Matching Items (24)
Description
Cognitive function is multidimensional and complex, and research indicates that it is impacted by age, lifetime experience, and ovarian hormone milieu. One particular domain of cognitive function that is susceptible to age-related decrements is spatial memory. Cognitive practice can affect spatial memory when aged in both males and females, and in females alone ovarian hormones have been found to alter spatial memory via modulating brain microstructure and function in many of the same brain areas affected by aging. The research in this dissertation has implications that promote an understanding of the effects of cognitive practice on aging memory, why males and females respond differently to cognitive practice, and the parameters and mechanisms underlying estrogen's effects on memory. This body of work suggests that cognitive practice can enhance memory when aged and that estrogen is a probable candidate facilitating the observed differences in the effects of cognitive practice depending on sex. This enhancement in cognitive practice effects via estrogen is supported by data demonstrating that estrogen enhances spatial memory and hippocampal synaptic plasticity. The estrogen-facilitated memory enhancements and alterations in hippocampal synaptic plasticity are at least partially facilitated via enhancements in cholinergic signaling from the basal forebrain. Finally, age, dose, and type of estrogen utilized are important factors to consider when evaluating estrogen's effects on memory and its underlying mechanisms, since age alters the responsiveness to estrogen treatment and the dose of estrogen needed, and small alterations in the molecular structure of estrogen can have a profound impact on estrogen's efficacy on memory. Collectively, this dissertation elucidates many parameters that dictate the outcome, and even the direction, of the effects that cognitive practice and estrogens have on cognition during aging. Indeed, many parameters including the ones described here are important considerations when designing future putative behavioral interventions, behavioral therapies, and hormone therapies. Ideally, the parameters described here will be used to help design the next generation of interventions, therapies, and nootropic agents that will allow individuals to maintain their cognitive capacity when aged, above and beyond what is currently possible, thus enacting lasting improvement in women's health and public health in general.
ContributorsTalboom, Joshua S (Author) / Bimonte-Nelson, Heather A. (Thesis advisor) / Conrad, Cheryl D. (Committee member) / Neisewander, Janet L (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2011
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
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
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
Description
For this thesis a Monte Carlo simulation was conducted to investigate the robustness of three latent interaction modeling approaches (constrained product indicator, generalized appended product indicator (GAPI), and latent moderated structural equations (LMS)) under high degrees of nonnormality of the exogenous indicators, which have not been investigated in previous literature. Results showed that the constrained product indicator and LMS approaches yielded biased estimates of the interaction effect when the exogenous indicators were highly nonnormal. When the violation of nonnormality was not severe (symmetric with excess kurtosis < 1), the LMS approach with ML estimation yielded the most precise latent interaction effect estimates. The LMS approach with ML estimation also had the highest statistical power among the three approaches, given that the actual Type-I error rates of the Wald and likelihood ratio test of interaction effect were acceptable. In highly nonnormal conditions, only the GAPI approach with ML estimation yielded unbiased latent interaction effect estimates, with an acceptable actual Type-I error rate of both the Wald test and likelihood ratio test of interaction effect. No support for the use of the Satorra-Bentler or Yuan-Bentler ML corrections was found across all three methods.
