what is variable in research with example

Scale: Nominal and ordinal.

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

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

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

What is a variable?

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

What is a dependent variable?

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

What is a variable in programming?

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

What is a control variable?

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

What is a controlled variable in science?

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

How many independent variables should an investigation have?

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

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

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Independent and Dependent Variables

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In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

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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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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.

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.

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 :

Phenomenon 2: Crime and violence in the streets

Phenomenon 3: poor performance of students in college entrance exams.

Examples of variables related to poor academic performance :

Phenomenon 4: Fish kill

Examples of variables related to fish kill :

Phenomenon 5: Poor crop growth

Examples of variables related to poor crop growth :

Phenomenon 6:  How Content Goes Viral

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.

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

Independent variables.

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:

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

Dependent Variables

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

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

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

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

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

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?

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Independent vs Dependent Variables: Definitions & Examples

A variable is an important element of research. It is a characteristic, number, or quantity of any category that can be measured or counted and whose value may change with time or other parameters.  

Variables are defined in different ways in different fields. For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value. In algebra, a variable represents an unknown entity, mostly denoted by a, b, c, x, y, z, etc. In statistics, variables represent real-world conditions or factors. Despite the differences in definitions, in all fields, variables represent the entity that changes and help us understand how one factor may or may not influence another factor.  

Variables in research and statistics are of different types—independent, dependent, quantitative (discrete or continuous), qualitative (nominal/categorical, ordinal), intervening, moderating, extraneous, confounding, control, and composite. In this article we compare the first two types— independent vs dependent variables .  

Table of Contents

What is a variable?  

Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments. They help in understanding how some factors influence others. Some examples of variables include age, gender, race, income, weight, etc.   

As mentioned earlier, different types of variables are used in research. Of these, we will compare the most common types— independent vs dependent variables . The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let’s explain this with an independent and dependent variable example : In a study to analyze the effect of antibiotic use on microbial resistance, antibiotic use is the independent variable and microbial resistance is the dependent variable because antibiotic use affects microbial resistance.( 1)  

What is an independent variable?  

Here is a list of the important characteristics of independent variables .( 2,3)  

• An independent variable is the factor that is being manipulated in an experiment.  
• In a research study, independent variables affect or influence dependent variables and cause them to change.  
• Independent variables help gather evidence and draw conclusions about the research subject.  
• They’re also called predictors, factors, treatment variables, explanatory variables, and input variables.  
• On graphs, independent variables are usually placed on the X-axis.  
• Example: In a study on the relationship between screen time and sleep problems, screen time is the independent variable because it influences sleep (the dependent variable).  
• In addition, some factors like age are independent variables because other variables such as a person’s income will not change their age.  

Types of independent variables  

Independent variables in research are of the following two types:( 4)  

Quantitative  

Quantitative independent variables differ in amounts or scales. They are numeric and answer questions like “how many” or “how often.”  

Here are a few quantitative independent variables examples :  

• Differences in treatment dosages and frequencies: Useful in determining the appropriate dosage to get the desired outcome.  
• Varying salinities: Useful in determining the range of salinity that organisms can tolerate.  

Qualitative  

Qualitative independent variables are non-numerical variables.  

A few qualitative independent variables examples are listed below:  

• Different strains of a species: Useful in identifying the strain of a crop that is most resistant to a specific disease.  
• Varying methods of how a treatment is administered—oral or intravenous.  

A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.  

What is a dependent variable ?  

Here are a few characteristics of dependent variables: ( 3)  

• A dependent variable represents a quantity whose value depends on the independent variable and how it is changed.  
• The dependent variable is influenced by the independent variable under various circumstances.  
• It is also known as the response variable and outcome variable.  
• On graphs, dependent variables are placed on the Y-axis.  

Here are a few dependent variable examples :  

• In a study on the effect of exercise on mood, the dependent variable is mood because it may change with exercise.  
• In a study on the effect of pH on enzyme activity, the enzyme activity is the dependent variable because it changes with changing pH.   

Types of dependent variables  

Dependent variables are of two types:( 5)  

Continuous dependent variables

These variables can take on any value within a given range and are measured on a continuous scale, for example, weight, height, temperature, time, distance, etc.  

Categorical or discrete dependent variables

These variables are divided into distinct categories. They are not measured on a continuous scale so only a limited number of values are possible, for example, gender, race, etc.  

Differences between independent and dependent variables  

The following table compares independent vs dependent variables .  

Independent and dependent variable examples  

Listed below are a few examples of research questions from various disciplines and their corresponding independent and dependent variables.( 6)

  Independent vs dependent variables in research  

Experiments usually have at least two variables—independent and dependent. The independent variable is the entity that is being tested and the dependent variable is the result. Classifying independent and dependent variables as discrete and continuous can help in determining the type of analysis that is appropriate in any given research experiment, as shown in the table below. ( 7)  

  Here are some more research questions and their corresponding independent and dependent variables. ( 6)  

  How to identify independent vs dependent variables  

In addition to all the characteristics of independent and dependent variables listed previously, here are few simple steps to identify the variable types in a research question.( 8)  

• Keep in mind that there are no specific words that will always describe dependent and independent variables.  
• If you’re given a paragraph, convert that into a question and identify specific words describing cause and effect.  
• The word representing the cause is the independent variable and that describing the effect is the dependent variable.  

Let’s try out these steps with an example.  

A researcher wants to conduct a study to see if his new weight loss medication performs better than two bestseller alternatives. He wants to randomly select 20 subjects from Richmond, Virginia, aged 20 to 30 years and weighing above 60 pounds. Each subject will be randomly assigned to three treatment groups.  

To identify the independent and dependent variables, we convert this paragraph into a question, as follows: Does the new medication perform better than the alternatives? Here, the medications are the independent variable and their performances or effect on the individuals are the dependent variable.  

Visualizing independent vs dependent variables  

Data visualization is the graphical representation of information by using charts, graphs, and maps. Visualizations help in making data more understandable by making it easier to compare elements, identify trends and relationships (among variables), among other functions.  

Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data. The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis.  

Figure 1 is a scatter plot that depicts the relationship between the number of household members and their monthly grocery expenses. 9 The number of household members is the independent variable and the expenses the dependent variable. The graph shows that as the number of members increases the expenditure also increases.  

Key takeaways   

Let’s summarize the key takeaways about independent vs dependent variables from this article:  

• A variable is any entity being measured in a study.  
• A dependent variable is often the focus of a research study and is the response or outcome. It depends on or varies with changes in other variables.  
• Independent variables cause changes in dependent variables and don’t depend on other variables.  
• An independent variable can influence a dependent variable, but a dependent variable cannot influence an independent variable.  
• An independent variable is the cause and dependent variable is the effect.  

Frequently asked questions  

• What are the different types of variables used in research?  

The following table lists the different types of variables used in research.( 10)  

 2. Why is it important to differentiate between independent vs dependent variables ?  

  Differentiating between independent vs dependent variables is important to ensure the correct application in your own research and also the correct understanding of other studies. An incorrectly framed research question can lead to confusion and inaccurate results. An easy way to differentiate is to identify the cause and effect.  

