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Variables in Research – Definition, Types and Examples

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In research, variables are critical components that represent the characteristics or attributes being studied. They are the elements that researchers measure, control, or manipulate to observe their effects on other variables, ultimately aiming to answer research questions or test hypotheses. Variables are central to both quantitative and qualitative research, enabling scientists to gather data and draw meaningful conclusions.

Variables in Research

A variable is a characteristic, attribute, or value that can change or vary across participants, objects, or conditions within a research study. Variables allow researchers to quantify or categorize aspects of the subject under investigation, serving as the foundation for data collection and analysis. Variables may represent observable qualities like age or income, as well as abstract constructs like intelligence or satisfaction.

Key Features of Variables in Research :

Measurability : Variables must be quantifiable or classifiable for observation.
Variability : Variables can differ among individuals, groups, or experimental conditions.
Relevance : Variables should align with the research objectives to ensure meaningful results.

Types of Variables in Research

Research variables are typically classified into several types based on their roles, characteristics, and nature of measurement. The primary types include independent variables , dependent variables , extraneous variables , and control variables , among others.

1. Independent Variable (IV)

Definition : An independent variable is the variable that is manipulated or controlled by the researcher to observe its effect on the dependent variable. The independent variable is often the “cause” in a cause-and-effect relationship.

Characteristics :

Controlled or manipulated by the researcher.
Its changes are intended to produce an effect on another variable.
Also known as a predictor or explanatory variable.

Examples of Independent Variables :

Treatment Type : Different types of medication or therapy administered to test their effects on patients.
Study Hours : Number of hours spent studying in an experiment examining its impact on test scores.
Advertising Method : Types of advertising methods used to determine their effect on consumer interest.

Example Scenario : In an experiment studying the effect of sleep on cognitive performance, the amount of sleep (e.g., 4, 6, or 8 hours) is the independent variable, as it is controlled by the researcher to observe its impact on cognitive performance.

2. Dependent Variable (DV)

Definition : The dependent variable is the outcome or effect that is measured in response to changes in the independent variable. It is the “effect” in a cause-and-effect relationship and is influenced by the independent variable.

Dependent on the independent variable.
Also known as the outcome or response variable.
Changes in the dependent variable are observed to determine the effect of the independent variable.

Examples of Dependent Variables :

Test Scores : Used to measure the impact of study hours (IV) on academic performance.
Blood Pressure : Measured to observe the effects of different medications (IV) on blood pressure levels.
Sales Volume : Analyzed to determine the impact of advertising methods (IV) on sales.

Example Scenario : In a study examining the impact of exercise on weight loss, weight loss is the dependent variable because it is expected to change in response to different levels or types of exercise (independent variable).

3. Extraneous Variable

Definition : Extraneous variables are additional variables that are not the main focus of a study but could influence the relationship between the independent and dependent variables if not controlled. They can introduce bias and affect the study’s internal validity.

Not directly related to the hypothesis.
Can potentially impact the dependent variable if not controlled.
Should be minimized or controlled to prevent interference.

Examples of Extraneous Variables :

Room Temperature : In an experiment on cognitive performance, variations in room temperature could influence participants’ concentration levels.
Participant Mood : In a study examining the effects of a new teaching method, a participant’s mood could influence their engagement and performance.
Time of Day : In research on reaction times, the time of day may affect participant alertness and thus reaction speed.

Example Scenario : In a study testing the effect of a new diet on weight loss, extraneous variables such as participants’ exercise habits or stress levels could impact the outcome, potentially confounding the relationship between the diet (IV) and weight loss (DV).

4. Control Variable

Definition : Control variables are variables that are intentionally kept constant or controlled throughout a study to ensure that they do not influence the dependent variable. By controlling these variables, researchers isolate the effects of the independent variable on the dependent variable.

Remain constant across all conditions.
Ensure that changes in the dependent variable are due to the independent variable alone.
Increase the reliability of the results by reducing potential confounding factors.

Examples of Control Variables :

Room Lighting : Keeping lighting constant in an experiment on reading comprehension.
Equipment Type : Using the same equipment across experimental conditions to ensure consistency.
Participant Age Range : Keeping the age range of participants within a specific bracket to control for age-related effects.

Example Scenario : In an experiment studying the effect of study methods on test scores, controlling the time of day the test is taken would help to ensure that test performance is not influenced by participant alertness at different times.

