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      Representational and decorative pictures in science and mathematics tests: Do they make a difference?

      Learning and Instruction

      Elsevier BV

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Random effects structure for confirmatory hypothesis testing: Keep it maximal.

            Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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              Mixed-effects modeling with crossed random effects for subjects and items

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                Author and article information

                Journal
                Learning and Instruction
                Learning and Instruction
                Elsevier BV
                09594752
                August 2020
                August 2020
                : 68
                : 101345
                Article
                10.1016/j.learninstruc.2020.101345
                © 2020

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