 3. How are independent and dependent variables used in non-experimental research?  

  So far in this article we talked about variables in relation to experimental research, wherein variables are manipulated or measured to test a hypothesis, that is, to observe the effect on dependent variables. Let’s examine non-experimental research and how variable are used. 11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable. Instead, the researcher would observe and measure the levels of each variable in the sample population. The level of control researchers have is the major difference between experimental and non-experimental research. Another difference is the causal relationship between the variables. In non-experimental research, it is not possible to establish a causal relationship because other variables may be influencing the outcome.  

  4. Are there any advantages and disadvantages of using independent vs dependent variables ?

  Here are a few advantages and disadvantages of both independent and dependent variables.( 12)

Advantages: 

• Dependent variables are not liable to any form of bias because they cannot be manipulated by researchers or other external factors.  
• Independent variables are easily obtainable and don’t require complex mathematical procedures to be observed, like dependent variables. This is because researchers can easily manipulate these variables or collect the data from respondents.  
• Some independent variables are natural factors and cannot be manipulated. They are also easily obtainable because less time is required for data collection.

Disadvantages: 

• Obtaining dependent variables is a very expensive and effort- and time-intensive process because these variables are obtained from longitudinal research by solving complex equations.  
• Independent variables are prone to researcher and respondent bias because they can be manipulated, and this may affect the study results.  

We hope this article has provided you with an insight into the use and importance of independent vs dependent variables , which can help you effectively use variables in your next research study.    

• Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size. Indian Dermatol Online J. 2019 Jan-Feb; 10(1): 82–86. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362742/  
• What Is an independent variable? (with uses and examples). Indeed website. Accessed March 11, 2024. https://www.indeed.com/career-advice/career-development/what-is-independent-variable  
• Independent and dependent variables: Differences & examples. Statistics by Jim website. Accessed March 10, 2024. https://statisticsbyjim.com/regression/independent-dependent-variables/  
• Independent variable. Biology online website. Accessed March 9, 2024. https://www.biologyonline.com/dictionary/independent-variable#:~:text=The%20independent%20variable%20in%20research,how%20many%20or%20how%20often .  
• Dependent variables: Definition and examples. Clubz Tutoring Services website. Accessed March 10, 2024. https://clubztutoring.com/ed-resources/math/dependent-variable-definitions-examples-6-7-2/  
• Research topics with independent and dependent variables. Good research topics website. Accessed March 12, 2024. https://goodresearchtopics.com/research-topics-with-independent-and-dependent-variables/  
• Levels of measurement and using the correct statistical test. Univariate quantitative methods. Accessed March 14, 2024. https://web.pdx.edu/~newsomj/uvclass/ho_levels.pdf  
• Easiest way to identify dependent and independent variables. Afidated website. Accessed March 15, 2024. https://www.afidated.com/2014/07/how-to-identify-dependent-and.html  
• Choosing data visualizations. Math for the people website. Accessed March 14, 2024. https://web.stevenson.edu/mbranson/m4tp/version1/environmental-racism-choosing-data-visualization.html  
• Trivedi C. Types of variables in scientific research. Concepts Hacked website. Accessed March 15, 2024. https://conceptshacked.com/variables-in-scientific-research/  
• Variables in experimental and non-experimental research. Statistics solutions website. Accessed March 14, 2024. https://www.statisticssolutions.com/variables-in-experimental-and-non-experimental-research/#:~:text=The%20independent%20variable%20would%20be,state%20instead%20of%20manipulating%20them .  
• Dependent vs independent variables: 11 key differences. Formplus website. Accessed March 15, 2024. https://www.formpl.us/blog/dependent-independent-variables

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Stats and R

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

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

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

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

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

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

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

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

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

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

In statistics, variables are classified into 4 different types:

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

Quantitative

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

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

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

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

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

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

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

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

Qualitative

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

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

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

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

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

A nominal variable can have:

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

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

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

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

Variable transformations

There are two main variable transformations:

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

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

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

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

Additional notes

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

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

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

Thanks for reading.

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

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

Related articles

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

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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

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• v.10(1); Jan-Feb 2019

Types of Variables, Descriptive Statistics, and Sample Size

Feroze kaliyadan.

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

Vinay Kulkarni

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

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

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

Quantitative vs qualitative

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

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

Quantitative variables can be either discrete or continuous

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

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

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

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

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

Dependent and independent variables

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

Descriptive Statistics

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

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

Descriptive statistics can be broadly put under two categories:

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

Sorting and grouping

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

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

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

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

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

Stem and leaf plot

Illustration/visual display of data

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

Composite bar chart

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

Scatter diagram

Summary statistics

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

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

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

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

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

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

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

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

Location of mode, median, and mean

Measures of dispersion

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

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

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

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

The box plot

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

The concept of skewness and kurtosis

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

Positive skew

High kurtosis (positive kurtosis – also called leptokurtic)

Negative skew

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

Sample Size

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

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

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

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

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

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

Effect size and minimal clinically relevant difference

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

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

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

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

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

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

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Types of Variables in Research – Definition & Examples

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A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design . This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes.

Inhaltsverzeichnis

• 1 Types of Variables in Research – In a Nutshell
• 2 Definition: Types of variables in research
• 3 Types of variables in research – Quantitative vs. Categorical
• 4 Types of variables in research – Independent vs. Dependent
• 5 Other useful types of variables in research

Types of Variables in Research – In a Nutshell

• A variable is an attribute of an item of analysis in research.
• The types of variables in research can be categorized into: independent vs. dependent , or categorical vs. quantitative .
• The types of variables in research (correlational) can be classified into predictor or outcome variables.
• Other types of variables in research are confounding variables , latent variables , and composite variables.

Definition: Types of variables in research

A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas , or other research objects . There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study.

Note that the correct variable will help with your research design , test selection, and result interpretation.

In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors.

Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents.

Types of variables in research – Quantitative vs. Categorical

Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes.

Quantitative or numerical data represents amounts, while categorical data represents collections or groupings.

The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better.

Quantitative variables

The scores you record when collecting quantitative data usually represent real values you can add, divide , subtract , or multiply . There are two types of quantitative variables: discrete variables and continuous variables .

The table below explains the elements that set apart discrete and continuous types of variables in research:

Categorical variables

Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers . However, the numbers represent categories instead of real amounts.

There are three categorical types of variables in research: nominal variables, ordinal variables , and binary variables . Here is a tabular summary.

It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete.

Data sheet of quantitative and categorical variables

A data sheet is where you record the data on the variables in your experiment.

In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet.

The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process.

Below is an example of a data sheet containing binary, nominal, continuous , and ordinal types of variables in research.

Types of variables in research – Independent vs. Dependent

The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival.

Therefore, the researcher manipulates the independent variables and measures the dependent variables . Additionally, you may have control variables that you hold constant.

The table below summarizes independent variables, dependent variables , and control variables .

Data sheet of independent and dependent variables

In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent.

Below is a data sheet based on our experiment:

Types of variables in correlational research

The types of variables in research may differ depending on the study.

