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Repeated Measures/Within-Subjects Design

by: Amy Turk

Repeated Measures/Within-Subjects Design Psyc-21621

Marketplace > Kent State University > Psychlogy > Psyc-21621 > Repeated Measures Within Subjects Design
Amy Turk

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About this Document

Notes on Within-Subjects Testing
Quantitative Methods Psych I
Dr. Gordon
Class Notes
Psychology, Statistics, Stats, repeated measures anova, repeated measures t test, within-subjects, Design, null hypothesis, population, Robert Cohen, cohens d, standard deviation, Variability, order, effects, counterbalancing, normal, distribution, independent, Observations
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This 2 page Class Notes was uploaded by Amy Turk on Wednesday April 27, 2016. The Class Notes belongs to Psyc-21621 at Kent State University taught by Dr. Gordon in Spring 2016. Since its upload, it has received 5 views. For similar materials see Quantitative Methods Psych I in Psychlogy at Kent State University.


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Date Created: 04/27/16
Chapter 11 REPEATED MEASURES/WITHIN-SUBJECTS DESIGN ● two separate scores are obtained for each person in the sample ○ ex. group measured before therapy and after therapy ● the same group of subjects is used ● matched-subjects design = each person in one sample is matched with a person in the other sample ○ the matching is done so that the two individuals are equivalent with respect to a specific variable that the researcher would like to control ○ the more variables used, the harder it is to find matching pairs ○ the goal is to simulate a repeated-measures design as closely as possible Difference Scores ● X2 - X1 ● measures the amount of change in reaction time for each person ● subtracting the first score from the second score ● researcher is interested in the mean for the population of difference scores ● for a repeated-measures study, the null hypothesis states that the mean difference for the general population is zero ○ although the mean population is zero, the individual scores in the population are not all equal to zero ○ even when the null hypothesis is true, we still expect some people to have positive difference scores and some to have negative difference scores Cohen’s D ● a standardized measure of the mean differences between treatments ● (population mean difference) divided by (standard deviation) Variability ● describes the consistency of the treatment effect ● high variability means that there is no consistent treatment effect ● with small variability, the treatment effect is statistically significant Benefits of Repeated Measures ● requires fewer subjects ● suited for changes that take place over time ● reduces or eliminates problems caused by individual differences = primary advantage ○ age ○ IQ ○ gender ● primary disadvantage = the structure of the design allows for factors other than the treatment effect to cause a person’s score to change from one treatment to the next ○ it’s possible that participation in the first treatment influences the person’s score in the second treatment ■ order effects = changes in scores that are caused by participation in an earlier treatment ● can distort the mean differences ● counterbalancing = participants are randomly divided into two groups, with one group receiving treatment 1 followed by treatment 2 and the other group receiving treatment 2 followed by treatment 1 ○ the goal is to distribute any outside effects evenly over the two treatments Assumptions ● independent observations ● normal population distribution


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