Cluster sampling results in response variable variation both among respondents (i.e., within cluster or Level 1) and among clusters (i.e., between-cluster or Level 2). Properly modeling within- and between-cluster variation could be of substantive interest in numerous settings, but applied researchers typically test only within-cluster (i.e., individual difference) theories. Specifying a between-cluster model in the absence of theory requires a specification search in multilevel structural equation modeling. This study examined a variety of within-cluster and between-cluster sample sizes, intraclass correlation coefficients, start models, parameter addition and deletion methods, and Type I error control techniques to identify which combination of start model, parameter addition or deletion method, and Type I error control technique best recovered the population of the between-cluster model. Results indicated that a "saturated" start model, univariate parameter deletion technique, and no Type I error control performed best, but recovered the population between-cluster model in less than 1 in 5 attempts at the largest sample sizes. The accuracy of specification search methods, suggestions for applied researchers, and future research directions are discussed.
ASJC Scopus subject areas
- Decision Sciences(all)
- Modeling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)