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- Genre: Masters Thesis
- Genre: Diaries
Description
This project studied a four-variable single mediator model, a single mediator model: X (independent variable) to M (mediator) to Y (dependent variable), and a confounder (U) that influences M and Y. Confounding represents a threat to the causal interpretation in mediation analysis. For instance, if X represents random assignment to control and treatment conditions, the effect of X on M and the effect of X on Y have a causal interpretation under certain reasonable assumptions. However, the randomization of X does not allow for a causal interpretation of the M to Y effect unless certain confounding assumptions are satisfied. The aim of this project was to develop a significance test and an effect size comparison for two sensitivity to confounding analyses methods: Left Out Variables Error (L.O.V.E.) and the correlated residuals method. Further, the project assessed the accuracy of the methods for identifying confounding bias by simulating data with and without confounding bias.
ContributorsAlvarez Bartolo, Diana (Author) / Mackinnon, David P. (Thesis advisor) / Grimm, Kevin J. (Committee member) / McNeish, Daniel (Committee member) / Arizona State University (Publisher)
Created2022
Description
Statistical inference from mediation analysis applies to populations, however, researchers and clinicians may be interested in making inference to individual clients or small, localized groups of people. Person-oriented approaches focus on the differences between people, or latent groups of people, to ask how individuals differ across variables, and can help researchers avoid ecological fallacies when making inferences about individuals. Traditional variable-oriented mediation assumes the population undergoes a homogenous reaction to the mediating process. However, mediation is also described as an intra-individual process where each person passes from a predictor, through a mediator, to an outcome (Collins, Graham, & Flaherty, 1998). Configural frequency mediation is a person-oriented analysis of contingency tables that has not been well-studied or implemented since its introduction in the literature (von Eye, Mair, & Mun, 2010; von Eye, Mun, & Mair, 2009). The purpose of this study is to describe CFM and investigate its statistical properties while comparing it to traditional and casual inference mediation methods. The results of this study show that joint significance mediation tests results in better Type I error rates but limit the person-oriented interpretations of CFM. Although the estimator for logistic regression and causal mediation are different, they both perform well in terms of Type I error and power, although the causal estimator had higher bias than expected, which is discussed in the limitations section.
ContributorsSmyth, Heather Lynn (Author) / Mackinnon, David P (Thesis advisor) / Grimm, Kevin J. (Committee member) / Edwards, Michael C (Committee member) / Arizona State University (Publisher)
Created2019
Description
Data from 749 Mexican-origin families across a seven-year span was used to test a model of the processes that moderate and mediate the transmission of religious values from parent to child. There were four separate reports of parenting practices (mother-report, father-report, adolescent’s report on mother, and adolescents report on father) and models were tested separately based on each report. Results suggest the mother’s role was more influential than fathers in transmitting religious values to their child, across parent and adolescent-report. In addition, results revealed different, and opposing effects for mother’s self-report of parenting practices and adolescents report on mother’s parenting behavior. Adolescents’ perceptions of maternal acceptance and consistency increased the likelihood of adolescents maintaining their religious values across adolescence, whereas mothers’ self-reported parenting practices negatively predicted late adolescents’ religious values. Lastly, results of this study lend support for the differential role of mothers in fathers in the development of adolescents’ social competence, specifically in the context of their religious values and use of positive parenting practices. The findings highlight the unique contributions of each reports’ perceptions in studying the transmission of religious values in families, as well, as the distinct role of mothers and fathers in the development of adolescents’ social competence.
ContributorsPerez, Vanesa Marie (Author) / Gonzales, Nancy A. (Thesis advisor) / Lemery-Chalfant, Kathryn (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Statistical mediation analysis allows researchers to identify the most important the mediating constructs in the causal process studied. Information about the mediating processes can be used to make interventions more powerful by enhancing successful program components and by not implementing components that did not significantly change the outcome. Identifying mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to external criteria. However, current methods do not allow researchers to study the relationships between general and specific aspects of a construct to an external criterion simultaneously. This study proposes a bifactor measurement model for the mediating construct as a way to represent the general aspect and specific facets of a construct simultaneously. Monte Carlo simulation results are presented to help to determine under what conditions researchers can detect the mediated effect when one of the facets of the mediating construct is the true mediator, but the mediator is treated as unidimensional. Results indicate that parameter bias and detection of the mediated effect depends on the facet variance represented in the mediation model. This study contributes to the largely unexplored area of measurement issues in statistical mediation analysis.
ContributorsGonzález, Oscar (Author) / Mackinnon, David P (Thesis advisor) / Grimm, Kevin J. (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2016
Description
Currently, there is a clear gap in the missing data literature for three-level models.
