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.
- 4 English
- 4 Public
- 3 Multilevel Modeling
- 3 Multiple Imputation
- 2 Missing Data
- 2 Psychology
- 1 Bayesian Estimation
- 1 Categorical Data Analysis
- 1 Educational tests & measurements
- 1 Fully Conditional Specification
- 1 Hierarchical
- 1 Missing Data Theory
- 1 Missing at Random Data
- 1 Quantitative psychology
- 1 Three-level
- 1 confirmatory factor analysis
- 1 fit indices
- 1 heterogeneity
- 1 invariance
- 1 psychometrics
Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen analysis model(s), the presence of missing data, and the type(s) of data collected. If analysis models are used which a) do not accurately capture the structure of relationships in the data such as clustered/hierarchical data, b) do not allow or control for missing values present in the data, or c) do not accurately compensate for different data types such as categorical data, then the assumptions associated with the model have not been met and the …
- Kunze, Katie Lynn, Levy, Roy, Enders, Craig K, et al.
- Created Date
Currently, there is a clear gap in the missing data literature for three-level models. To date, the literature has only focused on the theoretical and algorithmic work required to implement three-level imputation using the joint model (JM) method of imputation, leaving relatively no work done on fully conditional specication (FCS) method. Moreover, the literature lacks any methodological evaluation of three-level imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in order to implement FCS in the context of a three-level model and (2) to evaluate both imputation methods. The simulation investigated a random intercept model under both …
- Keller, Brian Tinnell, Enders, Craig K, Grimm, Kevin J, et al.
- Created Date
Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent …
- Mistler, Stephen Andrew, Enders, Craig K, Aiken, Leona, et al.
- Created Date
Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful cross-group comparisons, failure to attend to possible sources of latent class heterogeneity in the form of class-based differences in factor structure has the potential to compromise conclusions with respect to observed groups and may result in misguided attempts at instrument development and theory refinement. The present studies examined the sensitivity …
- Blackwell, Kimberly Carol, Millsap, Roger E, Aiken, Leona S, et al.
- Created Date