what is variable in thesis

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Organizing Your Social Sciences Research Paper

• Independent and Dependent Variables
• Purpose of Guide
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• Glossary of Research Terms
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• Narrowing a Topic Idea
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• Background Information
• The Research Problem/Question
• Theoretical Framework
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• Primary Sources
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• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
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• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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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

Conceptual and Theoretical Frameworks for Thesis Studies: What you must know

A theoretical framework is a conceptual model that provides a systematic and structured way of thinking about a research problem or question. It helps to identify key variables and the relationships between them and to guide the selection and interpretation of data. Theoretical frameworks draw on existing theories and research and can be used to develop new hypotheses or test existing ones. They provide a foundation for research design, data collection, and analysis and can help to ensure that research is relevant, rigorous, and coherent. Theoretical frameworks are common in many disciplines, including social sciences, natural sciences, and humanities, and are essential for building knowledge and advancing understanding in a field.

This article explains the importance of frameworks in a thesis study and the differences between conceptual frameworks and theoretical frameworks. It provides guidelines on how to write a thesis framework, definitions of variable types, and examples of framework types.

What is a research framework and why do I need one?

When planning your thesis study, you need to justify your research and explain its design to your readers. This is called the research framework.

When planning your thesis study, you need to justify your research and explain its design to your readers. This is called the research framework. Think of it as the foundation of a building. A good building needs a strong foundation. Similarly, your research needs to be supported by reviewing and explaining the existing knowledge in the field, describing how your research study will fit within or contribute to the existing literature (e.g., it could challenge or test an existing theory or address a knowledge gap), and informing the reader how your study design aligns with your thesis question or hypothesis.

Important components of the framework are a literature review of recent studies associated with your thesis topic as well as theories/models used in your field of research. The literature review acts as a filtering tool to select appropriate thesis questions and guide data collection, analysis, and interpretation of your findings. Think broadly! Apart from reviewing relevant published papers in your field of research, also explore theories that you have come across in your undergraduate courses, other published thesis studies, encyclopedias, and handbooks.

There are two types of research frameworks: theoretical and conceptual .

What is a conceptual framework?

A conceptual framework is a written or visual representation that explains the study variables and their relationships with each other. The starting point is a literature review of existing studies and theories about your topic.

Steps to develop a conceptual framework

• Clarify your study topic by identifying and defining key concepts in your thesis problem statement and thesis question. Essentially, your thesis should address a knowledge gap.
• Perform a literature review to provide a background to interpret and explain the study findings. Also, draw on empirical knowledge that you have gained from personal experience.
• Identify crucial variables from the literature review and your empirical knowledge, classify them as dependent or independent variables, and define them.
• Brainstorm all the possible factors that could affect each dependent variable.
• Propose relationships among the variables and determine any associations that exist between all variables.
• Use a flowchart or tree diagram to present your conceptual framework.

Types of variables

When developing a conceptual framework, you will need to identify the following:

• Independent variables
• Dependent variables
• Moderating variables
• Mediating variables
• Control variables

First, identify the independent (cause) and dependent (effect) variables in your study. Then, identify variables that influence this relationship, such as moderating variables, mediating variables, and control variables. A moderating variable changes the relationship between independent and dependent variables when its value increases or decreases. A mediating variable links independent and dependent variables to better explain the relationship between them. A control variable could potentially impact the cause-and-effect relationship but is kept constant throughout the study so that its effects on the findings/outcomes can be ruled out.

Example of a conceptual framework

You want to investigate the hours spent exercising (cause) on childhood obesity (effect).

Now, you need to consider moderating variables that affect the cause-and-effect relationship. In our example, the amount of junk food eaten would affect the level of obesity.

Next, you need to consider mediating variables. In our example, the maximum heart rate during exercise would affect the child’s weight.

Finally, you need to consider control variables. In this example, because we do not want to investigate the role of age in obesity, we can use this as a control variable. Thus, the study subjects would be children of a specific age (e.g., aged 6–10 years).

What is a theoretical framework?

A theoretical framework provides a general framework for data analysis. It defines the concepts used and explains existing theories and models in your field of research.

A theoretical framework provides a general framework for data analysis. It defines the concepts used and explains existing theories and models in your field of research. It also explains any assumptions that were used to inform your approach and your choice of specific rationales. Theoretical frameworks are often used in the fields of social sciences.

Purpose of a theoretical framework

• Test and challenge existing theories
• Establish orderly connections between observations and facts
• Predict and control situations
• Develop hypotheses

Steps to develop a theoretical framework

• Identify and define key concepts in your thesis problem statement and thesis question.
• Explain and evaluate existing theories by writing a literature review that describes the concepts, models, and theories that support your study.
• Choose the theory that best explains the relationships between the key variables in your study.
• Explain how your research study fills a knowledge gap or fits into existing studies (e.g., testing if an established theory applies to your thesis context).
• Discuss the relevance of any theoretical assumptions and limitations.

A thesis topic can be approached from a variety of angles, depending on the theories used.

• In psychology, a behavioral approach would use different methods and assumptions compared with a cognitive approach when treating anxiety.
• In literature, a book could be analyzed using different literary theories, such as Marxism or poststructuralism.

