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Mixed-effects logistic regression for estimating transitional probabilities in sequentially coded observational data
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DOI：10.1037/1082-989X.12.3.317.png)
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Abstract
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This article demonstrates the use of mixed-effects logistic regression (NILR) for conducting sequential analyses of binary observational data. MLR is a special case of the mixed-effects logit modeling framework, which may be applied to multicategorical observational data. ne MLR approach is motivated in part by G. A. Dagne, G. W. Howe, C. H. Brown, & B. O. Muthen (2002) advances in general linear mixed models for sequential analyses of observational data in the form of contingency table frequency counts. The advantage of the NILR approach is that it circumvents obstacles in the estimation of random sampling error encountered using Dagne and colleagues' approach. This article demonstrates the MLR model in an analysis of observed sequences of communication in a sample of young adult same-sex peer dyads. The results obtained using MLR are compared with those of a parallel analysis using Dagne and colleagues' linear rrLixed model for binary observational data in the form of log odds ratios. Similarities and differences between the results of the 2 approaches are discussed. Implications for the use of linear mixed models versus mixed-effects logit models for sequential analyses are considered.
Keywords:
observational data
sequential analysis
multilevel modeling
mixed-effects logit modeling
empirical Bayes estimation
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