This Monte Carlo simulation examined the effects of variable selection (combinations of confounders with four patterns of relationships to outcome and assignment to treatment) and number of strata (5, 10, or 20) in propensity score analyses. The focus was on how the variations affected the average effect size compared to quasi-assignment without adjustment for bias. Results indicate that if a propensity score model does not include variables strongly related to both outcome and assignment, not only will bias not decrease, but it may possibly increase. Furthermore, models that include a variable highly related to assignment to treatment but do not also include a variable highly related to the outcome could increase bias. In regards to the number of strata, results varied depending on the propensity score model and sample size. In 75% of the models that resulted in a significant reduction in bias, quintiles outperformed the other stratification schemes. In fact, the richer that the propensity score model was (i.e., including multiple covariates of varying relationships to the outcome and to assignment to treatment), the more likely that the model required fewer strata to balance the covariates. In models without that same richness, additional strata were necessary. Finally, the study suggests that when developing a rich propensity score model with stratification, it is crucial to examine the strata for overlap.

To Rod, my time as your graduate assistant was a tremendously rewarding experience but even more satisfying were the countless hours spent in your office conversing. It was during one of those conversations with Rod that the idea to explore student course evaluations was born.To Bob, I want to thank you for your support and for believing in the merit of this research, without which I would not have been able to conduct my study. I must also thank you and your research staff for dedicating innumerable hours to procuring and deidentifying data. Becky Patterson and Arnold Hook were tremendous assets and must be commended for their work in linking and mining the data that were analyzed in my study.To Joe, thank you for believing in me when I came to you as a student in counseling psychology and asked for admission into the ELFH program. Your extensive knowledge of student course evaluations, in particular the instrument used in the current study, was a great resource. concerning the instructor's teaching ability, preparation, grading, the course text and organization to which the student rates their agreement with the statement on a 5 point Likert-type scale ranging from 1 "Strongly Disagree", "Poor", or "Very Low" to 5 "Strongly Agree", "Excellent" or "Very High".In order to assess the relationship between the student, course, and instructor-level variables and the student course rating, hierarchical linear modeling (HLM) analyses were conducted. Most of the variability in student course rating was estimated at the student-level and this was reflected in the fact that most of the statistically significant relationships were found at the student-level. Prior student course interest and the amount v of student effort were statistically significant predictors of student course rating in all of the regression models. These findings were supported by previous studies and provide further evidence of such relationships.Additional HLM analyses were conducted to assess the relationship between student course rating and final course grade. Results of the HLM analyses indicated that student course rating was a statistically significant predictor of student course grade. This finding is consistent with the existing literature which posits a weak positive relationship between expected course grade and student course rating.vi

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This study examined community and institutional factors that influence offering online workforce development programs in community colleges. The study included a random sample of 321 community college in the United States. Findings conclude that colleges operating under statewide governance structures and in states with more highly centralized statewide practices have more online occupational programs than other types of institutions. In addition, student racial demographics factor into online course offerings. Institutions with higher percentages of White students are more likely to offer online occupational programs. These findings illustrate a potential need for additional online program development in colleges with larger percentages of students of color and raise questions about how states with decentralized systems can increase educational access by facilitating additional online workforce development programs.