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ERIC Number: EJ1005801
Record Type: Journal
Publication Date: 2013-Apr
Pages: 24
Abstractor: As Provided
Reference Count: 0
ISBN: N/A
ISSN: ISSN-0749-596X
Random Effects Structure for Confirmatory Hypothesis Testing: Keep It Maximal
Barr, Dale J.; Levy, Roger; Scheepers, Christoph; Tily, Harry J.
Journal of Memory and Language, v68 n3 p255-278 Apr 2013
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[subscript 1] and F[subscript 2] tests, and in many cases, even worse than F[subscript 1] alone. Maximal LMEMs should be the "gold standard" for confirmatory hypothesis testing in psycholinguistics and beyond. (Contains 6 tables and 6 figures.)
Elsevier. 3251 Riverport Lane, Maryland Heights, MO 63043. Tel: 800-325-4177; Tel: 314-447-8000; Fax: 314-447-8033; e-mail: JournalCustomerService-usa@elsevier.com; Web site: http://www.elsevier.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A