Doctoral Dissertation Research: Cross-Classified, Multiple-Membership Modeling for Multilevel, Nonnested Data

University Of Cincinnati Main Campus, Cincinnati OH

Investigators

Abstract

Data collected in many social sciences often are characterized by multilevel or nested structures in which a lower-level unit belongs to one and only one higher-level unit; for instance, students attend one and only one school, and patients are treated by one and only one health care provider. Conventional multilevel modeling for nested data is now well understood and frequently applied in different research areas. However, many data structures are multilevel but do not qualify as nested: Students may attend more than one school, and patients may be treated by more than one health care provider. Cross-classified, multiple-membership (CCMM) modeling, a general statistical framework for modeling multilevel, nonnested data, was set forth by Browne, Goldstein, and Rasbash (2001). It has a wide range of potential applications in many research areas, including education, health research and epidemiology, sociology, and human genetics. Though applications of CCMM modeling have started to appear in the literature, the statistical aspects of CCMM modeling have not been investigated extensively, and the practical experiences of model building are still very limited. This dissertation research will evaluate the estimation performance of CCMM modeling and investigate the consequences of ignoring multilevel, nonnested data structures using both real data analyses and Monte Carlo simulation. CCMM modeling will be applied to the Early Childhood Longitudinal Study Kindergarten Cohort (ECLS-K) data to model reading and mathematics growth from kindergarten to fifth grade after incorporating student mobility. Guided by real data analyses, a comprehensive Monte Carlo simulation will be conducted to evaluate the estimation performance of CCMM modeling and consequences of ignoring CCMM data structures under manipulated data conditions that emulate real data structures. User-friendly computer program codes and step-by-step tutorials will be written to facilitate the use of CCMM modeling in applied research. This research includes not only a state-of-the-art review of advanced statistical modeling for complex data structures and their applications, but also the first systematic investigation of the statistical performance of CCMM modeling for multilevel nonnested data using Bayesian estimation. It will demonstrate the flexibility of CCMM modeling in analyzing multilevel nonnested data and lead to advancements of scientific knowledge regarding appropriate modeling of complex data in real research settings. The real data analyses with ECLS-K will show the applicability of CCMM modeling in applied research and help educators and researchers to better understand how student mobility affects early reading and mathematics development. The Monte Carlo simulation study will provide evidence regarding the statistical performance of CCMM modeling and methodological instructions on CCMM model building for the research community, which eventually will facilitate translating innovative quantitative research methods into rigorous applied research in the social sciences and beyond. As a Doctoral Dissertation Research Improvement award, support is provided to enable a promising student to establish a strong, independent research career.