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- All Subjects: psychology
- Genre: Doctoral Dissertation
- Creators: West, Stephen G.
- Creators: Mackinnon, David P
Description
Research has shown that a developmental process of maturing out of alcohol involvement occurs during young adulthood, and that this process is related to both young adult role transitions (e.g., marriage) and personality developmental (e.g., decreased disinhibition and neuroticism). The current study extended past research by testing whether protective marriage and personality effects on maturing out were stronger among more severe late adolescent drinkers, and whether protective marriage effects were stronger among those who experienced more personality development. Parental alcoholism and gender were tested as moderators of marriage, personality, and late adolescent drinking effects on maturing out; and as distal predictors mediated by these effects. Participants were a subsample (N = 844; 51% children of alcoholics; 53% male, 71% non-Hispanic Caucasian, 27% Hispanic; Chassin, Barrera, Bech, & Kossak-Fuller, 1992) from a larger longitudinal study of familial alcoholism. Hypotheses were tested with latent growth models characterizing alcohol consumption and drinking consequence trajectories from late adolescence to adulthood (age 17-40). Past findings were replicated by showing protective effects of becoming married, sensation-seeking reductions, and neuroticism reductions on the drinking trajectories. Moderation tests showed that protective marriage effects on the drinking trajectories were stronger among those with higher pre-marriage drinking in late adolescence (i.e., higher growth intercepts). This might reflect role socialization mechanisms such that more severe drinking produces more conflict with the demands of new roles (i.e., role incompatibility), thus requiring greater drinking reductions to resolve this conflict. In contrast, little evidence was found for moderation of personality effects by late adolescent drinking or for moderation of marriage effects by personality. Parental alcoholism findings suggested complex moderated mediation pathways. Parental alcoholism predicted less drinking reduction through decreasing the likelihood of marriage (mediation) and muting marriage's effect on the drinking trajectories (moderation), but parental alcoholism also predicted more drinking reduction through increasing initial drinking in late adolescence (mediation). The current study provides new insights into naturally occurring processes of recovery during young adulthood and suggests that developmentally-tailored interventions for young adults could harness these natural recovery processes (e.g., by integrating role incompatibility themes and addressing factors that block role effects among those with familial alcoholism).
ContributorsLee, Matthew R. (Author) / Chassin, Laurie (Thesis advisor) / Corbin, William R. (Committee member) / Mackinnon, David P (Committee member) / Presson, Clark C. (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
Postpartum depression (PPD) is a significant public health concern affecting up to half a million U.S. women annually. Mexican-American women experience substantially higher rates of PPD, and represent an underserved population with significant health disparities that put these women and their infants at greater risk for substantial psychological and developmental difficulties. The current study utilized data on perceived stress, depression, maternal parenting behavior, and infant social-emotional and cognitive development from 214 Mexican-American mother-infant dyads. The first analysis approach utilized a latent intercept (LI) model to examine how overall mean levels and within-person deviations of perceived stress, depressive symptoms, and maternal parenting behavior are related across the postpartum period. Results indicated large, positive between- and within-person correlations between perceived stress and depression. Neither perceived stress nor depressive symptoms were found to have significant between- or within-person associations with the parenting variables. The second analysis approach utilized an autoregressive cross-lagged model with tests of mediation to identify underlying mechanisms among perceived stress, postpartum depressive symptoms, and maternal parenting behavior in the prediction of infant social-emotional and cognitive development. Results indicated that increased depressive symptoms at 12- and 18-weeks were associated with subsequent reports of increased perceived stress at 18- and 24-weeks, respectively. Perceived stress at 12-weeks was found to be negatively associated with subsequent non-hostility at 18-weeks, and both sensitivity and non-hostility were found to be associated with infant cognitive development and social-emotional competencies at 12 months of age (52-weeks), but not with social-emotional problems. The results of the mediation analyses showed that non-hostility at 18- and 24-weeks significantly mediated the association between perceived stress at 12-weeks and infant cognitive development and social-emotional competencies at 52-weeks. The findings extend research that sensitive parenting in early childhood is as important to the development of cognitive ability, social behavior, and emotion regulation in ethnic minority cultures as it is in majority culture families; that maternal perceptions of stress may spillover into parenting behavior, resulting in increased hostility and negatively influencing infant cognitive and social-emotional development; and that symptoms of depressed mood may influence the experience of stress.
ContributorsCiciolla, Lucia (Author) / Crnic, Keith A (Thesis advisor) / West, Stephen G. (Thesis advisor) / Luecken, Linda J. (Committee member) / Presson, Clark C. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers.
ContributorsMistler, Stephen (Author) / Enders, Craig K. (Thesis advisor) / Aiken, Leona (Committee member) / Levy, Roy (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful cross-group comparisons, failure to attend to possible sources of latent class heterogeneity in the form of class-based differences in factor structure has the potential to compromise conclusions with respect to observed groups and may result in misguided attempts at instrument development and theory refinement. The present studies examined the sensitivity of two widely used confirmatory factor analytic model fit indices, the chi-square test of model fit and RMSEA, to latent class differences in factor structure. Two primary questions were addressed. The first of these concerned the impact of latent class differences in factor loadings with respect to model fit in a single sample reflecting a mixture of classes. The second question concerned the impact of latent class differences in configural structure on tests of factorial invariance across observed groups. The results suggest that both indices are highly insensitive to class-based differences in factor loadings. Across sample size conditions, models with medium (0.2) sized loading differences were rejected by the chi-square test of model fit at rates just slightly higher than the nominal .05 rate of rejection that would be expected under a true null hypothesis. While rates of rejection increased somewhat when the magnitude of loading difference increased, even the largest sample size with equal class representation and the most extreme violations of loading invariance only had rejection rates of approximately 60%. RMSEA was also insensitive to class-based differences in factor loadings, with mean values across conditions suggesting a degree of fit that would generally be regarded as exceptionally good in practice. In contrast, both indices were sensitive to class-based differences in configural structure in the context of a multiple group analysis in which each observed group was a mixture of classes. However, preliminary evidence suggests that this sensitivity may contingent on the form of the cross-group model misspecification.
