This dissertation provides historical insight into the design and implementation of one strategic plan of a public higher education system in an effort to inform future similar strategic planning processes. On July 1, 1994, the Board of Regents appointed Stephen R. Portch the ninth Chancellor of the University System of Georgia. The timing was advantageous because then Governor Zell Miller was determined to leave his legacy as Georgia’s “education governor,” and in those prosperous economic times, the Governor was eager to pour money into the university system. The regents selected Portch because they recognized his potential to lead the system through a period of unprecedented transformation. They were looking for a leader with vision, and they saw that in Portch. The goal of the Portch chancellorship was to move the University System of Georgia into the national forefront, and he achieved this objective via strategic planning. The strategic planning process occurred in three phases. The first phase was the development of a vision statement; the second stage was the ratification of 34 guiding principles; and the final stage was implementation of the plan. This dissertation provides an analysis of the strategic planning process and its resulting policy directives. Using historical research methods, I carefully examine the primary goals the strategic plan set forth, and whether and how it met those goals. Further, I examine Portch’s leadership style, identifying both strengths and weaknesses, as well as how his leadership influenced the success of the plan. Using oral history methods, I interviewed the Chancellor Emeritus as well as members of the Board of Regents and the University System Office staff who played key roles in the development and implementation of the strategic plan. I also interviewed faculty members, students, and legislators to solicit their perspectives on the Chancellor, the plan, and their legacy. In the mid-1990s, the university system benefited greatly from the synergy of having a unified board, a supportive governor and legislature, and a booming economy. These conditions enabled Portch, a chancellor with a vision and strong leadership skills, to move the system forward significantly in a relatively short time.

ABSTRACT LUCY DIGGS SLOWE, HOWARD UNIVERSITY DEAN OF WOMEN, 1922-1937: EDUCATOR, ADMINISTRATOR, ACTIVIST by Lisa R. Rasheed Within the last twenty years, some educational researchers initiated an emphasis to study the accomplishments and contributions of African-American women in higher education. Although they were marginally recognized, some African-American women forged into uncharted territories by providing examples of administrative leadership in post-secondary settings. Their triumphs and failures have gone unnoticed, leaving a vacant space in the chronicles of history in higher education. Little is know about one African-American woman, as an administrator at a co-educational institution in terms of her vision about her position as a professional, her view of student-oriented services and activities, and her acknowledgement and realization of the need for a student-centered community as a vital context for learning. Using historical methods, this study examines the life and work of Lucy Diggs Slowe, Howard University Dean of Women from 1922 until her death in 1937. The purpose of this study is to offer a more comprehensive illumination about Slowe’s experiences and contributions as an educational leader. Lucy Diggs Slowe was a woman of strong constitution and substance. A woman of many firsts, she was one of the founding members of the African-American sorority Alpha Kappa Alpha in 1908. Slowe would go on to leave an indelible imprint philosophically, professionally, and personally on the lives she touched as both an administrator at Howard University and a member of the Washington, D.C. community. Slowe’s contributions are worthy of study to better understand how she embodied leadership by focusing on her career in higher education as an administrator.

When researchers are unable to randomly assign students to treatment conditions, selection bias is introduced into the estimates of treatment effects. Random assignment to treatment conditions, which has historically been the scientific benchmark for causal inference, is often impossible or unethical to implement in educational systems. For example, researchers cannot deny services to those who stand to gain from participation in an academic program. Additionally, students select into a particular treatment group through processes that are impossible to control, such as those that result in a child dropping-out of high school or attending a resource-starved school. Propensity score methods provide valuable tools for removing the selection bias from quasi-experimental research designs and observational studies through modeling the treatment assignment mechanism. The utility of propensity scores has been validated for the purposes of removing selection bias when the observations are assumed to be independent; however, the ability of propensity scores to remove selection bias in a multilevel context, in which group membership plays a role in the treatment assignment, is relatively unknown. A central purpose of the current study was to begin filling in the gaps in knowledge regarding the performance of propensity scores for removing selection bias, as defined by covariate balance, in multilevel settings using a Monte Carlo simulation study. The performance of propensity scores were also examined using a large-scale national dataset. Results from this study provide support for the conclusion that multilevel characteristics of a sample have a bearing upon the performance of propensity scores to balance covariates between treatment and control groups. Findings suggest that propensity score estimation models should take into account the cluster-level effects when working with multilevel data; however, the numbers of treatment and control group individuals within each cluster must be sufficiently large to allow estimation of those effects. Propensity scores that take into account the cluster-level effects can have the added benefit of balancing covariates within each cluster as well as across the sample as a whole.

