Temporal patterns of variable relationships in person-oriented research: longitudinal models of configural frequency analysis.
This article reviews the premises of configural frequency analysis (CFA), including methods of choosing significance tests and base models, as well as protecting alpha, and discusses why CFA is a useful approach when conducting longitudinal person-oriented research. CFA operates at the manifest variable level. Longitudinal CFA seeks to identify those temporal patterns that stand out as more frequent (CFA types) or less frequent (CFA antitypes) than expected with reference to a base model. A base model that has been used frequently in CFA applications, prediction CFA, and a new base model, auto-association CFA, are discussed for analysis of cross-classifications of longitudinal data. The former base model takes the associations among predictors and among criteria into account. The latter takes the auto-associations among repeatedly observed variables into account. Application examples of each are given using data from a longitudinal study of domestic violence. It is demonstrated that CFA results are not redundant with results from log-linear modeling or multinomial regression and that, of these approaches, CFA shows particular utility when conducting person-oriented research.
von Eye
Alexander
A
Department of Psychology, Michigan State University, East Lansing, MI 48824-1116, USA. voneye@msu.edu
Mun
Eun Young
EY
Bogat
G Anne
GA
D000328:Adult; D001008:Anxiety Disorders / Q000175:diagnosis / Q000453:epidemiology / Q000523:psychology; D002675:Child, Preschool; D003866:Depressive Disorder / Q000175:diagnosis / Q000453:epidemiology / Q000523:psychology; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008137:Longitudinal Studies; D008297:Male; D008432:Mathematical Computing; D015233:Models, Statistical; D010552:Personality Assessment / Q000706:statistics & numerical data; D010553:Personality Development; D011247:Pregnancy; D011594:Psychometrics / Q000706:statistics & numerical data*; D012106:Research / Q000706:statistics & numerical data; D012307:Risk Factors; D012984:Software; D013179:Spouse Abuse / Q000523:psychology / Q000706:statistics & numerical data