Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…

In this study, we reanalyzed the classic Hawai'i Family Study of Cognition (HFSC) data using contemporary multilevel modeling techniques. We used the HFSC baseline data ("N" = 6,579) and reexamined the factorial structure of 16 cognitive variables using confirmatory (restricted) measurement models in an explicit sequence. These models…

The current study evaluated the relationship between various operationalizations of the Advanced Placement[R] (AP) exam and course information with first-year grade point average (FYGPA) in college to better understand the role of AP in college admission decisions. In particular, the incremental validity of the different AP variables, above…

This pedagogical article is intended to explain the similarities and differences between the parameterizations of two multilevel measurement model (MMM) frameworks. The conventional two-level MMM that includes item indicators and models item scores (Level 1) clustered within examinees (Level 2) and the two-level cross-classified MMM (in which item…

Intraclass correlations are used to summarize the variance decomposition in populations with multilevel hierarchical structure. There has recently been considerable interest in estimating intraclass correlations from surveys or designed experiments to provide design parameters for planning future large-scale randomized experiments. The large…