The estimation of variance components serves as an integral part of the evaluation of variation, and is of interest and required in a variety of applications (Hugo, 2012). Estimation of the among-group variance components is often desired for quantifying the variability and effectively understanding these measurements (Van Der Rijst, 2006). The methodology for determining Bayesian tolerance intervals for the one – way random effects model has originally been proposed by Wolfinger (1998) using both informative and non-informative prior distributions (Hugo, 2012). Wolfinger (1998) also provided relationships with frequentist methodologies. From a Bayesian point of view, it is important to investigate and compare the effect on coverage probabilities if negative variance components are either replaced by zero, or completely disregarded from the simulation process. This research presents a simulation-based approach for determining Bayesian tolerance intervals in variance component models when negative variance components are either replaced by zero, or completely disregarded from the simulation process. This approach handles different kinds of tolerance intervals in a straightforward fashion. It makes use of a computer-generated sample (Monte Carlo process) from the joint posterior distribution of the mean and variance parameters to construct a sample from other relevant posterior distributions. This research makes use of only non-informative Jeffreys‟ prior distributions and uses three Bayesian simulation methods. Comparative results of different tolerance intervals obtained using a method where negative variance components are either replaced by zero or completely disregarded from the simulation process, is investigated and discussed in this research.

Statistics / M. Sc. (Statistics)

This thesis includes three parts. The overarching theme is how to analyze structured hierarchical data, with applications to astronomy and sociology. The first part discusses how expectation propagation can be used to parallelize the computation when fitting big hierarchical bayesian models. This methodology is then used to fit a novel, nonlinear mixture model to ultraviolet radiation from various regions of the observable universe. The second part discusses how the Stan probabilistic programming language can be used to numerically integrate terms in a hierarchical bayesian model. This technique is demonstrated on supernovae data to significantly speed up convergence to the posterior distribution compared to a previous study that used a Gibbs-type sampler. The third part builds a formal latent kernel representation for aggregate relational data as a way to more robustly estimate the mixing characteristics of agents in a network. In particular, the framework is applied to sociology surveys to estimate, as a function of ego age, the age and sex composition of the personal networks of individuals in the United States.

There is a growing awareness that informal supports such as family-supportive supervision are critical in assuring the success of work-life policies and benefits. Furthermore, it is believed that family-supportive supervision may have positive effects regardless of the number or quality of work-life polices and benefits an organization has in place. Given this recognition, work-life experts have emphasized the need for supervisor training to increase family-supportive supervision. To date however, there has been a paucity of research on the predictors of family-supportive supervision which could be used as the target of such a training intervention. This dissertation had three major aims: 1) to investigate which supervisor-level (e.g., reward system, productivity maintenance, salience of changing workforce, belief in business case, awareness of organizational policies and benefits, role-modeling) and employee-level (e.g., support sought) factors are most strongly related to family-supportive supervision; 2) to explore whether supervisor factors moderate the relationship between support sought and family-supportive supervision; 3) and to use a multilevel design to confirm the association between family-supportive supervision and work-family conflict. This study used a cross-sectional, two-level (e.g., supervisor, and employee) hierarchical design. The data were collected from supervisors (Nurse Managers N=67) and employees (Nurses N=757) at five hospitals in the Pacific Northwest. All of the major analyses were conducted using multi-level regression in HLM. The results indicated that family-supportive supervision was higher for employees who worked for managers with a stronger belief in the business case and for employees who sought support. None of the other supervisor-level factors were found to be significant predictors of family supportive supervision. There was no evidence that supervisor-level factors moderated that relationship between support sought and family-supportive supervision. Higher levels of family-supportive supervision were related to lower work-to-family conflict. These findings suggest that organizations seeking to reduce work-family conflict and increase family supportive supervision should consider intervening at multiple levels. This dissertation reviews a rich body of evidence demonstrating the business case for offering work-life supports that could serve as a starting point for developing a training to increase supervisors' belief in the business case. In addition, strategies for organizations to increase support seeking, which has been shown to be an important coping mechanism, are discussed. The multi-level design of this dissertation also contributes to the literature by demonstrating that the largest proportion of variability in family-supportive supervision is at the employee-level. This finding suggests the importance of measuring family-supportive supervision at the employee-level and suggests that future research should focus on the employee-level predictors of family-supportive supervision.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 264-275).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 98-101).

Thesis supervisor: Walter P. Vispoel. Includes bibliographic references (p. 140-143).

Thesis (Ph. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed Nov. 21, 2007). Vita. Includes bibliographical references.

Thesis (Ph. D.)--Oregon State University, 2009. / Printout. Includes bibliographical references. Also available on the World Wide Web.

Thesis (Ph. D.)--University of Notre Dame, 2009. / Thesis directed by Rory McVeigh and William Carbonaro for the Department of Sociology. "October 2009." Includes bibliographical references (leaves 200-210).