Using latent class analysis to detect behavioral patterns in systems of observational variables

• Published in: Evaluation and program planning Vol. 19; no. 4; pp. 321 - 331
• Main Authors: Weβels, Holger, Von Eye, Alexander
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.1996; Elsevier
• Series: Evaluation and Program Planning
• Subjects: Behavior Patterns; Child Care Services; Classification; Computer Assisted Research; Data Reduction Methods; Day Care; Evaluation Methods; Evaluation Research; Infants; Latent Class Analysis; Latent Class Models; Latent Structure Analysis; Longitudinal Studies; Methodology (Data Analysis); Observation; Preference Data; Program Evaluation; Research Methodology; Sweden
• ISSN: 0149-7189
• DOI: 10.1016/S0149-7189(96)00030-4

Behavioral observation is widely used for data gathering in evaluation research. Yet it leaves the investigator with unique problems. Usually, multiple observations result in a hierarchical data set, where numerous data records exist for each subject. Researchers face data reduction problems at least at two levels. First, there is the well-known and often-addressed problem of reducing the number of variables in a data set with only one information record per individual. Second, there is the problem of summarizing data at the individual subject level. The easiest way to perform this latter type of aggregation involves using univariate summary measures as probabilities of “using” an item, means, or standard deviations for each item per subject. Other standard procedures include first order interactions between pairs of items. However, use of pair-wise interactions is restricted because of variable dependence within each subject (this Affects e.g. factor analysis), or because of the relatively high number of single observations (this Affects e.g., cluster analysis). In this paper we propose employing Latent Class Analysis (LCA) to reduce the amount of information in observational data sets. In a first step, LCA allows one to specify intraindividual behavior patterns. In a second step, LCA allows one to derive meaningful summary scores for each individual. The two steps are illustrated using data that describe peer play competence in Swedish toddlers.