Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    A FINITE MIXTURE MULTILEVEL STRUCTURAL EQUATION MODEL FOR UNOBSERVED HETEROGENEITY IN RANDOM VARIABILITY
    (2023) Feng, Yi; Hancock, Gregory R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Variability is often of key interest in various research and applied settings. Important research questions about intraindividual variability (e.g., consistency across repeated measurements) or intragroup variability (e.g., cohesiveness among members within a team) are piquing the interest of researchers from a variety of disciplines. To address the research needs in modeling random variability as the key construct, Feng and Hancock (2020, 2022) proposed a multilevel SEM-based modeling approach where variability can be modeled as a random variable. This modeling framework is a highly flexible analytical tool that can model variability in observed measures or latent constructs, variability as the predictor or the outcome, as well as the between-subject comparison of variability across observed groups. A huge challenge still remains, however, when it comes to modeling the unobserved heterogeneity in random variability. Given that no existing research addresses the methodological considerations of uncovering the unobserved sub-populations that differ in intraindividual variability or intragroup variability, or sub-populations that differ in the various processes and mechanisms involving intraindividual variability or intragroup variability, the current dissertation study aims to fill this gap in literature. In the current study, a finite-mixture MSEM for modeling unobserved heterogeneity in random variability (MMSEM-RV) is introduced. Bayesian estimation via MCMC is proposed for model estimation. The performance of MMSEM-RV with Bayesian estimation is systematically evaluated in a simulation study across varying conditions. An illustrative example with empirical PISA data is also provided to demonstrate the practical application of MMSEM-RV.
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    The Predictors of Family Cohesion and Conflict in Transracially Adoptive Families
    (2010) Jackson, Dawnyea Dominique; Leslie, Leigh A; Family Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Transracial adoption in the United States has a short, but controversial history. Between 1971 and 2001, U.S. citizens adopted 265, 677 children from other countries. The increased prevalence and controversial history of transracial adoption makes it very important to learn more about the well being of transracially adoptive families. The purpose of the current study was to investigate the extent to which the diversity of the community in which a family lives and the parent's multiethnic experiences are predictors of family cohesion and conflict in transracially adoptive families. This relationship was examined for a sample (N=47) of Asian (n=24) Black (n=12) and Latino (n=11) participants. Results yielded no significant results, except for one interesting finding for the Latino racial/ethnic group. The results indicated that for the Latino racial/ethnic group the higher the parent's multiethnic experiences the lower the level of family cohesion, which was not in the predicted direction. The empirical implications of these findings are discussed.