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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2010 - 20122

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    The Broad Autism Phenotype Within Mother-Child Interactions
    (2012) Royster, Christina; Ratner, Nan; Hearing and Speech Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This study sought to identify features of the Broad Autism Phenotype (BAP) expressed by mothers during interactions with their infants to further understand how these features relate to early indicators of autism. Twelve mothers were selected who had an older child with autism, and the control group included twelve mothers who did not. Results demonstrated that the groups of mothers did not have significantly different responses on the BAP assessment, and they did not differ in any features of interactions, except that the experimental group used less inhibitory language. Children in the experimental group had lower language scores than the controls. When subjects were divided into groups based upon both child responsiveness and maternal BAP traits, subsequent patterns indicated four mother-child profiles, suggesting that a combination of maternal BAP characteristics and child behavior might influence interaction outcomes. Further research regarding BAP features as an early indicator for autism is discussed.
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    A Comparison of Methods for Testing for Interaction Effects in Structural Equation Modeling
    (2010) Weiss, Brandi A.; Harring, Jeffrey R.; Hancock, Gregory R.; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The current study aimed to determine the best method for estimating latent variable interactions as a function of the size of the interaction effect, sample size, the loadings of the indicators, the size of the relation between the first-order latent variables, and normality. Data were simulated from known population parameters, and data were analyzed using nine latent variable methods of testing for interaction effects. Evaluation criteria used for comparing the methods included proportion of relative bias, the standard deviation of parameter estimates, the mean standard error estimate, a relative ratio of the mean standard error estimate to the standard deviation of parameter estimates, the percent of converged solutions, Type I error rates, and empirical power. It was found that when data were normally distributed and the sample size was 250 or more, the constrained approach results in the least biased estimates of the interaction effect, had the most accurate standard error estimates, high convergence rates, and adequate type I error rates and power. However, when sample sizes were small and the loadings were of adequate size, the latent variable scores approach may be preferable to the constrained approach. When data were severely non-normal, all of the methods were biased, had inaccurate standard error estimates, low power, and high Type I error rates. Thus, when data were non-normal, relative comparisons were made regarding the approaches rather than absolute comparisons. In relative terms, the marginal-maximum likelihood approach performed the least poorly of the methods for estimating the interaction effect, but requires sample sizes of 500 or greater. However, when data were non-normal, the latent moderated structure analysis resulted in the least biased estimates of the first-order effects and had bias similar to that of the marginal-maximum likelihood approach. Recommendations are made for researchers who wish to test for latent variable interaction effects.