CSUF Department of Psychology
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Variables

A variable is a quantity or quality that varies across a group of cases.  For example, the height of CSUF students is a variable because it varies across CSUF students.  The sex of CSUF students is a variable because it varies across CSUF students.  In both of these examples, CSUF students are the cases

A quantity or quality that does not vary across a group of cases is called a constant.  Note that quantities or qualities that are variables in one situation can be constants in another.  For example, although the sex of CSUF students is a variable, the sex of Wellesley College students is a constant because Wellesley is a women’s college.

Quantitative variables are quantities that are measured using numbers.  Categorical variables (or qualitative variables) are qualities that are measured using category labels or names.  So the height of CSUF students is a quantitative variable, but the sex of CSUF students is a categorical variable.

Different Kinds of Cases

In psychology, the cases are often individual people and the variables are characteristics of those people.  But this is not always true.  For example, we might be interested in the populations of 100 U.S. cities, so that the cases are the 100 cities (and the variable—population—varies across these 100 cities).  Or we might be interested in the number of times a particular child misbehaves each day over a period of a month, so that the cases are the 30 days in the month (and the variable—the number of times the child misbehaves—varies across these 30 days).  Or we might be interested in the noise levels in three different popular study locations on campus: the Union, Madden Library, and the Satellite.  Here the cases are the three locations.

Values and Scores

A variable can take on more than one value.  People’s heights (measured to the nearest inch) can take on the values … 60, 61, 62, 63, ….  People’s sexes can take on the values male and female.  The values of a variable are also referred to as levels.  When a case takes on a particular value, however, we will call this a score.

Imagine that three people rate their moods on a 10-point (1 to 10) rating scale.  Judy rates her mood a 9, Angela a 6, Blake a 2, and Todd a 6.  The values or levels of the variable are the integers 1 through 10, but the scores are 9, 6, 2, and 6.

Operational Definitions

An operational definition of a variable is a definition of the variable in terms of how, specifically, it is to be measured.  For some variables, this is not much of an issue.  You can usually determine whether someone is female or male by looking at them or by asking them.  But what about a variable like honesty?  You might operationally define it as a person’s score on an honesty questionnaire—perhaps a questionnaire that other researchers have used and found to work.  Or you could put people in a situation in which they have an opportunity to cheat or steal and you could secretly observe them.  In this case, you might operationally define honesty in terms of whether or not they do cheat or steal in this situation.

Note that this is different from the question of what units you are going to use to measure a variable.  Saying that you are going to measure people’s heights “in inches” is not an operational definition.  An operational definition would specify how you are actually going to come up with a number for each person.  For example, you could operationally define height as people’s self-reported heights in inches in response to an item on a questionnaire.

Naming and Talking About Variables

Many variables that psychologists are interested in have simple and obvious names: height, sex, mood, etc.  Many others do not.  For example, you might be interested in the number of words from a study list that participants in a memory experiment correctly recall.  What should you call this variable?  To be clear and unambiguous, you should call it something like “the number of words correctly recalled.”   To be even clearer, you should also specify the group of cases: “the number of words correctly recalled by 30 participants in a memory experiment.”

So when you are first talking or writing about a variable, or when you are labeling a table or graph, be sure to use a clear and unambiguous variable name—even if that name seems long and clunky.  In practice, once you have defined a variable, you can eventually resort to a shorter name (e.g., “recall”) for the sake of efficiency.  Beware, however, because many of these shorter names can seem to refer only to a single value or only to one end of the scale.  For example, when a psychologist says she is studying “happiness,” it might sound as though she is studying happy people.  In reality, she is probably studying people whose happiness scores range from extremely low (unhappy) to extremely high (happy).  It works the same with variables like “depression,” “intelligence,” “shyness,” and so on.  A psychologist interested in the variable “shyness” is probably interested in people who range from extremely shy to extremely outgoing.

None of this is just a matter of our being picky.  Experience tells us that a student’s inability to name a variable clearly and unambiguously often reflects a failure to understand what the variable is, or worse, a failure to understand the general concept of a variable.

