Stimulus effect matters: The importance and cautionary notes of linear mixed-effects model with random stimulus effects

Abstract

When researchers analyze data from an experiment with multiple experimental stimuli, they tend to aggregate responses to the experimental stimuli before performing a statistical test e.g., t-test, analysis of variance. This common practice, however, ignores sampling errors of experimental stimuli, resulting in a substantial increase in Type-1 error rate. This article reviews the relevant literature and provides conceptual explanations about the mechanisms underlying the inflation of Type-1 error rate. The article also illustrates how linear mixed-effects model with random stimulus effects can address the issue, with the emphasis on the correct model specification when using linear mixed-effects model. Ke ywords: random stimulus effects, mixed-effects model, multilevel model, hierarchical linear model International Af-fective Picture System IAPS; Lang, Bradley, & Cuthbert, 1997 6 12 20 12 20 20 vs. t 1 5 random stimulus effect/random item effect