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In investigating mediating processes, researchers usually use randomized experiments and linear regression or structural equation modeling to determine if the treatment affects the hypothesized mediator and if the mediator affects the targeted outcome. However, randomizing the treatment will not yield accurate causal path estimates unless certain assumptions are satisfied. Since

In investigating mediating processes, researchers usually use randomized experiments and linear regression or structural equation modeling to determine if the treatment affects the hypothesized mediator and if the mediator affects the targeted outcome. However, randomizing the treatment will not yield accurate causal path estimates unless certain assumptions are satisfied. Since randomization of the mediator may not be plausible for most studies (i.e., the mediator status is not randomly assigned, but self-selected by participants), both the direct and indirect effects may be biased by confounding variables. The purpose of this dissertation is (1) to investigate the extent to which traditional mediation methods are affected by confounding variables and (2) to assess the statistical performance of several modern methods to address confounding variable effects in mediation analysis. This dissertation first reviewed the theoretical foundations of causal inference in statistical mediation analysis, modern statistical analysis for causal inference, and then described different methods to estimate causal direct and indirect effects in the presence of two post-treatment confounders. A large simulation study was designed to evaluate the extent to which ordinary regression and modern causal inference methods are able to obtain correct estimates of the direct and indirect effects when confounding variables that are present in the population are not included in the analysis. Five methods were compared in terms of bias, relative bias, mean square error, statistical power, Type I error rates, and confidence interval coverage to test how robust the methods are to the violation of the no unmeasured confounders assumption and confounder effect sizes. The methods explored were linear regression with adjustment, inverse propensity weighting, inverse propensity weighting with truncated weights, sequential g-estimation, and a doubly robust sequential g-estimation. Results showed that in estimating the direct and indirect effects, in general, sequential g-estimation performed the best in terms of bias, Type I error rates, power, and coverage across different confounder effect, direct effect, and sample sizes when all confounders were included in the estimation. When one of the two confounders were omitted from the estimation process, in general, none of the methods had acceptable relative bias in the simulation study. Omitting one of the confounders from estimation corresponded to the common case in mediation studies where no measure of a confounder is available but a confounder may affect the analysis. Failing to measure potential post-treatment confounder variables in a mediation model leads to biased estimates regardless of the analysis method used and emphasizes the importance of sensitivity analysis for causal mediation analysis.
ContributorsKisbu Sakarya, Yasemin (Author) / Mackinnon, David Peter (Thesis advisor) / Aiken, Leona (Committee member) / West, Stephen (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase the power to detect a relation between two variables has not been investigated. The first study identified

Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase the power to detect a relation between two variables has not been investigated. The first study identified situations where empirical and analytical power of two tests of significance for a single mediator model was greater than power of a bivariate significance test. Results from the first study indicated that including a mediator increased statistical power in small samples with large effects and in large samples with small effects. Next, a study was conducted to assess when power was greater for a significance test for a two mediator model as compared with power of a bivariate significance test. Results indicated that including two mediators increased power in small samples when both specific mediated effects were large and in large samples when both specific mediated effects were small. Implications of the results and directions for future research are then discussed.
ContributorsO'Rourke, Holly Patricia (Author) / Mackinnon, David P (Thesis advisor) / Enders, Craig K. (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Possible selves researchers have uncovered many issues associated with the current possible selves measures. For instance, one of the most famous possible selves measures, Oyserman (2004)'s open-ended possible selves, has proven to be difficult to score reliably and also involves laborious scoring procedures. Therefore, this study was initiated to develo

Possible selves researchers have uncovered many issues associated with the current possible selves measures. For instance, one of the most famous possible selves measures, Oyserman (2004)'s open-ended possible selves, has proven to be difficult to score reliably and also involves laborious scoring procedures. Therefore, this study was initiated to develop a close-ended measure, called the Persistent Academic Possible Selves Scale for Adolescents (PAPSS), that meets these challenges. The PAPSS integrates possible selves theories (personal and social identities) and educational psychology (self-regulation in social cognitive theory). Four hundred and ninety five junior high and high school students participated in the validation study of the PAPSS. I conducted confirmatory factor analyses (CFA) to compare fit for a baseline model to the hypothesized models using Mplus version 7 (Muthén & Muthén, 2012). A weighted least square means and a variance adjusted (WLSMV) estimation method was used for handling multivariate nonnormality of ordered categorical data. The final PAPSS has validity evidence based on the internal structure. The factor structure is composed of three goal-driven factors, one self-regulated factor that focuses on peers, and four self-regulated factors that emphasize the self. Oyserman (2004)'s open-ended questionnaire was used for exploring the evidence of convergent validity. Many issues regarding Oyserman (2003)'s instructions were found during the coding process of academic plausibility. It was complicated to detect hidden academic possible selves and strategies from non-academic possible selves and strategies. Also, interpersonal related strategies were over weighted in the scoring process compared to interpersonal related academic possible selves. The study results uncovered that all of the academic goal-related factors in the PAPSS are significantly related to academic plausibility in a positive direction. However, self-regulated factors in the PAPSS are not. The correlation results between the self-regulated factors and academic plausibility do not provide the evidence of convergent validity. Theoretical and methodological explanations for the test results are discussed.
ContributorsLee, Ji Eun (Author) / Husman, Jenefer (Thesis advisor) / Green, Samuel (Committee member) / Millsap, Roger (Committee member) / Brem, Sarah (Committee member) / Arizona State University (Publisher)
Created2013
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Description
This study utilized targeted messages and expert and referent sources in an effort to promote physical activity behavior in college students. College students aged 18-25, excluding collegiate athletes, were randomized into three conditions using their current physical activity level. Two of the conditions received targeted messages highlighting three primary components

This study utilized targeted messages and expert and referent sources in an effort to promote physical activity behavior in college students. College students aged 18-25, excluding collegiate athletes, were randomized into three conditions using their current physical activity level. Two of the conditions received targeted messages highlighting three primary components of social cognitive theory - self-efficacy, outcome expectations, and goals - while the third condition received no messages. In addition, the experimental conditions received the messages from either an expert (i.e., a personal trainer) or a referent (i.e., a close friend). In this way, this experiment analyzed whether receiving social cognitive theory messages increased physical activity indicators compared to the control condition, as well as if the message source caused differences in the physical activity indicators. Moreover, participants completed Time 1 and Time 2 measures to determine if receiving messages or not caused changes over a one week time period. Seven physical activity indicators were assessed: self-efficacy, positive outcome expectations, negative outcome expectations, attitudes, response-efficacy, intentions, and physical activity behavior. Results revealed that both the personal trainer and close friend conditions had significantly higher scores than the control condition for intentions at Time 1 and Time 2, as well as physical activity behavior at Time 2. Moreover, the personal trainer condition had significantly higher positive outcome expectations compared to both the friend and control conditions. No other significant differences were found across conditions for Time 1 attitudes, response-efficacy, negative outcome expectations, self-efficacy, and Time 2 attitudes, and self-efficacy. Overall, targeted messages were effective in increasing physical activity intentions and behavior regardless of the message source.
ContributorsMarmo, Jennifer Lauren (Author) / Roberto, Anthony J. (Thesis advisor) / Mongeau, Paul A. (Committee member) / Ainsworth, Barbara E. (Committee member) / Arizona State University (Publisher)
Created2012