Repeated measures design

Statistics

Engineering

Yield (engineering)

Pathology

Clinical study design

Econometrics

Artificial intelligence

Psychology

Metallurgy

Materials science

Reliability engineering

Consistency (knowledge bases)

Research design

Treatment effect

Mathematics

Design of experiments

Computer science

Clinical trial

Traditional medicine

Medicine

American Political Science Review

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.

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