THE NATURE OF FEEDBACK PROVIDED TO ELEMENTARY STUDENTS BY TEACHERS IN SCHOOLS WHERE GRADING AND REPORTING ARE STANDARDS-BASED Feedback is one of the most powerful influences on learning and achievement. Hattie (2002) found that the giving of quality feedback to students is one of the top five strategies teachers can use to improve student achievement. Research has confirmed that the right kind of feedback is essential for effective teaching and learning (McMillan, 2007). The University of Queensland (Australia) notes that feedback is the entity that brings assessment into the learning process (1998). The evidence also shows, however, that how feedback is given and the types of feedback given can provide disparate results with both achievement and student motivation. One mitigating factor to the giving and receiving of feedback in classrooms is a climate of evaluation, competition, rewards, punishments, winners and losers. In fact, research shows that while the giving of descriptive feedback enhances learning and motivation, the giving of norm-referenced grades has a negative impact on students (Bandura, 1993; Black & Wiliam, 1998; Butler & Nisan, 1986; Butler, 1987). This qualitative study used interviews, teacher observations, and document analysis to seek out the nature of feedback provided to students in a standards-based school district, where grading is standards-based rather than norm-referenced. The literature review suggests particular properties and circumstances that make feedback effective, and the researcher has used this research to analyze the oral and written feedback that teachers provide students. The analysis describes the use of feedback and feedback loops in these classrooms and the findings add to the current knowledge-base about the giving and receiving of feedback in standards-based schools and suggests areas for teacher improvement and development.

This study aimed to explore the effectiveness of graduation coach services in reducing student risk factors for dropping out of high school and increasing student academic performance, a strong correlate of student persistence to high school graduation (Battin-Pearson et al., 2000). The study employed a quasiexperimental nonequivalent control group design utilizing student risk ratio and individualized Georgia High School Graduation Test (GHSGT) scores in English/language arts and mathematics as measures to compare students who received the services of a graduation coach to those who did not. The sample for this study included 39,326 Georgia students continuously enrolled in the 11th grade during the 2008 – 2009 school year and characterized as at risk for high school noncompletion by virtue of possessing a student risk ratio greater than zero. Of these students, 9,076 (23.08%) were selected as caseload students to receive the intervention and support services of a graduation coach (GaDOE, 2009c). To assess the differential effects of graduation coach services on student risk ratio across school improvement regions, gender, and ethnicity, the researcher conducted a series of general linear model (GLM) multivariate repeated measures analyses. Risk ratios for students served by a graduation coach were found to be significantly lower in May 2009 than in August 2008. Differences in student risk ratios existed regionally across the state regardless of graduation coach caseload status. While analyses related to the differential effects of graduation coach service provision revealed no significant difference in the student risk ratio metric according to gender, they did reflect a significant difference in the student risk ratio metric according to graduation coach caseload status and ethnicity. Regression analyses determined that student risk ratio may account for 12.5 percent of the variance in student GHSGT mathematics scores and 9.6 percent of the variance in student GHSGT English/language arts scores for first-time 11th graders. Adding student caseload status into the equation increases the variance accounted for to 12.9 percent in mathematics and 10.4 percent in English/language arts.

Most quantitative research is conducted by randomly selecting members of a population on which to conduct a study. When statistics are run on a sample, and not the entire population of interest, they are subject to a certain amount of error. Many factors can impact the amount of error, or bias, in statistical estimates. One important factor is sample size; larger samples are more likely to minimize bias than smaller samples. Therefore, determining the necessary sample size to obtain accurate statistical estimates is a critical component of designing a quantitative study. Much research has been conducted on the impact of sample size on simple statistical techniques such as group mean comparisons and ordinary least squares regression. Less sample size research, however, has been conducted on complex techniques such as hierarchical linear modeling (HLM). HLM, also known as multilevel modeling, is used to explain and predict an outcome based on knowledge of other variables in nested populations. Ordinal logistic HLM (OLHLM) is used when the outcome variable has three or more ordered categories. While there is a growing body of research on sample size for two-level HLM utilizing a continuous outcome, there is no existing research exploring sample size for OLHLM. The purpose of this study was to determine the impact of sample size on statistical estimates for ordinal logistic hierarchical linear modeling. A Monte Carlo simulation study was used to investigate this research query. Four variables were manipulated: level-one sample size, level-two sample size, sample outcome category allocation, and predictor-criterion correlation. Statistical estimates explored include bias in level-one and level-two parameters, power, and prediction accuracy. Results indicate that, in general, holding other conditions constant, bias decreases as level-one sample size increases. However, bias increases or remains unchanged as level-two sample size increases, holding other conditions constant. Power to detect the independent variable coefficients increased as both level-one and level-two sample size increased, holding other conditions constant. Overall, prediction accuracy is extremely poor. The overall prediction accuracy rate across conditions was 47.7%, with little variance across conditions. Furthermore, there is a strong tendency to over-predict the middle outcome category.

