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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
Past literature has indicated that the majority of people with alcohol problems never seek treatment and that this is especially true of women. Relatively few studies have investigated how different types of alcohol-related consequences longitudinally predict men and women's perceived need for treatment and their utilization of treatment services. The current study sought to expand the literature by examining whether gender moderates the links between four frequently endorsed types of consequences and perceived need for or actual utilization of treatment. Two-hundred thirty-seven adults ages 21-36 completed a battery of questionnaires at two time points five years apart. Results indicated that there were four broad types of consequences endorsed by both men and women. Multiple-group models and Wald chi square tests indicated that there were no significant relationships between consequences and treatment outcomes. No gender moderation was found but post-hoc power analyses indicated that the study was underpowered to detect moderation. Researchers need to continue to study factors that predict utilization of alcohol treatment services and the process of recovery so that treatment providers can better address the needs of people with alcohol-related consequences in the areas of referral procedures, clinical assessment, and treatment service provision and planning.
ContributorsBeltran Gonzalez, Iris (Author) / Chassin, Laurie (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Corbin, William (Committee member) / Barrera, Jr., Manuel (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
Although social learning and attachment theories suggest that parent-adolescent relationships influence adult romantic relationships, research on this topic is limited. Most research examining relations between mother-adolescent and father-adolescent relationship quality and young adult romantic relationship quality has found significant effects of mother-adolescent relationship quality. Findings on fathers have been less consistent. These relations have not been examined among youth who experienced parental divorce, which often negatively impacts parent-child relationships and romantic outcomes. Further, no prior studies examined interactive effects of mother-adolescent and father-adolescent relationship quality on romantic attachment. The current study used longitudinal data from the control group of a randomized controlled trial of a preventative intervention for divorced families to examine unique and interactive effects of mother-adolescent and father-adolescent relationship quality on young adult romantic attachment. The 72 participants completed measures of mother-adolescent relationship quality and father-adolescent relationship quality during adolescence (ages 15-19), and completed a measure of romantic attachment (anxiety and avoidance) during young adulthood (ages 24-28). Findings revealed significant interactive effects of mother-adolescent and father-adolescent relationship quality on young adult romantic anxiety. The pattern of results suggests that having a high quality relationship with one's father can protect children from negative effects of having a low quality relationship with one's mother on romantic anxiety. These results suggest the importance of examining effects of one parent-adolescent relationship on YA romantic attachment in the context of the other parent-adolescent relationship. Exploratory analyses of gender revealed that father-adolescent relationship quality significantly interacted with gender to predict romantic avoidance; this relation was stronger for males. These results suggest that nonresidential fathers play an important role in adolescents' working models of relationships and their subsequent romantic attachment.
ContributorsCarr, Colleen (Author) / Wolchik, Sharlene A (Thesis advisor) / Doane, Leah (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2013
Description
Daily dairies and other intensive measurement methods are increasingly used to study the relationships between two time varying variables X and Y. These data are commonly analyzed using longitudinal multilevel or bivariate growth curve models that allow for random effects of intercept (and sometimes also slope) but which do not address the effects of weekly cycles in the data. Three Monte Carlo studies investigated the impact of omitting the weekly cycles in daily dairy data under the multilevel model framework. In cases where cycles existed in both the time-varying predictor series (X) and the time-varying outcome series (Y) but were ignored, the effects of the within- and between-person components of X on Y tended to be biased, as were their corresponding standard errors. The direction and magnitude of the bias depended on the phase difference between the cycles in the two series. In cases where cycles existed in only one series but were ignored, the standard errors of the regression coefficients for the within- and between-person components of X tended to be biased, and the direction and magnitude of bias depended on which series contained cyclical components.
