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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    Understanding the Mechanism of Panel Attrition
    (2009) Lemay, Michael; Kreuter, Frauke; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Nonresponse is of particular concern in longitudinal surveys (panels) for several reasons. Cumulative nonresponse over several waves can substantially reduce the proportion of the original sample that remains in the panel. Reduced sample size increases the variance of the estimates and reduces the possibility for subgroup analysis. Also, the higher the attrition, the greater the concern that error (bias) will arise in the survey estimates. The fundamental purpose of most panel surveys is to allow analysts to estimate dynamic behavior. However, current research on attrition in panel surveys focuses on the characteristics of respondents at wave 1 to explain attrition in later waves, essentially ignoring the role of life events as determinants of panel attrition. If the dynamic behaviors that panel surveys are designed to examine are also prompting attrition, estimates of those behaviors and correlates of those behaviors may be biased. Also, current research on panel attrition generally does not differentiate between attrition through non-contacts and attrition through refusals. As these two source of nonresponse have been shown to have different determinants, they can also be expected to have different impacts on data quality. The goal of this research is to examine these issues. Data for this research comes from the Panel Survey of Income Dynamics (PSID) conducted by the University of Michigan. The PSID is an ongoing longitudinal survey that began in 1968 and with a focus on the core topics of income, employment, and health.
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    Generalized Confirmatory Factor Mixture Modeling: A Tool for Assessing Factorial Invariance Across Unspecified Populations
    (2004-04-30) Gagne, Phill; Hancock, Gregory R; Measurement, Statistics and Evaluation
    Mixture modeling is an increasingly popular analysis in applied research settings. Confirmatory factor mixture modeling can be used to test for the presence of multiple populations that differ on one or more parameters of a factor model in a sample lacking a priori information about population membership. There have, however, been considerable difficulties regarding convergence and parameter recovery in confirmatory factor mixture models. The present study uses a Monte Carlo simulation design to expand upon a previous study by Lubke, Muthén, & Larsen (2002) which investigated the effects on convergence and bias of introducing intercept heterogeneity across latent classes, a break from the standard approach of intercept invariance in confirmatory factor modeling when the mean structure is modeled. Using convergence rates and percent bias as outcome measures, eight design characteristics of confirmatory factor mixture models were manipulated to investigate their effects on model performance: N; mixing proportion; number of indicators; factor saturation; number of heterogeneous intercepts, location of intercept heterogeneity, magnitude of intercept heterogeneity, and the difference between the latent means (Δκ) of the two modeled latent classes. A small portion of the present study examined another break from standard practice by having models with noninvariant factor loadings. Higher rates of convergence and lower bias in the parameter estimates were found for models with intercept and/or factor loading noninvariance than for models that were completely invariant. All manipulated model conditions affected convergence and bias, often in the form of interaction effects, with the most influential facets after the presence of heterogeneity being N and Δκ, both having a direct relation with convergence rates and an inverse relation with bias magnitude. The findings of the present study can be used to some extent to inform design decisions by applied researchers, but breadth of conditions was prioritized over depth, so the results are better suited to guiding future methodological research into confirmatory factor mixture models. Such research might consider the effects of larger Ns in models with complete invariance of intercepts and factor loadings, smaller values of Δκ in the presence of noninvariance, and additional levels of loading heterogeneity within latent classes.