Matching Items (6)
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- All Subjects: Adolescence
- All Subjects: Psychology--Statistical methods.
- Genre: Doctoral Dissertation
- Creators: Grimm, Kevin J.
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
Research has consistently shown that gay/lesbian/bisexual (GLB) or sexual minority youth are at an increased risk for adverse outcomes resulting from the stress caused by continual exposure to negative events (e.g., victimization, discrimination). The present study used a nationally representative sample of adolescents to test mechanisms that may be responsible for the differences in offending behaviors among sexual minority and heterosexual adolescents. Specifically, this study tested whether bisexual adolescents received less maternal support than did heterosexual adolescents because of their sexual orientation, thus increasing the likelihood that they run away from home. This study then examined whether the greater likelihood that bisexual adolescents running away would lead to them committing a significantly higher variety of income-based offenses, but not a significantly higher variety of aggression-based offenses. This study tested the hypothesized mediation model using two separate indicators of sexual orientation measured at two different time points, modeled outcomes in two ways, as well as estimated the models separately for boys and girls. Structural equation modeling was used to test the hypothesized direct and indirect relations. Results showed support for maternal support and running away mediating the relations between sexual orientation and offending behaviors for the model predicting the likelihood of committing either an aggressive or an income offense, but only for girls who identified as bisexual in early adulthood. Results did not support these relations for the other models, suggesting that bisexual females have unique needs when it comes to prevention and intervention. Results also highlight the need for a greater understanding of sexual orientation measurement methodology.
ContributorsMansion, Andre (Author) / Chassin, Laurie (Thesis advisor) / Barrera, Manuel (Committee member) / Grimm, Kevin J. (Committee member) / Toomey, Russell B (Committee member) / Arizona State University (Publisher)
Created2018
Description
Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection of ordered-categorical indicators (e.g., Likert scale items). To evaluate longitudinal measurement invariance with ordered-categorical indicators, a set of hierarchical models can be sequentially tested and compared. If the statistical tests of measurement invariance fail to be supported for one of the models, it is useful to have a method with which to gauge the practical significance of the differences in measurement model parameters over time. Drawing on studies of latent growth models and second-order latent growth models with continuous indicators (e.g., Kim & Willson, 2014a; 2014b; Leite, 2007; Wirth, 2008), this study examined the performance of a potential sensitivity analysis to gauge the practical significance of violations of longitudinal measurement invariance for ordered-categorical indicators using second-order latent growth models. The change in the estimate of the second-order growth parameters following the addition of an incorrect level of measurement invariance constraints at the first-order level was used as an effect size for measurement non-invariance. This study investigated how sensitive the proposed sensitivity analysis was to different locations of non-invariance (i.e., non-invariance in the factor loadings, the thresholds, and the unique factor variances) given a sufficient sample size. This study also examined whether the sensitivity of the proposed sensitivity analysis depended on a number of other factors including the magnitude of non-invariance, the number of non-invariant indicators, the number of non-invariant occasions, and the number of response categories in the indicators.
ContributorsLiu, Yu, Ph.D (Author) / West, Stephen G. (Thesis advisor) / Tein, Jenn-Yun (Thesis advisor) / Green, Samuel (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2016
Description
The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit individual differences by allowing within-person slopes to vary across individuals. This study examined the statistical performance of the contextual model with a random slope for longitudinal within-person fluctuation data.
A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
ContributorsWurpts, Ingrid Carlson (Author) / Mackinnon, David P (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Created2016
Description
Familism values have been shown to have a multitude of benefits for Mexican American youth. Understanding different pathways of the adoption of familism values from adolescence and young adulthood, and predictors of these pathways, is critical. The current study assessed different classes of change in familism values across five waves from fifth grade to young adulthood, and fifth-grade predictors of these profiles, among a sample of 749 Mexican American youth. Univariate and growth mixture modeling was used to determine classes of familism change and found two classes—one class that showed small, insignificant declines across adolescence that accelerated into young adulthood and one class that showed significant declines across adolescence that stabilized and increased into young adulthood. The three-step procedure was then used to examine the following fifth-grade predictors of familism classes: family conflict, family cohesion, harsh parenting, parental acceptance, economic hardship, and perceived ethnic discrimination. Family conflict and perceived ethnic discrimination were significant predictors of familism class membership. Greater family conflict predicted a greater probability of being in the class of significant declines in familism across adolescence that stabilized and increased into young adulthood. Greater perceived ethnic discrimination predicted a greater probability of being in the class of small, insignificant decreases across adolescence that accelerated into young adulthood. Gender moderated the impact of family cohesion. For females, greater father-reported family cohesion predicted a greater probability of being in the class with significant declines during adolescence that stabilized and increased into young adulthood. For males, greater father-reported family cohesion predicted a greater probability of being in the class with slight, insignificant declines in adolescence that accelerated into young adulthood. Youth nativity moderated the impact of maternal acceptance. For youth born in the U.S., greater mother-reported acceptance predicted a greater probability of being in the class of slight, insignificant declines across adolescence that accelerated into young adulthood. For youth born in Mexico, greater mother-reported acceptance predicted a greater probability of being in the class of significant declines in familism across adolescence that stabilized and increased into young adulthood. Limitations and implications for prevention and future research are discussed.
ContributorsJenchura, Emily C. (Author) / Gonzales, Nancy A. (Thesis advisor) / Knight, George P (Committee member) / Grimm, Kevin J. (Committee member) / Perez, Marisol (Committee member) / Arizona State University (Publisher)
Created2019
Description
This study aimed to advance understanding of the relation between social media and adolescent alcohol use while accounting for offline peer alcohol use, exploring offline peer alcohol use separately as a covariate and as a moderator, with an additional exploratory analysis of the relation between social media and alcohol use without offline peer alcohol use in the model. A total of 868 students (55% female) in grade 7 (n = 468) and grade 8 (n = 400) at wave 1, self-reported on alcohol use, binge drinking, and social media use as well as nominated friends from their school and grade. Data from nominated peers who also completed the questionnaires were used for peer-report of alcohol use. Data were collected annually from students at grades 8, 9, 10, and 11 were used in analyses. Final structural models consisted of a cross-lagged panel design with saved factor scores for social media and peer alcohol use predicting a categorical alcohol use variable or a binary binge drinking variable. With offline peer alcohol use as a covariate in the model, social media did not prospectively relate to subsequent grade alcohol use or binge drinking. However, without offline peer alcohol use, the path from social media use to subsequent grade alcohol use was significant but not the path to binge drinking. Offline peer alcohol use did not significantly moderate the relation between social media and subsequent grade alcohol use or binge drinking.
ContributorsCurlee, Alexandria Stephanie (Author) / Corbin, William R. (Thesis advisor) / Chassin, Laurie (Committee member) / Doane, Leah D (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2020