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- Genre: Masters Thesis
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
In the past, it has been assumed that measurement and predictive invariance are consistent so that if one form of invariance holds the other form should also hold. However, some studies have proven that both forms of invariance only hold under certain conditions such as factorial invariance and invariance in the common factor variances. The present research examined Type I errors and the statistical power of a method that detects violations to the factorial invariant model in the presence of group differences in regression intercepts, under different sample sizes and different number of predictors (one or two). Data were simulated under two models: in model A only differences in the factor means were allowed, while model B violated invariance. A factorial invariant model was fitted to the data. Type I errors were defined as the proportion of samples in which the hypothesis of invariance was incorrectly rejected, and statistical power was defined as the proportion of samples in which the hypothesis of factorial invariance was correctly rejected. In the case of one predictor, the results show that the chi-square statistic has low power to detect violations to the model. Unexpected and systematic results were obtained regarding the negative unique variance in the predictor. It is proposed that negative unique variance in the predictor can be used as indication of measurement bias instead of the chi-square fit statistic with sample sizes of 500 or more. The results of the two predictor case show larger power. In both cases Type I errors were as expected. The implications of the results and some suggestions for increasing the power of the method are provided.
ContributorsAguilar, Margarita Olivera (Author) / Millsap, Roger E. (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2010
Description
Aging and the menopause transition are both intricately linked to cognitive changes
during mid-life and beyond. Clinical literature suggests the age at menopause onset can differentially impact cognitive status later in life. Yet, little is known about the relationship between behavioral and brain changes that occur during the transitional stage into the post-menopausal state. Much of the pre-clinical work evaluating an animal model of menopause involves ovariectomy in rodents; however, ovariectomy results in an abrupt loss of circulating hormones and ovarian tissue, limiting the ability to evaluate gradual follicular depletion. The 4-vinylcyclohexene diepoxide (VCD) model simulates transitional menopause in rodents by selectively depleting the immature ovarian follicle reserve and allowing animals to retain their follicle-deplete ovarian tissue, resulting in a profile similar to the majority of menopausal women. Here, Vehicle or VCD treatment was administered to ovary-intact adult and middle-aged Fischer-344 rats to assess the cognitive effects of transitional menopause via VCD-induced follicular depletion over time, as well as to understand potential interactions with age, with VCD treatment beginning at either six or twelve months of age. Results indicated that subjects that experience menopause onset at a younger age had impaired spatial working memory early in the transition to a follicle-deplete state. Moreover, in the mid- and post- menopause time points, VCD-induced follicular depletion amplified an age effect, whereby Middle-Aged VCD-treated animals had poorer spatial working and reference memory performance than Young VCD-treated animals. Correlations suggested that in middle age, animals with higher circulating estrogen levels tended to perform better on spatial memory tasks. Overall, these findings suggest that the age at menopause onset is a critical parameter to consider when evaluating learning and memory across the transition to reproductive senescence. From a translational perspective, this study informs the field with respect to how the age at menopause onset might impact cognition in menopausal women, as well as provides insight into time points to explore for the window of opportunity for hormone therapy during the menopause transition to attenuate age- and menopause- related cognitive decline, and produce healthy brain aging profiles in women who retain their ovaries throughout the lifespan.
during mid-life and beyond. Clinical literature suggests the age at menopause onset can differentially impact cognitive status later in life. Yet, little is known about the relationship between behavioral and brain changes that occur during the transitional stage into the post-menopausal state. Much of the pre-clinical work evaluating an animal model of menopause involves ovariectomy in rodents; however, ovariectomy results in an abrupt loss of circulating hormones and ovarian tissue, limiting the ability to evaluate gradual follicular depletion. The 4-vinylcyclohexene diepoxide (VCD) model simulates transitional menopause in rodents by selectively depleting the immature ovarian follicle reserve and allowing animals to retain their follicle-deplete ovarian tissue, resulting in a profile similar to the majority of menopausal women. Here, Vehicle or VCD treatment was administered to ovary-intact adult and middle-aged Fischer-344 rats to assess the cognitive effects of transitional menopause via VCD-induced follicular depletion over time, as well as to understand potential interactions with age, with VCD treatment beginning at either six or twelve months of age. Results indicated that subjects that experience menopause onset at a younger age had impaired spatial working memory early in the transition to a follicle-deplete state. Moreover, in the mid- and post- menopause time points, VCD-induced follicular depletion amplified an age effect, whereby Middle-Aged VCD-treated animals had poorer spatial working and reference memory performance than Young VCD-treated animals. Correlations suggested that in middle age, animals with higher circulating estrogen levels tended to perform better on spatial memory tasks. Overall, these findings suggest that the age at menopause onset is a critical parameter to consider when evaluating learning and memory across the transition to reproductive senescence. From a translational perspective, this study informs the field with respect to how the age at menopause onset might impact cognition in menopausal women, as well as provides insight into time points to explore for the window of opportunity for hormone therapy during the menopause transition to attenuate age- and menopause- related cognitive decline, and produce healthy brain aging profiles in women who retain their ovaries throughout the lifespan.
ContributorsKoebele, Stephanie Victoria (Author) / Bimonte-Nelson, Heather A. (Thesis advisor) / Aiken, Leona S. (Committee member) / Conrad, Cheryl D. (Committee member) / Wynne, Clive DL (Committee member) / Arizona State University (Publisher)
Created2015
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
Description
Researchers are often interested in estimating interactions in multilevel models, but many researchers assume that the same procedures and interpretations for interactions in single-level models apply to multilevel models. However, estimating interactions in multilevel models is much more complex than in single-level models. Because uncentered (RAS) or grand mean centered (CGM) level-1 predictors in two-level models contain two sources of variability (i.e., within-cluster variability and between-cluster variability), interactions involving RAS or CGM level-1 predictors also contain more than one source of variability. In this Master’s thesis, I use simulations to demonstrate that ignoring the four sources of variability in a total level-1 interaction effect can lead to erroneous conclusions. I explain how to parse a total level-1 interaction effect into four specific interaction effects, derive equivalencies between CGM and centering within context (CWC) for this model, and describe how the interpretations of the fixed effects change under CGM and CWC. Finally, I provide an empirical example using diary data collected from working adults with chronic pain.
ContributorsMazza, Gina L (Author) / Enders, Craig K. (Thesis advisor) / Aiken, Leona S. (Thesis advisor) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2015