5. Moderator Variable

Definition : A moderator variable is a variable that affects the strength or direction of the relationship between the independent and dependent variables. It reveals how the relationship between variables changes under different conditions.

Influences the relationship between IV and DV without being directly manipulated.
Helps identify for whom or under what conditions an effect is strongest.
Used to understand context-dependent effects.

Examples of Moderator Variables :

Age : In a study on exercise and mental health, age may moderate the effect, with exercise benefiting younger adults more than older ones.
Income Level : In research on education and career success, income level may moderate the relationship by impacting access to resources.
Social Support : In a study on stress and job performance, social support may strengthen or weaken the impact of stress.

Example Scenario : In a study examining the effect of workload on job satisfaction, social support might act as a moderator variable. High social support could weaken the negative impact of workload on job satisfaction, while low support could intensify it.

6. Mediator Variable

Definition : A mediator variable explains the process through which the independent variable influences the dependent variable. It acts as a “middle link” in the causal chain, showing how or why an effect occurs.

Provides insight into the mechanism of an effect.
Positioned between the IV and DV in the causal pathway.
Identified through statistical analysis to explain mediation effects.

Examples of Mediator Variables :

Job Satisfaction : In a study on salary and employee retention, job satisfaction may mediate the relationship, as higher salary might improve satisfaction, leading to higher retention.
Stress Levels : In research on workload and health outcomes, stress may mediate the relationship, with higher workload leading to increased stress, which in turn affects health.
Self-Efficacy : In a study on training and job performance, self-efficacy may act as a mediator by showing how training improves confidence, which leads to better performance.

Example Scenario : In a study examining the impact of education level on career success, self-confidence could act as a mediator. Higher education might boost self-confidence, which in turn leads to greater career success.

Examples of Variables in Real Research

Independent Variable : Type of teaching method.
Dependent Variable : Student test scores.
Control Variable : Class size and subject matter.
Moderator Variable : Student motivation level.
Independent Variable : Dosage of medication.
Dependent Variable : Patient blood pressure.
Extraneous Variable : Patient diet and exercise habits.
Control Variable : Administration time.
Independent Variable : Sleep duration.
Dependent Variable : Cognitive performance.
Mediator Variable : Alertness levels.
Moderator Variable : Participant age.

Variables are fundamental elements of research, serving as the building blocks for hypotheses, measurements, and analyses. By understanding different types of variables—including independent, dependent, control, extraneous, moderator, and mediator variables—researchers can design studies that accurately capture the effects and relationships they aim to explore. Proper use of variables enhances the reliability and validity of findings, leading to more meaningful contributions to scientific knowledge.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . SAGE Publications.
Trochim, W. M., & Donnelly, J. P. (2008). The Research Methods Knowledge Base . Cengage Learning.
Babbie, E. (2016). The Practice of Social Research . Cengage Learning.
Kerlinger, F. N., & Lee, H. B. (2000). Foundations of Behavioral Research . Harcourt College Publishers.
Punch, K. F. (2013). Introduction to Social Research: Quantitative and Qualitative Approaches . SAGE Publications.

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Module 3 Chapter 4: Overview of Quantitative Study Variables

The first thing we need to understand is the nature of variables and how variables are used in a study’s design to answer the study questions. In this chapter you will learn:

different types of variables in quantitative studies,
issues surrounding the unit of analysis question.

Understanding Quantitative Variables

The root of the word variable is related to the word “vary,” which should help us understand what variables might be. Variables are elements, entities, or factors that can change (vary); for example, the outdoor temperature, the cost of gasoline per gallon, a person’s weight, and the mood of persons in your extended family are all variables. In other words, they can have different values under different conditions or for different people.

We use variables to describe features or factors of interest. Examples might include the number of members in different households, the distance to healthful food sources in different neighborhoods, the ratio of social work faculty to students in a BSW or MSW program, the proportion of persons from different racial/ethnic groups incarcerated, the cost of transportation to receive services from a social work program, or the rate of infant mortality in different counties. In social work intervention research, variables might include characteristics of the intervention (intensity, frequency, duration) and outcomes associated with the intervention.

