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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Now showing 1 - 4 of 4
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    INVESTIGATING METHODS OF INCORPORATING COVARIATES IN GROWTH MIXING MODELING: A SIMULATION STUDY
    (2015) Li, Ming; Harring, Jeffrey R.; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The current research aims to evaluate the performance of various approaches for estimating covariates within the latent class membership regression model in the context of growth mixture models. Researchers have been searching for more efficient and accurate estimation methods for incorporating covariate information in mixture modeling in order to clearly differentiate between subjects from different groups and to make interpretation of the growth trajectories more meaningful. However, few studies have considered more complicated models such as growth mixture models where the latent class variable is more difficult to identify. To this end, two Monte Carlo simulations were conducted. In Simulation I, four estimation approaches were investigated to examine parameter recovery, variance and standard error efficacy related to both categorical and continuous covariates that defined the regression model for the latent class membership part of the model. Data generated for Simulation II include three covariates, with one dichotomous variable linked to latent class membership and the other two (one dichotomous and one continuous) associated with measurement part of the growth mixture model. Three estimation approaches were then compared using the population data generation model as well as a misspecified model.
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    Taxometric Analysis of Negative Symptoms in An International Sample of Ten Countries
    (2011) Wilson, Amy; Blanchard, Jack J; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Negative symptoms have emerged as a replicable factor of symptomatology within schizophrenia. Although rating scales provide assessments along dimensions of severity, categorization of a negative symptom subtype is typically concluded. Despite an accumulation of findings that support categorical conceptualization, the data are also consistent with a dimensional-only model where negative symptom subtypologies simply reflect an extreme on a continuum of severity. Previous studies (Blanchard, et al, 2005) have used taxometric statistical methods to confirm the existence of a negative symptom subtype; however, the nature of taxometric methods requires replication (Waller & Meehl, 1998). The current investigation is a taxometric analysis of the World Health Organization Ten-Country Study of Schizophrenia. Data from a subset of 694 individuals were analyzed using the taxometric methods of maximum covariance analysis (MAXCOV) and mean above minus below a cut (MAMBAC) and a latent class with a base rate of approximately .14 - .16 was identified.
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    An Empirical Investigation of Unscalable Components in Scaling Models
    (2009) Braaten, Kristine Norene; Dayton, C. M.; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Guttman (1947) developed a scaling method in which the items measuring an attribute can be ordered according to the strength of the attribute. The Guttman scaling model assumes that every member of the population belongs to a scale type and does not allow for response errors. The Proctor (1970) and the intrusion-omission (Dayton and Macready, 1976) models introduced the notion that observed response patterns deviate from Guttman scale types because of response error. The Goodman (1975) model posited that part of the population is intrinsically unscalable. The extended Proctor and intrusion-omission (Dayton and Macready, 1980) models, commonly called extended Goodman models, include both response error and an intrinsically unscalable class (IUC). An alternative approach to the Goodman and extended Goodman models is the two-point mixture index of fit developed by Rudas, Clogg, and Lindsay (1994). The index, pi-star, is a descriptive measure used to assess fit when the data can be summarized in a contingency table for a hypothesized model. It is defined as the smallest proportion of cases that must be deleted from the observed frequency table to result in a perfect fit for the postulated model. In addition to contingency tables, pi-star can be applied to latent class models, including scaling models for dichotomous data. This study investigates the unscalable components in the extended Goodman models and the two-point mixture where the hypothesized model is the Proctor or intrusion-omission model. The question of interest is whether the index of fit associated with the Proctor or intrusion-omission model provides a potential alternative to the IUC proportion for the extended Proctor or intrusion-omission model, or in other words, whether or not pi-star and the IUC proportion are comparable. Simulation results in general did not support the notion that pi-star and the IUC proportion are comparable. Six-variable extended models outperformed their respective two-point mixture models with regard to the IUC proportion across almost every combination of condition levels. This is also true for the four-variable case except the pi-star models showed overall better performance when the true IUC proportion is small. A real data application illustrates the use of the models studied.
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    Testing for Differentially Functioning Indicators Using Mixtures of Confirmatory Factor Analysis Models
    (2009) Mann, Heather Marie; Hancock, Gregory R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Heterogeneity in measurement model parameters across known groups can be modeled and tested using multigroup confirmatory factor analysis (CFA). When it is not reasonable to assume that parameters are homogeneous for all observations in a manifest group, mixture CFA models are appropriate. Mixture CFA models can add theoretically important unmeasured characteristics to capture heterogeneity and have the potential to be used to test measurement invariance. The current study investigated the ability of mixture CFA models to identify differences in factor loadings across latent classes when there is no mean separation in both the latent and measured variables. Using simulated data from models with known parameters, parameter recovery, classification accuracy, and the power of the likelihood-ratio test were evaluated as impacted by model complexity, sample size, latent class proportions, magnitude of factor loading differences, percentage of noninvariant factor loadings, and pattern of noninvariant factor loadings. Results suggested that mixture CFA models may be a viable option for testing the invariance of measurement model parameters, but without impact and differences in measurement intercepts, larger sample sizes, more noninvariant factor loadings, and larger amounts of heterogeneity are needed to distinguish different latent classes and successfully estimate their parameters.