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Autor/inn/enVerkuilen, Jay; Smithson, Michael
TitelMixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution
QuelleIn: Journal of Educational and Behavioral Statistics, 37 (2012) 1, S.82-113 (32 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1076-9986
DOI10.3102/1076998610396895
SchlagwörterResponses; Regression (Statistics); Statistical Analysis; Models; Statistical Distributions; Data Analysis; Computation; Markov Processes; Monte Carlo Methods; Maximum Likelihood Statistics; Bayesian Statistics; Goodness of Fit; Simulation; Cognitive Psychology; Probability; Response Style (Tests)
AbstractDoubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite poorly modeled by the normal distribution. The authors extend the beta-distributed generalized linear model (GLM) proposed in Smithson and Verkuilen (2006) to discrete and continuous mixtures of beta distributions, which enables modeling dependent data structures commonly found in real settings. The authors discuss estimation using both deterministic marginal maximum likelihood and stochastic Markov chain Monte Carlo (MCMC) methods. The results are illustrated using three data sets from cognitive psychology experiments. (Contains 6 tables, 2 figures, and 1 note.) (As Provided).
AnmerkungenSAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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