Demographic Variables . Social workers are often interested in what we call demographic variables . Demographic variables are used to describe characteristics of a population, group, or sample of the population. Examples of frequently applied demographic variables are

national origin,
religious affiliation,
sexual orientation,
marital/relationship status,
employment status,
political affiliation,
geographical location,
education level, and

At a more macro level, the demographics of a community or organization often includes its size; organizations are often measured in terms of their overall budget.

Independent and Dependent Variables . A way that investigators think about study variables has important implications for a study design. Investigators make decisions about having them serve as either independent variables or as dependent variables . This distinction is not something inherent to a variable, it is based on how the investigator chooses to define each variable. Independent variables are the ones you might think of as the manipulated “input” variables, while the dependent variables are the ones where the impact or “output” of that input variation would be observed.

Intentional manipulation of the “input” (independent) variable is not always involved. Consider the example of a study conducted in Sweden examining the relationship between having been the victim of child maltreatment and later absenteeism from high school: no one intentionally manipulated whether the children would be victims of child maltreatment (Hagborg, Berglund, & Fahlke, 2017). The investigators hypothesized that naturally occurring differences in the input variable (child maltreatment history) would be associated with systematic variation in a specific outcome variable (school absenteeism). In this case, the independent variable was a history of being the victim of child maltreatment, and the dependent variable was the school absenteeism outcome. In other words, the independent variable is hypothesized by the investigator to cause variation or change in the dependent variable. This is what it might look like in a diagram where “ x ” is the independent variable and “ y ” is the dependent variable (note: you saw this designation earlier, in Chapter 3, when we discussed cause and effect logic):

quantitative research variables examples

For another example, consider research indicating that being the victim of child maltreatment is associated with a higher risk of substance use during adolescence (Yoon, Kobulsky, Yoon, & Kim, 2017). The independent variable in this model would be having a history of child maltreatment. The dependent variable would be risk of substance use during adolescence. This example is even more elaborate because it specifies the pathway by which the independent variable (child maltreatment) might impose its effects on the dependent variable (adolescent substance use). The authors of the study demonstrated that post-traumatic stress (PTS) was a link between childhood abuse (physical and sexual) and substance use during adolescence.

Take a moment to complete the following activity.

Types of Quantitative Variables

There are other meaningful ways to think about variables of interest, as well. Let’s consider different features of variables used in quantitative research studies. Here we explore quantitative variables as being categorical, ordinal, or interval in nature. These features have implications for both measurement and data analysis.

Categorical Variables. Some variables can take on values that vary, but not in a meaningful numerical way. Instead, they might be defined in terms of the categories which are possible. Logically, these are called categorical variables . Statistical software and textbooks sometimes refer to variables with categories as nominal variables . Nominal can be thought of in terms of the Latin root “nom” which means “name,” and should not be confused with number. Nominal means the same thing as categorical in describing variables. In other words, categorical or nominal variables are identified by the names or labels of the represented categories. For example, the color of the last car you rode in would be a categorical variable: blue, black, silver, white, red, green, yellow, or other are categories of the variable we might call car color.

What is important with categorical variables is that these categories have no relevant numeric sequence or order. There is no numeric difference between the different car colors, or difference between “yes” or “no” as the categories in answering if you rode in a blue car. There is no implied order or hierarchy to the categories “Hispanic or Latino” and “Not Hispanic or Latino” in an ethnicity variable; nor is there any relevant order to categories of variables like gender, the state or geographical region where a person resides, or whether a person’s residence is owned or rented.

If a researcher decided to use numbers as symbols related to categories in such a variable, the numbers are arbitrary—each number is essentially just a different, shorter name for each category. For example, the variable gender could be coded in the following ways, and it would make no difference, as long as the code was consistently applied.

Race and ethnicity. One of the most commonly explored categorical variables in social work and social science research is the demographic referring to a person’s racial and/or ethnic background. Many studies utilize the categories specified in past U.S. Census Bureau reports. Here is what the U.S. Census Bureau has to say about the two distinct demographic variables, race and ethnicity ( https://www.census.gov/mso/www/training/pdf/race-ethnicity-onepager.pdf ):

What is race? The Census Bureau defines race as a person’s self-identification with one or more social groups. An individual can report as White, Black or African American, Asian, American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, or some other race. Survey respondents may report multiple races. What is ethnicity? Ethnicity determines whether a person is of Hispanic origin or not. For this reason, ethnicity is broken out into two categories, Hispanic or Latino and Not Hispanic or Latino. Hispanics may report as any race.