ContributorsCham, Hei Ning (Author) / West, Stephen G. (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2010
Description
Mediation analysis is a statistical approach that examines the effect of a treatment (e.g., prevention program) on an outcome (e.g., substance use) achieved by targeting and changing one or more intervening variables (e.g., peer drug use norms). The increased use of prevention intervention programs with outcomes measured at multiple time points following the intervention requires multilevel modeling techniques to account for clustering in the data. Estimating multilevel mediation models, in which all the variables are measured at individual level (Level 1), poses several challenges to researchers. The first challenge is to conceptualize a multilevel mediation model by clarifying the underlying statistical assumptions and implications of those assumptions on cluster-level (Level-2) covariance structure. A second challenge is that variables measured at Level 1 potentially contain both between- and within-cluster variation making interpretation of multilevel analysis difficult. As a result, multilevel mediation analyses may yield coefficient estimates that are composites of coefficient estimates at different levels if proper centering is not used. This dissertation addresses these two challenges. Study 1 discusses the concept of a correctly specified multilevel mediation model by examining the underlying statistical assumptions and implication of those assumptions on Level-2 covariance structure. Further, Study 1 presents analytical results showing algebraic relationships between the population parameters in a correctly specified multilevel mediation model. Study 2 extends previous work on centering in multilevel mediation analysis. First, different centering methods in multilevel analysis including centering within cluster with the cluster mean as a Level-2 predictor of intercept (CWC2) are discussed. Next, application of the CWC2 strategy to accommodate multilevel mediation models is explained. It is shown that the CWC2 centering strategy separates the between- and within-cluster mediated effects. Next, Study 2 discusses assumptions underlying a correctly specified CWC2 multilevel mediation model and defines between- and within-cluster mediated effects. In addition, analytical results for the algebraic relationships between the population parameters in a CWC2 multilevel mediation model are presented. Finally, Study 2 shows results of a simulation study conducted to verify derived algebraic relationships empirically.
ContributorsTofighi, Davood (Author) / West, Stephen G. (Thesis advisor) / Mackinnon, David P (Thesis advisor) / Enders, Craig C (Committee member) / Millsap, Roger E (Committee member) / Arizona State University (Publisher)
Created2010
Description
An individual’s reputation can be beneficial or detrimental to their exchanges with others,
and these exchanges may be critical for achieving evolutionary goals, such as reproduction.
Depending on their reputation, an individual may or may not gain access to resources in order to
achieve their evolutionary goals. Reputation is typically described as being “positive” and
“negative,” but the current study aimed to identify potential nuances to reputations beyond the
traditional dichotomy. It was hypothesized that different types of reputations (such as “friendly”,
“dishonest”, and “aggressive”) would group together in categories beyond “positive” and
“negative.” Additionally, individuals with different life history strategies might find different
reputations important, because the reputations they find most important may help them get the
kinds of resources they need to attain their specific evolutionary goals. Therefore, it was also
predicted that the importance individuals place on different types of reputations would vary as a
function of life history strategy. Exploratory factor analysis identified a five factor structure for
reputations. Individuals also placed varying levels of importance on different types of
reputations, and found some reputations more important than others depending on their life
history strategy. This study demonstrates that reputational information is more nuanced than
previously thought and future research should consider that there may be more than just
“positive” and “negative” reputations in social interactions.
and these exchanges may be critical for achieving evolutionary goals, such as reproduction.
Depending on their reputation, an individual may or may not gain access to resources in order to
achieve their evolutionary goals. Reputation is typically described as being “positive” and
“negative,” but the current study aimed to identify potential nuances to reputations beyond the
traditional dichotomy. It was hypothesized that different types of reputations (such as “friendly”,
“dishonest”, and “aggressive”) would group together in categories beyond “positive” and
“negative.” Additionally, individuals with different life history strategies might find different
reputations important, because the reputations they find most important may help them get the
kinds of resources they need to attain their specific evolutionary goals. Therefore, it was also
predicted that the importance individuals place on different types of reputations would vary as a
function of life history strategy. Exploratory factor analysis identified a five factor structure for
reputations. Individuals also placed varying levels of importance on different types of
reputations, and found some reputations more important than others depending on their life
history strategy. This study demonstrates that reputational information is more nuanced than
previously thought and future research should consider that there may be more than just
“positive” and “negative” reputations in social interactions.
