College of Education
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations..
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Item 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.Item Urbanicity and Academic Self-Concept(2009-02-27) Strein, William; Pickering, Cyril; Grossman, JulieThe main focus of this study was the relationships between school urbanicity (size of community in which the school is located) and fifth-grade students’ academic self-concepts. Using multi-level modeling methodology (HLM) we were able to investigate “school effects”, net of individual students’ characteristics. School urbanicity had no effect on reading, math, or general academic self-concept. School-level effects were found consistently for aggregate school achievement in reading and math, congruent with Marsh’s Big-Fish-Little-Pond effect. Less consistent school-level effects were found for proportion of minority students and school-average SES. Individual level effects mirrored those reported in other literature with tested achievement having the greatest effectItem Big Fish and Other School Effects on Academic Self-Concept(2010-08-14) Strein, William; Grossman, JuliieA substantial amount of research indicates that academic self-concept is a function of both individual characteristics, and school effects that impact on the development of self-perceptions. Few studies have studied a cohort of students as they progress through the transition from elementary to middle school. The present study uses multi-level modeling to examine school effects on students’ academic self-concept in reading and math as they transition from elementary to middle school. Data come from the ECLS-K data set. Few school effects were found, but students’ SES was found to be a strong moderator of the relationship between reading achievement and self-perceptions of students’ ability and interest in reading.