Thesis (Ed.D.)--University of Tulsa, 1974. / Bibliography: leaf 26.

Thesis (Ed.D.)--University of Tulsa, 1974. / Bibliography: leaf 26.

Thesis (Ph. D.)--UCLA, 2006. / Vita. Includes bibliographical references (leaves 151-156).

Thesis (Ph. D.)--University of Hawaii at Manoa, 1990. / Includes bibliographical references (leaves 94-95) / Microfiche. / vii, 95 leaves, bound 29 cm

"The need for efficient means of testing has long been recognized. To obtain efficiency in testing requires the study of four attributes of the testing instrument--namely: reliability, validity, interpretability and administrability. It is the purpose of this paper to examine in some detail the first of these attributes, reliability. In particular, this is an attempt to analyse the reliability of Mathematics 101 Test D which was administered at Florida State University in the fall of 1948"--Introduction. / Typescript. / "July, 1949." / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Science under Plan II." / Includes bibliographical references (leaf 28).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisors: Jere Confrey and Jill A. Marshall. Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Numerous school effectiveness studies have utilized least squares regression techniques to produce school effectiveness indices despite the fact that they are subject to serious sampling fluctuations when sample sizes are small. If the sample size is smaller than normally thought adequate for accurate prediction a larger sample can be analyzed by pooling students from similar programs from different schools. Even though the regression weights for similar programs should be similar across schools, direct pooling of students may be less than satisfactory. A technique such as Bayesian m-group regression can be used that will incorporate both the similarity of the regressions across schools as well as the uniqueness of the individual programs. This study empirically examines the predictive efficiency of four regression techniques that utilize individual student data as input. Cross-validation analyses were performed and mean squared errors, mean absolute errors, and correlations between observed and predicted scores were compared for four methods: (1) within-school least squares regression, (2) pooled least squares regression, (3) pooled least squares regression with adjusted alphas, and (4) Bayesian m-group regression with identical regression coefficients. In addition, school effectiveness indices were obtained for the four regression techniques as well as least squares regression using school means and mean difference scores. These effectiveness indices were compared, and the stability of these indices across random samples of students, and across consecutive classes examined. The within-school least squares regression method was found to be somewhat inferior to the other three models in terms of predictive efficiency. The Bayesian m-group equal slope model showed no appreciable advantage over the pooled least squares regression model or the pooled least squares regression model with adjusted alphas. The indices produced by all six methods appear to be capable of representing the relative effectiveness of the schools involved in the study. In addition, those indices that moderate the importance of extreme values remained relatively stable from one subsample to another with correlations ranging from .75 to .85. Stability from class to class were of a much lower magnitude than those values reflecting stability from sample to sample. Correlations between school effectiveness indices of consecutive classes ranged from .28 to .47. / Ph. D.

Since its establishment the Institute of Education Sciences has been creating opportunities and driving standards to generate research in education that is high quality rigorous, and relevant. This dissertation is an analysis of current practices in Goal III and Goal IV studies, in order to (1) better understand of the types of schools that agree to take part in these studies, and (2) an assess how representative these schools are in comparison to important policy relevant populations. This dissertation focuses on a subset of studies that were funded from 2005-2014 by the Department of Education, IES, under the NCER grants-funding arm. Studies included were those whose interventions were aimed at elementary students across core curriculum and ELL program areas. Study schools were compared to two main populations, the U.S population of elementary schools and Title I elementary schools, as well as these populations on a state level. The B-index, proposed by Tipton (2014) was the main value of comparison used to assess the compositional similarity, or generalizability, of study schools to these identified inference populations. The findings show that across all studies included in this analysis, participating schools were representative of the U.S. population of schools, B-index = 0.9. Comparisons were also made between this collection of schools and the respective populations at the state level. Results showed that these schools were not representative of any individual states (no B-index values were greater than 0.90). Across all included studies, schools that agreed to participate were more often located in urban areas, had higher rates of FRL students, had more minority students enrolled, and had more total students, in both district and school, than those schools in the population of U.S. schools. It is clear that the movement of education research is to be relevant to a larger audience. Through this study it is clear that, across studies, we are achieving some representation in IES funded studies. However, the finer comparisons, study samples to individual state and individual studies to these populations, show limited similarity between study schools and populations of interest to policy makers using these study findings to make decisions about their schools.

A longitudinal analysis of transactional data for an entire college cohort was mined from administrative student records systems to identify individual student behaviors and establish correlations between individual students’ behaviors and academic outcomes. Conducted at one large urban community college, this study determined curricular peer association behavior between individual students, and also evaluated late registration and course schedule change behaviors. Findings demonstrated a strong correlation between these three behavioral patterns and a lasting influence on academic outcomes, such as: semestrial GPA and cumulative GPA, credit accumulation, persistence and graduation rates. Finding also indicated a correlation among the three behaviors themselves. Furthermore, conducting a longitudinal analysis of individual students made it possible to identify the temporal tipping-points which differentiated at-risk behavior from otherwise benign behavior. The intrinsic factors associated with individual students’ behaviors were followed over a period of thirteen consecutive semesters. Mining Transactional Student-Level Data at the scale achieved in this study, when compared to traditional methods of data collection, provided the precision needed to determine the actual proximity among specific peers, and the identification of registration behavior patterns. The extraction of transactional data from the records of each student in an entire cohort resulted in a method of inquiry immune to the negative effects of student’s non-response or selection bias. Complimenting previous research, this study provides a detailed descriptive analysis of those behaviors not only at the semestrial level, but also cumulatively across consecutive semesters. This study demonstrates that curricular peer association can be measured directly from common, ubiquitous, transactional records. The rates of Peer Association among individual students was very dynamic: While the majority of students had some peer associations while enrolled, in the aggregate two thirds of students had no peer association (were soloists) at some point in time, while more than a quarter of all students were soloists for at least half of their entire enrollment period. Soloists differed from students with peer associations. They were likely to be older, international students, African Americans, transfer students, or those entering fully prepared for college level coursework (no remedial coursework). Peer association was positively correlated, both in the semester in which it occurred and cumulatively, with: GPA, credits earned, and retention or graduation rates. These correlations to academic outcomes varied with the number of peer associations established, and the intensity of peer encounters. The study revealed that nearly a quarter of all students practiced late registration at least once; and more than 10 percent have registered late multiple times during their studies. Nearly three quarters of students made modifications to their course schedule at least once after the semester began. Overall, two fifths of students changed their initial schedule every semester. These behaviors were unrecorded in previous studies that were limited in the evaluation of longitudinal behaviors, used subsets of students and were subject to non-response bias. Late registration and student schedule changes was correlated with lower semestrial and cumulative academic outcomes. Late registration behavior subsequently increased the likelihood of a student being a soloist. When compared to previous studies, the analysis conducted here not only accounted for academic, demographic and financial variables at baseline, but went on to perform updates at key points in time each semester to reflect changes over time. The exhaustive revisiting of the covariates each semester provided enhanced control to the ‘order of time’ influence. All covariates were re-measured each semester allowing to better evaluate the correlation of student behavioral indicators for a given semester, and cumulatively. This enhanced the study’s ability to account for common unobserved variables inherent to academic, demographic and financial attributes that might influence student outcomes correlated with peer association, late registration and schedule changes. This study contributes to the literature by showing that peer association can be evaluated in the setting of an open admission commuter institution, and that peer association has consistent and positive correlation with academic outcomes. It provides new insights regarding the magnitude of late registration and schedule changes, as well as their negative immediate and longitudinal correlation with student outcomes. Further implications to community colleges’ faculty, administrators, researchers and policymakers, as well as future directions for research employing transactional level data are discussed.