Doctor of Education (Ed.D.), Bowling Green State University, 2020, Leadership Studies

This study explored the influence of randomized belonging interventions on academic outcomes among first-generation college students at a mid-sized, Midwest, four-year public institution. Astin's Input-Environment-Outcome (I-E-O) model served as the conceptual framework for investigating the impact of an environmental sense of belonging intervention on outcomes such as first-year grade point average and continuous enrollment. A convenience sampling technique was utilized to recruit a total of 10,281 students from across three cohorts (2015, 2016, and 2017) of incoming first-time undergraduate students. Participants were invited to complete an online, text-based intervention, the College Transition Collaborative Social Belonging Intervention (CTCSBI) during the summer prior to the beginning of their first semester. Among the sample population, 7,278 students were randomized to one of three treatment conditions. A balanced design was used to give equal representation in each condition. A non-treatment control group (N = 3,003) was included as part of a quasi-experimental component of the study. Factorial analysis of covariance (ANCOVA) was used to analyze and examine six research questions, to test the independent variables (Generation Status and Treatment Condition) with respect to the dependent variables (First-Year Grade Point Average and Continuous Enrollment), and to examine interaction effects while controlling for variables known to influence academic outcomes (High School Grade Point Average, Standardized College Entrance Exam Score, and Ethnicity). The results of the study suggested that completing a pre-matriculation intervention significantly influenced first-year grade point average and continuous enrollment of first-generation college students. This study and the related findings are especially important given the opportunity for such interventions to address and reduce achievement gaps of underserved students, align university initiatives wi (open full item for complete abstract)

Committee: Kristina LaVenia Ph.D. (Committee Chair); Margaret McCubbin MFA (Other); Julia Matuga Ph.D. (Committee Member); Mary Murphy Ph.D. (Committee Member); Patrick Pauken Ph.D., J.D. (Committee Member) Subjects: Education; Higher Education

Master of Arts, The Ohio State University, 2016, Psychology

Moderation analysis is used throughout many scientific fields, including psychology and other social sciences, to model contingencies in the relationship between some independent variable (X) and some outcome variable (Y) as a function of some other variable, typically called a moderator (M). Inferential methods for testing moderation provide only a simple yes/no decision about whether the relationship is contingent. These contingencies can often be complicated. Researcher often need to look closer. Probing the relationship between X and Y at different values of the moderator provides the researcher with a better understanding of how the relationship changes across the moderator. There are two popular methods for probing an interaction: simple slopes analysis and the Johnson-Neyman procedure. The Johnson-Neyman procedure is used to identify the point(s) along a continuous moderator where the relationship between the independent variable and the outcome variable transition(s) between being statistically significant to nonsignificant or vice versa. Implementation of the Johnson-Neyman procedure when X is either dichotomous of continuous is well described in the literature; however, when X is a multicategorical variable it is not clear how to implement this method. I begin with a review of moderation and popular probing techniques for dichotomous and continuous X. Next, I derive the Johnson-Neyman solutions for three groups and continue with a partial derivation for four groups. Solutions for the four-group derivation rely on finding the roots of an eighth-degree polynomial for which there is no algebraic solution. I provide an iterative computer program for SPSS and SAS that solves for the Johnson-Neyman boundaries for any number of groups. I describe the performance of this program, relative to known solutions, and typical run-times under a variety of circumstances. Using a real dataset, I show how to analyze data using the tool and how to interpret the results. I co (open full item for complete abstract)

Committee: Andrew Hayes (Advisor); Michael Edwards (Committee Member); Duane Wegener (Committee Member) Subjects: Applied Mathematics; Behavioral Sciences; Biostatistics; Experimental Psychology; Psychology; Quantitative Psychology; Statistics

Master of Science, The Ohio State University, 2014, Biostatistics

Pre-post studies, where outcomes are measured both before and after an intervention, are common in biomedical research. When the outcomes at both pre- and post-test are completely observed, previous studies have shown that analysis of covariance (ANCOVA) is more powerful than the change score analysis in testing the treatment effect and therefore is usually recommended in analyzing pre-post studies. However, methods for analyzing pre-post studies with missing outcome values have not been compared. The goal of this study was to compare the power of two analysis methods in testing for a treatment effect when post-test values are missing: ANCOVA after multiple imputation (MI) and the mixed model. To do so, we analyzed data from a real study, the BePHIT study, and performed simulation studies. Four analysis methods were used to analyze the BePHIT and simulated data: ANCOVA after MI, ANCOVA using only complete cases (CC), the mixed model using all-available data, and the mixed model using complete cases. Simulation studies were conducted under various sample sizes, missingness rates, and missingness scenarios. In the analysis of the BePHIT data, ANCOVA after MI produced the smallest p-value for the test of a treatment effect. However, in the simulation studies, CC ANCOVA was generally the most powerful method. The simulation studies also showed that the power of ANCOVA after MI dropped the fastest when the percentage of missingness increased and, for most scenarios, was the least powerful method when 50% of the post-test outcomes were missing.

