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ERIC Number: EJ818418
Record Type: Journal
Publication Date: 2008-Nov
Pages: 13
Abstractor: As Provided
Reference Count: 0
ISSN: ISSN-0749-596X
Categorical Data Analysis: Away from ANOVAs (Transformation or Not) and towards Logit Mixed Models
Jaeger, T. Florian
Journal of Memory and Language, v59 n4 p434-446 Nov 2008
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.)
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A