Matching Items (46)
Description
With improvements in technology, intensive longitudinal studies that permit the investigation of daily and weekly cycles in behavior have increased exponentially over the past few decades. Traditionally, when data have been collected on two variables over time, multivariate time series approaches that remove trends, cycles, and serial dependency have been used. These analyses permit the study of the relationship between random shocks (perturbations) in the presumed causal series and changes in the outcome series, but do not permit the study of the relationships between cycles. Liu and West (2016) proposed a multilevel approach that permitted the study of potential between subject relationships between features of the cycles in two series (e.g., amplitude). However, I show that the application of the Liu and West approach is restricted to a small set of features and types of relationships between the series. Several authors (e.g., Boker & Graham, 1998) proposed a connected mass-spring model that appears to permit modeling of more general cyclic relationships. I showed that the undamped connected mass-spring model is also limited and may be unidentified. To test the severity of the restrictions of the motion trajectories producible by the undamped connected mass-spring model I mathematically derived their connection to the force equations of the undamped connected mass-spring system. The mathematical solution describes the domain of the trajectory pairs that are producible by the undamped connected mass-spring model. The set of producible trajectory pairs is highly restricted, and this restriction sets major limitations on the application of the connected mass-spring model to psychological data. I used a simulation to demonstrate that even if a pair of psychological time-varying variables behaved exactly like two masses in an undamped connected mass-spring system, the connected mass-spring model would not yield adequate parameter estimates. My simulation probed the performance of the connected mass-spring model as a function of several aspects of data quality including number of subjects, series length, sampling rate relative to the cycle, and measurement error in the data. The findings can be extended to damped and nonlinear connected mass-spring systems.
ContributorsMartynova, Elena (M.A.) (Author) / West, Stephen G. (Thesis advisor) / Amazeen, Polemnia (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2019
Description
To make meaningful comparisons on a construct of interest across groups or over time, measurement invariance needs to exist for at least a subset of the observed variables that define the construct. Often, chi-square difference tests are used to test for measurement invariance. However, these statistics are affected by sample size such that larger sample sizes are associated with a greater prevalence of significant tests. Thus, using other measures of non-invariance to aid in the decision process would be beneficial. For this dissertation project, I proposed four new effect size measures of measurement non-invariance and analyzed a Monte Carlo simulation study to evaluate their properties and behavior in addition to the properties and behavior of an already existing effect size measure of non-invariance. The effect size measures were evaluated based on bias, variability, and consistency. Additionally, the factors that affected the value of the effect size measures were analyzed. All studied effect sizes were consistent, but three were biased under certain conditions. Further work is needed to establish benchmarks for the unbiased effect sizes.
ContributorsGunn, Heather J (Author) / Grimm, Kevin J. (Thesis advisor) / Edwards, Michael C (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Anderson, Samantha F. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Longitudinal data from European-American (EA) and Mexican-American (MA) families (n = 179 mothers, fathers, and youth; 41% MA) was used to test a bio-psycho-cultural model of the effect of non-responsive parenting on externalizing problems in young adult offspring through the effect on the stress response system. Parenting behavior (acceptance, rejection, harsh discipline) was assessed when children were in late childhood (12-13 years), cortisol samples were collected during late adolescence (18-19 years), and externalizing problems were measured in young adulthood (21-22 years). Latent profile analyses were used to examine patterns of parenting behavior in EA and MA families. A path analysis framework was used to examine how non-responsive parenting interacted with acceptance to predict adolescent stress response and subsequent externalizing problems in EA and MA young adults. Results showed different patterns of parenting behavior in EA versus MA families, with MA families demonstrating a profile of high acceptance and high non-responsiveness at higher rates than EA families. In MA families, youth adherence to the traditional cultural value of familismo related to more positive perceptions of parenting behavior. Across ethnic groups, parent rejection only predicted higher externalizing problems in young adults when acceptance was high. The effect of parent harsh discipline on offspring stress response differed by ethnicity. In MA families, harsh discipline predicted dysregulated stress response in youth when acceptance was low. In EA families, harsh discipline did not relate to youth stress response. Overall, results increase the understanding of normative and adaptive parenting behaviors in MA families. Findings inform the development of culturally-competent parenting-focused interventions that can better prevent dysregulated stress response and externalizing behavior problems in ethnically diverse youth.
