Categorical Data Analysis: Away from ANOVAs (Transformation or Not) and towards Logit Mixed Models

Autor/in	Jaeger, T. Florian
Titel	Categorical Data Analysis: Away from ANOVAs (Transformation or Not) and towards Logit Mixed Models
Quelle	In: Journal of Memory and Language, 59 (2008) 4, S.434-446 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0749-596X
DOI	10.1016/j.jml.2007.11.007
Schlagwörter	School Choice; Statistical Analysis; Geometric Concepts; Mathematical Models; Priming; Comparative Analysis; Data Analysis + Suchen Sie Ihr Suchwort? Choice of school; Schulwahl; Statistische Analyse; Elementare Geometrie; Mathematical model; Mathematisches Modell; Auswertung
Abstract	This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow and Clayton [Breslow, N. E. & Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. "Journal of the American Statistical Society 88"(421), 9-25]), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods. (Contains 4 figures and 10 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10