Matching Items (40)
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
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
The primary aim of this study was to investigate resilient profiles in low-income Mexican American (MA) mothers. MA mothers are part of an under researched population, the fastest growing ethnic minority group, and have the highest birth rate in the United States, presenting a significant public health concern. The transition to motherhood can be an emotionally and physically complex time for women, particularly in the context of a stressful low-income environment. Although most low-income women navigate this transition well, a significant number of mothers develop moderate to severe depressive symptoms. The proposed research investigated profiles of resilience during the prenatal period using a person-centered approach via latent profile analysis. In alignment with current resilience theories, several domains of resilience were investigated including psychological, social, and cultural adherence (e.g., maintaining specific cultural traditions). Concurrent prenatal depressive symptoms and stress were correlated with the profiles in order to establish validity. Six week postpartum depressive symptoms and physiological processes (e.g., overall cortisol output, heart rate variability, and sleep) were also predicted by the prenatal resilient profiles. The resulting data revealed three separate profiles: low-resource, high-resource Anglo, and high-resource Mexican. These resilience profiles had differential associations with concurrent depressive symptoms and stress, such that women in the high-resource profiles reported less depressive symptoms and stress prenatally. Further, profile differences regarding cortisol output, resting heart rate variability, were also found, but there were no differences in insomnia symptoms. Profile classification also moderated the effects of prenatal economic stress on postpartum depressive symptoms, such that women in the high-resource Mexican profile were at risk for higher postpartum depressive symptoms under high economic stress compared to the high-resource Anglo group, which demonstrated a more resilient response. Overall, the results suggest the presence of multiple clusters of prenatal resilience within a sample of MA mothers facing health disparities, with various effects on perinatal mental health and postpartum physiological processes. The results also highlight the need for multi-dimensional models of resilience and the possible implications for interventions.
ContributorsGress Smith, Jenna L (Author) / Luecken, Linda J. (Thesis advisor) / Gonzales, Nancy (Committee member) / Okun, Morris (Committee member) / Zautra, Alex (Committee member) / Arizona State University (Publisher)
Created2014
Description
Research demonstrating the importance of the paternal role has been largely conducted using samples of Caucasian men, leaving a gap in what is known about fathering in minority cultures. Family systems theories highlight the dynamic interrelations between familial roles and relationships, and suggest that comprehensive studies of fathering require attention to the broad family and cultural context. During the early infancy period, mothers' and fathers' postpartum adjustment may represent a critical source of influence on father involvement. For the current study, Mexican American (MA) women (N = 125) and a subset of their romantic partners/biological fathers (N = 57) reported on their depressive symptoms and levels of father involvement (paternal engagement, accessibility, and responsibility) during the postpartum period. Descriptive analyses suggested that fathers are involved in meaningful levels of care during infancy. Greater paternal postpartum depression (PPD) was associated with lower levels of father involvement. Maternal PPD interacted with paternal gender role attitudes to predict father involvement. At higher levels of maternal PPD, involvement increased among fathers adhering to less segregated gender role attitudes and decreased among fathers who endorsed more segregated gender role attitudes. Within select models, differences in the relations were observed between mothers' and fathers' reports of paternal involvement. Results bring attention to the importance of examining contextual influences on early fathering in MA families and highlight the unique information that may be gathered from separate maternal and paternal reports of father involvement.
ContributorsRoubinov, Danielle S (Author) / Luecken, Linda J. (Thesis advisor) / Crnic, Keith A (Committee member) / Enders, Craig K. (Committee member) / Gonzales, Nancy A. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Low-income Mexican American women face significant risk for poor health during the postpartum period. Chronic stressors are theorized to negatively impact mental and physical health outcomes. However, physiological factors associated with increased self-regulatory capacity, such as resting heart rate variability, may buffer the impact of stress. In a sample of 322 low-income Mexican American women (mother age 18-42; 84% Spanish-speaking; modal family income $10,000-$15,000), the interactive influence of resting heart rate variability and three chronic prenatal stressors (daily hassles, negative life events, economic stress) on maternal cortisol output, depressive symptoms, and self-rated health at 12 weeks postpartum was assessed. The hypothesized interactive effects between resting heart rate variability and the chronic prenatal stressors on the health outcomes were not supported by the data. However, results showed that a higher number of prenatal daily hassles was associated with increased postpartum depressive symptoms, and a higher number of prenatal negative life events was associated with lower postpartum cortisol output. These results suggest that elevated chronic stress during the prenatal period may increase risk for poor health during the postpartum period.