Structuring a theoretical framework

The structure of a theoretical framework is fluid, and there are no specific rules that need to be followed, as long as it is clearly and logically presented.

The theoretical framework is a natural extension of your literature review. The literature review should identify gaps in the field of your research, and reviewing existing theories will help to determine how these can be addressed. The structure of a theoretical framework is fluid, and there are no specific rules that need to be followed, as long as it is clearly and logically presented. The theoretical framework is sometimes integrated into the literature review chapter of a thesis, but it can also be included as a separate chapter, depending on the complexity of the theories.

Example of a theoretical framework

The sales staff at Company X are unmotivated and struggling to meet their monthly targets. Some members of the management team believe that this could be achieved by implementing a comprehensive product-training program, but others believe that introducing a sales commission structure will help.

Company X is not achieving their monthly sales targets

To increase monthly sales.

Research question:

How can Company X motivate their sales team to achieve its monthly sales targets?

Sub-questions:

• Why do the sales staff feel unmotivated?
• What is the relationship between motivation and monetary rewards?
• Do the sales staff feel that they have sufficient product knowledge?

Theoretical framework:

A literature search will need to be performed to understand the background of the many different theories of motivation in psychology. For example, Maslow’s Hierarchy of Needs (basic human needs—physiological, safety, love/belonging, esteem, and self-actualization—have to be fulfilled before one can live up to their true potential), Vroom’s Theory of Expectancy (people decide upon their actions based on the outcomes they expect), and Locke’s Goal-Setting Theory (goals are a key driver of one’s behavior). These theories would need to be investigated to determine which would be the best approach to increase the motivation of the sales staff in Company X so that the monthly sales targets are met.

A robust conceptual or theoretical framework is crucial when writing a thesis/dissertation. It defines your research gap, identifies your approach, and guides the interpretation of your results.

A thesis is the most important document you will write during your academic studies. For professional thesis editing and thesis proofreading services, check out Enago's Thesis Editing service s for more information.

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What type of framework is used in the Humanities and Social Sciences (HSS) domain? +

Theoretical frameworks are typically used in the HSS domain, while conceptual frameworks are used in the Sciences domain.

What is the difference between mediating versus moderating variables? +

The difference between mediators and moderators can be confusing. A moderating variable is unaffected by the independent variable and can increase or decrease the strength of the relationship between the independent and dependent variables. A mediating variable is affected by the independent variable and can explain the relationship between the independent and dependent variables. T he statistical correlation between the independent and dependent variables is higher when the mediating variable is excluded.

What software should I use to present my conceptual framework? +

The software program Creately provides some useful templates that can help you get started. Other recommended programs are SmartDraw , Inkscape , and diagrams.net .

What are Examples of Variables in Research?

Table of contents, introduction.

In writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing excellent research. What are variables, and how do you use variables in your research?

I explain this key research concept below with lots of examples of variables commonly used in a study.

You may find it challenging to understand just what variables are in research, especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this vital concept of research, as well as statistics.

Understanding what variables mean is crucial in writing your thesis proposal because you will need these in constructing your conceptual framework  and in analyzing the data that you have gathered.

Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will use your data for statistical analysis.

I will strengthen your understanding by providing examples of phenomena and their corresponding variables below.

Definition of Variable

Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary,” meaning, changing in amount, volume, number, form, nature, or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.

The next section provides examples of variables related to climate change , academic performance, crime, fish kill, crop growth, and how content goes viral. Note that the variables in these phenomena can be measured, except the last one, where a bit more work is required.

Examples of Variables in Research: 6 Phenomena

The following are examples of phenomena from a global to a local perspective. The corresponding list of variables is given to illustrate how complex phenomena can be broken down into manageable pieces for better understanding and to subject the phenomena to research.

Phenomenon 1: Climate change

Examples of variables related to climate change :

• temperature
• the amount of carbon emission
• the amount of rainfall

Phenomenon 2: Crime and violence in the streets

Examples of variables related to crime and violence :

• number of robberies
• number of attempted murders
• number of prisoners
• number of crime victims
• number of laws enforcers
• number of convictions
• number of carnapping incidents

Phenomenon 3: Poor performance of students in college entrance exams

Examples of variables related to poor academic performance :

• entrance exam score
• number of hours devoted to studying
• student-teacher ratio
• number of students in the class
• educational attainment of teachers
• teaching style
• the distance of school from home
• number of hours devoted by parents in providing tutorial support

Phenomenon 4: Fish kill

Examples of variables related to fish kill :

• dissolved oxygen
• water salinity
• age of fish
• presence or absence of parasites
• presence or absence of heavy metal
• stocking density

Phenomenon 5: Poor crop growth

Examples of variables related to poor crop growth :

• the amount of nitrogen in the soil
• the amount of phosphorous in the soil
• the amount of potassium in the ground
• frequency of weeding
• type of soil

Phenomenon 6:  How Content Goes Viral

• interesting,
• surprising, and
• causing physiological arousal.

Notice in the above variable examples that all the factors listed under the phenomena can be counted or measured using an ordinal, ratio, or interval scale, except for the last one. The factors that influence how content goes viral are essentially subjective.

