Pairwise Multiple Comparisons in Repeated Measures Designs

1980 ◽  
Vol 5 (3) ◽  
pp. 269 ◽  
Author(s):  
Scott E. Maxwell
Keyword(s):  
Repeated Measures ◽  
Multiple Comparisons ◽  
Repeated Measures Designs

Pairwise Multiple Comparisons in Repeated Measures Designs

1980 ◽  
Vol 5 (3) ◽  
pp. 269-287 ◽  
Author(s):  
Scott E. Maxwell
Keyword(s):  
Repeated Measures ◽  
Mixed Model ◽  
Multiple Comparisons ◽  
Error Rates ◽  
Type I ◽  
Omnibus Test ◽  
Mixed Model Approach ◽  
Significant Difference ◽  
Repeated Measures Designs ◽  
Necessary And Sufficient

Five methods of performing pairwise multiple comparisons in repeated measures designs were investigated. Tukey's Wholly Significant Difference (WSD) test, recommended by most experimental design texts, requires that all differences between pairs of means have a common variance. However, this assumption is equivalent to the sphericity condition that is necessary and sufficient for the validity of the mixed-model approach to the omnibus test. Monte Carlo methods revealed that Tukey's WSD leads to an inflated alpha level when the sphericity assumption is not met. Consideration of both Type I and Type II error rates found in the simulated conditions for the five procedures suggests that a Bonferroni method utilizing a separate error term for each comparison should be employed.

Increasing Precision without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments

2021 ◽  
pp. 1-18
Author(s):  
SCOTT CLIFFORD ◽  
GEOFFREY SHEAGLEY ◽  
SPENCER PISTON
Keyword(s):  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Repeated Measurements ◽  
Experimental Designs ◽  
Treatment Effect Size ◽  
Standard Design ◽  
Survey Experiments ◽  
Repeated Measures Designs ◽  
Alternative Designs

The use of survey experiments has surged in political science. The most common design is the between-subjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to precisely estimate treatment effects. Alternative designs that involve repeated measurements of the dependent variable promise greater precision, but they are rarely used out of fears that these designs will yield different results than a standard design (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Contrary to common fears, repeated measures designs tend to yield the same results as more common designs while substantially increasing precision. These designs also offer new insights into treatment effect size and heterogeneity. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.

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