Multi-Modal Selection Effects in the Study of Adult Development: A Perspective on Multivariate, Replicated, Single-Subject, Repeated Measures Designs

1991 ◽  
Vol 17 (1) ◽  
pp. 21-27 ◽  
Author(s):  
John R. Nesselroade ◽  
Constance J. Jones
Keyword(s):  
Adult Development ◽  
Repeated Measures ◽  
Single Subject ◽  
Selection Effects ◽  
Modal Selection ◽  
Repeated Measures Designs

Repeated Measures Designs

1999 ◽  
Vol 27 (4) ◽  
pp. 552-578 ◽  
Author(s):  
Michael V. Ellis
Keyword(s):  
Repeated Measures ◽  
Single Subject ◽  
Psychology Research ◽  
Applied Psychology ◽  
Single Subject Research ◽  
Repeated Measures Design ◽  
Wide Range ◽  
Research Designs ◽  
Repeated Measures Designs

To facilitate innovation in applied psychology research, investigators need to be well-informed about available research designs. The purpose of this article is to provide an overview of repeated measures research designs (e.g., participants exposed to more than one treatment or measured on more than one occasion). My intent is twofold. First, I underscore the wide range of repeated measures research designs available to researchers in applied psychology. Second, I argue that the differentiation and polarity of group and single-subject research designs is largely arbitrary. I use examples to illustrate each repeated measures design and present its strengths and limitations.

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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