variables and Their Uses

  5. Identifying Variables

  6. Research Variables

COMMENTS

  1. Chapter 5: Variables and measurement IN research.

    1 Chapter 5: Variables and measurement IN research. 2 Dependent Variables Dependent/response variable: a variable that is measured or observed from an individual. Reliability: the degree to which the results of a study can be replicated under similar conditions. Operational definition: the definition of an abstract concept used by a researcher ...

  2. PPT

    Presentation Transcript. Quantitative Research Methodology Session 2 Variables, Population, and Sampling. Variable A characteristic that varies • Independent Variable "the factor that is measured, manipulated, or selected by the experimenter to determine its relationship with an observed phenomenon" (Tuckman, 1999, p.93) Variable (2) 2.

  3. PPT Research Questions, Hypotheses, and Variables

    Research Questions, Variables, and Hypotheses: Part 1 PHC 6700/RCS 6740 2/14/06 Happy Valentine's Day!

  4. PowerPoint Slides: SOWK 621.01: Research I: Basic Research Methodology

    DeCarlo and his team developed a complete package of materials that includes a textbook, ancillary materials, and a student workbook as part of a VIVA Open Course Grant. The PowerPoint slides associated with the twelve lessons of the course, SOWK 621.01: Research I: Basic Research Methodology, as previously taught by Dr. Matthew DeCarlo at ...

  5. A COURSE IN RESEARCH METHODOLOGY 2018.pptx

    A COURSE IN RESEARCH METHODOLOGY 2018.pptx. Naimi AMARA. This teaching paper is an introdcution to the field of research methodology as it enables beginners (students) to understand basic things about research, research techniques , research design and research procedure. The general aim behind this teaching paper is to facilitate the task of ...

  6. Lecture Notes on Research Methodology

    New York: Prentice-Hall, 1960. Download ppt "Lecture Notes on Research Methodology". 1 Research Methodology: An Introduction: MEANING OF RESEARCH: Research in common parlance refers to a search for knowledge. Once can also define research as a scientific & systematic search for pertinent information on a specific topic.

  7. Variables in Research

    Categorical Variable. This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

  8. RESEARCH METHODS Lecture 5. CONCEPTS AND VARIABLES.

    Presentation on theme: "RESEARCH METHODS Lecture 5. CONCEPTS AND VARIABLES."— ... 1 RESEARCH METHODS Lecture 5 2 CONCEPTS AND VARIABLES 3 Concept Things we observe Observable realities physical or abstract For purposes of identification of a reality we try to give a name to it. By using name we communicate with others. ... Download ppt ...

  9. PPT

    Presentation Transcript. Research Variables. Research Definitions • An experiment is a process in which an investigator devises two or more different experiences (treatments) for subjects or participants. • Involves a control group and one or more treatment groups. Research Variables • Independent Variable (IV): the controlled variable in ...

  10. (PPT) introduction to research methodology

    Research can be defined as the search for knowledge, or as any systematic investigation, with an open mind, to establish novel facts, solve new or existing problems, prove new ideas, or develop new theories, usually using a scientific method. Egesa Romans. According to Mugenda & Mugenda (2010), research is process of carrying out a diligent ...

  11. 10 Types of Variables in Research

    Organization is an entity. Name, Size, Type, Learning, Innovation are the attributes of an organization. So all these attributes are the variables. 2- Other example is that Employee is also an entity. Name, Age, Gender, Experience, Stress level, satisfaction, Performance are the attributes of an employee.

  12. Types of Variables

    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 Vs. Dependent Vs.

  13. Types of Variables in Research & Statistics

    Examples. Discrete variables (aka integer variables) Counts of individual items or values. Number of students in a class. Number of different tree species in a forest. Continuous variables (aka ratio variables) Measurements of continuous or non-finite values. Distance.

  14. Research Methodology Lecture No : 10 (Measurement of Variables/Scales

    Download ppt "Research Methodology Lecture No : 10 (Measurement of Variables/Scales)" Recap Measurement is necessary to give answers or to the research question , or to test our hypotheses. The opeationalizing of certain subjective variables are necessary for measurement. The abstract concepts are broken down to dimensions and its elements.

  15. [Updated 2023] Top 20 PowerPoint Templates for a Systematic Research

    Template 13: Graph of Primary Research Methodology PPT Template. Experience the power of data-driven insights with this professional and appealing PPT template. Designed for primary research, this template offers a comprehensive framework that includes field trials, observations, interviews, focus groups, and surveys.