Committee: Michael Pennell (Advisor); Rebecca Andridge (Committee Member) Subjects: Biostatistics

Doctor of Philosophy (PhD), Ohio University, 2012, Curriculum and Instruction (Education)

This study examined teachers' attitudes and perceptions toward transition services for students with mild intellectual disabilities in Saudi Arabia using a descriptive non-experimental quantitative research design. This study also examined the relationship between teachers' attitudes regarding transition services for students with mild intellectual disabilities and teachers' gender and educational background. Three hundred and sixty nine teachers responded to the study survey, including 223 males and 146 females. A two-way ANCOVA and descriptive statistics were used to answer the research questions. The findings indicated that teachers hold positive attitudes toward transition services. Also, this study found no differences in teachers' attitudes based on their gender or educational background. However, this study found differences in teachers' attitudes related to years of teaching experience, and having a relative or someone close with a disability. In addition, teachers in this study reported they felt unprepared to provide transition services. A lack of job opportunities and an anticipated reluctance by employers to hire youth with disabilities were perceived as the greatest potential challenges in providing transition services. Supporting youth with mild disabilities in real work environments was perceived by teachers as the best option for exposing them to work experiences. Finally, this study presents the major potential implications regarding transition services for students with mild intellectual disabilities in Saudi Arabia in terms of teachers' preparation for transition services, best practices for transition services, options for providing work experience, and potential challenges that could be encountered.

Committee: Dianne Gut (Committee Chair); George Johanson (Committee Member); Ronaldo Vigo (Committee Member); Greg Kessler (Committee Member); Danielle Dani (Committee Member) Subjects: Middle School Education; Special Education; Vocational Education

Doctor of Philosophy (PhD), Ohio University, 2007, Educational Research and Evaluation (Education)

This study focuses on the development and examination of a new method to construct frequency tables for grouped data. This method is called the iteration algorithm in that it proceeds by successive iterations to determine the four key elements that are essential in building a grouped-data frequency distribution. The algorithm also uses five formulas and stops running as soon as the first solution is attained (for teaching purposes only). Two major interests emerged. The first interest was to evaluate how accurate the iteration algorithm is as a process. The second and main focus of this study was to assess the effectiveness of the iteration algorithm as an instructional method. The findings of the Monte Carlo simulations to address the first main interest showed that the results yielded by the iteration algorithm are comparable to those produced by a well-known statistical package. To tackle the second foremost aspect of this study, the multivariate analysis of covariance (MANCOVA) results indicated that the students expressed, on average, more positive attitudes towards the iteration algorithm than towards a traditional method in learning how to construct their own grouped-data frequency tables.

Committee: Gordon Brooks (Advisor) Subjects: Education, Tests and Measurements

PHD, Kent State University, 2009, College of Education, Health and Human Services / School of Lifespan Development and Educational Sciences

This study investigated the effectiveness of a standard protocol Tier 2 reading intervention among third and fifth grade students and the methodologies used to determine the intervention's effectiveness. Several confounding covariates were observed as a result of utilizing eligibility criteria for assignment to the Tier 2 intervention condition. The biasing effects of these covariates were controlled using traditional ANCOVA and a methodology typically utilized in medical observational studies, propensity score analysis. Although a large amount of research is available on the effectiveness of particular Tier 2 interventions, no research has compared the merits of ANCOVA and propensity score analysis in estimating the effectiveness of these interventions in an applied setting. Three significant findings were obtained in this study. First, although third grade students receiving Tier 2 reading intervention made significant gains toward closing the grade level achievement gap, their gains were smaller than those of peers receiving only Tier 1 intervention. Among fifth grade students, both groups gained at least one grade level, although there was no difference in gains of students receiving Tier 1 and Tier 2 interventions. Third, similar effect sizes were reported by ANCOVA and propensity score analysis approaches in both the third and fifth grade studies. Propensity score analysis resulted in similar conclusions while reporting treatment effects in terms of actual criterion scores (i.e., Ohio Achievement Test-Reading). Traditional ANCOVA analysis reported treatment effects as adjusted criterion scores which are not necessarily reflective of achievement test scaling. This study has significant implications for future research and current practice regarding school psychologists' role in systems consultation, improving achievement for all students, and data-based decision making within a response-to-intervention framework.

Committee: Richard Cowan PhD (Committee Co-Chair); Shawn Fitzgerald PhD (Committee Co-Chair); Frank Sansosti PhD (Committee Member) Subjects: Developmental Psychology; Education; Educational Evaluation; Educational Psychology; Elementary Education; Psychology; School Administration; Statistics