ContributorsPatterson, Danielle Nicole (Author) / Aktipis, Athena (Thesis director) / Corbin, William (Committee member) / West, Stephen G. (Committee member) / Ayers, Jessica D. (Committee member) / Department of Psychology (Contributor) / School of Human Evolution & Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
This paper examines the effect of a weekly student self-assessment assignment on student performance in an undergraduate math course. Self-assessment is an increasingly popular type of formative assessment with close ties to self-regulated learning theory. In this randomized controlled trial, 88 students enrolled in MAT 142 were divided into four treatment groups, receiving the self-assessment assignment for either half the semester, the full semester, or not at all. There was no main effect of the treatment on students’ course performance (F(3,80) = 0.154, p = 0.999). However, students’ level of compliance with the assignments (F(1, 63) = 6.87, p = 0.011) and class attendance (F(1, 83) = 12.34, p < 0.001) both significantly predicted exam scores, suggesting that conscientiousness predicts performance. I conducted focus groups to understand how students felt toward the self-assessments. Participants expressed distaste toward the assignments and provided suggestions for improvements. I describe these improvements, among others, in an effort to outline future directions for this research. I also describe a new model of student self-assessment based on theories of adaptive testing and self-regulated learning.
ContributorsDuque-Baird, Santiago (Author) / Zheng, Yi (Thesis director) / West, Stephen G. (Thesis director) / Slominski, Tara (Committee member) / Barrett, The Honors College (Contributor) / Department of English (Contributor) / Department of Psychology (Contributor)
Created2024-05
Description
Although there is extensive research on temperament and its relation to psychopathology, most studies to date have used a dimensional approach to temperament, assessing one or two temperament traits at a time, which does not account for the interrelatedness between temperament dimensions. Recent studies have identified statistical methods that can capture these heterogenous relations that take a more typological approach. The current study used data from a racially/ethnically and socioeconomically diverse sample of twins to investigate whether using these different analytic methods together to capture temperament contribute to the conceptualization of phenotypic and genetic temperamental risk for later cross-reporter factors of anxiety, depression, externalizing, and ADHD. In breaking psychopathology down into its respective components, as measured by multiple reporters, I found that temperamental genetic risk for psychopathology differed depending on the psychopathology factors assessed. In using both dimensional and typological approaches to temperament, I found that high negative emotionality may only act as a risk factor for anxiety and ADHD when children also have low effortful control and high impulsivity. Results also showed an inconsistent role of genetics and the environment that explain the relation between temperament and psychopathology depending on the approach used. Examining typological and dimensional approaches together supported well-known findings and challenged others, highlighting importance of taking multiple approaches in investigating the phenotypic relation between temperament and psychopathology, and its etiology.
ContributorsMurillo, Alexys S. (Author) / Lemery-Chalfant, Kathryn (Thesis advisor) / Lucca, Kelsey (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2024
Description
Mediation analysis is used to investigate how an independent variable, X, is related to an outcome variable, Y, through a mediator variable, M (MacKinnon, 2008). If X represents a randomized intervention it is difficult to make a cause and effect inference regarding indirect effects without making no unmeasured confounding assumptions using the potential outcomes framework (Holland, 1988; MacKinnon, 2008; Robins & Greenland, 1992; VanderWeele, 2015), using longitudinal data to determine the temporal order of M and Y (MacKinnon, 2008), or both. The goals of this dissertation were to (1) define all indirect and direct effects in a three-wave longitudinal mediation model using the causal mediation formula (Pearl, 2012), (2) analytically compare traditional estimators (ANCOVA, difference score, and residualized change score) to the potential outcomes-defined indirect effects, and (3) use a Monte Carlo simulation to compare the performance of regression and potential outcomes-based methods for estimating longitudinal indirect effects and apply the methods to an empirical dataset. The results of the causal mediation formula revealed the potential outcomes definitions of indirect effects are equivalent to the product of coefficient estimators in a three-wave longitudinal mediation model with linear and additive relations. It was demonstrated with analytical comparisons that the ANCOVA, difference score, and residualized change score models’ estimates of two time-specific indirect effects differ as a function of the respective mediator-outcome relations at each time point. The traditional model that performed the best in terms of the evaluation criteria in the Monte Carlo study was the ANCOVA model and the potential outcomes model that performed the best in terms of the evaluation criteria was sequential G-estimation. Implications and future directions are discussed.
ContributorsValente, Matthew J (Author) / Mackinnon, David P (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Keving (Committee member) / Chassin, Laurie (Committee member) / Arizona State University (Publisher)
Created2018