To date, the literature has only focused on the theoretical and algorithmic work
required to implement three-level imputation using the joint model (JM) method of
imputation, leaving relatively no work done on fully conditional specication (FCS)
method. Moreover, the literature lacks any methodological evaluation of three-level
imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in
order to implement FCS in the context of a three-level model and (2) to evaluate
both imputation methods. The simulation investigated a random intercept model
under both 20% and 40% missing data rates. The ndings of this thesis suggest
that the estimates for both JM and FCS were largely unbiased, gave good coverage,
and produced similar results. The sole exception for both methods was the slope for
the level-3 variable, which was modestly biased. The bias exhibited by the methods
could be due to the small number of clusters used. This nding suggests that future
research ought to investigate and establish clear recommendations for the number of
clusters required by these imputation methods. To conclude, this thesis serves as a
preliminary start in tackling a much larger issue and gap in the current missing data
literature.
To date, the literature has only focused on the theoretical and algorithmic work
required to implement three-level imputation using the joint model (JM) method of
imputation, leaving relatively no work done on fully conditional specication (FCS)
method. Moreover, the literature lacks any methodological evaluation of three-level
imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in
order to implement FCS in the context of a three-level model and (2) to evaluate
both imputation methods. The simulation investigated a random intercept model
under both 20% and 40% missing data rates. The ndings of this thesis suggest
that the estimates for both JM and FCS were largely unbiased, gave good coverage,
and produced similar results. The sole exception for both methods was the slope for
the level-3 variable, which was modestly biased. The bias exhibited by the methods
could be due to the small number of clusters used. This nding suggests that future
research ought to investigate and establish clear recommendations for the number of
clusters required by these imputation methods. To conclude, this thesis serves as a
preliminary start in tackling a much larger issue and gap in the current missing data
literature.
ContributorsKeller, Brian Tinnell (Author) / Enders, Craig K. (Thesis advisor) / Grimm, Kevin J. (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2015
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
Description
The present study aimed to advance the current understanding of the relation between disability and subjective well-being by examining the extent to which different facets of subjective well-being (life satisfaction, positive affect, and negative affect) change before and after disability onset, and the extent to which age and type of disability moderate such changes. Multiphase growth-curve models to prospective longitudinal survey data from Waves 1-16 of the Household, Income and Labour Dynamics in Australia (HILDA) survey (N = 3,795; mean age = 50.22; age range: 16-99; 51% women). On average, life satisfaction remained relatively stable across the disability transition, whereas positive affect declined and negative affect increased the year surrounding disability onset; in the years thereafter, neither positive affect nor negative affect returned to pre-onset levels. Individuals who acquired disability in old age were more likely to report sustained declines in subjective well-being than were individuals who became disabled in midlife or young adulthood. Psychological disability was associated with the strongest declines across each indicator of subjective well-being at disability onset but also greater adaptation in the years thereafter. The findings provide further evidence against the set-point theory of hedonic adaptation and for a more moderate viewpoint that allows for processes of adaptation to vary based on the outcome examined, the type of stressor, and individual characteristics. The discussion focuses on possible mechanisms underlying the moderating roles of age and type of disability.
ContributorsFraire, Nicoletta (Author) / Infurna, Frank J. (Thesis advisor) / Luthar, Suniya S. (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Lifespan psychological perspectives have long suggested the context in which individuals live having the potential to shape the course of development across the adult lifespan. Thus, it is imperative to examine the role of both the objective and subjective neighborhood context in mitigating the consequences of lifetime adversity on mental and physical health. To address the research questions, data was used from a sample of 362 individuals in midlife who were assessed on lifetime adversity, multiple outcomes of mental and physical health and aspects of the objective and subjective neighborhood. Results showed that reporting more lifetime adversity was associated with poorer mental and physical health. Aspects of the objective and subjective neighborhood, such as green spaces moderated these relationships. The discussion focuses on potential mechanisms underlying why objective and subjective indicators of the neighborhood are protective against lifetime adversity.
ContributorsStaben, Omar E (Author) / Infurna, Frank J. (Thesis advisor) / Luthar, Suniya S. (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Measurement invariance exists when a scale functions equivalently across people and is therefore essential for making meaningful group comparisons. Often, measurement invariance is examined with independent and identically distributed data; however, there are times when the participants are clustered within units, creating dependency in the data. Researchers have taken different approaches to address this dependency when studying measurement invariance (e.g., Kim, Kwok, & Yoon, 2012; Ryu, 2014; Kim, Yoon, Wen, Luo, & Kwok, 2015), but there are no comparisons of the various approaches. The purpose of this master's thesis was to investigate measurement invariance in multilevel data when the grouping variable was a level-1 variable using five different approaches. Publicly available data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K) was used as an illustrative example. The construct of early behavior, which was made up of four teacher-rated behavior scales, was evaluated for measurement invariance in relation to gender. In the specific case of this illustrative example, the statistical conclusions of the five approaches were in agreement (i.e., the loading of the externalizing item and the intercept of the approaches to learning item were not invariant). Simulation work should be done to investigate in which situations the conclusions of these approaches diverge.
ContributorsGunn, Heather (Author) / Grimm, Kevin J. (Thesis advisor) / Aiken, Leona S. (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Created2016