ContributorsBlackwell, Kimberly Carol (Author) / Millsap, Roger E (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2011
Description
Mediation analysis is used to investigate how an independent variable, X, is related to an outcome variable, Y, through a mediator variable, M (MacKinnon, 2008). If X represents a randomized intervention it is difficult to make a cause and effect inference regarding indirect effects without making no unmeasured confounding assumptions using the potential outcomes framework (Holland, 1988; MacKinnon, 2008; Robins & Greenland, 1992; VanderWeele, 2015), using longitudinal data to determine the temporal order of M and Y (MacKinnon, 2008), or both. The goals of this dissertation were to (1) define all indirect and direct effects in a three-wave longitudinal mediation model using the causal mediation formula (Pearl, 2012), (2) analytically compare traditional estimators (ANCOVA, difference score, and residualized change score) to the potential outcomes-defined indirect effects, and (3) use a Monte Carlo simulation to compare the performance of regression and potential outcomes-based methods for estimating longitudinal indirect effects and apply the methods to an empirical dataset. The results of the causal mediation formula revealed the potential outcomes definitions of indirect effects are equivalent to the product of coefficient estimators in a three-wave longitudinal mediation model with linear and additive relations. It was demonstrated with analytical comparisons that the ANCOVA, difference score, and residualized change score models’ estimates of two time-specific indirect effects differ as a function of the respective mediator-outcome relations at each time point. The traditional model that performed the best in terms of the evaluation criteria in the Monte Carlo study was the ANCOVA model and the potential outcomes model that performed the best in terms of the evaluation criteria was sequential G-estimation. Implications and future directions are discussed.
ContributorsValente, Matthew J (Author) / Mackinnon, David P (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Keving (Committee member) / Chassin, Laurie (Committee member) / Arizona State University (Publisher)
Created2018
Description
Research and theory in social psychology and related fields indicates that people simultaneously hold many cultural identities. And it is well evidenced across relevant fields (e.g., sociology, marketing, economics) that salient identities are instrumental in a variety of cognitive and behavioral processes, including decision-making. It is not, however, well understood how the relative salience of various cultural identities factors into the process of making identity-relevant choices, particularly ones that require an actor to choose between conflicting sets of cultural values or beliefs. It is also unclear whether the source of that salience (e.g., chronic or situational) is meaningful in this regard. The current research makes novel predictions concerning the roles of cultural identity centrality and cultural identity situational salience in three distinct aspects of the decision-making process: Direction of decision, speed of decision, and emotion related to decision. In doing so, the research highlights two under-researched forms of culture (i.e., political and religious) and uses as the focal dependent variable a decision-making scenario that forces participants to choose between the values of their religious and political cultures and, to some degree, behave in an identity-inconsistent manner. Results indicate main effects of Christian identity centrality and democrat identity centrality on preference for traditional versus gender-neutral (i.e., non-traditional/progressive) restrooms after statistically controlling for covariates. Additionally, results show a significant main effect of democrat identity centrality and a significant interaction effect of Christian and democrat identity centrality on positive emotion linked to the decision. Post hoc analyses further reveal a significant quadratic relationship between Christian identity centrality and emotion related to the decision. There was no effect of situational strength of democrat identity salience on the decision. Neither centrality or situational strength had any effect on the speed with which participants made their decisions. This research theoretically and empirically advances the study of cultural psychology and carries important implications for identity research and judgment and decision-making across a variety of fields, including management, behavioral economics, and marketing.
ContributorsBarbour, Joseph Eugene (Author) / Cohen, Adam B. (Thesis advisor) / Kenrick, Douglas T. (Committee member) / Mackinnon, David P (Committee member) / Mandel, Naomi (Committee member) / Arizona State University (Publisher)
Created2019
Description
This study examined an adverse effect of an adolescent group intervention. Group interventions represent one of the most economical, convenient, and common solution to adolescent behavior problems, although prior findings from program evaluation studies have suggested that these groups can unexpectedly increase the externalizing behaviors that they were designed to reduce or prevent. The current study used data from a longitudinal, randomized controlled trial of the Bridges to High School / Puentes a La Secundaria Program, a multicomponent prevention program designed to reduce risk during the middle school transition, which has demonstrated positive effects across an array of outcomes. Data were collected at the beginning of 7th grade, with follow-up data collected at the end of the 7th, 8th, 9th, and 12th grade from a sample of Mexican American adolescents and their mothers. Analyses evaluated long-term effects on externalizing outcomes, trajectories of externalizing behaviors across adolescence, and potential mediators of observed effects. Results showed that the adverse effect that was originally observed based on adolescent self-report of externalizing symptoms at 1-year posttest among youth with high pretest externalizing symptoms was not maintained over time and was not reflected in changes in adolescents' trajectories of externalizing behaviors. Moreover, neither of the peer mediators that theory suggests would explain adverse effects were found to mediate the relationship between intervention status and externalizing symptoms at 1-year posttest. Finally, only beneficial effects were found on externalizing symptoms based on mother report. Together, these findings suggest that the Bridges intervention did not adversely affect adolescent problem behaviors and that future studies should use caution when interpreting unexpected adverse effects.
ContributorsWong, Jessie Jong-Chee (Author) / Gonzales, Nancy A. (Thesis advisor) / West, Stephen G. (Thesis advisor) / Chassin, Laurie (Committee member) / Dishion, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
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