Browsing by Author "Dr. Jason Osborne, Committee Co-Chair"

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    Evaluating the Perceptions of Success Inventory for Beginning Teachers and its Connection to Teacher Retention.
    (2008-04-09) Corbell, Kristen Anne; Dr. Alan Reiman, Committee Co-Chair; Dr. John Nietfeld, Committee Member; Dr. Jason Osborne, Committee Co-Chair; Dr. Adam Meade, Committee Member
    This study evaluated the psychometric properties of the Perceptions of Success Inventory for Beginning Teachers (PSI-BT). The PSI-BT assessed areas that contribute to beginning teachers' perceptions of success as well as beginning teacher retention. Corbell, Reiman, and Nietfeld constructed the first version of the PSI-BT in 2005. In this study, I revised and evaluated the PSI-BT. The PSI-BT was designed to assess ten factors associated with beginning teachers' perceptions of success: Mentor Support, Colleague Support, Administration Support, Classroom Management, Student Success, Instructional Resources, Assignment and Workload, Parental Contacts, Satisfaction, and Commitment. Each of the factors was assessed based on two categories: the beginning teacher's current experience and what the beginning teacher perceived to be essential for effective teaching. In addition to these 10 factors, the PSI-BT also assessed a beginning teacher's retention intentions. Three research questions comprised this study. The first question addressed the psychometric properties of the current experience category of the PSI-BT including construct, convergent, discriminant, and predictive validity. An extensive literature review, expert opinions, and confirmatory factor analysis established the construct validity of the PSI-BT. Significant correlations between the factors of the PSI-BT with the factors of the Teacher's Sense of Efficacy Scale established convergent validity. Non-significant correlations between the PSI-BT with the e-mail subscale of the Teacher's Attitudes Toward Computers confirmed discriminant validity. Binary logistic regression ascertained the PSI-BT factors that predicted beginning teacher retention. Retention was measured using school system retention and attrition data gathered during the school year following the administration of the PSI-BT. The second research question used multiple regression analyses to determine the factors that predicted beginning teacher retention intentions, Satisfaction, and Commitment. The third research question addressed the gap between the current experience and essential for effective teaching categories. The final analysis investigated how the gap for each of the factors predicted beginning teacher retention. The culmination of this research has provided a psychometrically sound instrument that school systems can use to reliably assess beginning teachers' perceptions of success. The gathered data can then be used to make informed decisions for improving induction programs and ultimately to retain beginning teachers
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    The Orthogonal Interactions Model for Unreplicated Factorial Experiments
    (2007-04-22) Barker, Clayton Adam; Dr. David Dickey, Committee Member; Dr. Jason Osborne, Committee Co-Chair; Dr. Cavell Brownie, Committee Member; Dr. Leonard Stefanski, Committee Co-Chair
    Unreplicated factorial experiments arise frequently in practice because of limited resources. When fitting the standard ANOVA model to data from unreplicated experiments, the data are not sufficient to estimate the interaction terms and error variance, thus limiting the possible inferences. In experiments such as crop yield trials, investigators are interested in estimating interactions but are unable to form replications. Existing methods, such as Tukey's One-Degree-of-Freedom model and the AMMI model, fit constrained interactions which allow for the error variance estimation. We present the orthogonal interactions (OI) model for unreplicated factorial experiments. The model frees degrees of freedom for error by assuming that the main effects are orthogonal to the interactions. Through simulation we find that approximate F-statistics are appropriate in testing for main effects and interactions. The likelihood ratio test to compare the OI model to the ANOVA model leads to high type-I error rates, so we use a simulation corrected likelihood ratio test. Two real data sets (one with replication, one without) suggest that the OI model is appropriate for real data. In working with the OI model and the existing models, we found a need for reliable degrees of freedom for complex statistical models. The resampling method for estimating degrees of freedom is motivated by the linear model, where there is a linear relationship between expected sums of squares and the variance of errors added to the response. Through simulation, we find that the resampling method provides reliable degrees of freedom for the OI model and for existing models as well.