Matching Items (25)
Description
Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase the power to detect a relation between two variables has not been investigated. The first study identified situations where empirical and analytical power of two tests of significance for a single mediator model was greater than power of a bivariate significance test. Results from the first study indicated that including a mediator increased statistical power in small samples with large effects and in large samples with small effects. Next, a study was conducted to assess when power was greater for a significance test for a two mediator model as compared with power of a bivariate significance test. Results indicated that including two mediators increased power in small samples when both specific mediated effects were large and in large samples when both specific mediated effects were small. Implications of the results and directions for future research are then discussed.
ContributorsO'Rourke, Holly Patricia (Author) / Mackinnon, David P (Thesis advisor) / Enders, Craig K. (Committee member) / Millsap, Roger (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
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
The present study examined the association of pain intensity and goal progress in a community sample of 132 adults with chronic pain who participated in a 21 day diary study. Multilevel modeling was employed to investigate the effect of morning pain intensity on evening goal progress as mediated by pain's interference with afternoon goal pursuit. Moderation effects of pain acceptance and pain catastrophizing on the associations between pain and interference with both work and lifestyle goal pursuit were also tested. The results showed that the relationship between morning pain and pain's interference with work goal pursuit in the afternoon was significantly moderated by a pain acceptance. In addition, it was found that the mediated effect differed across levels of pain acceptance; that is: (1) there was a significant mediation effect when pain acceptance was at its mean and one standard deviation below the mean; but (2) there was no mediation effect when pain acceptance was one standard deviation above the mean. It appears that high pain acceptance significantly attenuates the power of nociception in disrupting one's work goal pursuit. However, in the lifestyle goal model, none of the moderators were significant nor was there a significant association between pain interference with goal pursuit and goal progress. Only morning pain intensity significantly predicted afternoon interference with lifestyle goal pursuit. Further interpretation of the present findings and potential explanations of those inconsistencies are elaborated on discussion. Limitations and the clinical implication of the current study were considered, along with suggestions for future studies.
ContributorsMun, Chung Jung (Author) / Karoly, Paul (Thesis advisor) / Okun, Morris A. (Committee member) / Enders, Craig K. (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
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
Parenting approaches that are firm yet warm (i.e., authoritative parenting) have been found to be robustly beneficial for mainstream White Americans youths, but do not demonstrate similarly consistent effects among Chinese Americans (CA) adolescents. Evidence suggests that CA adolescents interpret and experience parenting differently than their mainstream counterparts given differences in parenting values and child-rearing norms between traditional Chinese and mainstream American cultures. The current study tests the theory that prospective effects of parenting on psychological and academic functioning depends on adolescents' cultural frameworks for interpreting and understanding parenting. CA adolescents with values and expectations of parenting that are more consistent with mainstream American parenting norms were predicted to experience parenting similar to their White American counterparts (i.e., benefiting from a combination of parental strictness and warmth). In contrast, CA adolescents with parenting values and expectations more consistent with traditional Chinese parenting norms were predicted to experience parenting and its effects on academic and psychological outcomes differently than patterns documented in the mainstream literature. This study was conducted with a sample of Chinese American 9th graders (N = 500) from the Multicultural Family Adolescent Study. Latent Class Analysis (LCA), a person-centered approach to modeling CA adolescents' cultural frameworks for interpreting parenting, was employed using a combination of demographic variables (e.g., nativity, language use at home, mother's length of stay in the U.S.) and measures of parenting values and expectations (e.g., parental respect, ideal strictness & laxness). The study then examined whether prospective effects of parenting behaviors (strict control, warmth, and their interaction effect) on adolescent adjustment (internalizing and externalizing symptoms, substance use, and GPA) were moderated by latent class membership. The optimal LCA solution identified five distinct cultural frameworks for understanding parenting. Findings generally supported the idea that effects of parenting on CA adolescent adjustment depend on adolescents' cultural framework for parenting. The classic authoritative parenting effect (high strictness and warmth leads to positive outcomes) was found for the two most acculturated groups of adolescents. However, only one of these groups overtly endorsed mainstream American parenting values.
ContributorsLiu, Freda Fangfang (Author) / Gonzales, Nancy A. (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Yoo, Hyung Chol (Committee member) / Barrera, Manuel (Committee member) / Arizona State University (Publisher)
Created2011
Description
Designing studies that use latent growth modeling to investigate change over time calls for optimal approaches for conducting power analysis for a priori determination of required sample size. This investigation (1) studied the impacts of variations in specified parameters, design features, and model misspecification in simulation-based power analyses and (2) compared power estimates across three common power analysis techniques: the Monte Carlo method; the Satorra-Saris method; and the method developed by MacCallum, Browne, and Cai (MBC). Choice of sample size, effect size, and slope variance parameters markedly influenced power estimates; however, level-1 error variance and number of repeated measures (3 vs. 6) when study length was held constant had little impact on resulting power. Under some conditions, having a moderate versus small effect size or using a sample size of 800 versus 200 increased power by approximately .40, and a slope variance of 10 versus 20 increased power by up to .24. Decreasing error variance from 100 to 50, however, increased power by no more than .09 and increasing measurement occasions from 3 to 6 increased power by no more than .04. Misspecification in level-1 error structure had little influence on power, whereas misspecifying the form of the growth model as linear rather than quadratic dramatically reduced power for detecting differences in slopes. Additionally, power estimates based on the Monte Carlo and Satorra-Saris techniques never differed by more than .03, even with small sample sizes, whereas power estimates for the MBC technique appeared quite discrepant from the other two techniques. Results suggest the choice between using the Satorra-Saris or Monte Carlo technique in a priori power analyses for slope differences in latent growth models is a matter of preference, although features such as missing data can only be considered within the Monte Carlo approach. Further, researchers conducting power analyses for slope differences in latent growth models should pay greatest attention to estimating slope difference, slope variance, and sample size. Arguments are also made for examining model-implied covariance matrices based on estimated parameters and graphic depictions of slope variance to help ensure parameter estimates are reasonable in a priori power analysis.
ContributorsVan Vleet, Bethany Lucía (Author) / Thompson, Marilyn S. (Thesis advisor) / Green, Samuel B. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2011