In correlational research , dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables.

However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable.

Other useful types of variables in research

The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study.

Below are other types of variables in research worth understanding.

What is the definition for independent and dependent variables?

An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study.

What are quantitative and categorical variables?

Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design.

Discrete and continuous variables: What is their difference?

Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight.

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RESEARCH VARIABLES: TYPES, USES AND DEFINITION OF TERMS

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15 Independent and Dependent Variable Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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An independent variable (IV) is what is manipulated in a scientific experiment to determine its effect on the dependent variable (DV).

By varying the level of the independent variable and observing associated changes in the dependent variable, a researcher can conclude whether the independent variable affects the dependent variable or not.

This can provide very valuable information when studying just about any subject.

Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.

The term causation is vitally important. Scientists want to know what causes changes in the dependent variable. The only way to do that is to manipulate the independent variable and observe any changes in the dependent variable.

Definition of Independent and Dependent Variables

The independent variable and dependent variable are used in a very specific type of scientific study called the experiment .

Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.

The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).

Other variations of an experiment might include having multiple levels of the independent variable.

If the independent variable affects the dependent variable, then it should be possible to observe changes in the dependent variable based on the presence or absence of the independent variable.  

Of course, there are a lot of issues to consider when conducting an experiment, but these are the basic principles.

These concepts should not be confused with predictor and outcome variables .

Examples of Independent and Dependent Variables

1. gatorade and improved athletic performance.

A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.

If they can back up that claim with hard scientific data, that would be great for sales.

So, the researcher goes to a nearby university and randomly selects both male and female athletes from several sports: track and field, volleyball, basketball, and football. Each athlete will run on a treadmill for one hour while their heart rate is tracked.

All of the athletes are given the exact same amount of liquid to consume 30-minutes before and during their run. Half are given Gatorade, and the other half are given water, but no one knows what they are given because both liquids have been colored.

In this example, the independent variable is Gatorade, and the dependent variable is heart rate.  

2. Chemotherapy and Cancer

A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression.

The patients are randomly assigned to one of three groups: one group receives no chemotherapy, one group receives a low dose of chemotherapy, and one group receives a high dose of chemotherapy.

Each group receives chemotherapy treatment three times a week for two months, except for the no-treatment group. At the end of two months, the doctors measure the size of each patient’s tumor.

In this study, despite the ethical issues (remember this is just a hypothetical example), the independent variable is chemotherapy, and the dependent variable is tumor size.

3. Interior Design Color and Eating Rate

A well-known fast-food corporation wants to know if the color of the interior of their restaurants will affect how fast people eat. Of course, they would prefer that consumers enter and exit quickly to increase sales volume and profit.

So, they rent space in a large shopping mall and create three different simulated restaurant interiors of different colors. One room is painted mostly white with red trim and seats; one room is painted mostly white with blue trim and seats; and one room is painted mostly white with off-white trim and seats.

Next, they randomly select shoppers on Saturdays and Sundays to eat for free in one of the three rooms. Each shopper is given a box of the same food and drink items and sent to one of the rooms. The researchers record how much time elapses from the moment they enter the room to the moment they leave.

The independent variable is the color of the room, and the dependent variable is the amount of time spent in the room eating.

4. Hair Color and Attraction

A large multinational cosmetics company wants to know if the color of a woman’s hair affects the level of perceived attractiveness in males. So, they use Photoshop to manipulate the same image of a female by altering the color of her hair: blonde, brunette, red, and brown.

Next, they randomly select university males to enter their testing facilities. Each participant sits in front of a computer screen and responds to questions on a survey. At the end of the survey, the screen shows one of the photos of the female.

At the same time, software on the computer that utilizes the computer’s camera is measuring each male’s pupil dilation. The researchers believe that larger dilation indicates greater perceived attractiveness.

The independent variable is hair color, and the dependent variable is pupil dilation.

5. Mozart and Math

After many claims that listening to Mozart will make you smarter, a group of education specialists decides to put it to the test. So, first, they go to a nearby school in a middle-class neighborhood.

During the first three months of the academic year, they randomly select some 5th-grade classrooms to listen to Mozart during their lessons and exams. Other 5 th grade classrooms will not listen to any music during their lessons and exams.

The researchers then compare the scores of the exams between the two groups of classrooms.

Although there are a lot of obvious limitations to this hypothetical, it is the first step.

The independent variable is Mozart, and the dependent variable is exam scores.

6. Essential Oils and Sleep

A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. They hire a sleep research lab to conduct the study. The researchers at the lab have their usual test volunteers sleep in individual rooms every night for one week.

The conditions of each room are all exactly the same, except that half of the rooms have lavender released into the rooms and half do not. While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment.

At the end of the study, the researchers compare the total amount of time spent in deep sleep of the lavender-room participants with the no lavender-room participants.

The independent variable in this sleep study is lavender, and the dependent variable is the total amount of time spent in deep sleep.

7. Teaching Style and Learning

A group of teachers is interested in which teaching method will work best for developing critical thinking skills.

So, they train a group of teachers in three different teaching styles : teacher-centered, where the teacher tells the students all about critical thinking; student-centered, where the students practice critical thinking and receive teacher feedback; and AI-assisted teaching, where the teacher uses a special software program to teach critical thinking.

At the end of three months, all the students take the same test that assesses critical thinking skills. The teachers then compare the scores of each of the three groups of students.

The independent variable is the teaching method, and the dependent variable is performance on the critical thinking test.

8. Concrete Mix and Bridge Strength

A chemicals company has developed three different versions of their concrete mix. Each version contains a different blend of specially developed chemicals. The company wants to know which version is the strongest.

So, they create three bridge molds that are identical in every way. They fill each mold with one of the different concrete mixtures. Next, they test the strength of each bridge by placing progressively more weight on its center until the bridge collapses.

In this study, the independent variable is the concrete mixture, and the dependent variable is the amount of weight at collapse.

9. Recipe and Consumer Preferences

People in the pizza business know that the crust is key. Many companies, large and small, will keep their recipe a top secret. Before rolling out a new type of crust, the company decides to conduct some research on consumer preferences.

The company has prepared three versions of their crust that vary in crunchiness, they are: a little crunchy, very crunchy, and super crunchy. They already have a pool of consumers that fit their customer profile and they often use them for testing.

Each participant sits in a booth and takes a bite of one version of the crust. They then indicate how much they liked it by pressing one of 5 buttons: didn’t like at all, liked, somewhat liked, liked very much, loved it.

The independent variable is the level of crust crunchiness, and the dependent variable is how much it was liked.

10. Protein Supplements and Muscle Mass

A large food company is considering entering the health and nutrition sector. Their R&D food scientists have developed a protein supplement that is designed to help build muscle mass for people that work out regularly.

The company approaches several gyms near its headquarters. They enlist the cooperation of over 120 gym rats that work out 5 days a week. Their muscle mass is measured, and only those with a lower level are selected for the study, leaving a total of 80 study participants.

They randomly assign half of the participants to take the recommended dosage of their supplement every day for three months after each workout. The other half takes the same amount of something that looks the same but actually does nothing to the body.