But researchers devised ways to measure those variables by grouping the respondents’ answers on whether content is positive, interesting, prominent, among others (see the  full description here ).

Thus, the variables in the last phenomenon represent the  nominal scale of measuring variables .

The expected values derived from these variables will be in terms of numbers, amount, category, or type. Quantified variables allow statistical analysis . Variable descriptions, correlations, or differences are then determined.

Difference Between Independent and Dependent Variables

Which of the above examples of variables are the independent and the dependent variables?

Independent Variables

The independent variables are those variables that may influence or affect the other variable, i.e., the dependent variable.

For example, in the second phenomenon, i.e., crime and violence in the streets, the independent variables are the number of law enforcers. If there are more law enforcers, it is expected that it will reduce the following:

• number of robberies,
• number of attempted murders,
• number of prisoners, 
• number of crime victims, and
• the number of carnapping incidents.

The five variables listed under crime and violence in the streets as the theme of a study are all dependent variables.

Dependent Variables

The dependent variable, as previously mentioned, is the variable affected or influenced by the independent variable.

For example, in the first phenomenon on climate change, temperature as the independent variable influences sea level rise, the dependent variable. Increased temperature will cause the expansion of water in the sea. Thus, sea-level rise on a global scale will occur.

I will leave the classification of the other variables to you. Find out whether those are independent or dependent variables. Note, however, that some variables can be both independent or dependent variables, as the context of the study dictates.

Finding the relationship between variables

How will you know that one variable may cause the other to behave in a certain way?

Finding the relationship between variables requires a thorough  review of the literature . Through a review of the relevant and reliable literature, you will find out which variables influence the other variable. You do not just guess relationships between variables. The entire process is the essence of research.

At this point, I believe that the concept of the variable is now clear to you. Share this information with your peers, who may have difficulty in understanding what the variables are in research.

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About the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

128 Comments

Your question is unclear to me Biyaminu. What do you mean? If you want to cite this, see the citation box after the article.

• Pingback: Quantitative Research Design: 4 Common Ways to Gather Your Data Efficiently October 15, 2020

Dear Calvin, when you state your research objectives that’s where you will know if you need to use variables or not.

Great work. I’d just like to know in which situations are variables not used in scientific research please. thank you.

• Pingback: Nonparametric Tests: 8 Important Considerations Before Using Them October 11, 2020

I salute your work, before I was have no enough knowledge about variable I think I was claimed from my lecturers, but the real meaning I was in the mid night. thanks

Thank you very much for your nice NOTE! I have a question: Can you please give me any examples of variables in students’ indiscipline?

A well articulated exposition! Pls, I need a simple guide on the variables of the following topic : IMPACT OF TAX REFORMS ON REVENUE GENERATION IN NIGERIA: A CASE STUDY OF KOGI STATE. THANKS A LOT.

thanks for the explanation a bout variables. keep on posting information a bout reseach on my email.

This was extremely helpful and easy to digest

Dear Hamse, That depends on what variables you are studying. Are you doing a study on cause and effect?

Dear Sophia and Hamse,

As I mentioned earlier, please read the last part of the above article on how to determine the dependent and independent variables.

CHALLENGES FACING DEVELOPMENT OF COOPERATIVE MOVEMENT IN TANA RIVER COUNTY

What is the IV and DV of this Research topic?

You can see in the last part of the above article an explanation about dependent and independent variables.

Dear Maur, what you just want to do is to describe the challenges. No need for a conceptual framework.

Hey, I really appreciate your explanation however I’m having a hard time figuring out the IV and DV on the topic about fish kill, can you help me?

I am requested to write 50 variables in my research as per my topic which is about street vending. I am really clueless.

Hi Regoniel…your articles are much more guiding….pls am writing my thesis on impact of insurgency on Baga Road fish market Maiduguri.

How will my conceptual framework looks like What do I need to talk on

Dear Alhaji, just be clear about what you want to do. Your research question must be clearly stated before you build your conceptual framework.

• Pingback: How to Analyze Frequency Data | SimplyEducate.Me December 4, 2018

Thanks so much ! This article is so much simple to my understanding. A friend of my referred me to this site and I am so greatful. Please Sir, when writing the dependent and independent variables should it be in a table form ?

Dear Grace, Good day. I don’t understand what you mean. But if your school requires that the independent and dependent variables be written in table form, I see no problem with that. It’s just a way for you to clearly show what variables you are analyzing. And you need to justify that.

Can you please give me what are the possible variables in terms of installation of street lights along barangay roads of calauan, laguna: an assessment?

Hello sir, sorry to bother you but what are the guidelines for writing a good report

Guidelines for writing a good research report?

• Cookies & Privacy
• GETTING STARTED
• Introduction
• FUNDAMENTALS
• Acknowledgements
• Research questions & hypotheses
• Concepts, constructs & variables
• Research limitations
• Getting started
• Sampling Strategy
• Research Quality
• Research Ethics
• Data Analysis

Types of variables

Understanding the types of variables you are investigating in your dissertation is necessary for all types of quantitative research design , whether you using an experimental , quasi-experimental , relationship-based or descriptive research design. When you carry out your dissertation, you may need to measure , manipulate and/or control the variables you are investigating. In the section on Research Designs , you can learn more about the various types of quantitative research design. In this article, we present and illustrate the different types of variables you may come across in your dissertation. First, we discuss the main groups of variables: categorical variables and continuous variables . Second, we explain what dependent and independent variables are. This will provide you with one of the foundations required to tackle a dissertation based on a quantitative research design.

