Statistics for the Behavioral Sciences by Frederick J. Gravetter, Larry B. Wallnau ISBN 10: 1305504917 ISBN 13: 9781305504912

PREVIEW
It’s the night before an exam. It is getting late and you
are trying to decide whether to study or to sleep. There
are obvious advantages to studying, especially if you feel
that you do not have a good grasp of the material to be
tested. On the other hand, a good night’s sleep will leave
you better prepared to deal with the stress of taking an
exam. “To study or to sleep?” was the question addressed
by a recent research study (Gillen-O’Neel, Huynh, &
Fuligni, 2013). The researchers started with a sample of
535 9 th -grade students and followed up when the students
were in the 10 th and 12 th grades. Each year the students
completed a diary every day for two weeks, recording
how much time they spent studying outside of school
and how much time they slept the night before. The students
also reported the occurrence of academic problems
each day such as “did not understand something taught
in class” and “did poorly on a test, quiz, or homework.”
The data showed a general trade-off between study time
and sleep time, especially for the older students. The
primary result, however is that the students reported
more academic problems following nights with less than
average sleep than they did after nights with more than
average sleep. Notice that the researchers recorded academic
performance for each student in two conditions,
after nights with more sleep and after nights with less
sleep, and the goal is to compare these two sets of scores.
In the previous chapter, we introduced a statistical
procedure for evaluating the mean difference between
two sets of data (the independent-measures t statistic).
However, the independent-measures t statistic is intended
for research situations involving two separate and independent
samples. You should realize that the two sets of
scores in the sleep-or-study example are not independent
samples. In fact, the same group individuals participated
in both of the treatment conditions. What is needed is a
new statistical analysis for comparing two means that
are both obtained from the same group of participants.
In this chapter, we introduce the repeated-measures
t statistic, which is used for hypothesis tests evaluating
the mean difference between two sets of scores obtained
from the same group of individuals. As you will see,
however, this new t statistic is very similar to the original
t statistic that was introduced in Chapter 9.
11.1 Introduction to Repeated-Measures Designs
LEARNING OBJECTIVES
1. Define a repeated-measures design and explain how it differs from an independentmeasures
design.
2. Define a matched-subjects design and explain how it differs from repeatedmeasures
and independent-measures designs.
In the previous chapter, we introduced the independent-measures research design as one
strategy for comparing two treatment conditions or two populations. The independentmeasures
design is characterized by the fact that two separate samples are used to obtain
the two sets of scores that are to be compared. In this chapter, we examine an alternative
strategy known as a repeated-measures design, or a within-subjects design. With a
repeated-measures design, one group of participants is measured in two different treatment
conditions so there are two separate scores for each individual in the sample. For
example, a group of patients could be measured before therapy and then measured again
after therapy. Or, response time could be measured in a driving simulation task for a
group of individuals who are first tested when they are sober and then tested again after
two alcoholic drinks. In each case, the same variable is being measured twice for the same
set of individuals; that is, we are literally repeating measurements on the same sample.
DEFINITION
A repeated-measures design, or a within-subject design, is one in which the
dependent variable is measured two or more times for each individual in a single
sample. The same group of subjects is used in all of the treatment conditions.
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