At the end of three months, the muscle mass of all participants is measured.

The independent variable is the supplement, and the dependent variable is muscle mass.  

11. Air Bags and Skull Fractures

In the early days of airbags , automobile companies conducted a great deal of testing. At first, many people in the industry didn’t think airbags would be effective at all. Fortunately, there was a way to test this theory objectively.

In a representative example: Several crash cars were outfitted with an airbag, and an equal number were not. All crash cars were of the same make, year, and model. Then the crash experts rammed each car into a crash wall at the same speed. Sensors on the crash dummy skulls allowed for a scientific analysis of how much damage a human skull would incur.

The amount of skull damage of dummies in cars with airbags was then compared with those without airbags.

The independent variable was the airbag and the dependent variable was the amount of skull damage.

12. Vitamins and Health

Some people take vitamins every day. A group of health scientists decides to conduct a study to determine if taking vitamins improves health.

They randomly select 1,000 people that are relatively similar in terms of their physical health. The key word here is “similar.”

Because the scientists have an unlimited budget (and because this is a hypothetical example, all of the participants have the same meals delivered to their homes (breakfast, lunch, and dinner), every day for one year.

In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The other half do not take the vitamins.

At the end of one year, the health of all participants is assessed, using blood pressure and cholesterol level as the key measurements.

In this highly unrealistic study, the independent variable is vitamins, and the dependent variable is health, as measured by blood pressure and cholesterol levels.

13. Meditation and Stress

Does practicing meditation reduce stress? If you have ever wondered if this is true or not, then you are in luck because there is a way to know one way or the other.

All we have to do is find 90 people that are similar in age, stress levels, diet and exercise, and as many other factors as we can think of.

Next, we randomly assign each person to either practice meditation every day, three days a week, or not at all. After three months, we measure the stress levels of each person and compare the groups.

How should we measure stress? Well, there are a lot of ways. We could measure blood pressure, or the amount of the stress hormone cortisol in their blood, or by using a paper and pencil measure such as a questionnaire that asks them how much stress they feel.

In this study, the independent variable is meditation and the dependent variable is the amount of stress (however it is measured).

14. Video Games and Aggression

When video games started to become increasingly graphic, it was a huge concern in many countries in the world. Educators, social scientists, and parents were shocked at how graphic games were becoming.

Since then, there have been hundreds of studies conducted by psychologists and other researchers. A lot of those studies used an experimental design that involved males of various ages randomly assigned to play a graphic or non-graphic video game.

Afterward, their level of aggression was measured via a wide range of methods, including direct observations of their behavior, their actions when given the opportunity to be aggressive, or a variety of other measures.

So many studies have used so many different ways of measuring aggression.

In these experimental studies, the independent variable was graphic video games, and the dependent variable was observed level of aggression.

15. Vehicle Exhaust and Cognitive Performance

Car pollution is a concern for a lot of reasons. In addition to being bad for the environment, car exhaust may cause damage to the brain and impair cognitive performance.

One way to examine this possibility would be to conduct an animal study. The research would look something like this: laboratory rats would be raised in three different rooms that varied in the degree of car exhaust circulating in the room: no exhaust, little exhaust, or a lot of exhaust.

After a certain period of time, perhaps several months, the effects on cognitive performance could be measured.

One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy.

In this animal study, the independent variable would be car exhaust and the dependent variable would be amount of time to run a maze.

Read Next: Extraneous Variables Examples

The experiment is an incredibly valuable way to answer scientific questions regarding the cause and effect of certain variables. By manipulating the level of an independent variable and observing corresponding changes in a dependent variable, scientists can gain an understanding of many phenomena.

For example, scientists can learn if graphic video games make people more aggressive, if mediation reduces stress, if Gatorade improves athletic performance, and even if certain medical treatments can cure cancer.

The determination of causality is the key benefit of manipulating the independent variable and them observing changes in the dependent variable. Other research methodologies can reveal factors that are related to the dependent variable or associated with the dependent variable, but only when the independent variable is controlled by the researcher can causality be determined.

Ferguson, C. J. (2010). Blazing Angels or Resident Evil? Can graphic video games be a force for good? Review of General Psychology, 14 (2), 68-81. https://doi.org/10.1037/a0018941

Flannelly, L. T., Flannelly, K. J., & Jankowski, K. R. (2014). Independent, dependent, and other variables in healthcare and chaplaincy research. Journal of Health Care Chaplaincy , 20 (4), 161–170. https://doi.org/10.1080/08854726.2014.959374

Manocha, R., Black, D., Sarris, J., & Stough, C.(2011). A randomized, controlled trial of meditation for work stress, anxiety and depressed mood in full-time workers. Evidence-Based Complementary and Alternative Medicine , vol. 2011, Article ID 960583. https://doi.org/10.1155/2011/960583

Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work (Reading, Mass.) , 22 (3), 255–260.

Taylor, J. M., & Rowe, B. J. (2012). The “Mozart Effect” and the mathematical connection, Journal of College Reading and Learning, 42 (2), 51-66.  https://doi.org/10.1080/10790195.2012.10850354

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Variables in Research

In the realm of research, particularly in mathematics and the sciences understanding the concept of the variables is fundamental. The Variables are integral to the formulation of hypotheses the design of the experiments and interpretation of data. They serve as the building blocks for the mathematical models and statistical analyses making it possible to describe, analyze and predict phenomena.

This article aims to provide a comprehensive overview of the variables in the research explaining their significance, types and roles. By the end of this article, students and researchers will have a clearer understanding of how to identify, use and interpret variables in their research projects.

Table of Content

What are Variables?

Types of variables, independent variables, dependent variables, control variables, extraneous variables, moderator variables, mediator variables.

Variables are elements that can change or vary within the experiment or study. They can represent different types of data such as numerical values, categories or even qualitative attributes. In mathematical terms, variables are symbols that can assume different values.

Various types of variables are:

Definition: Variables that are manipulated or controlled in an experiment to observe their effect on other variables.

Example: In a study examining the effect of study time on the test scores the amount of the study time is the independent variable.

Definition: Variables that are measured or observed in response to the changes in the independent variable.

Example: In the same study the test scores are the dependent variable.

Definition: Variables that are kept constant to ensure that the results are due to the manipulation of the independent variable.

Example: The study environment could be a control variable in the study on the study time and test scores.

Definition: Variables that are not intentionally studied but could affect the outcome of the experiment.

Example: The amount of the sleep students get before the test could be an extraneous variable.

Definition: V ariables that influence the strength or direction of the relationship between independent and dependent variables.

Example: The difficulty of the test could be a moderator variable affecting the relationship between the study time and test scores.

Definition: Variables that explain the process through which the independent variable affects the dependent variable.

Example: The level of the understanding of the material could be a mediator variable in the study on study time and test scores.