Categorical and continuous variables

Dependent and independent variables, ambiguities in classifying variables.

There are two groups of variables that you need to know about: categorical variables and continuous variables . We use the word groups of variables because both categorical and continuous variables include additional types of variable. However, there can also be some ambiguities when deciding whether a variable is categorical or continuous. We discuss the two groups of variable, as well as these potential ambiguities, in the sections that follow:

Categorical variables

Categorical variables are also known as qualitative (or discrete ) variables . These categorical variables can be further classified as being nominal , dichotomous or ordinal variables. Each of these types of categorical variable (i.e., nominal , dichotomous and ordinal ) has what are known as categories or levels . These categories or levels are the descriptions that you give a variable that help to explain how variables should be measured, manipulated and/or controlled. Take the following example:

Career choices of university students You are interested in the career choices of university students . You could ask university students a number of closed questions related to their career choices. For example: What is your planned occupation? What is the most important factor influencing your career choice?

The first question highlights the use of categories and the second question levels . For example:

Question 1 : What is your planned occupation? Variables with categories

Architect Attorney Biochemist Engineer Dentist Doctor Entrepreneur Social Worker Teacher ETC...

Career prospects Nature of the work Physical working conditions Salary and benefits ETC...

What is important to note about the categories in question 1 and the levels in question 2 is that these will be created by you. Ideally, you will have included these categories or levels based on some primary or secondary research. Ultimately, you choose which categories or levels to include and how many categories or levels there should be.

Each of these types of categorical variable (i.e., nominal , dichotomous and ordinal ) are described below with associated examples:

Nominal variables

The following are examples of nominal variables. These nominal variables could address questions like:

These examples highlight two core characteristics of nominal variables:

Nominal variables have two or more categories.

Nominal variables do not have an intrinsic order.

When we talk about nominal variables not having an intrinsic order , we mean that they can only have categories (e.g., black, blond, brown and red hair); not levels (e.g., a Likert scale from 1 to 5).

Dichotomous variables

The following are examples of dichotomous variables. These dichotomous variables could address questions like:

Dichotomous variables are nominal variables that have just two categories. They have a number of characteristics:

Dichotomous variables are designed to give you an either/or response

For example, you are either male or female. You either like watching television (i.e., you answer YES ) or you don't (i.e., you answer NO ).

Dichotomous variables can either be fixed or designed

For example, some variables (e.g., your sex ) can only be dichotomous (i.e., you can only be male or female ). They are therefore fixed . In other cases, dichotomous variables are designed by the researcher. For example, take the question: Do you like watching television? We have determined that the respondent can only select YES (i.e., I like watching television) or NO (i.e., I don't like watching television). However, another researcher could provide the respondent with more than two categories to this question (e.g., most of the time, sometimes , hardly ever ). Where more than two categories are used, these variables become known as nominal variables rather than dichotomous ones.

Ordinal variables

Just like nominal variables, ordinal variables have two or more categories. However, unlike nominal variables, ordinal variables can also be ordered or ranked (i.e., they have levels ). For example, take the following example of an ordinal variable:

So if you asked someone if they liked the policies of the Democratic Party and you presented them with the following three categories: Not very much , They are OK , or Yes, a lot ; 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 three categories , we cannot place a value to them. For example, we cannot say that the response, They are OK , is twice as positive as the response, Not very much .

Other examples of ordinal variables are:

When it comes to Likert scales, as highlighted in the previous example, there can be some disagreement over whether these should be considered ordinal variables or continuous variables [see the section: Ambiguities in classifying variables ].

Continuous variables

Continuous variables, which are also known as quantitative variables, can be further classified a being either interval or ratio variables. Each of these types of continuous variable (i.e., interval and ratio ) has numerical properties. These numerical properties are the values by which continuous variables can be measured, manipulated and/or controlled. We illustrate the two types of continuous variable (i.e., interval and ratio ) and some associated values in the sections that follow:

Interval variables

Interval variables have a numerical value and can be measured along a continuum . Some examples of interval variables are:

However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable. This is because temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no temperature.

Ratio variables

Ratio variables are interval variables that meet an additional condition: a measurement value of 0 (zero) must mean that there is none of that variable. Some examples of ratio variables are:

Sometimes, the measurement scale for data is ordinal , but the variable is treated as though it were continuous . This is more often the case when using Likert scales. When a Likert scale has five values (e.g., strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree), it is treated as an ordinal variable. However, when a Likert scale has seven or more values (e.g., strongly agree, moderately agree, agree, neither agree nor disagree, disagree, moderately disagree, and strongly disagree), the variable is sometimes treated as a continuous variable. Nonetheless, this is a matter of dispute. Some researchers would argue that a Likert scale should never be treated as a continuous variable, even with seven levels/values.

