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An introduction to restriktor : evaluating informative hypotheses for linear models

Book Contribution - Chapter

Many researchers have specific expectations about the relation between the means of different groups or between (standardized) regression coefficients. For example, in an experimental setting, the comparison of two or more treatment groups may be subject to order constraints (e.g., H 1: µ1 < µ2 < µ3 = µ4). In practice, hypothesis H 1 is usually tested using a classical one-way ANOVA with additional pairwise comparisons if the corresponding F test is significant. In this chapter, we introduce the freely available R package restriktor for evaluating order-constrained hypotheses directly. Testing specific expectations directly does not require multiple significance tests. This way, researchers avoid inflated Type I error rates that might occur without any corrections that control the familywise Type I error rate, and decreases in power that occur due to such corrections. The procedure is illustrated using four examples.
Book: Small sample size solutions : a guide for applied researchers and practitioners
Series: European Association of Methodology Series
Pages: 157 - 172
Publication year:2020