Role of Variables in Research

Variables are crucial in research for the several reasons:

• Formulating Hypotheses: Variables help in defining and formulating hypotheses in which are essential for the conducting experiments and studies.
• Designing Experiments: Variables determine the structure and design of the experiments guiding the procedures for the data collection and analysis.
• Analyzing Data: Understanding the relationships between the variables is key to the analyzing data and drawing meaningful conclusions from the research.
• Predicting Outcomes: Variables are used in mathematical models to the predict outcomes and make informed decisions based on the data.

Example: Using Variables in a Mathematical Research Study

Let’s consider a study investigating the relationship between the number of the hours spent practicing a mathematical problem and the performance on the test.

• Independent Variable: Number of hours spent practicing.
• Dependent Variable: Test performance (score).
• Control Variable: Type of the mathematical problems practiced.
• Extraneous Variable: Prior knowledge of the subject.
• Moderator Variable: Complexity of the problems.
• Mediator Variable: Confidence level of the student.

Step-by-Step Example

Formulating the Hypothesis: “Increasing the number of hours spent practicing mathematical problems will improve the test performance.”

• Designing Experiment: Students are divided into groups based on the different practice hours (1 hour, 2 hours, 3 hours).
• Data Collection: Test scores are collected after the practice sessions.
• Data Analysis: The relationship between the practice hours and test scores is analyzed using the statistical methods.
• Conclusion: The findings are used to the draw conclusions about the impact of the practice on test performance.

Visualizing Data

Visualizing data helps in understanding the relationships between the variables. Here are some common methods:

• Scatter Plots: To visualize the relationship between the two continuous variables.
• Bar Charts: To compare the means of the different groups.
• Histograms: To show the distribution of the single variable.

Questions on Variables in Research

Question 1: In a study examining the effect of the sleep on the academic performance identify the independent, dependent and control variables.

Independent Variable: Amount of sleep. Dependent Variable: Academic performance (grades). Control Variable: Study environment, type of the academic tasks.

Question 2: Explain how an extraneous variable can affect the outcome of an experiment.

An extraneous variable such as the amount of the caffeine consumed could affect the academic performance of the students in a study examining the effect of sleep on the academic performance. If not controlled it could confound the results by the influencing the dependent variable independently of the independent variable.

Question 3: Describe how you would control for extraneous variables in a study.

To control for the extraneous variables researchers can use the random assignment ensure consistent conditions or include the extraneous variables in the statistical analysis to the account for their potential impact.

Practice Questions on Variables in Research

Q1: How can you identify the independent variable in a given research study?

Q2: What steps can you take to ensure that control variables are effectively managed in an experiment?

Q3: How does the presence of extraneous variables impact the validity of research findings?

Q4: In what ways can a moderator variable affect the relationship between independent and dependent variables?

Q5: What are some common methods for visualizing the relationship between independent and dependent variables?

Q6: How can you determine if a variable should be classified as a mediator in your research?

Q7: What are the key differences between categorical and continuous variables, and how do they influence data analysis?

Q8: How do you formulate a hypothesis involving multiple variables in a complex study?

Q9: What strategies can be employed to reduce the impact of extraneous variables in field research?

Q10: How can statistical methods be used to account for control variables in the analysis of research data?

Variables are the cornerstone of the research in mathematics and other sciences. They allow researchers to the formulate hypotheses, design experiments analyze data and draw meaningful conclusions. By understanding and effectively managing different types of the variables researchers can enhance the validity and reliability of their studies.

Concept of variable and Raw data Dependent and Independent variable

FAQs on Variables in Research

What is an independent variable.

An independent variable is the variable that is manipulated in an experiment to the observe its effect on the dependent variable.

What is a dependent variable?

A dependent variable is the variable that is measured or observed in the response to the changes in the independent variable.

Why are control variables important?

Control variables are important because they help ensure that the results of an experiment are due to the manipulation of the independent variable and not other factors.

What is the difference between moderator and mediator variables?

Moderator variables influence the strength or direction of the relationship between the independent and dependent variables while mediator variables explain the process through which the independent variable affects the dependent variable.

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You’re on a business trip in Oakland, CA. You've been working late in downtown and now you're looking for a place nearby to grab a late dinner. You decided to check Zomato to try and find somewhere to eat. (Don't begin searching yet).

• Look around on the home page. Does anything seem interesting to you?
• How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
• What do the reviews say about the restaurant you've chosen?
• What was the most important factor for you in choosing this spot?
• You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
• Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
• Now go to any restaurant's page and try to leave a review (don't actually submit it).

What was the worst thing about your experience?

It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer

What other aspects of the experience could be improved?

Feedback from the owners would be nice

What did you like about the website?

The flow was good, lots of bright photos

What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.

• Please begin by downloading the app to your device.
• Choose Italian and get started with the first lesson (stop once you reach the first question).
• Now go all the way through the rest of the first lesson, describing your thoughts as you go.
• Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
• After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
• What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.

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What is Explanatory Research? Definition, Method and Examples

What is Explanatory Research?

Explanatory research is defined as a type of research designed to explain the reasons behind a phenomenon or the relationships between variables. Unlike exploratory research, which seeks to understand and identify new aspects of a topic, explanatory research aims to clarify how and why certain variables influence each other. It is often used to test theories and establish causal relationships, providing a deeper understanding of the underlying mechanisms at play.

In explanatory research, researchers use structured methodologies to investigate hypotheses about causal relationships. This type of research often involves quantitative methods, such as experiments or surveys, to test these hypotheses and determine the strength and nature of the relationships between variables. By employing statistical analysis and controlled experimentation, explanatory research can identify causal links and provide evidence-based explanations for observed phenomena.

For instance, consider a study investigating the impact of employee motivation on productivity in a company. Explanatory research would aim to determine not only whether there is a relationship between motivation and productivity but also how motivation influences productivity. Researchers might conduct controlled experiments or use statistical models to measure the effect of different motivational strategies on employee output, thereby providing a clearer understanding of the causal relationship between these variables.

Key Characteristics of Explanatory Research

Explanatory research is characterized by several key features that differentiate it from other types of research. These characteristics include:

1. Causal Analysis

Explanatory research focuses on understanding and establishing causal relationships between variables. It aims to determine how changes in one variable (independent variable) affect another variable (dependent variable). This involves identifying the mechanisms or processes through which these causal effects occur.

2. Hypothesis Testing

This type of research typically involves formulating and testing hypotheses to validate or refute theoretical propositions. Researchers design studies to test specific predictions about how variables interact, often using statistical methods to assess the strength and significance of these relationships.

3. Quantitative Methods

Explanatory research often employs quantitative methods, such as experiments, surveys, and statistical analysis. These methods provide a structured approach to data collection and analysis, enabling researchers to measure the impact of independent variables on dependent variables with precision and reliability.

4. Controlled and Systematic Approach

To ensure accurate results, explanatory research involves a controlled and systematic approach to data collection and analysis. Researchers use experimental designs, control groups, and randomization to minimize biases and isolate the effects of the independent variable on the dependent variable.

5. Theory Testing and Validation

Explanatory research is instrumental in testing and validating theories or models. By providing empirical evidence on causal relationships, it helps in refining or challenging existing theories and contributes to the development of new theoretical frameworks.