Since you are responsible for setting the measurement scale for a variable, you will need to think carefully about how you characterise a variable. For example, social scientists may be more likely to consider the variable gender to be a nominal variable. This is because they view gender as having a number of categories, including male, female, bisexual and transsexual. By contrast, other researchers may simply view gender as a dichotomous variable, having just two categories: male and female. In such cases, it may be better to refer to the variable gender as sex .

A variable is not only something that you measure , but also something that you can manipulate and control for. 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 this has on a dependent variable (sometimes called an outcome variable). The dependent variable is simply that; a variable that is dependent on an independent variable(s). We discuss these concepts in the example below:

For example: 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:

Some students spend more time revising for their test; and

Some students are naturally more intelligent than others.

Therefore, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. As such, the dependent and independent variables for the study are:

The dependent variable is simply that; a variable that is dependent on an independent variable(s). In our case, the test mark (i.e. the dependent variable) that a student achieves is dependent on revision time and intelligence (i.e., the independent variables). Whilst revision time and intelligence (i.e., independent variables) may (or may not) cause a change in the test mark (i.e., 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 would not make any sense.

Therefore, the aim of the tutor's investigation is to examine whether these independent variables (i.e., revision time and IQ) result in a change in the dependent variable (i.e., 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 (i.e., revision time and IQ) are also connected in some way.

You can find out more about the different uses of variables, especially in quantitative research designs (i.e., descriptive , experimental , quasi-experimental and relationship-based research designs), in the section on Research Designs .

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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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 variable. 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."

Variables: Types and Characteristics

Variable is a quantity or a characteristic that has or more mutually exclusive values or properties of objects or people that can be classified, measured or labeled in different ways.

Types of Variables

• Discrete Variable – only a finite or potentially countable set of values.
• Continuous Variable – an infinite set of values between any two levels of the variables.  They are result of measurement.
• Independent Variable – a stimulus variable which is chosen by the researcher to determine its relationship to an observed phenomena.
• Dependent Variable – a response variable which is observed and measured to determine the effect of the independent variable.
• Moderate Variable – a secondary or special type of independent variable chosen by the researcher to ascertain if it alters or modifies.
• Control Variable – a variable controlled by the research in which the effects can be neutralized by removing the variable.
• Intervening Variable – a variable which interferes with the independent and dependent variables, but its effects can either strengthen or weaken the independent and dependent variables.

Characteristics of Variable

1.    Capable of assuming several values representing a certain category. 2.    Values that may arise from counting and or from measurement. 3.    Raw data or figures gathered by a research for statistical purposes. 4.    Predicted values of one variable on the basis of another 5.    Observable characteristic of a person or objects being studied.

Operational Definition in Research

In addition to careful planning, one of the keys to successful research is the use of operational definitions in measuring the concepts and variables we are studying or the terms we are using in our research documents.

The operational definition is the specific way a variable is measured in a particular study.

It is critical to operationally define a variable to lend credibility to the methodology and ensure the reproducibility of the study’s results. Another study may identify the same variable differently, making it difficult to compare the results of these two studies.

To begin with, the operational definition is different from the dictionary definition, which is often conceptual, descriptive, and consequently imprecise.

In contrast, an operational definition gives an obvious, precise, and communicable meaning to a concept used to ensure comprehensive knowledge of the idea by specifying how the idea is measured and applied within a particular set of circumstances.

This definition highlights two important things about an operational definition:

• It gives a precise meaning to the spoken or written word, forming a ‘common language between two or more people.
• It defines how a term, word, or phrase is used when it is applied in a specific context. This implies that a word may have different meanings when used in different situations.

An operational definition must be valid, which implies that it should measure what it is supposed to measure. It must also be reliable, meaning that the results should be the same even when done by different people or by one person at different times.

An operational definition ensures a succinct description of concepts and terms applied to a specific situation to facilitate the collection of meaningful and standardized data.

When collecting data, it is important to define every term very clearly to assure all those who collect and analyze the data have the same understanding.

Therefore, operational definitions should be very precise and framed to avoid variation and confusion in interpretation.

Suppose, for example, we want to know whether a professional journal may be considered as a ‘standard journal’ or not. Here is a possible operational definition of a standard journal.

We set in advance that a journal is considered standard if

• It contains an ISSN number.
• It is officially published from a public or private university or from an internationally recognized research organization;
• It is peer-reviewed;
• It has a recognized editorial /advisory board;
• It is published on a regular basis at least once a year,
• It has an impact factor.

Thus, the researcher knows exactly what to look for when determining whether a published journal is standard or not.

The operational definition of literacy rate as adopted by the Bangladesh Bureau of Statistics (BBS) in their Vital Registration System is as follows:

“Percentage of the population of 7 years and above who can write a letter among the total population.”

In sum, an operational definition serves four purposes:

• It establishes the rules and procedures the researcher uses to measure the variable.
• It provides unambiguous and consistent meaning to terms/variables that can be interpreted differently.
• It makes the collection of data and analysis more focused and efficient.
• It guides what type of data and information we are looking for.

By operationally defining a variable, a researcher can communicate a common methodology to another researcher.

