Variables may be attributes that are different for different people, such as weight, gender, religious affiliation, political affiliation, and so forth. Variables can also be conditions in the environment that can affect the results of a study, such as the time of day when an experiment takes place.
A variable is a characteristic, attribute, or condition that has different values for different individuals.
When variables are measured, researchers often identify the variables by a letter, such as X. If two variables are used, then the researcher may denote the first variable as X and the second as Y. This shorthand is useful in describing the relationships between variables.
A value that does not change from person to person is called a constant. The idea of constancy is closely related to the scientific concept of control, which we will discuss in more detail in a later section.
A constant is an attribute of a person or a condition that does not change from person to person but stays the same for every individual.
Independent and Dependent Variables
An attribute is a specific value of a variable. For example, the variable gender has two attributes: male and female. Attributes are commonly referred to as a level of the variable or simply the category. Take care not to confuse a variable with its categories. Male, for example, is not a variable. It is a category of the variable gender, along with the category female. Ideally, researchers define variables in such a way that each category is mutually exclusive. That is, all observations will fall into one and only one category. It is also desirable that the categories be exhaustive. This means that every observation has a category to which it can be assigned.
Special names are used for the two variables that are being studied by a researcher in an experiment. The variable that is manipulated by the researcher—the one thought to cause a change in the other—is called the independent variable (IV). The variable that is observed to see if it was changed by the independent variable is called the dependent variable (DV). It is called dependent because its value depends on the independent variable. Recall that a basic purpose of experiments is to determine the extent to which a change in the independent variable causes changes in the dependent variable.
In an experiment, the independent variable often reflects that the researcher administered some type of treatment—something we do to the participants. In other words, the researcher must manipulate the level of the independent variable to have a true experiment. In its simplest form, an experiment involves two groups. The first is the group that got the treatment—the experimental group. A second group does not get the treatment. Individuals in this group are said to be in the control group.
In correlation and regression-based research where the researcher is using one or more variables to predict values of another variable, the independent variable (IV) is often called the predictor variable because it is used to predict the dependent variable. The dependent variable (the outcome) is called the criterion variable in these cases.
Discrete and Continuous Variables
The variables in a study can also be described in terms of the types of values that can be assigned to them. A discrete variable consists of separate categories that cannot be divided. Take the variable gender for example. Generally, you are either male or female—the categories are indivisible. Discrete variables usually define categories, or are restricted to whole, countable numbers. The variable felony convictions is an example. Either you have been convicted of no felonies, or you have been convicted of a whole number of felonies. You cannot have been convicted of 2.78 felonies.
A continuous variable, on the other hand, can be subdivided into an infinite (or practically infinite) number of fractional parts. Annual household income (measured in dollars and cents) is a good example of a continuous variable. Variables that are continuous can be imagined to be along a line (like the number line) with no obvious points of separation. Note that it will be rare for any two subjects to have the same exact score on a continuous variable.
Latent v. Observable Variables
There is a big difference between variables that we can directly observe and the more abstract variables that cannot be observed that we refer to as constructs. One way to look at constructs is as nonobservables. This is related to what are called latent variables. Latent variables are unobserved “things” that a researcher presumes to underlie an observable variable. Intelligence is a common example of a latent variable. We cannot directly measure intelligence, but we can observe things that we think are related to it, such as verbal ability and mathematical ability (operationalized as scores on a standardized test).
Most of the problems that social scientists are interested in are latent variables. We as social scientists are not interested in specific children hitting each other on the playground; our real concern is understanding the latent variable aggression. We are not interested in a child’s ability to select correct responses on a test; we are interested in the latent variable intelligence. Generally, we cannot measure these variables we are interested in. Thus, we are forced to measure behaviors that we think indicate the presence of the latent (unobservable) variable that we are interested in.
Modification History File Created: 07/25/2018 Last Modified: 07/25/2018
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