TY - JOUR PY - 2009// TI - Temporal patterns of variable relationships in person-oriented research: Prediction and auto-association models of configural frequency analysis JO - Applied developmental science A1 - von Eye, Alexander A1 - Mun, Eun Young A1 - Bogat, G. Anne SP - 172 EP - 187 VL - 13 IS - 4 N2 - Longitudinal Configural Frequency Analysis (CFA) seeks to identify, at the manifest variable level, those temporal patterns that are observed more frequently (CFA types) or less frequently (CFA antitypes) than expected with reference to a base model. This article discusses, compares, and extends two base models of interest in longitudinal data analysis. The first of these, Prediction CFA (P-CFA), is a base model that can be used in the configural analysis of both cross-sectional and longitudinal data. This model takes the associations among predictors and among criteria into account. The second base model, Auto-Association CFA (A-CFA), was specifically designed for longitudinal data. This model takes the auto-associations among repeatedly observed variables into account. Both models are extended to accommodate covariates, for example, stratification variables. Application examples are given using data from a longitudinal study of domestic violence. It is illustrated that CFA is able to yield results that are not redundant with results from log-linear modeling or multinomial regression. It is concluded that CFA is particularly useful in the context of person-oriented research. (PsycINFO Database Record (c) 2010 APA, all rights reserved) (journal abstract)

LA - SN - 1088-8691 UR - http://dx.doi.org/10.1080/10888690903287864 ID - ref1 ER -