ContributorsMahrer, Nicole Eva (Author) / Luecken, Linda (Thesis advisor) / Wolchik, Sharlene (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Pina, Armando (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
Although substantial research has examined individual, family, and peer factors that contribute to predicting adolescent alcohol use, limited attention has been devoted to the unique role of romantic partners and little consideration has been given to the potential importance of romantic relationship seriousness. Data from Waves I and II of the National Longitudinal Study of Adolescent Health (Add Health) were used to assess the relation between romantic relationship seriousness and binge drinking and drinking consequences one year later among 14-18 year-olds (n= 928 adolescents; 54.1% female). Main effects of relationship seriousness and moderating effects of adolescent age, partner age, adolescent age by partner age, parental alcoholism, and gender were examined separately for each drinking outcome using zero-inflated Poisson regression (ZIP) models. Relationship seriousness and study covariate interactions were also examined. ZIP models estimate (a) a logistic regression that distinguishes between individuals whose values can only be zero on the outcome (i.e., a structural zero class) and individuals with count values ranging from zero to any other positive integer (i.e., a non-structural zero class), and (b) a Poisson regression predicting count values among the non-structural zero class. Results showed trends towards significance for relations between relationship seriousness and binge drinking and drinking consequences among non-structural zero classes. As hypothesized, increased relationship seriousness predicted less frequent binge drinking and fewer drinking consequences. The relation between relationship seriousness and binge drinking was moderated by peer alcohol use; the negative relation between relationship seriousness and binge drinking frequency was significant among adolescents who reported 0-2, but not 3, close friends who drink. The relation between relationship seriousness and number of drinking consequences was moderated by gender, adolescent delinquency (covariate), peer alcohol use (covariate), and Wave I drinking consequences (control variable). Specifically, a significant relation between relationship seriousness and number of drinking consequences was revealed only for females and only for adolescents who reported high consequences at Wave I, and was significant among adolescents who reported 0-2 close friends who drink and low delinquency. Results indicate that relationship seriousness can protect adolescents in terms of drinking outcomes, which could have implications for prevention efforts.
ContributorsCarr, Colleen (Author) / Wolchik, Sharlene (Thesis advisor) / Chassin, Laurie (Thesis advisor) / Dishion, Thomas (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2016
Description
Parental over-control (excessively restrictive and regulatory parenting behaviors) has been consistently identified as a robust risk factor in the development and maintenance of child anxiety problems. However, current understanding of the parental over-control to child anxiety relation is limited by a lack of specificity. The broad ‘parental over-control’ construct represents a heterogeneous category of related but distinct parenting behaviors each of which may exert a unique effect on child anxious emotion. Still, research to date has generally failed to consider this possibility. Moreover, culturally cognizant theory and emerging empirical evidence suggest cross-ethnic (Caucasian vs. Hispanic/Latino) differences in the utilization of various parenting strategies as well as the effects of parenting behaviors on child outcomes. But, only a handful of studies have considered the potential differences in the functioning of parental over-control behaviors within a Hispanic/Latino cultural framework. Using a sample of 98 pre-adolescent children at-risk for anxiety problems, the present study sought to further explicate the association between parental over-control and child anxiety symptoms in the context of ethnic and cultural diversity. Results suggest that parents’ use of overprotection and (lack of) autonomy granting might be particularly relevant to child anxiety, compared to parental intrusiveness and behavioral control. Findings also indicate that some youth may be more vulnerable to parental over-control and suggest that cultural values may play a role in the relation between over-controlling parenting and child anxiety symptoms. Knowledge about cross-cultural variations in the relation among parental over-control behaviors and the development of anxiety symptoms is important because it can improve the cultural robustness of child anxiety theory and has potential to inform culturally sensitive child anxiety prevention and intervention efforts.
ContributorsHolly, Lindsay E (Author) / Pina, Armando A (Thesis advisor) / Crnic, Keith (Committee member) / Tein, Jenn-Yun (Committee member) / Barrera, Manuel (Committee member) / Arizona State University (Publisher)
Created2016
Description
Time-to-event analysis or equivalently, survival analysis deals with two variables simultaneously: when (time information) an event occurs and whether an event occurrence is observed or not during the observation period (censoring information). In behavioral and social sciences, the event of interest usually does not lead to a terminal state such as death. Other outcomes after the event can be collected and thus, the survival variable can be considered as a predictor as well as an outcome in a study. One example of a case where the survival variable serves as a predictor as well as an outcome is a survival-mediator model. In a single survival-mediator model an independent variable, X predicts a survival variable, M which in turn, predicts a continuous outcome, Y. The survival-mediator model consists of two regression equations: X predicting M (M-regression), and M and X simultaneously predicting Y (Y-regression). To estimate the regression coefficients of the survival-mediator model, Cox regression is used for the M-regression. Ordinary least squares regression is used for the Y-regression using complete case analysis assuming censored data in M are missing completely at random so that the Y-regression is unbiased. In this dissertation research, different measures for the indirect effect were proposed and a simulation study was conducted to compare performance of different indirect effect test methods. Bias-corrected bootstrapping produced high Type I error rates as well as low parameter coverage rates in some conditions. In contrast, the Sobel test produced low Type I error rates as well as high parameter coverage rates in some conditions. The bootstrap of the natural indirect effect produced low Type I error and low statistical power when the censoring proportion was non-zero. Percentile bootstrapping, distribution of the product and the joint-significance test showed best performance. Statistical analysis of the survival-mediator model is discussed. Two indirect effect measures, the ab-product and the natural indirect effect are compared and discussed. Limitations and future directions of the simulation study are discussed. Last, interpretation of the survival-mediator model for a made-up empirical data set is provided to clarify the meaning of the quantities in the survival-mediator model.