In other words, the Census Bureau defines two categories for the variable called ethnicity (Hispanic or Latino and Not Hispanic or Latino), and seven categories for the variable called race. While these variables and categories are often applied in social science and social work research, they are not without criticism.

Based on these categories, here is what is estimated to be true of the U.S. population in 2016:

Dichotomous variables. There exists a special category of categorical variable with implications for certain statistical analyses. Categorical variables comprised of exactly two options, no more and no fewer are called dichotomous variables . One example was the U.S. Census Bureau dichotomy of Hispanic/Latino and Non-Hispanic/Non-Latino ethnicity. For another example, investigators might wish to compare people who complete treatment with those who drop out before completing treatment. With the two categories, completed or not completed, this treatment completion variable is not only categorical, it is dichotomous. Variables where individuals respond “yes” or “no” are also dichotomous in nature.

The past tradition of treating gender as either male or female is another example of a dichotomous variable. However, very strong arguments exist for no longer treating gender in this dichotomous manner: a greater variety of gender identities are demonstrably relevant in social work for persons whose identity does not align with the dichotomous (also called binary) categories of man/woman or male/female. These include categories such as agender, androgynous, bigender, cisgender, gender expansive, gender fluid, gender questioning, queer, transgender, and others.

Ordinal Variables. Unlike these categorical variables, sometimes a variable’s categories do have a logical numerical sequence or order. Ordinal, by definition, refers to a position in a series. Variables with numerically relevant categories are called ordinal variables. For example, there is an implied order of categories from least-to-most with the variable called educational attainment. The U.S. Census data categories for this ordinal variable are:

1st-4thgrade
5th-6thgrade
7th-8thgrade
high school graduate
some college, no degree
associate’s degree, occupational
associate’s degree academic
bachelor’s degree
master’s degree
professional degree
doctoral degree

In looking at the 2016 Census Bureau estimate data for this variable, we can see that females outnumbered males in the category of having attained a bachelor’s degree: of the 47,718,000 persons in this category, 22,485,000 were male and 25,234,000 were female. While this gendered pattern held for those receiving master’s degrees, the pattern was reversed for receiving doctoral degrees: more males than females obtained this highest level of education. It is also interesting to note that females outnumbered males at the low end of the spectrum: 441,000 females reported no education compared to 374,000 males.

Here is another example of using ordinal variables in social work research: when individuals seek treatment for a problem with alcohol misuse, social workers may wish to know if this is their first, second, third, or whatever numbered serious attempt to change their drinking behavior. Participants enrolled in a study comparing treatment approaches for alcohol use disorders reported that the intervention study was anywhere from their first to eleventh significant change attempt (Begun, Berger, Salm-Ward, 2011). This change attempt variable has implications for how social workers might interpret data evaluating an intervention that was not the first try for everyone involved.

Rating scales. Consider a different but commonly used type of ordinal variable: rating scales. Social, behavioral, and social work investigators often ask study participants to apply a rating scale to describe their knowledge, attitudes, beliefs, opinions, skills, or behavior. Because the categories on such a scale are sequenced (most to least or least to most), we call these ordinal variables.

Examples include having participants rate:

how much they agree or disagree with certain statements (not at all to extremely much);
how often they engage in certain behaviors (never to always);
how often they engage in certain behaviors (hourly, daily, weekly, monthly, annually, or less often);
the quality of someone’s performance (poor to excellent);
how satisfied they were with their treatment (very dissatisfied to very satisfied)
their level of confidence (very low to very high).

Interval Variables. Still other variables take on values that vary in a meaningful numerical fashion. From our list of demographic variables, age is a common example. The numeric value assigned to an individual person indicates the number of years since a person was born (in the case of infants, the numeric value may indicate days, weeks, or months since birth). Here the possible values for the variable are ordered, like the ordinal variables, but a big difference is introduced: the nature of the intervals between possible values. With interval variables the “distance” between adjacent possible values are equal. Some statistical software packages and textbooks use the term scale variable : this is exactly the same thing as what we call an interval variable.

