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- W2070275643 endingPage "1306" @default.
- W2070275643 startingPage "1287" @default.
- W2070275643 abstract "In order to examine metacognitive accuracy (i.e., the relationship between metacognitive judgment and memory performance), researchers often rely on by-participant analysis, where metacognitive accuracy (e.g., resolution, as measured by the gamma coefficient or signal detection measures) is computed for each participant and the computed values are entered into group-level statistical tests such as the t test. In the current work, we argue that the by-participant analysis, regardless of the accuracy measurements used, would produce a substantial inflation of Type I error rates when a random item effect is present. A mixed-effects model is proposed as a way to effectively address the issue, and our simulation studies examining Type I error rates indeed showed superior performance of mixed-effects model analysis as compared to the conventional by-participant analysis. We also present real data applications to illustrate further strengths of mixed-effects model analysis. Our findings imply that caution is needed when using the by-participant analysis, and recommend the mixed-effects model analysis." @default.
- W2070275643 created "2016-06-24" @default.
- W2070275643 creator A5024782365 @default.
- W2070275643 creator A5037181915 @default.
- W2070275643 creator A5079354298 @default.
- W2070275643 creator A5089165062 @default.
- W2070275643 date "2014-01-01" @default.
- W2070275643 modified "2023-09-29" @default.
- W2070275643 title "Type I error inflation in the traditional by-participant analysis to metamemory accuracy: A generalized mixed-effects model perspective." @default.
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