Explanatory Research Method: Key Stages with Examples

Explanatory research involves several key stages to systematically investigate causal relationships and provide a deeper understanding of how and why variables are related. Here’s an outline of these stages with examples for clarity:

1. Defining the Research Problem and Hypotheses

• Identify the Issue: Clearly define the problem or phenomenon that needs explanation. This involves understanding what specific causal relationships you aim to explore.
• Formulate Hypotheses: Develop hypotheses that propose a causal link between variables.

Example: If researching how employee motivation affects productivity, the hypothesis might be: “Increased employee motivation leads to higher productivity.”

2. Designing the Research Methodology

• Select Research Design: Common designs include experiments, longitudinal studies, and controlled surveys.
• Define Variables: Identify and define the independent (predictor) and dependent (outcome) variables in the study. Also, determine any control variables needed to isolate the effects of the independent variable.

Example: In an experiment, you might design a study where one group of employees receives a new motivational incentive (independent variable), and their productivity (dependent variable) is measured over time versus a control group who did not receive the incentive.

3. Data Collection

• Gather Data: Collect data using methods consistent with your research design.
• Ensure Validity and Reliability: Use reliable and valid measures to ensure the data accurately represents the variables being studied.

Example: Administer a productivity survey to employees before and after the introduction of the motivational incentive, ensuring that the survey questions are consistently applied and valid for measuring productivity.

4. Data Analysis

• Analyze Data: Use statistical methods to test the hypotheses and examine the relationships between variables. Techniques such as regression analysis, ANOVA, or structural equation modeling may be employed to determine causal links.
• Interpret Results: Evaluate the results in the context of the hypotheses. Assess whether the data supports the proposed causal relationships and consider the implications for the theory or model being tested.

Example: Perform a regression analysis to determine if there is a statistically significant relationship between the motivational incentive and changes in productivity. Analyze the data to see if the increased motivation leads to measurable improvements in productivity.

5. Reporting and Drawing Conclusions

• Compile Findings: Prepare a detailed report of the research findings, including the methodology, data analysis, and results. Discuss how the findings support or refute the hypotheses.
• Draw Conclusions: Based on the results, draw conclusions about the causal relationships between the variables. Consider the implications for theory, practice, and future research.

Example: Conclude that the motivational incentive has a positive effect on productivity based on the data analysis. Discuss how these findings support the hypothesis and suggest potential applications for improving employee performance in the workplace.

6. Review and Refinement

• Evaluate the Study: Review the research process to identify any limitations or areas for improvement. Consider how the study could be refined or expanded in future research.
• Suggest Future Research: Based on the findings, propose additional research to explore related questions or further validate the results.

Example: Reflect on whether the study’s design could be improved, such as including more diverse samples or different motivational strategies, and suggest future research to explore these aspects.

Best Practices for Explanatory Research in 2024

To conduct effective explanatory research in 2024, adhering to best practices ensures robust, reliable, and insightful findings. Here are the key best practices:

1. Utilize Advanced Statistical Techniques

• Modern Analytical Tools: Employ advanced statistical methods and software, such as machine learning algorithms, to analyze complex datasets and uncover causal relationships. Techniques like structural equation modeling (SEM) and causal inference methods can provide deeper insights into the data.
• Big Data Integration: Integrate big data analytics to handle large volumes of data from various sources. This approach can reveal patterns and causal links that might not be apparent with smaller datasets.

2. Implement Rigorous Experimental Designs

• Controlled Experiments: Design experiments with control groups, randomization, and manipulation of independent variables to ensure robust causal inference. Use random assignment to minimize biases and enhance the validity of results.
• Longitudinal Studies: Consider longitudinal designs to track changes over time and establish temporal causality. This approach helps in understanding how variables influence each other over extended periods.

3. Ensure Data Quality and Integrity

• Accurate Measurement: Use validated instruments and measures to ensure the accuracy and reliability of data collection. Regularly calibrate tools and check the consistency of measurements.
• Data Cleaning: Implement thorough data cleaning processes to address missing values, outliers, and inconsistencies. Ensuring data integrity is crucial for valid analysis and conclusions.

4. Adopt a Multi-Method Approach

• Combining Quantitative and Qualitative Methods: Use a mixed-methods approach to enrich explanatory research. Qualitative data can provide context and insights into quantitative findings, offering a more comprehensive understanding of causal relationships.

5. Focus on Ethical Standards

• Informed Consent and Transparency: Ensure participants provide informed consent and are aware of how their data will be used. Maintain transparency about the research’s purpose, funding, and potential conflicts of interest.
• Data Protection: Adhere to relevant data protection regulations and best practices.

6. Maintain Flexibility and Adaptability

• Iterative Refinement: Be prepared to adapt research designs and hypotheses based on preliminary findings. An iterative approach allows for adjustments and refinements that improve the research’s relevance and accuracy.
• Responsive to New Insights: Stay open to emerging trends and insights that may require modifications to the research approach or hypotheses.

7. Clear and Effective Communication

• Detailed Reporting: Provide comprehensive reports that clearly explain the methodology, data analysis, and conclusions. Use visualizations and accessible language to communicate findings effectively.
• Stakeholder Engagement: Present findings to relevant stakeholders and discuss implications for practice or policy. Engaging with stakeholders ensures that research results are used effectively and have a real-world impact.

8. Continuous Learning and Improvement

• Stay Updated: Keep abreast of the latest developments in research methodologies and technologies. Incorporate new techniques and best practices into research designs.
• Reflect and Revise: Regularly review research processes and outcomes to identify areas for improvement. Incorporate feedback and lessons learned into future research endeavors.

Interested in learning more about the fields of product, research, and design? Search our articles here for helpful information spanning a wide range of topics!

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Title Page Setup

A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.

Student title page

The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.

Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6

Related handouts

• Student Title Page Guide (PDF, 263KB)
• Student Paper Setup Guide (PDF, 3MB)

Student papers do not include a running head unless requested by the instructor or institution.

Follow the guidelines described next to format each element of the student title page.

Professional title page

The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.

Follow the guidelines described next to format each element of the professional title page.

• Return to Zillow.com

What is a Zestimate?

The Zestimate® home valuation model is Zillow’s estimate of a home’s market value. A Zestimate incorporates public, MLS and user-submitted data into Zillow’s proprietary formula, also taking into account home facts, location and market trends. It is not an appraisal and can’t be used in place of an appraisal.

How accurate is the Zestimate?

The nationwide median error rate for the Zestimate for on-market homes is 2.4%, while the Zestimate for off-market homes has a median error rate of 7.49%. The Zestimate’s accuracy depends on the availability of data in a home’s area. Some areas have more detailed home information available — such as square footage and number of bedrooms or bathrooms — and others do not. The more data available, the more accurate the Zestimate value will be. 

These tables break down the accuracy of Zestimates for both active listings and off-market listings.

Active listings accuracy

Last updated: April 27, 2023

Note: The Zestimate’s accuracy is computed by comparing the final sale price to the Zestimate that was published on or just prior to the sale date.

Download an Excel spreadsheet of this data .