Operational definitions lay down the ground rules and procedures that the investigator will use to observe and record behavior and write down facts without bias.

The sole purpose of defining the variables operationally is to keep them unambiguous, thereby reducing errors.

How to operationalize a variable?

There is no hard and first rule for operationally defining a variable. Operational definitions may vary depending on your purpose and how you measure them.

Neither are there any universally accepted definitions of all the variables. A researcher can logically choose a definition of a variable that will serve his or her purpose.

Whenever possible, operational definitions used by others in their work of good standing could be used to compare the results.

Suppose a study classifies students according to their grades: A, B, C, etc. But the task is not easy if you must determine which students fall in which grade since there is seldom any universal rule for grades.

To do this, you need an operational definition.

In the goiter prevalence survey of 2004a person was classified as iodine deficient for a urinary iodine excretion (IUE) <100 pg/L and severely iodine-deficient for a urinary iodine excretion (IUE) <20 pg/L. One may choose a different threshold, too, in defining the iodine deficiency.

As another example, suppose it is intended to assess mothers’ knowledge of family planning. A set of 20 questions has been designed such that for every correct answer, a score of 1 will be given to the respondents.

Suppose further that we want to make 4 categories of knowledge: ‘no knowledge,’ ‘low knowledge,’ ‘medium knowledge,’ and ‘high knowledge.’ We decide to define these knowledge levels as follows:

One might, however, choose a different range of scores to define the knowledge levels.

Based on the body mass index (BMI), for example, the international health risk classification is operationally defined as follows

For the classification of nutritional status, internationally accepted categories already exist, which are based on the so-called NCHS/WHO standard growth curves. For the indicator ‘weight-for-age’, for example, children are assessed to be

• Well-nourished (normal) if they are above 80% of the standard.
• Moderately malnourished (moderate underweight) if they are between 60% and 80% of the standard.
• Severely malnourished (severely under-weight) if they are below 60% of the standard.

The nutritional status can also be classified based on the weight-for-age Z-score (WAZ) values. The Z- score of cut-off values are:

A farmer may be classified as landless, medium, and big, depending on his possession of landholding size. One such classification is as follows:

Similarly, a business firm may be classified as large, medium, or small in terms of its investment, capital, and a number of employees or assets, which may vary widely by the type of business firm.

In demographic research, a person may be categorized as a child, those who are under five years of age, adolescents in the age range 12-19, adults aged 20-65, and old aged 65 and over.

Not only that, but variables also need operationally defined. The terms that indicate the relationship between variables need to be defined.

For example, in many stated hypotheses, we use such terms as ‘frequent,’ ‘greater than,’ ‘less than,’ ‘significant,’ ‘higher than,’ ‘favorable,’ ‘different,’ ‘efficient,’ and the like.

These terms must be clearly and unambiguously defined so that they make sense and allow the researcher to measure the variables in question.

Consider the following hypothesis.

• Visits of Family Welfare Assistants will motivate the women resulting in significantly higher use of contraceptives.

‘Visit’ is the independent variable to which we might associate numbers 0, 1, and 2, to mean the frequency of visits made during a stipulated period. The term ‘higher use’ may mean a higher rate (dependent variable) than before.

This can be measured as the difference between the present and the past rate or between a post-test and a pretest measurement:

Difference =.Average CPR (pretest) – Average CPR (posttest)

But how much ‘higher’ will be regarded as significant? Thus the term’ significant’ needs to be defined clearly. We may decide to statistically verify at a 5% level with a probability of at least 95% that the difference in usage level is significant.

Thus, the operational definition of terms tells us the meaning of their use and the way of measuring the difference and testing its statistical significance, thereby accepting or rejecting the hypothesis.

In a study on the comparison of the performance of nationalized commercial banks (NCB) and private commercial banks (PCB) by Hasan (1995), one of the hypotheses was of the following form:

• PCBs are more efficient than NCBs in private deposit collection.

The term ‘more efficient’ was assessed by testing the statistical significance of the differences in the mean deposits of the two banks in question at a 5% level.

The concept of operational definition also applies to other technical terms that are not universally defined.

Here are some examples of such terms with their operational definitions:

Operational Definition of Terms

What is the primary purpose of an operational definition in research?

The primary purpose of an operational definition is to provide a clear, precise, and communicable meaning to a concept, ensuring comprehensive understanding by specifying how the concept is measured and applied within a specific set of circumstances.

How does an operational definition differ from a dictionary definition?

While a dictionary definition is often conceptual, descriptive, and may be imprecise, an operational definition offers a specific, clear, and applicable meaning to a term or concept when used in a particular context.

Why is it essential to operationally define a variable in research?

Operationally defining a variable is crucial to lend credibility to the research methodology, ensure reproducibility of the study’s results, and provide a common methodology for communication between researchers.

What are the key characteristics of a good operational definition?

A good operational definition should be valid (measuring what it’s supposed to measure), reliable (providing consistent results across different instances or by different people), precise, and framed to avoid variation and confusion in interpretation.

Can operational definitions vary between studies or researchers?

Yes, operational definitions can vary depending on the study’s purpose and how variables are measured. There are no universally accepted definitions for all variables, allowing researchers to choose definitions that best serve their objectives.

