UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

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 given thesis/dissertation in DRUM.

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

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    MODELING CLUSTERED DATA WITH FEW CLUSTERS: A CROSS-DISCIPLINE COMPARISON OF SMALL SAMPLE METHODS
    (2015) McNeish, Daniel; Hancock, Gregory R.; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Small sample inference with clustered data has received increased attention recently in the methodological literature with several simulation studies being presented on the small sample behavior of various methods. There are several different classes of methods that can be implemented to account for clustering and disciplinary allegiances are quite rigid: for instance, recent reviews have found that 94% of psychology studies use multilevel models whereas only 3% of economics studies use multilevel models. In economics, fixed effects models are far more popular and in biostatistics there is a tendency to employ generalized estimating equations. As a result of these strong disciplinary preferences, methodological studies tend to focus only a single class of methods (e.g., multilevel models in psychology) while largely ignoring other possible methods. Therefore, the performance of small sample methods have been investigated within classes of methods but studies have not expanded investigations across disciplinary boundaries to more broadly compare the performance of small sample methods that exist in the various classes of methods to accommodate clustered data. Motivated by an applied educational psychology study with a few clusters, in this dissertation the various methods to accommodate clustered data and their small sample extensions are introduced. Then a wide ranging simulation study is conducted to compare 12 methods to model clustered data with a small number of clusters. Many small sample studies generate data from fairly unrealistic models that only feature a single predictor at each level so this study generates data from a more complex model with 8 predictors that is more reminiscent of data researchers might have in an applied study. Few studies have also investigated extremely small numbers of clusters (less than 10) that are quite common in many researchers areas where clusters contain many observations and are there expensive to recruit (e.g., schools, hospitals) and the simulation study lowers the number of clusters well into the single digits. Results show that some methods such as fixed effects models and Bayes estimation clearly perform better than others and that researchers may benefit from considering methods outside those typically employed in their specific discipline.
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    Women's Paid Labor Force Participation and Child Immunization: A Multilevel Model
    (2006-05-07) Strayhorn, Kali-Ahset Amen; DeRose, Laurie F.; Sociology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    I estimated the effect of women's cash work on child immunization in 25 countries across Africa, Asia and Latin America using a multi-level fixed-effects model and found support for the hypothesis that all children benefit in areas with higher rates of women's labor force participation. The proportion of women working within a sub-national region (province) has a strong, positive impact on the likelihood of complete child immunization. While all children benefit from increasing levels of women's work, the children of those who work benefit more from living in areas where women's work is at higher rates. Thus, this analysis supports the view that a child's complete immunization is influenced by the larger social context associated with women's work.