ContributorsKim, Han Joe (Author) / Mackinnon, David P. (Thesis advisor) / Tein, Jenn-Yun (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2017
Description
The present study utilized longitudinal data from a high-risk community sample (n= 377; 166 trauma-exposed; 54% males; 52% children of alcoholics; 73% non-Hispanic/Latino Caucasian; 22% Hispanic/Latino; 5% other ethnicity) to test a series of hypotheses that may help explain the risk pathways that link traumatic stress, posttraumatic stress disorder (PTSD) symptomatology, and problematic alcohol and drug use. Specifically, this study examined whether pre-trauma substance use problems increase risk for trauma exposure (the high-risk hypothesis) or PTSD symptoms (the susceptibility hypothesis), whether PTSD symptoms increase risk for later alcohol/drug problems (the self-medication hypothesis), and whether the association between PTSD symptoms and alcohol/drug problems is due to shared risk factors (the shared vulnerability hypothesis). This study also examined the roles of gender and ethnicity in these pathways. A series of logistic and negative binomial regressions were performed in a path analysis framework. A composite pre-trauma family adversity variable was formed from measures of family conflict, family life stress, parental alcoholism, and other parent psychopathology. Results provided the strongest support for the self-medication hypothesis, such that PTSD symptoms predicted higher levels of later alcohol and drug problems among non-Hispanic/Latino Caucasian participants, over and above the influences of pre-trauma family adversity, pre-trauma substance use problems, trauma exposure, and demographic variables. Results partially supported the high-risk hypothesis, such that adolescent substance use problems had a marginally significant unique effect on risk for assaultive violence exposure but not on overall risk for trauma exposure. There was no support for the susceptibility hypothesis, as pre-trauma adolescent substance use problems did not significantly influence risk for PTSD diagnosis/symptoms over and above the influence of pre-trauma family adversity. Finally, there was little support for the shared vulnerability hypothesis. Neither trauma exposure nor preexisting family adversity accounted for the link between PTSD symptoms and later substance use problems. These results add to a growing body of literature in support of the self-medication hypothesis. Findings extend previous research by showing that PTSD symptoms may influence the development of alcohol and drug problems over and above the influence of trauma exposure itself, preexisting family risk factors, and baseline levels of substance use.
ContributorsHaller, Moira (Author) / Chassin, Laurie (Thesis advisor) / Davis, Mary (Committee member) / Pina, Armando (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Sexual risk taking is prevalent in adolescence, particularly among Latino teens, and can have serious consequences in the form of contraction of STIs, HIV, and increased risk of unintended pregnancy. Family contexts characterized by conflict and lack of support are antecedents of adolescent sexual risk taking, but evidence elucidating the mechanisms underlying this association is lacking. The current study sought to test two potential pathways to sexual risk taking within the framework of social developmental theory, among a sample of 189 Mexican origin adolescents and their caregivers interviewed in the 7th, 8th, and 12th grades. Structural equation modeling was utilized to examine pathways from 7th grade family risk to age of sexual initiation, number of lifetime sexual partners, and condom nonuse reported in the 12th grade. Deviant peer affiliations and academic engagement at 8th grade were tested as mediators of this relationship for boys and girls. Results confirm the importance of the family context, with family risk exerting direct effects on the number of lifetime sexual partners for both genders, and on age of sexual initiation for females only. Deviant peer affiliations serve as a mediator of family risk for males, but not females. When included in a model alongside deviant peers, academic engagement does not play the hypothesized mediating role between family risk and any of the sexual risk outcomes. Future research ought to consider additional mediators that better account for the relation between family risk and sexual risk taking among females.
ContributorsJensen, Michaeline R (Author) / Gonzales, Nancy A. (Thesis advisor) / Lopez, Vera (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2012
Description
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