ContributorsLiu, Yu (Author) / West, Stephen G. (Thesis advisor) / Enders, Craig K. (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is well-established that maternal depression is significantly related to internalizing and externalizing behavioral problems and psychopathology in general. However, research suggests maternal depression does not account for all the variance of these outcomes and that other family contextual factors should be investigated. The role of fathers beyond their simple presence or absence is one factor that needs to be further investigated in the context of maternal depression. The proposed study used prospective and cross-sectional analyses to examine father effects (i.e., paternal depression, alcohol use, involvement, and familism) on youth internalizing and externalizing symptoms within the context of maternal depression. The sample consisted of 405 Mexican-American families who had a student in middle school. Data were collected when the students were in 7th and 10th grade. Results from path analyses revealed that maternal depression significantly predicted concurrent youth internalizing symptoms in 7th and 10th grade and externalizing symptoms in 10th grade. In contrast, paternal depression was not related to adolescent symptomatology at either time point, nor was paternal alcoholism, and analyses failed to support moderating effects for any of the paternal variables. However, paternal involvement (father-report) uniquely predicted youth internalizing and externalizing symptoms over and above maternal depression in 7th grade. Youth report of paternal involvement uniquely predicted both internalizing and externalizing in 7th and 10th grade. Paternal familism uniquely predicted youth externalizing symptoms in 7th grade. The present findings support that maternal depression, but not paternal depression, is associated with concurrent levels of youth symptomatology in adolescence. The study did not support that fathers adjustment moderated (exacerbate or buffer) maternal depression effects. However, paternal involvement and paternal familism showed compensatory effects on youth symptomatology in concurrent analyses.
ContributorsMontano, Zorash (Author) / Gonzales, Nancy A. (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Roosa, Mark (Committee member) / Arizona State University (Publisher)
Created2013
Description
Comorbidity is a significant problem for current classification systems of psychopathology (i.e., DSM-V). One issue is that the underlying mechanisms shared among comorbid disorders are poorly understood, especially potential psychosocial mechanisms such as family dynamics. The current study used latent class analysis to empirically classify patterns of psychopathology within a large community sample of late adolescents (age 18-19) based on their lifetime psychological adjustment measured using the World Health Organization Composite International Diagnostic Interview. Videotaped family interactions of adolescents (age 16-17) and their parents were micro and macro coded and the resulting family dynamics were compared across the three empirically defined groups of psychological adjustment which emerged from the latent class analysis: 1) an early onset, persistent antisocial behavior class; 2) an emotionally distressed and substance using class; and 3) a typically developing class. It was found that some directly observed family dynamics, including parental monitoring, dyadic positive engagement and coercive engagement discriminated among empirically derived classes. It was also found that particular tasks better discriminated among classes with regard to specific family dynamics (e.g., family activity task best discriminated among classes on dyadic positive engagement). Overall, findings suggest that novel methodologies like latent class analysis can be useful in attempting to map underlying transdiagnostic mechanisms onto the current diagnostic framework. The findings also highlight the importance of taking many variables into consideration when attempting to understand how family dynamics are associated with psychological adjustment.
ContributorsPanza, Kaitlyn E (Author) / Dishion, Thomas J (Thesis advisor) / Crnic, Keith A (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2015
Description
Research methods based on the frequentist philosophy use prior information in a priori power calculations and when determining the necessary sample size for the detection of an effect, but not in statistical analyses. Bayesian methods incorporate prior knowledge into the statistical analysis in the form of a prior distribution. When prior information about a relationship is available, the estimates obtained could differ drastically depending on the choice of Bayesian or frequentist method. Study 1 in this project compared the performance of five methods for obtaining interval estimates of the mediated effect in terms of coverage, Type I error rate, empirical power, interval imbalance, and interval width at N = 20, 40, 60, 100 and 500. In Study 1, Bayesian methods with informative prior distributions performed almost identically to Bayesian methods with diffuse prior distributions, and had more power than normal theory confidence limits, lower Type I error rates than the percentile bootstrap, and coverage, interval width, and imbalance comparable to normal theory, percentile bootstrap, and the bias-corrected bootstrap confidence limits. Study 2 evaluated if a Bayesian method with true parameter values as prior information outperforms the other methods. The findings indicate that with true values of parameters as the prior information, Bayesian credibility intervals with informative prior distributions have more power, less imbalance, and narrower intervals than Bayesian credibility intervals with diffuse prior distributions, normal theory, percentile bootstrap, and bias-corrected bootstrap confidence limits. Study 3 examined how much power increases when increasing the precision of the prior distribution by a factor of ten for either the action or the conceptual path in mediation analysis. Power generally increases with increases in precision but there are many sample size and parameter value combinations where precision increases by a factor of 10 do not lead to substantial increases in power.
ContributorsMiocevic, Milica (Author) / Mackinnon, David P. (Thesis advisor) / Levy, Roy (Committee member) / West, Stephen G. (Committee member) / Enders, Craig (Committee member) / Arizona State University (Publisher)
Created2014
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