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.
Browse
3 results
Search Results
Item Cultural humility, therapeutic relationship, and outcome: Between-therapist, within-therapist, and within-client effects(2019) Morales, Katherine Chante; Kivlighan, Jr., Dennis M; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The present study longitudinally examined the association between client-perceived cultural humility (CH) of the therapist, dyadic working alliance (WA), and dyadic session evaluation (SES). We analyzed cultural humility scores at three levels: a) between therapist b) within-therapist and c) within-client. Using a sample of 79 clients, 15 therapists, and 231 time periods, we conducted two multilevel analyses using dyadic WA and dyadic SES as predictors. We found that high between-therapist, within-therapist, and within-client CH yielded higher dyadic WA scores. We also found that within-therapist and within-client CH yielded higher dyadic SES scores. However, importance of client identity did not act as moderator as predicted for CH and dyadic WA; nor did importance of client identity moderate the relationships between within-therapist and within-client CH and dyadic SES. We did find that importance of client identity moderated the relationship between-therapist CH and dyadic SES. Implications for future research will be discussed. Keywords: cultural humility, working alliance, session evaluation, psychodynamic, HLMItem 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.Item THE EFFECT OF TWO MENTORING MODELS ON TEACHER ATTRITION(2015) Kuhaneck, Michael Patrick; Strein, William; Education Policy, and Leadership; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This quantitative study employs Hierarchical Linear Modeling (HLM) to complete a path analysis that determines the effect of two different mentoring models on teacher attrition in a local education agency (LEA). The research focuses on 38 comprehensive public schools to determine if teacher attrition was impacted by a countywide teacher mentoring model employed from 2007 to 2012 compared to a school-based teacher mentoring program employed from 2012 to 2014. The research also assessed if these models had varying impact based on the level of the school (elementary, middle, or high), the setting of the school (urban or rural), and the poverty level of the school as measured by free and reduced meal rate. The results illustrate there was no statistically significant correlation between teacher attrition and the mentoring model employed irrespective of the level, setting, or poverty rate of the school.