How is the Zestimate calculated?

Zillow publishes Zestimate home valuations for 104 million homes across the country, and uses state of the art statistical and machine learning models that can examine hundreds of data points for each individual home.

To calculate a Zestimate, Zillow uses a sophisticated neural network-based model that incorporates data from county and tax assessor records and direct feeds from hundreds of multiple listing services and brokerages. The Zestimate also incorporates:

• Home characteristics including square footage, location or the number of bathrooms.
• On-market data such as listing price, description, comparable homes in the area and days on the market
• Off-market data — tax assessments, prior sales and other publicly available records
• Market trends, including seasonal changes in demand

Currently, we have data for over 110 million U.S. homes and we publish Zestimates for 104 million of them.

What changes are in the latest Zestimate?

The latest Zestimate model is our most accurate Zestimate yet. It’s based on a neural network model and uses even more historical data to produce off-market home valuations. This means the Zestimate is more responsive to market trends & seasonality that may affect a home’s market value. We also reduced overall errors and processing time in the Zestimate.

My Zestimate seems too low or too high. What gives?

The amount of data we have for your home and homes in your area directly affects the Zestimate’s accuracy, including the amount of demand in your area for homes. If the data is incorrect or incomplete, update your home facts — this may affect your Zestimate. To ensure the most accurate Zestimate, consider reporting any home updates to your local tax assessor. Unreported additions, updates and remodels aren’t reflected in the Zestimate.

Check that your tax history and price history (the sale price and date you bought your home) are accurate on Zillow. If data is missing or incorrect, let us know .

Be aware that the model that creates the Zestimate factors in changing market trends, including seasonal fluctuations in demand. So in some cases that may be the reason for a change in your Zestimate.

I just listed my home for sale. Why did my Zestimate change?

When a home goes on the market, new data can be incorporated into the Zestimate algorithm. In the simplest terms, the Zestimate for on-market homes includes listing data that provides valuable signals about the home’s eventual sale price. This data isn’t available for off-market homes.

My home is on the market. Why is the Zestimate so far off?

Properties that have been listed for a full year transition to off-market valuations because they have been listed longer than normal for that local market. This can result in a large difference between the list price and the Zestimate.

I just changed my home facts. When will my Zestimate update?

Updates to your home facts are factored into the Zestimate. However, if the updates are not significant enough to affect the home’s value (eg: paint colors), your Zestimate may not change. Zestimates for all homes update multiple times per week, but on rare occasions this schedule is interrupted by algorithmic changes or new analytical features.

How are changes to my home facts (like an additional bedroom or bathroom) valued?

The Zestimate is based on complex and proprietary algorithms that can incorporate millions of data points. The algorithms determine the approximate added value that an additional bedroom or bathroom contributes, though the amount of the change depends on many factors, including local market trends, location and other home facts.

Is the Zestimate an appraisal?

No. The Zestimate is not an appraisal and can’t be used in place of an appraisal. It is a computer-generated estimate of the value of a home today, given the available data.

We encourage buyers, sellers and homeowners to supplement the Zestimate with other research, such as visiting the home, getting a professional appraisal of the home, or requesting a comparative market analysis (CMA) from a real estate agent.

Why do I see home values for the past?

We generate historical Zestimates for most homes if we have sufficient data to do so.

Do you ever change historical Zestimates?

We occasionally recalculate historical Zestimate values along with major data upgrades or improvements to the algorithm.  These recalculations are based on a variety of considerations and, therefore, not every new algorithm release will get a corresponding update of historical values.

However, we never allow future information to influence a historical Zestimate (for example, a sale in 2019 could not influence a 2018 Zestimate). Historical Zestimates only use information known prior to the date of that Zestimate.

Does the Zestimate algorithm ever change?

Yes — Zillow’s team of researchers and engineers work every day to make the Zestimate more accurate. Since Zillow’s founding in 2006, we have deployed multiple major Zestimate algorithm updates and other incremental improvements are consistently released between major upgrades.

How often are Zestimates for homes updated?

We refresh Zestimates for all homes multiple times per week, but on rare occasions this schedule is interrupted by algorithmic changes or new analytical features.

Are foreclosure sales included in the Zestimate algorithm?

No. The Zestimate is intended to provide an estimate of the price that a home would fetch if sold for its full value, where the sale isn’t for partial ownership of the property or between family members. Our extensive analysis of foreclosure resale transactions supports the conclusion that these sales are generally made at substantial discounts compared to non-foreclosure sales. For this reason, the Zestimate does not incorporate data about these sales.

Who calculates the Zestimate? Can someone tamper with my home’s Zestimate?

The Zestimate is an automated valuation model calculated by a software process. It’s not possible to manually alter the Zestimate for a specific property.

Can the Zestimate be updated?

Yes. The Zestimate’s accuracy depends on the amount of data we have for the home. Public records can be outdated or lag behind what homeowners and real estate agents know about a property, so it’s best to update your home facts and fix any incorrect or incomplete information — this will help make your Zestimate as accurate as possible.

You can also add info about the architectural style, roof type, heat source, building amenities and more. Remember: updating home information doesn’t guarantee an increase in the value of Zestimate, but will increase the Zestimate’s accuracy.

Does Zillow delete Zestimates? Can I have my Zestimate reviewed if I believe there are errors?

We do not delete Zestimates. However, for some homes we may not have enough data to provide a home valuation that meets our standards for accuracy. In these instances, we do not publish the Zestimate until more data can be obtained. The Zestimate is designed to be a neutral estimate of the fair market value of a home, based on publicly available and user-submitted data. For this purpose, it is important that the Zestimate is based on information about all homes (e.g., beds, baths, square footage, lot size, tax assessment, prior sale price) and that the algorithm itself is consistently applied to all homes in a similar manner.

I don’t know of any homes that have sold recently in my area. How are you calculating my Zestimate?

Zestimates rely on much more than comparable sales in a given area. The home’s physical attributes, historical information and on-market data all factor into the final calculation. The more we know about homes in an area (including your home), the better the Zestimate. Our models can find neighborhoods similar to yours and use sales in those areas to extrapolate trends in your housing market. Our estimating method differs from that of a comparative market analysis completed by a real estate agent. We use data from a geographical area that is much larger than your neighborhood — up to the size of a county — to help calculate the Zestimate. Though there may not be any recent sales in your neighborhood, even a few sales in the area allow us to extrapolate trends in the local housing market.

I’m trying to sell my home and I think my Zestimate should be higher.

The Zestimate was created to give customers more information about homes and the housing market. It is intended to provide user-friendly data to promote transparent real estate markets and allow people to make more informed decisions — it should not be used to drive up the price of a home. Zestimates are designed to track the market, not drive it.

Can I use the Zestimate to get a loan?

No. The Zestimate is an automated value model and not an appraisal. Most lending professionals and institutions will only use professional appraisals when making loan-related decisions.

I have two Zestimates for my home. How do I fix this?

If you see two Zestimates for the same property, please let us know by visiting the Zillow Help Center and s e lecting Submit a request. You may see more than one Zestimate for your address if you are a homeowner with multiple parcels of land. Zillow matches the parcels on record with the county. If you officially combine parcels, the county will send us updated information.