A Taxonomy of Variables

There are so many different kinds of quantities and qualities that psychologists study that it is helpful to have a rough taxonomy of them.  Here is one that emphasizes the variety of variables that psychologists are interested in.

Demographics – Examples: Age, sex, race, religion, income, etc.

Physical Characteristics – Examples: Height, weight, etc.

Physical and Mental Health – These concern the presence (vs. absence) of physical and mental disorders, along with their symptoms.  Examples: medical diagnosis, psychiatric diagnosis, number of headaches per week, number of panic attacks per week, etc.

Personal History – These concern experiences that people have had.  Examples: number of lifetime sexual partners,

Individual Differences – These include standard personality traits (e.g., the Big Five), along with other relatively stable psychological characteristics.  Examples: Extroversion, intelligence, financial responsibility, self-esteem, etc.

Beliefs and Attitudes – This category also includes knowledge, opinions, and judgments.  Examples: attitude toward divorce, opinion of the President’s job performance, beliefs about the seriousness of the AIDS epidemic, etc.

Affective Variables – These include emotions, moods, and feelings about the goodness or badness of a stimulus.  Examples: depression, subjective well being (i.e., happiness), anger, how much one “likes” a photograph.

Performance Variables – This refers to people’s performance on all sorts of physical and cognitive tasks.  Examples: number of points on a final exam, number of errors on a memory test, time taken to recognize a stimulus, etc.

Naturally Occurring Behaviors – These include essentially all other behaviors that people might engage in and that might be of interest to psychologists.  Examples: whether or not one person helps another, number of hours of television watched per week, whether or not use public recycling containers, etc.

Treatments – These are actions that are taken (vs. not taken), which are intended to have a positive (i.e., beneficial) effect on another variable.  Although the term treatment comes from medicine, neither the action nor the effect needs to be medical or biological.  Examples: taking a drug (vs. not taking it) to reduce anxiety, using cognitive therapy (vs. psychotherapy) to treat depression, using a new method of studying (vs. the standard method) to improve exam performance, etc.

Other Situation / Task Variables – This is an enormous catch-all category that includes anything about a person’s situation or the task he or she is performing.  Number of people in the room, difficulty of a test, noise level, time allowed to complete a task,  

Another Taxonomy of Variables

Another way to classify variables focuses on the role of the variables in the research process.

Dependent variables are the behaviors of primary interest to the researcher.  For example, a researcher interested in what makes people better or worse drivers will study dependent variables that are related to driving ability.  These might include the number of past accidents (a personal history variable), the number of errors made in driving through an obstacle course (a task performance variable), how fast one can respond to a hazard on a driving simulator (another task performance variable), and so on.

Independent variables are variables that the researchers believes affect dependent variables.  For example, a researcher might hypothesize that extroverted people have more car accidents than introverted people, in which case the level of extroversion (an individual difference variable) would be the independent variable and the number of accidents would be the dependent variable.  [WARNING: Some researchers and teachers reserve the term independent variable for variables that are manipulated in the context of an experiment.  We will have more to say about this in other readings.]

Extraneous variables are, essentially, any variables other than the independent and dependent variables.  Of special interest are extraneous variables that might affect the dependent variable.  For example, in an experiment on the effect of vitamin supplements on participants' ability to memorize a list of words, any other variable that might affect participants' ability to memorize (e.g., their motivation, their general intelligence) would be an extraneous variable worth considering.  In general, researchers try to control extraneous variable, where "control" means to hold them constant.  Confounding variables are a special kind of extraneous variable that are discussed in another reading.

In this taxonomy of variables, both independent variables and extraneous variables can be broken down into essentially two types.  One type is called participant variables (also called subject or person variables), which are characteristics of research participants—demographics, individual differences, and so on.  A second type is called situation variables, which are characteristics of the situation.  When people are performing a task set up by the researcher, situation variables are often called task variables.  For example, consider a researcher interested in the effect of noise on short-term memory capacity among both high IQ and low IQ students.  Here the dependent variable is short-term memory capacity (a performance variable).  The independent variables are noise (a situation or task variable) and IQ (a participant variable).  An extraneous variable would be the participants' level of alertness at the start of the experiment (another participant variable).