Performing Contrast Analysis in Factorial Designs: From NHST to Confidence Intervals and Beyond. (August 2017)

• Record Type: Journal Article
• Authors: Wiens, Stefan; Nilsson, Mats E.
• Is Part Of: Educational and psychological measurement. Volume 77:Number 4(2017:Aug.)
• Page Start: 690
• Page End: 715
• Publication Date: 2017-08
• Subjects: analysis of variance -- contrast analysis -- confidence interval -- null hypothesis significance testing -- Bayesian analysis; Educational tests and measurements -- Periodicals; Psychological tests -- Periodicals; 151.205
• DOI: 10.1177/0013164416668950
• Languages: English
• ISSNs: 0013-1644

Abstract

Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful because it can be used to test specific questions of central interest in studies with factorial designs. It weighs several means and combines them into one or two sets that can be tested with t tests. The effect size produced by a contrast analysis is simply the difference between means. The CI of the effect size informs directly about direction, hypothesis exclusion, and the relevance of the effects of interest. However, any interpretation in terms of precision or likelihood requires the use of likelihood intervals or credible intervals (Bayesian). These various intervals and even a Bayesian t test can be obtained easily with free software. This tutorial reviews these methods to guide researchers in answering the following questions: When I analyze mean differences in factorial designs, where can I find the effects of central interest, and what can I learn about their effect sizes?