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Lecture 12

PSYC 305 Lecture Notes - Lecture 12: Repeated Measures Design, Migraine, Analysis Of Variance


Department
Psychology
Course Code
PSYC 305
Professor
Heungsun Hwang
Lecture
12

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February 21st
Repeated measures design
We often use an experimental design in which measurements on a single DV are
repeated a number of times within the same subject.
Such designs in which subjects are crossed with at least one experimental factor are
called repeated-measures designs.
The simplest experiment of this kind may be a before- and after-treatment
design (two conditions)
One-way repeated measures design
One-way means the number of independent variables is one
N subjects are measured on a single DV under K conditions or levels of a
single IV or factor.
Subjects are repeatedly measured across all levels of independent
variables
One-way ANOVA example
IV: there is one independent variable
This factor has multiple levels
k is the amount of levels
Example: class rowthere are three levels of class row
This is the same independent factor, but separated into several groups
This affects our continuous dependent variable
DV: for example, this could be exam scores
We assume that all groups have different subjects in regular one-way ANOVA
k different groups have different subjects
But in one-way repeated measures ANOVA, we have the same participants go
to all of the levels
k different groups always have the same subjects
This is the main difference between one-way ANOVA and one-way
repeated measures ANOVA
Example 1 (simplest)
We have two subjects and both have an acne problem, and we observe them before
and after using an acne product
Subject 1: beforehas 10 pimples after5 pimples on the left
Subject 2: beforehas 10 pimples after5 pimples on the right
So, we can tell that both subjects reacted to the acne product positively, but
somewhat differently
Example 2
National college entrance exam application essay test interview
You take the college entrance exam, and if you do well enough on it, you fill in your
university application. If you do well enough in the application, you do the essay
test. If this goes well enough, you do the interview.
You are the participant, and you, the same participant, go through all the tests
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Four separate professors interview you, one after the other
The independent variable is the interviewthere are four levels (four
professors)
The dependent score would be the score you receive after your interview
The subject also changes throughout all levels
The subject could be getting more and more comfortable as the interview
goes on, or there could be other factors (they could be getting more and more
tired)
So, when the mean changes across professors, you cannot be certain whether
the professor or the subject initiated this change
Example: one-way repeated measures design
Four migraine sufferers were asked to record the duration of their migraine
headaches. After the baseline recording, during which no training was given, each
subject had a six-week period of relaxation training. We would like to know if the
relaxation therapy can help to relieve the migraine. The dependent variable is the
duration (hours/week) of headaches recorded at the third and sixth week of the
therapy.
We may be interested to find out if the relaxation training is effective in reducing the
duration of migraine headaches.
DV = Duration of headache
IV = Migraine therapy
The four same patients are repeatedly measured across three levels of
migraine therapy
The scores are the dependent variable (duration of headache)
There is only one independent variable, which is migraine therapy
Group means plot
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Subject scores plot
The subjects may change over the different conditions
This is one source of change we may want to look at
Superimposed
Possible questions
Overall, are there significant differences in the mean scores of DV across groups?
Between-group effect of treatment
Overall, are there significant differences across subjects?
Subject-level variability (between-subject effect)
Technically, we can test both of these things, but in reality we dont care very much
about subject-level variability
One-way repeated measures ANOVA
It can examine the effect of treatment (IV) (between-group effects)
H0T: μ1 = μ2 = μk
It can also test the effect of subjects (between-subject effect)
As the levels of the subject factor are individual subjects, this effect
represents the variance of subjects.
If variance is large, subjects are more different from each other
H0s: Vs = 0
Meaning that all subjects are the same
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