ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at email@example.com.
- Wilson, Jeffrey
- 4 Arizona State University
- 4 Reiser, Mark
- 3 Kao, Ming-Hung
- 2 Barber, Jarrett
- 1 Dassanayake, Mudiyanselage Maduranga Kasun
- 1 Eubank, Randall
- 1 Kamarianakis, Ioannis
- 1 Milovanovic, Jelena
- 1 St Louis, Robert D
- 1 St Louis, Robert D.
- 1 St. Louis, Robert
- 1 Yang, Yan
- 1 Young, Dennis
- 1 Yu, Wanchunzi
- 1 Zhang, Jun
- 4 Public
- 1 Asymptotic Power
- 1 Bootstrap
- 1 Chi-Square goodness-of-fit tests
- 1 Confidence Set
- 1 Item Response Model
- 1 Mixed Models
- 1 Orthogonal Components
- 1 Orthogonal components of chi-square statistic
- 1 Power Analysis
- 1 Quadratic Growth Curves
- 1 Random Effects
- 1 Sparseness
- 1 Vertex
- 1 decomposition of chi-square statistic
Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable or missing at random (MAR). However, this assumption leads to unrealistic simplification and is implausible for many cases. For example, an investigator is examining the effect of treatment on depression. Subjects are scheduled with doctors on a regular basis and asked questions about recent emotional situations. Patients who are experiencing severe depression are more likely to miss an appointment and leave the data missing for that particular visit. Data that are not missing at random may produce bias …
- Zhang, Jun, Reiser, Mark, Barber, Jarrett, et al.
- Created Date
The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by the cross-classification of a large number of variables, these goodness-of-fit statistics may have lower power and inaccurate Type I error rate due to sparseness. Pearson's statistic can be decomposed into orthogonal components associated with the marginal distributions of observed variables, and an omnibus fit statistic can be obtained as a sum of these components. When the statistic is a sum of components for lower-order marginals, it has good performance for …
- Dassanayake, Mudiyanselage Maduranga Kasun, Reiser, Mark, Kao, Ming-Hung, et al.
- Created Date
Quadratic growth curves of 2nd degree polynomial are widely used in longitudinal studies. For a 2nd degree polynomial, the vertex represents the location of the curve in the XY plane. For a quadratic growth curve, we propose an approximate confidence region as well as the confidence interval for x and y-coordinates of the vertex using two methods, the gradient method and the delta method. Under some models, an indirect test on the location of the curve can be based on the intercept and slope parameters, but in other models, a direct test on the vertex is required. We present a …
- Yu, Wanchunzi, Reiser, Mark, Barber, Jarrett, et al.
- Created Date
It is common in the analysis of data to provide a goodness-of-fit test to assess the performance of a model. In the analysis of contingency tables, goodness-of-fit statistics are frequently employed when modeling social science, educational or psychological data where the interest is often directed at investigating the association among multi-categorical variables. Pearson's chi-squared statistic is well-known in goodness-of-fit testing, but it is sometimes considered to produce an omnibus test as it gives little guidance to the source of poor fit once the null hypothesis is rejected. However, its components can provide powerful directional tests. In this dissertation, orthogonal components …
- Milovanovic, Jelena, Young, Dennis, Reiser, Mark, et al.
- Created Date