Physical Sciences and Mathematics Commons^™

A Descriptive Study Of The Effect Of Payer Source On Multiple Longitudinal Outcome Measures Within The Tbi Model Systems National Database Using Longitudinal Hlm Analyses, Melissa Carole Hofmann

Electronic Theses and Dissertations

Using longitudinal data from the TBIMS ND, this study utilized a longitudinal hierarchical linear modeling approach to describe the effect of primary payer source on individual level change in outcomes including the FIM and DRS. To facilitate the use of parametric statistics, Rasch-transformed FIM and DRS scores were utilized; thus approaching an interval level of measurement. The FIM was separated into 3 separate cognitive, mobility, and self-care subscales. In this way, rehabilitation professionals including speech, physical, and occupational therapists for this TBI sample could reference results to inform current clinical practice.

Results indicated that FIM and DRS trajectories were best …

A Comparison Of Latent Class Analysis And The Mixture Rasch Model: A Cross-Cultural Comparison Of 8th Grade Mathematics Achievement In The Fourth International Mathematics And Science Study (Timss-2011), Turker Toker

Electronic Theses and Dissertations

This study provides a comparison of the results of latent class analysis (LCA) and mixture Rasch model (MRM) analysis using data from the Trends in International Mathematics and Science Study - 2011 (TIMSS-2011) with a focus on the 8th-grade mathematics section. The research study focuses on the comparison of LCA with Mplus version 7.31 and MRM with WinMira 2011 to determine if results obtained differ when the assumed psychometric model differs. Also, a log-linear analysis was conducted to understand the interactions between latent classes identified by LCA and MRM. The data set used in the study was from four diverse …

A Comparison Between Propensity Score Matching, Weighting, And Stratification In Multiple Treatment Groups: A Simulation Study, Priyalatha Govindasamy

Electronic Theses and Dissertations

The application of propensity score techniques (matching, stratification, and weighting) with multiple treatment levels are similar to those used in binary groups. However, given that the application of propensity scores in multiple treatment groups is new, factors affecting the performance of matching, stratification, and weighting in multiple treatment groups are less explored. Therefore, this study was conducted to determine the performance of different propensity score techniques with multiple treatment groups under various circumstances. Specifically, the study focused on examining how the three propensity score corrective techniques perform in estimating treatment effects under (1) overt and (2) hidden types of selection …