Having dealt with the selection of subjects and their assignment to treatment groups, it
is time to consider how we treat each group and how we will characterize the data that will
result. Because we want to study the ability of subjects to deal with stress and maintain
high self-esteem under different kinds of treatments, and because the response to stress is a
function of many variables, a critical aspect of planning the study involves selecting the
variables to be studied. A variableis a property of an object or event that can take on dif-
ferent values. For example, hair color is a variable because it is a property of an object
(hair) and can take on different values (brown, yellow, red, gray, etc.). With respect to our
evaluation of the stress management program, such things as the treatments we use, the stu-
dent’s self-confidence, social support, gender, degree of personal control, and treatment
group are all relevant variables.
In statistics, we dichotomize the concept of a variable in terms of independent and de-
pendent variables. In our example, group membership is anindependent variable,because
we control it. We decide what the treatments will be and who will receive each treatment.
We decide that this group over here will receive the stress management treatment and that
group over there will not. If we had been comparing males and females we clearly do not
control a person’s gender, but we do decide on the genders to study (hardly a difficult deci-
sion) and that we want to compare males versus females. On the other hand the data—such
as the resulting self-esteem scores, scores on personal control, and so on—are thedependent
variables.Basically, the study is about the independent variables, and the results of the
study (the data) are the dependent variables. Independent variables may be either quantita-
tive or qualitative and discrete or continuous, whereas dependent variables are generally, but
certainly not always, quantitative and continuous, as we are about to define those terms.^1
We make a distinction between discrete variables,such as gender or high school class,
which take on only a limited number of values, and continuous variables,such as age and
self-esteem score, which can assume, at least in theory, any value between the lowest and
highest points on the scale.^2 As you will see, this distinction plays an important role in the
way we treat data.
Closely related to the distinction between discrete and continuous variables is the dis-
tinction between quantitative and categorical data. By quantitative data(sometimes called
measurement data), we mean the results of any sort of measurement—for example,
grades on a test, people’s weights, scores on a scale of self-esteem, and so on. In all cases,
some sort of instrument (in its broadest sense) has been used to measure something, and
we are interested in “how much” of some property a particular object represents.
On the other hand, categorical data(also known as frequency data orqualitative
data) are illustrated in such statements as, “There are 34 females and 26 males in our
study” or “Fifteen people were classed as ‘highly anxious,’ 33 as ‘neutral,’ and 12 as ‘low
anxious.’ ” Here we are categorizing things, and our data consist of frequencies for each
category (hence the name categorical data). Several hundred subjects might be involved in
our study, but the results (data) would consist of only two or three numbers—the number
of subjects falling in each anxiety category. In contrast, if instead of sorting people with re-
spect to high, medium, and low anxiety, we had assigned them each a score based on some4 Chapter 1 Basic Concepts
(^1) Many people have difficulty remembering which is the dependent variable and which is the independent
variable. Notice that both “dependent” and “data” start with a “d.”
(^2) Actually, a continuous variable is one in which anyvalue between the extremes of the scale (e.g., 32.485687.. .)
is possible. In practice, however, we treat a variable as continuous whenever it can take on many different values,
and we treat it as discrete whenever it can take on only a few different values.
quantitative data
measurement
data
categorical data
frequency data
qualitative data
dependent
variables
discrete
variables
continuous
variables
variable
independent
variable