Source: Dissertation Abstracts International, Volume: 27-11, Section: A, page: 3683. / Thesis (Ph.D.)--The Florida State University, 1966.

Source: Dissertation Abstracts International, Volume: 28-03, Section: A, page: 0915. / Thesis (Educat.D.)--The Florida State University, 1967.

Source: Dissertation Abstracts International, Volume: 18-05, page: 1705. / Thesis (Educat.D.)--The Florida State University, 1958.

Source: Dissertation Abstracts International, Volume: 23-10, page: 3733. / Thesis (Ph.D.)--The Florida State University, 1962.

Source: Dissertation Abstracts International, Volume: 25-12, page: 7037. / Thesis (Ph.D.)--The Florida State University, 1964.

Source: Dissertation Abstracts International, Volume: 25-12, page: 7038. / Thesis (Ph.D.)--The Florida State University, 1964.

Source: Dissertation Abstracts International, Volume: 25-12, page: 7045. / Thesis (Educat.D.)--The Florida State University, 1964.

Source: Dissertation Abstracts International, Volume: 40-07, Section: A, page: 3671. / Thesis (Ph.D.)--The Florida State University, 1979.

The Capabilities of four types of statistical models were examined to identify effective and ineffective schools through regression analysis. The models are referred to as incomplete since they included the following sets of predictors: unmatched longitudinal achievement and demographic (ULAD), cross-sectional scores and demographic (CSAD), demographic alone (ULDA), and longitudinal scores alone (MLA 3). The superiority of each model to other was determined by comparing results from each model with results from a complete model which included matched longitudinal achievement and demographic predictors. Regression equations with different numbers of predictors were investigated to determine the effect of the number of predictors on the ability of the incomplete models to produce results similar to those of the complete models. The analysis was based upon residuals from equations for each model computed for 277 schools. Residuals were examined by using three tools for classification of schools: confidence intervals based upon standard errors, performance index, and binomial distribution of scores from two different years. The summary of results of analysis are: (1) The two incomplete models, ULAD and CSAD, produced fewer misclassifications of schools than the other incomplete models. (2) No consistent effect of number of predictors was found. The number of different predictors in equations has a lesser effect in prediction than the type of predictors used in equations. (3) Each of the complete and incomplete equations showed a greater number of consistently overachieving schools than chance alone would allow. When such schools were identified, about 10 percent of the schools seemed able to raise their students, on average, by an amount equal to an increase from 50th to the 72nd percentile. (4) Confidence intervals of two standard errors were sufficient to avoid incorrect identification of schools; performance indices produced many classifications. / Source: Dissertation Abstracts International, Volume: 46-12, Section: A, page: 3561. / Thesis (Ph.D.)--The Florida State University, 1985.

A lack of accurate and useful information has contributed to the difficulty of developing new or enhanced computerized applications for admissions offices in private colleges and universities. Research questions were designed in six areas: (1) admissions office functions; (2) admissions planning and management; (3) system technical components; (4) system development process; (5) institutional demographic characteristics; (6) system hardware and software. / Responses to a survey of 169 private institutions were supplemented by demographic data from the annual survey of the National Center for Education Statistics, and analysed with the help of the Statistical Package for the Social Sciences, Version M. / Numeric hierarchies of typical application components were demonstrated in the first four areas of study; hierarchic scales were also established in areas one and three, indicating clustering of typical data processing components. When selected demographic data were used as independent variables, some groups of institutions tended to show a higher percentage of positive responses than the total population: (1) those with high freshmen SAT scores (over 999 combined); (2) those with higher enrollments (over 1499); (3) those which are universities; (4) those located outside the frostbelt. / It was not established that, as expected, the smaller, less selective institutions in the frostbelt were likely to manifest greater strength or sophistication in their automated admissions applications. / When sets of two independent variables were used to select subgroups of the survey population, however, and the hierarchic scales were tested for these subgroups, the results were less surprising. Results supported a hypothesis that the scales represent clusterings of components that are more likely to be found among private 2- and 4-year colleges in the frostbelt than among the overall survey population. / Findings of the study will be useful to practitioners of automated admissions applications in private colleges and universities, whether they be professionals in admissions offices or in computer centers. However, increased efforts planning, analysis, and design are greatly needed, lest such applications not develop in a more truly systematic manner. / Source: Dissertation Abstracts International, Volume: 47-01, Section: A, page: 0032. / Thesis (Ph.D.)--The Florida State University, 1985.