ContributorsJewell, Shannon Linda (Author) / Luecken, Linda J. (Thesis advisor) / Lemery-Chalfant, Kathryn (Committee member) / Perez, Marisol (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
Description
Daily life stressors and negative emotional experiences predict poor physical and psychological health. The stress response of the hypothalamic-pituitary-adrenal axis is a primary biological system through which stressful experiences impact health and well-being across development. Individuals differ in their capacity for self-regulation and utilize various coping strategies in response to stress. Everyday experiences and emotions are highly variable during adolescence, a time during which self-regulatory abilities may become particularly important for adapting to shifting social contexts. Many adolescents in the U.S. enter college after high school, a context characterized by new opportunities and challenges for self-regulation. Guided by biopsychosocial and daily process approaches, the current study explored everyday stress and negative affect (NA), cortisol reactivity, and self-regulation assessed at the momentary, daily, and trait level among a racially/ethnically and socioeconomically diverse sample of first-year college students (N = 71; Mage = 18.85; 23% male; 52% non-Hispanic White) who completed a modified ecological momentary assessment. It was expected that within-person increases in momentary stress level or NA would be associated with cortisol reactivity assessed in college students' naturalistic settings. It was predicted that these within-person associations would differ based on engagement coping responses assessed via momentary diary reports, by the range of engagement coping responses assessed via diary reports at the end of the day, and by higher trait levels of self-regulation assessed via standard self-report questionnaire. Within-person increases in momentary stress level were significantly associated with momentary elevations in cortisol only during moments characterized by greater than usual engagement coping efforts (i.e., within-person
increases). At a different level of analysis, within-person increases in momentary stress level were significantly associated with increases in cortisol only for those with low trait levels of coping efficacy and engagement coping. On average, within-person increases in momentary NA were significantly associated with cortisol reactivity. Tests of moderation revealed this momentary association was only significant for those with low trait levels of support-seeking coping.
increases). At a different level of analysis, within-person increases in momentary stress level were significantly associated with increases in cortisol only for those with low trait levels of coping efficacy and engagement coping. On average, within-person increases in momentary NA were significantly associated with cortisol reactivity. Tests of moderation revealed this momentary association was only significant for those with low trait levels of support-seeking coping.
ContributorsSladek, Michael Ronald (Author) / Doane, Leah D (Thesis advisor) / Eisenberg, Nancy (Committee member) / Luecken, Linda J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Coarsely grouped counts or frequencies are commonly used in the behavioral sciences. Grouped count and grouped frequency (GCGF) that are used as outcome variables often violate the assumptions of linear regression as well as models designed for categorical outcomes; there is no analytic model that is designed specifically to accommodate GCGF outcomes. The purpose of this dissertation was to compare the statistical performance of four regression models (linear regression, Poisson regression, ordinal logistic regression, and beta regression) that can be used when the outcome is a GCGF variable. A simulation study was used to determine the power, type I error, and confidence interval (CI) coverage rates for these models under different conditions. Mean structure, variance structure, effect size, continuous or binary predictor, and sample size were included in the factorial design. Mean structures reflected either a linear relationship or an exponential relationship between the predictor and the outcome. Variance structures reflected homoscedastic (as in linear regression), heteroscedastic (monotonically increasing) or heteroscedastic (increasing then decreasing) variance. Small to medium, large, and very large effect sizes were examined. Sample sizes were 100, 200, 500, and 1000. Results of the simulation study showed that ordinal logistic regression produced type I error, statistical power, and CI coverage rates that were consistently within acceptable limits. Linear regression produced type I error and statistical power that were within acceptable limits, but CI coverage was too low for several conditions important to the analysis of counts and frequencies. Poisson regression and beta regression displayed inflated type I error, low statistical power, and low CI coverage rates for nearly all conditions. All models produced unbiased estimates of the regression coefficient. Based on the statistical performance of the four models, ordinal logistic regression seems to be the preferred method for analyzing GCGF outcomes. Linear regression also performed well, but CI coverage was too low for conditions with an exponential mean structure and/or heteroscedastic variance. Some aspects of model prediction, such as model fit, were not assessed here; more research is necessary to determine which statistical model best captures the unique properties of GCGF outcomes.
ContributorsCoxe, Stefany (Author) / Aiken, Leona S. (Thesis advisor) / West, Stephen G. (Thesis advisor) / Mackinnon, David P (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2012