What’s the Estimated Sale Range?

While the Zestimate is the estimated market value for an individual home, the Estimated Sale Range describes the range in which a sale price is predicted to fall, including low and high estimated values. For example, a Zestimate may be $260,503, while the Estimated Sale Range is $226,638 to $307,394. This range can vary for different homes and regions. A wider range generally indicates a more uncertain Zestimate, which might be the result of unique home factors or less data available for the region or that particular home. It’s important to consider the size of the Estimated Sale Range because it offers important context about the Zestimate’s anticipated accuracy.

How can real estate professionals work with the Zestimate?

Millions of consumers visit Zillow every month. When combined with the guidance of real estate professionals, the Zestimate can help consumers make more informed financial decisions about their homes. Real estate professionals can also help their clients claim their home on Zillow, update the home facts and account for any work they have done on the property. A home’s Zillow listing is often the first impression for prospective buyers, and accurate information helps attract interest.

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Empirical Assessments of Wildfire-Treatment Outcomes

This research seeks to answer the following question: How do different variables – for example climate, weather, vegetation/fuels, topography, treatment characteristics – increase the likelihood that fuel treatments will achieve a particular outcome when they are confronted by a wildfire? This approach will allow us to learn from the many past fuels treatments that have been burned by wildfires and use that learning to inform our strategies for future fuels treatment investments. 

• Publications

As a warming climate increases the likelihood of wildfires, effective fuel management becomes ever more necessary. Improved understanding of the outcome of fuels treatments that are confronted by wildfire will allow managers to make targeted investments in treatments that are most likely to achieve desired outcomes. Outcomes in this research are intentionally tiered to the pillars of the National Cohesive Wildland Fire Management Strategy: reducing vegetation burn severity (a proxy for resilient landscapes), facilitating containment of wildfire spread (a proxy for safe and effective wildfire response), and reducing wildfire impacts to communities and infrastructure (a proxy for fire-adapted communities). 

This research takes a “big data” approach to fuel treatment effectiveness research, evaluating the conditions under which fires burn into thousands of prior fuels treatments, to determine when treatments are more likely to lead to desired outcomes when encountered by wildfires. This project uses scalable datasets as part of a reproducible workflow. We will apply this workflow retrospectively to particular landscapes and time periods of fires interacting with prior fuel treatments to determine which variables (climate, weather, vegetation/fuels, topography, treatment characteristics) increase the likelihood that fuel treatments will be effective at achieving a particular outcome. Using existing data available at landscape scales, including national datasets, when possible, will enable comparisons across research groups, agencies, and landscapes.

• Part one of this research is to develop a scalable, reproducible workflow to develop an analytical database of wildfire-treatment interactions in partnership with several university labs. The workflow will characterize a suite of variables for each wildfire-treatment interaction, including outcomes related to burn severity, containment effectiveness and infrastructure impacts. We will leverage services provided by the Interagency Fuel Treatment Decision Support System-Fuel Treatment Effectiveness Monitoring application and systems of record for federal fuel treatments to characterize treatment activities, to better understand how particular treatment approaches may be more or less likely to support desired outcomes on particular landscapes. 
• Part two of this research is to apply this workflow to particular landscapes in a suite of case studies. Each case study will focus on either one landscape or a set of landscapes, over a specific time period that could span one or more years. Climate and weather conditions and other factors such as vegetation type or fuel treatment type, will be evaluated for their relative contribution to wildfire outcomes in one particular area, how those factors may change with a warming climate in the future. 

There are many existing national datasets with information on vegetation, climate, and other fire-relevant information that will be involved in workflow development. The case studies will in turn be used to test and further improve the workflow, and to feed into predictive modeling efforts that are used for project planning, layout and design. Developing the workflow and datasets will involve several components or modules, including those related to characterizing burn severity, day/time of burn, fire progression information, fireline effectiveness, infrastructure damage assessments, treatment classification and characterization, change detection & validation, and more.

This project has two main objectives: 

• develop a scalable and reproducible workflow leveraging existing data, and
• apply the workflow to case studies that compare fuel treatment outcomes across regions. 
• A fuel treatment effectiveness workshop was held at the Association for Fire Ecology in December 2023. This workshop brought together fuel specialists and researchers to discuss components to a “big data” workflow on landscape scale treatment effectiveness.
• Reports, graphics and publications describing which fuel treatments will be provided to local management units who will then be able to use that information to report back on how effective those treatments were in the field. We will take this information and adapt as needed for the future.  
• This project will create a reproducible workflow that can be used by multiple research and management teams.  
• Produce datasets needed for future decision making.   

Expected Outcomes

• This project is expected to provide a comparison of possible fuel treatments to determine which ones will be the most effective in reducing undesirable wildfire effects as the climate continues to warm. The workflow that will be developed over the course of the research should make evaluations of future fuel treatments more efficient and informative.  

Metric of Success  

• Widespread usage of the techniques developed in this project by a variety of research groups.  
• Improved rapid monitoring and evaluating of fuel treatment outcomes across large landscapes.  
• Increased effectiveness of fuel treatments for specific outcomes.  
• More cohesive research and practitioner community around fuel treatment effectiveness.  

Principal Investigators

Jens Stevens

Eric E. Knapp

Morris C. Johnson

Mike A. Battaglia

Matthew B. Dickinson

Collaborators.

University Partners

Susan Prichard (University of Washington)

Ernesto Alvarado (University of Washington)

Camille Stevens-Rumann (Colorado State University)

Alina Cansler (University of Montana)

Jessica Miesel (University of Idaho)

Research and Analysis Team

Jonathan Batchelor (PNW Researcher)

Krista Thompson-Aue (PNW Analyst)

Alex Arkowitz (RMRS Researcher)

Hannah Van Dusen (RMRS Analyst)

Emily Sprague (NRS Analyst)

Betsy Black (PSW Analyst)

Leo O’Neill (PSW Analyst) 

Publications Relevant to this Project

• Emily Brodie, Eric E. Knapp, Wesley R. Brooks, Stacy A. Drury, Martin W. Ritchie. 2024. Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather
• C. Alina Cansler, Van R. Kane, Paul F. Hessburg, Jonathan T. Kane, Sean M.A. Jeronimo, James A. Lutz, Nicholas A. Povak, Derek J. Churchill, Andrew J. Larson. 2022. Previous wildfires and management treatments moderate subsequent fire severity
• Maureen C. Kennedy, Morris C. Johnson, Kendra Fallon, Deborah Mayer. 2019. How big is enough? Vegetation structure impacts effective fuel treatment width and forest resiliency
• Rebecca E. Lemons, Susan J. Prichard, Becky K. Kerns. 2023. Evaluating fireline effectiveness across large wildfire events in north-central Washington State
• Susan J. Prichard, Nicholas A. Povak, Maureen C. Kennedy, David W. Peterson. 2020. Fuel treatment effectiveness in the context of landform, vegetation, and large, wind‐driven wildfires