While use of hierarchical linear modeling (HLM) to predict an outcome is reasonable and desirable, employing the model for prediction without first establishing the model’s predictive validity is ill-advised. Estimating the predictive validity of a regression model by cross-validation has been thoroughly researched, but there is a dearth of research investigating the cross-validation of hierarchical linear models. One of the major obstacles in cross-validating HLM is the lack of a measure of explained variance similar to the squared multiple correlation coefficient in regression analysis. The purpose of this Monte Carlo simulation study is to explore the impact of sample size, centering, and predictor-criterion correlation magnitudes on potential cross-validation measurements for hierarchical linear modeling. This study considered the impact of 64 simulated conditions across three explained variance approaches: Raudenbush and Bryk’s (2002) proportional reduction in error variance, Snijders and Bosker’s (1994) modeled variance, and a measure of explained variance proposed by Gagné and Furlow (2009). For each of the explained variance approaches, a cross-validation measurement, shrinkage, was obtained. The results indicate that sample size, predictor-criterion correlations, and centering impact the cross-validation measurement. The degree and direction of the impact differs with the explained variance approach employed. Under some explained variance approaches, shrinkage decreased with larger level-2 sample sizes and increased in others. Likewise, in comparing group- and grand-mean centering, with some approaches grand-mean centering resulted in higher shrinkage estimates but smaller estimates in others. Larger total sample sizes yielded smaller shrinkage estimates, as did the predictor-criterion correlation combination in which the group-level predictor had a stronger correlation. The approaches to explained variance differed substantially in their usability for cross-validation. The Snijders and Bosker approach provided relatively large shrinkage estimates, and, depending on the predictor-criterion correlation, shrinkage under both Raudenbush and Bryk approaches could be sizable to the degree that the estimate begins to lack meaning. Researchers seeking to cross-validate HLM need to be mindful of the interplay between the explained variance approach employed and the impact of sample size, centering, and predictor-criterion correlations on shrinkage estimates when making research design decisions.

Standardized testing has been part of the American educational system for decades. Controversy from the beginning has plagued standardized testing, is plaguing testing today, and will continue to be controversial. Given the current federal educational policies supporting increased standardized testing, psychometricians, educators and policy makers must seek ways to ensure that tests are not biased towards one group over another. In measurement theory, if a test item behaves differently for two different groups of examinees, this test item is considered a differential functioning test item (DIF). Differential item functioning, often conceptualized in the context of item response theory (IRT) is a term used to describe test items that may favor one group over another after matched on ability. It is important to determine whether an item is functioning significantly different for one group over another regardless as to why. Hypothesis testing is used to determine statistical significant DIF items; an effect size measure quantifies a statistical significant difference. This study investigated the addition of reporting an effect size measure for differential item functioning of items and tests’ (DFIT) noncompensatory differential item functioning (NCDIF), and reporting empirically observed power. The Mantel-Haenszel (MH) parameter served as the benchmark for developing NCDIF’s effect size measure, for reporting moderate and large differential item functioning in test items. In addition, by modifying NCDIF’s unique method for determining statistical significance, NCDIF will be the first DIF statistic of test items where in addition to reporting an effect size measure, empirical power can also be reported. Furthermore, this study added substantially to the body of literature on effect size by also investigating the behavior of two other DIF measures, Simultaneous Item Bias Test (SIBTEST) and area measure. Finally, this study makes a significant contribution to the body of literature by verifying in a large-scale simulation study, the accuracy of software developed by Roussos, Schnipke, and Pashley (1999) to calculate the true MH parameter. The accuracy of this software had not been previously verified.

During a time when most institutions of higher education are in search of underrepresented student participation, Georgia State University (GSU), a majority White institution, has observed a lack of involvement of White students in co-curricular activities. The purpose of the research study was to critically examine White students’ (dis) engagement in traditional student organizations at this university that has a significant student of color population. I used case study methodology that allowed for a breadth of conceptual frameworks and research options. The methods of collecting data included interviews (formal, informal, and oral history) of current and former students, as well as campus administrators. In addition, the use of archived texts and photographs, yearbooks, organization rosters, and university enrollment statistics allowed for crystallization of data, layered interpretations, and document analyses. I used the data sources to interpret GSU White students’ perceptions of campus climate, racial interactions, leadership among students of color, and racial identity that influence their (dis) engagement in traditional student organizations and campus life. In exploring the “rhetoric of diversity,” I argue that the experiences and attitudes of White students can inform the policy debate on institutional mission and offerings.

Arts education must continually provide justification for its inclusion in the K-12 curriculum. This dissertation utilizes philosophical and conceptual analysis to probe the tensions, ironies, and contradictions that permeate the arts education advocacy discourse. Using evidence from advocacy materials published online, scholarly critiques of themes in the advocacy discourse, and research reports describing school-based arts programs, I construct an argument that posits generative consequences for student learning when arts-centered inquiry is reimagined as pragmatic instrumentalism. Such a reimagining of arts-centered inquiry seeks to draw a distinction between utilitarian justifications for the arts and instrumental benefits the arts provide individual students in mediating complex and connected learning. In reclaiming the term “instrumental” for arts-centered inquiry, I offer a way to restore the notion of generativity to arts learning and a means to promote greater understanding among practitioners, researchers, policymakers, and advocates.