Matching Items (4)
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- All Subjects: Quantitative Psychology and Psychometrics
- All Subjects: Mediation
- Creators: MacKinnon, David
- Member of: ASU Electronic Theses and Dissertations
Description
In order to analyze data from an instrument administered at multiple time points it is a common practice to form composites of the items at each wave and to fit a longitudinal model to the composites. The advantage of using composites of items is that smaller sample sizes are required in contrast to second order models that include the measurement and the structural relationships among the variables. However, the use of composites assumes that longitudinal measurement invariance holds; that is, it is assumed that that the relationships among the items and the latent variables remain constant over time. Previous studies conducted on latent growth models (LGM) have shown that when longitudinal metric invariance is violated, the parameter estimates are biased and that mistaken conclusions about growth can be made. The purpose of the current study was to examine the impact of non-invariant loadings and non-invariant intercepts on two longitudinal models: the LGM and the autoregressive quasi-simplex model (AR quasi-simplex). A second purpose was to determine if there are conditions in which researchers can reach adequate conclusions about stability and growth even in the presence of violations of invariance. A Monte Carlo simulation study was conducted to achieve the purposes. The method consisted of generating items under a linear curve of factors model (COFM) or under the AR quasi-simplex. Composites of the items were formed at each time point and analyzed with a linear LGM or an AR quasi-simplex model. The results showed that AR quasi-simplex model yielded biased path coefficients only in the conditions with large violations of invariance. The fit of the AR quasi-simplex was not affected by violations of invariance. In general, the growth parameter estimates of the LGM were biased under violations of invariance. Further, in the presence of non-invariant loadings the rejection rates of the hypothesis of linear growth increased as the proportion of non-invariant items and as the magnitude of violations of invariance increased. A discussion of the results and limitations of the study are provided as well as general recommendations.
ContributorsOlivera-Aguilar, Margarita (Author) / Millsap, Roger E. (Thesis advisor) / Levy, Roy (Committee member) / MacKinnon, David (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
Recent advances in hierarchical or multilevel statistical models and causal inference using the potential outcomes framework hold tremendous promise for mock and real jury research. These advances enable researchers to explore how individual jurors can exert a bottom-up effect on the jury’s verdict and how case-level features can exert a top-down effect on a juror’s perception of the parties at trial. This dissertation explains and then applies these technical advances to a pre-existing mock jury dataset to provide worked examples in an effort to spur the adoption of these techniques. In particular, the paper introduces two new cross-level mediated effects and then describes how to conduct ecological validity tests with these mediated effects. The first cross-level mediated effect, the a1b1 mediated effect, is the juror level mediated effect for a jury level manipulation. The second cross-level mediated effect, the a2bc mediated effect, is the unique contextual effect that being in a jury has on the individual the juror. When a mock jury study includes a deliberation versus non-deliberation manipulation, the a1b1 can be compared for the two conditions, enabling a general test of ecological validity. If deliberating in a group generally influences the individual, then the two indirect effects should be significantly different. The a2bc can also be interpreted as a specific test of how much changes in jury level means of this specific mediator effect juror level decision-making.
ContributorsLovis-McMahon, David (Author) / Schweitzer, Nicholas (Thesis advisor) / Saks, Michael (Thesis advisor) / Salerno, Jessica (Committee member) / MacKinnon, David (Committee member) / Arizona State University (Publisher)
Created2015
Description
Anxiety and depression are among the most prevalent disorders in youth, with prevalence rates ranging from 15% to 25% for anxiety and 5% to 14% for depression. Anxiety and depressive disorders cause significant impairment, fail to spontaneously remit, and have been prospectively linked to problematic substance use and legal problems in adulthood. These disorders often share a high-degree of comorbidity in both clinical and community samples, with anxiety disorders typically preceding the onset of depression. Given the nature and consequences of anxiety and depressive disorders, a plethora of treatment and preventative interventions have been developed and tested with data showing significant pre to post to follow-up reductions in anxiety and depressive symptoms. However, little is known about the mediators by which these interventions achieve their effects. To address this gap in the literature, the present thesis study combined meta-analytic methods and path analysis to evaluate the effects of youth anxiety and depression interventions on outcomes and four theory-driven mediators using data from 55 randomized controlled trials (N = 11,413). The mediators included: (1) information-processing biases, (2) coping strategies, (3) social competence, and (4) physiological hyperarousal. Meta-analytic results showed that treatment and preventative interventions reliably produced moderate effect sizes on outcomes and three of the four mediators (information-processing biases, coping strategies, social competence). Most importantly, findings from the path analysis showed that changes in information-processing biases and coping strategies consistently mediated changes in outcomes for anxiety and depression at both levels of intervention, whereas gains in social competence and reductions in physiological hyperarousal did not emerge as significant mediators. Knowledge of the mediators underlying intervention effects is important because they can refine testable models of treatment and prevention efforts and identify which anxiety and depression components need to be packaged or strengthened to maximize intervention effects. Allocating additional resources to significant mediators has the potential to reduce costs associated with adopting and implementing evidence-based interventions and improve dissemination and sustainability in real-world settings, thus setting the stage to be more readily integrated into clinical and non-clinical settings on a large scale.
ContributorsStoll, Ryan (Author) / Pina, Armando A (Thesis advisor) / MacKinnon, David (Committee member) / Knight, George (Committee member) / Arizona State University (Publisher)
Created2015