For example, in the graph below, the 1 ounce difference between this person consuming 1 ounce or 2 ounces of alcohol (Monday, Tuesday) is exactly the same as the 1 ounce difference between consuming 4 ounces or 5 ounces (Friday, Saturday). If we were to diagram the possible points on the scale, they would all be equidistant; the interval between any two points is measured in standard units (ounces, in this example).

With ordinal variables, such as a rating scales, no one can say for certain that the “distance” between the response options of “never” and “sometimes” is the same as the “distance” between “sometimes” and “often,” even if we used numbers to sequence these response options. Thus, the rating scale remains ordinal, not interval.

What might become a tad confusing is that certain statistical software programs, like SPSS, refer to an interval variable as a “scale” variable. Many variables used in social work research are both ordered and have equal distances between points. Consider for example, the variable of birth order. This variable is interval because:

the possible values are ordered (e.g., the third-born child came after the first- and second-born and before the fourth-born), and
the “distances” or intervals are measured in equivalent one-person units.

Continuous variables. There exists a special type of numeric interval variable that we call continuous variables. A variable like age might be treated as a continuous variable. Age is ordinal in nature, since higher numbers mean something in relation to smaller numbers. Age also meets our criteria for being an interval variable if we measure it in years (or months or weeks or days) because it is ordinal and there is the same “distance” between being 15 and 30 years old as there is between being 40 and 55 years old (15 calendar years). What makes this a continuous variable is that there are also possible, meaningful “fraction” points between any two intervals. For example, a person can be 20½ (20.5) or 20¼ (20.25) or 20¾ (20.75) years old; we are not limited to just the whole numbers for age. By contrast, when we looked at birth order, we cannot have a meaningful fraction of a person between two positions on the scale.

The Special Case of Income . One of the most abused variables in social science and social work research is the variable related to income. Consider an example about household income (regardless of how many people are in the household). This variable could be categorical (nominal), ordinal, or interval (scale) depending on how it is handled.

Categorical Example: Depending on the nature of the research questions, an investigator might simply choose to use the dichotomous categories of “sufficiently resourced” and “insufficiently resourced” for classifying households, based on some standard calculation method. These might be called “poor” and “not poor” if a poverty line threshold is used to categorize households. These distinct income variable categories are not meaningfully sequenced in a numerical fashion, so it is a categorical variable.

Ordinal Example: Categories for classifying households might be ordered from low to high. For example, these categories for annual income are common in market research:

Less than $25,000.
$25,000 to $34,999.
$35,000 to $49,999.
$50,000 to $74,999.
$75,000 to $99,999.
$100,000 to $149,999.
$150,000 to $199,999.
$200,000 or more.

Notice that the categories are not equally sized—the “distance” between pairs of categories are not always the same. They start out in about $10,000 increments, move to $25,000 increments, and end up in about $50,000 increments.

Interval Example . If an investigator asked study participants to report an actual dollar amount for household income, we would see an interval variable. The possible values are ordered and the interval between any possible adjacent units is $1 (as long as dollar fractions or cents are not used). Thus, an income of $10,452 is the same distance on a continuum from $9,452 and $11,452—$1,000 either way.

The Special Case of Age . Like income, “age” can mean different things in different studies. Age is usually an indicator of “time since birth.” We can calculate a person’s age by subtracting a date of birth variable from the date of measurement (today’s date minus date of birth). For adults, ages are typically measured in years where adjacent possible values are distanced in 1-year units: 18, 19, 20, 21, 22, and so forth. Thus, the age variable could be a continuous type of interval variable.

However, an investigator might wish to collapse age data into ordered categories or age groups. These still would be ordinal, but might no longer be interval if the increments between possible values are not equivalent units. For example, if we are more interested in age representing specific human development periods, the age intervals might not be equal in span between age criteria. Possibly they might be:

Infancy (birth to 18 months)
Toddlerhood (18 months to 2 ½ years)
Preschool (2 ½ to 5 years)
School age (6 to 11 years)
Adolescence (12 to 17 years)
Emerging Adulthood (18 to 25 years)
Adulthood (26 to 45 years)
Middle Adulthood (46 to 60 years)
Young-Old Adulthood (60 to 74 years)
Middle-Old Adulthood (75 to 84 years)
Old-Old Adulthood (85 or more years)

Age might even be treated as a strictly categorical (non-ordinal) variable. For example, if the variable of interest is whether someone is of legal drinking age (21 years or older), or not. We have two categories—meets or does not meet legal drinking age criteria in the United States—and either one could be coded with a “1” and the other as either a “0” or “2” with no difference in meaning.