Why is it important to define terms when collecting data?

Defining terms clearly ensures that everyone involved in collecting and analyzing the data has the same understanding, making the data collection process more focused and efficient and reducing potential errors or misinterpretations.

How can operational definitions help in comparing results across different studies?

By providing clear and specific criteria for measuring variables, operational definitions allow for a standardized approach. If multiple studies use the same or similar operational definitions, it becomes easier to compare and contrast their results.

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Home » Qualitative Variable – Types and Examples

Qualitative Variable – Types and Examples

Table of Contents

Qualitative Variable

Definition:

Qualitative variable, also known as a categorical variable, is a type of variable in statistics that describes an attribute or characteristic of a data point, rather than a numerical value.

Qualitative variables are typically represented by labels or categories, such as “male” or “female,” and are often used in surveys and polls to gather information about a population’s characteristics.

Types Qualitative Variable

There are two main types of qualitative variables:

Nominal Variables

A nominal variable is a Qualitative Variable where the categories are not ordered in any particular way. For example, gender (male or female), race (Asian, Black, Hispanic, etc.), or religion (Christian, Muslim, Hindu, etc.). Nominal variables can be represented using numbers, but the numbers do not have any quantitative meaning. For example, a researcher might assign the number “1” to male and “2” to female, but these numbers do not represent a quantitative difference between the categories.

Ordinal Variables

An ordinal variable is a Qualitative Variable where the categories are ordered in some way. For example, educational level (high school, college, graduate school), income level (low, medium, high), or level of agreement (strongly agree, somewhat agree, neutral, somewhat disagree, strongly disagree). Ordinal variables can be represented using numbers, and the numbers have a quantitative meaning, but the distance between the categories is not necessarily equal. For example, the difference between “high school” and “college” may not be the same as the difference between “college” and “graduate school.”

Examples of Qualitative Variables

Here are some examples of qualitative variables:

• Gender : Male or female
• Marital status: Married, single, divorced, widowed
• Race : Asian, Black, Hispanic, White, etc.
• Religious affiliation: Christian, Muslim, Hindu, Buddhist, etc.
• Political affiliation : Democrat, Republican, Independent, etc.
• Educational level : High school, college, graduate school
• Type of employment : Full-time, part-time, self-employed, unemployed
• Type of housing: Apartment, house, condo, etc.
• Method of transportation : Car, bus, train, bike, etc.
• Language spoken: English, Spanish, French, etc.

Applications of Qualitative Variable

Qualitative variables are used in many applications in different fields, including:

• Market research : Qualitative variables are often used in market research to understand consumer behavior and preferences. For example, a company might use qualitative variables such as age, gender, and income to segment their target market and create customized marketing campaigns.
• Public opinion polling : Qualitative variables are used in public opinion polling to gather information about people’s attitudes, beliefs, and opinions. Pollsters may ask questions about political affiliation, religious affiliation, or social issues to understand public opinion on a particular topic.
• Social sciences research: Qualitative variables are commonly used in social sciences research to study human behavior, culture, and society. Researchers may use qualitative variables to categorize people based on their demographic information or cultural background, and to analyze patterns and trends in behavior or attitudes.
• Healthcare research: Qualitative variables are used in healthcare research to identify risk factors and to understand the impact of treatments on patients. Researchers may use qualitative variables such as age, gender, or medical history to identify populations at risk for certain diseases, and to evaluate the effectiveness of different treatment options.
• Education research: Qualitative variables are used in education research to study the effectiveness of different teaching methods and to identify factors that influence student learning. Researchers may use qualitative variables such as socio-economic status, educational level, or learning style to analyze patterns and trends in student performance.

When to use Qualitative Variable

Qualitative variables should be used in research when the variable being studied is categorical and does not involve numerical values. Here are some situations where qualitative variables are appropriate:

• When studying demographic characteristics: Qualitative variables are useful for studying demographic characteristics such as age, gender, ethnicity, and religion. These variables can be used to segment a population into groups and to compare differences between groups.
• When studying attitudes and beliefs : Qualitative variables can be used to study people’s attitudes and beliefs about various topics, such as politics, social issues, or religion. Researchers can use surveys or interviews to gather data on these variables.
• When studying cultural differences: Qualitative variables are often used in cross-cultural research to study differences between cultures. Researchers may use qualitative variables such as language spoken, nationality, or cultural background to identify groups for comparison.
• When studying consumer behavior : Qualitative variables can be used in market research to study consumer behavior and preferences. Researchers can use qualitative variables such as brand loyalty, product preference, or buying habits to understand consumer behavior.
• When studying patient outcomes: Qualitative variables can be used in healthcare research to study patient outcomes, such as quality of life, satisfaction with treatment, or adherence to medication. Researchers can use qualitative variables to identify factors that influence patient outcomes and to develop interventions to improve patient care.

