Variables

A variable is simply anything that varies, anything that assumes different values or categories. For example, sex varies because there is more than one category or classification: female and male. Race is a variable because there is more than one category: Asian, Black, Hispanic, etc. Age is a variable because there is more than one category: 1 year, 2 years, 3 years, etc.

Conversely, a constant is anything that does not vary or take different values or categories. For example, everyone participating in this course is a student, so that is not a variable since it does not vary since it has only one category. As another example, consider a group of white females. With this group, neither race nor sex varies, so race and sex are constants for these people.

Exercise: Identifying Variables

In the following statements, identify the variables.

1. What is the relation between intelligence and achievement?
2. Do students learn more from a supportive teacher or a non-supportive teacher?
3. Are students aged 55 and older more likely to drop out of college than students of ages between 30 and 40?
4. What is the relationship between grade point average and dropping out of high school?
5. How do three counseling techniques—rational-emotive, gestalt, and no-counseling—differ in their effectiveness in decreasing test anxiety in high school juniors?
6. What is the relationship among leadership skills, intelligence, and achievement motivation of high school seniors?

Answers:

1. Intelligence and achievement.
2. Level of support (with two categories: support/non-support) and student learning.
3. Age (with two categories: 55 and over, 30 to 40) and dropping out of college (also with two categories: in or out).
4. Grade point average and dropping out of high school (two categories: in or out).
5. Counseling techniques (with three categories: rational-emotive, gestalt, and no-counseling) and test anxiety.
6. Leadership skills, intelligence, and achievement motivation.

Classification of Variables

In research it is often important to distinguish variables by the supposed or theoretical function they play. For example, if one states that a child's intelligence level influences the child's academic achievement in school, then the variable intelligence thought to have some impact, some effect on academic performance in school. In this example, intelligence is called the independent variable and academic achievement is the dependent variable. The logic here holds that achievement depends, to some degree, upon intelligence, hence it is called a dependent variable. Since intelligence does not depend upon achievement, intelligence in this example is referred to as the independent variable.

Here are two methods for identify8ing independent variables (IV) and dependent variables (DV). First, think in terms of chronological sequence--in terms of the time order. Which variable comes first, one's sex or one's achievement in school? Most would answer that one is born with a given sex (female or male), so it naturally precedes achievement in school. The variable that comes first in the time order is the IV and the variable that comes afterwards is the DV.

A second method for identifying the IVs and DVs is the ask yourself about the notion of causality. That is, if one does this with variable A, then what happens to variable B? For example, if one could increase intelligence, then achievement in school may result. But, if one increased achievement school, would this have any logical impact on one's intelligence? In this example, intelligence is the IV because it can affect achievement in school, and achievement is the DV because it is unlikely to affect intelligence.

Alternative labels for IV are cause and predictor, and other labels for the DV are effect and criterion.

Often it can be difficult to properly identify whether a variable is nominal, ordinal, interval, or ratio. A simpler approach is to identify variables as either qualitative (or categorical) or quantitative (or continuous). A qualitative/categorical variable is one that has categories that are not ranked--i.e., a nominal variable. All other variables have categories that can be ranked, therefore the categories differ by degree. These variables are quantitative or continuous, and are represented by the ordinal, interval, and ratio scales.