Typescript. Includes vita and abstract. Includes bibliographical references (leaves 94-101). Also available online in Scholars' Bank.

Thesis (D. Min.)--Abilene Christian University, 2001. / Includes abstract and vita. Includes bibliographical references (leaves 119-124).

Thesis (M.A.)--University of Notre Dame, 2009. / Thesis directed by Scott E. Maxwell for the Department of Psychology. "November 2009." Includes bibliographical references (leaves 105-107).

Thesis (Ph. D.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (leaves 279-292). Also available via the World Wide Web.

Thesis (M.S.)--Miami University, Dept. of Family and Child Studies, 2003. / Title from first page of PDF document. Document formatted into pages; contains iii, 73 p. Includes bibliographical references (p. 34-35).

Thesis (Ph. D.)--York University, 2000. Graduate Programme in Sociology. / Typescript. Includes bibliographical references (leaves 157-165). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ56229.

Thesis (doctoral)--Europa Universität Viadrina, Frankfurt/Oder, 2001.

Mediational analysis quantifies proposed causal mechanisms through which treatments act on outcomes. In the presence of clustered data, conventional multiple regression mediational methods break down, requiring the use of hierarchical linear modeling techniques. As an additional consideration, nonlinear relationships in multilevel mediation models require unique specifications that are ignored if modeled linearly. Improper specification of nonlinear relationships can lead to a consistently overestimated mediated effect. This has direct implications for inferences regarding intervention causality and efficacy. The current investigation examined a specific nonlinear multilevel mediation model parameterization to account for nonlinear relationships in clustered data. A simulation study was conducted to compare linear and nonlinear model specifications in the presence of truly nonlinear data. MacKinnon et al.’s (2007a) empirical-M based PRODCLIN method for estimating the confidence interval surrounding the instantaneous indirect effect was used to compare confidence interval coverage rates surrounding both the linear and nonlinear models’ estimates. Overall, the nonlinear model’s estimates were less biased, more efficient, and produced higher coverage rates than the linear model specification. For conditions containing a true value of zero for the instantaneous indirect effect, bias, efficiency, and coverage rate values were similar for the linear and nonlinear estimators. For conditions with a non-zero value for the instantaneous indirect effect, both the linear and nonlinear models were substantially biased. However, the nonlinear model was always less biased and always produced higher coverage rates than the linear model. The nonlinear model was more efficient than the linear model for all but two design conditions. / text

Partially clustered design is common in medicine, social sciences, intervention and psychological research. With some participants clustered and others not, the structure of partially clustering data is not parallel. Despite its common occurrence in practice, limited attention has been given regarding the evaluation of intervention effects in partially clustered data. Mediation analysis is used to identify the mechanism underlying the relationship between an independent variable and a dependent variable via a mediator variable. While most of the literature is focused on conventional frequentist mediation models, no research has studied a Bayesian mediation model in the context of a partially clustered design yet. Therefore, the primary objectives of this paper are to address conceptual considerations in estimating the mediation effects in the partially clustered randomized designs, and to examine the performances of the proposed model using both simulated data and real data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K). A small-scale simulation study was also conducted and the results indicate that under large sample sizes, negligible relative parameter bias was found in the Bayesian estimates of the indirect effects and of covariance between the components of the indirect effect. Coverage rates for the 95% credible interval for these two estimates were found to be close to the nominal level. These results supported use of the proposed Bayesian model for partially clustered mediation in conditions when the sample size is moderately large. / text

published_or_final_version / Social Work and Social Administration / Doctoral / Doctor of Philosophy