Purpose of Qualitative Variable

The purpose of a qualitative variable is to categorize data into distinct groups based on non-numerical characteristics or attributes. The use of qualitative variables allows researchers to describe and analyze non-quantifiable phenomena, such as attitudes, beliefs, behaviors, and demographic characteristics, and to identify patterns and trends in the data. The main purposes of qualitative variables are:

• To describe and categorize : Qualitative variables are used to describe and categorize data into meaningful groups based on characteristics or attributes that are not numerical.
• To compare and contrast: Qualitative variables allow researchers to compare and contrast different groups or categories of data, such as different demographic groups or cultural backgrounds.
• To identify patterns and trends: Qualitative variables allow researchers to identify patterns and trends in data that may not be apparent with numerical data. For example, a researcher may use qualitative variables to identify cultural differences in attitudes toward healthcare.
• To develop hypotheses: Qualitative variables can be used to develop hypotheses or research questions for further study. For example, a researcher may use qualitative variables to identify risk factors for a particular disease, which can then be further studied using quantitative methods.
• To inform decision-making: Qualitative variables can provide important information to inform decision-making in fields such as healthcare, education, and business. For example, healthcare providers may use qualitative variables to identify patient preferences and needs, which can inform treatment decisions.

Characteristics of Qualitative Variable

Here are some of the characteristics of qualitative variables:

• Categorical : Qualitative variables are categorical in nature, meaning that they describe characteristics or attributes that are not numerical. They can be nominal, ordinal or binary.
• Non-numeric : Qualitative variables do not involve numerical values, but rather descriptive or categorical data such as colors, shapes, types, or names.
• Limited number of categories: Qualitative variables are often limited to a small number of categories, such as male/female, married/single/divorced, or white/black/Asian.
• Mutually exclusive categories : Categories in a qualitative variable must be mutually exclusive, meaning that each observation can only belong to one category.
• No numerical order : Unlike quantitative variables, qualitative variables do not have a numerical order or ranking. Categories are assigned based on non-numerical criteria.
• Can be used for comparison : Qualitative variables are often used for comparison purposes, such as comparing the frequency of certain behaviors or attitudes across different demographic groups.
• Can be used for classification: Qualitative variables can be used to classify data into distinct groups based on common characteristics or attributes. For example, people can be classified into different racial or ethnic groups based on their ancestry.
• Can be used for hypothesis testing : Qualitative variables can be used to test hypotheses about differences between groups or categories of data. For example, a researcher may hypothesize that men and women have different attitudes toward a particular social issue, and use a qualitative variable to test this hypothesis.

Advantages of Qualitative Variable

There are several advantages of using qualitative variables.

• Rich data: Qualitative variables can provide rich data about complex phenomena such as attitudes, behaviors, and cultural differences. This data can be useful for gaining a deep understanding of a particular issue or topic.
• Flexibility : Qualitative variables are flexible and can be used in a variety of research methods, such as interviews, focus groups, and observations. This allows researchers to choose the method that best suits their research question and participants.
• Participant perspective : Qualitative variables allow researchers to capture the participant’s perspective and experience. By using open-ended questions or prompts, researchers can gain insight into how participants perceive and interpret a particular issue.
• Depth of understanding: Qualitative variables allow for a depth of understanding that may not be possible with quantitative variables alone. Qualitative data can provide details and context that quantitative data may miss.
• Contextualization : Qualitative variables can provide contextualization, allowing researchers to understand the cultural, social, and historical factors that shape attitudes and behaviors.
• Theory development: Qualitative variables can be useful for developing new theories or refining existing ones. By gathering rich data and analyzing it using qualitative methods, researchers can identify patterns and relationships that can inform the development of new theories.
• Researcher reflexivity : Qualitative variables require the researcher to be reflexive and acknowledge their own biases and assumptions. This can help to ensure that the research is ethical and inclusive, and that the data collected is valid and reliable.

Limitations of Qualitative Variable

Some Limitations of Qualitative Variable are as follows:

• Subjectivity : Qualitative data is often collected through open-ended questions or prompts, which can lead to subjective responses that are difficult to quantify or compare. This can make it challenging to establish inter-rater reliability and can limit the generalizability of the findings.
• Limited sample size : Qualitative research often involves small sample sizes, which can limit the generalizability of the findings. While qualitative research is typically focused on gaining a deep understanding of a particular issue, the findings may not be representative of the broader population.
• Time-consuming: Qualitative research can be time-consuming, particularly when collecting and analyzing data. Researchers must spend significant amounts of time in the field, conducting interviews or focus groups, and then transcribing and analyzing the data.
• Limited control: Qualitative research often involves limited control over the research environment and the participants. This can make it challenging to ensure that the data collected is valid and reliable.
• Limited generalizability: Qualitative research is typically focused on gaining a deep understanding of a particular issue, rather than testing hypotheses or making generalizations about the broader population. As a result, the findings may be less generalizable than those obtained through quantitative research methods.
• Ethical concerns: Qualitative research often involves collecting sensitive or personal information from participants. Researchers must take care to ensure that participants are fully informed about the research, that their privacy is protected, and that they are not harmed in any way by their participation.
• Bias : Qualitative research can be subject to bias, particularly if the researcher has a vested interest in the outcome of the research. Researchers must take care to acknowledge their own biases and assumptions, and to use multiple sources of data to ensure the validity and reliability of the findings.

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Muhammad Hassan

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