What is the “right” answer as to how to measure age (or income)? The answer is “it depends.” What it depends on is the nature of the research question: which conceptualization of age (or income) is most relevant for the study being designed.

Alphanumeric Variables. Finally, there are data which do not fit into any of these classifications. Sometimes the information we know is in the form of an address or telephone number, a first or last name, zipcode, or other phrases. These kinds of information are sometimes called alphanumeric variables . Consider the variable “address” for example: a person’s address might be made up of numeric characters (the house number) and letter characters (spelling out the street, city, and state names), such as 1600 Pennsylvania Ave. NW, Washington, DC, 20500.

Actually, we have several variables present in this address example:

the street address: 1600 Pennsylvania Ave.
the city (and “state”): Washington, DC
the zipcode: 20500.

This type of information does not represent specific quantitative categories or values with systematic meaning in the data. These are also sometimes called “string” variables in certain software packages because they are made up of a string of symbols. To be useful for an investigator, such a variable would have to be converted or recoded into meaningful values.

A Note about Unit of Analysis

An important thing to keep in mind in thinking about variables is that data may be collected at many different levels of observation. The elements studied might be individual cells, organ systems, or persons. Or, the level of observation might be pairs of individuals, such as couples, brothers and sisters, or parent-child dyads. In this case, the investigator may collect information about the pair from each individual, but is looking at each pair’s data. Thus, we would say that the unit of analysis is the pair or dyad, not each individual person. The unit of analysis could be a larger group, too: for example, data could be collected from each of the students in entire classrooms where the unit of analysis is classrooms in a school or school system. Or, the unit of analysis might be at the level of neighborhoods, programs, organizations, counties, states, or even nations. For example, many of the variables used as indicators of food security at the level of communities, such as affordability and accessibility, are based on data collected from individual households (Kaiser, 2017). The unit of analysis in studies using these indicators would be the communities being compared. This distinction has important measurement and data analysis implications.

A Reminder about Variables versus Variable Levels

A study might be described in terms of the number of variable categories, or levels, that are being compared. For example, you might see a study described as a 2 X 2 design—pronounced as a two by two design. This means that there are 2 possible categories for the first variable and 2 possible categories for the other variable—they are both dichotomous variables. A study comparing 2 categories of the variable “alcohol use disorder” (categories for meets criteria, yes or no) with 2 categories of the variable “illicit substance use disorder” (categories for meets criteria, yes or no) would have 4 possible outcomes (mathematically, 2 x 2=4) and might be diagrammed like this (data based on proportions from the 2016 NSDUH survey, presented in SAMHSA, 2017):

Reading the 4 cells in this 2 X 2 table tells us that in this (hypothetical) survey of 540 individuals, 500 did not meet criteria for either an alcohol or illicit substance use disorder (No, No); 26 met criteria for an alcohol use disorder only (Yes, No); 10 met criteria for an illicit substance use disorder only (No, Yes), and 4 met criteria for both an alcohol and illicit substance use disorder (Yes, Yes). In addition, with a little math applied, we can see that a total of 30 had an alcohol use disorder (26 + 4) and 14 had an illicit substance use disorder (10 + 4). And, we can see that 40 had some sort of substance use disorder (26 + 10 + 4).

Thus, when you see a study design description that looks like two numbers being multiplied, that is essentially telling you how many categories or levels of each variable there are and leads you to understand how many cells or possible outcomes exist. A 3 X 3 design has 9 cells, a 3 X 4 design has 12 cells, and so forth. This issue becomes important once again when we discuss sample size in Chapter 6.

Interactive Excel Workbook Activities

Complete the following Workbook Activity:

SWK 3401.3-4.1 Beginning Data Entry

Chapter Summary

In summary, investigators design many of their quantitative studies to test hypotheses about the relationships between variables. Understanding the nature of the variables involved helps in understanding and evaluating the research conducted. Understanding the distinctions between different types of variables, as well as between variables and categories, has important implications for study design, measurement, and samples. Among other topics, the next chapter explores the intersection between the nature of variables studied in quantitative research and how investigators set about measuring those variables.