Matching Items (15)

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Help-seeking models for Asian international and American students

Description

The relation of stigma to help-seeking attitudes and intentions and how these relations differed across cultures for American students, East Asian, and South Asian international students, were the focus of this study. Previous researchers had found that not seeking professional

The relation of stigma to help-seeking attitudes and intentions and how these relations differed across cultures for American students, East Asian, and South Asian international students, were the focus of this study. Previous researchers had found that not seeking professional psychological help when needed was prevalent for both American and international students. Stigma has been found to be a salient factor in influencing attitudes of individuals and may prevent individuals from getting the help they need. Both public and self-stigma were utilized to predict attitudes and intentions to seek psychological help in a sample of 806 students. Structural equation modeling analyses were conducted to assess the relationships in how self-stigma, public stigma, attitudes toward counseling and intentions to seek counseling will interplay for American, East Asian and South Asian international students, further expanding on previous help-seeking model (Vogel et al., 2007). Results indicated differences in factor structure of scales for the groups, and new factors were identified. With the new factors derived, different models of help-seeking intentions were established for each group, and distinct relations among the factors were explained. Furthermore, implications for future studies and clinical relevance were highlighted.

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Date Created
2015

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Impact of violations of longitudinal measurement invariance in latent growth models and autoregressive quasi-simplex models

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

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.

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Created

Date Created
2013

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Mathematical development: the role of broad cognitive processes

Description

This study investigated the role of broad cognitive processes in the development of mathematics skills among children and adolescents. The participants for this study were a subsample of a nationally representative sample used in the standardization of the Woodcock-Johnson III

This study investigated the role of broad cognitive processes in the development of mathematics skills among children and adolescents. The participants for this study were a subsample of a nationally representative sample used in the standardization of the Woodcock-Johnson III Tests of Cognitive Abilities and the Woodcock-Johnson III Tests of Achievement, Normative Update (Woodcock, McGrew, & Mather, 2007). Participants were between 5 years old and 18 years old (N = 4721; mean of 10.98 years, median of 10.00 years, standard deviation of 3.48 years), and were 50.7% male and 49.3% female. Structural equation models supported the theoretical suggestion that broad cognitive processes play significant and specific roles in the development of mathematical skills among children and adolescents. Implications for school psychology researchers and practitioners are discussed.

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2012

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Modeling lexical diversity across language sampling and estimation techniques

Description

Lexical diversity (LD) has been used in a wide range of applications, producing a rich history in the field of speech-language pathology. However, for clinicians and researchers identifying a robust measure to quantify LD has been challenging. Recently, sophisticated techniques

Lexical diversity (LD) has been used in a wide range of applications, producing a rich history in the field of speech-language pathology. However, for clinicians and researchers identifying a robust measure to quantify LD has been challenging. Recently, sophisticated techniques have been developed that assert to measure LD. Each one is based on its own theoretical assumptions and employs different computational machineries. Therefore, it is not clear to what extent these techniques produce valid scores and how they relate to each other. Further, in the field of speech-language pathology, researchers and clinicians often use different methods to elicit various types of discourse and it is an empirical question whether the inferences drawn from analyzing one type of discourse relate and generalize to other types. The current study examined a corpus of four types of discourse (procedures, eventcasts, storytelling, recounts) from 442 adults. Using four techniques (D; Maas; Measure of textual lexical diversity, MTLD; Moving average type token ratio, MATTR), LD scores were estimated for each type. Subsequently, data were modeled using structural equation modeling to uncover their latent structure. Results indicated that two estimation techniques (MATTR and MTLD) generated scores that were stronger indicators of the LD of the language samples. For the other two techniques, results were consistent with the presence of method factors that represented construct-irrelevant sources. A hierarchical factor analytic model indicated that a common factor underlay all combinations of types of discourse and estimation techniques and was interpreted as a general construct of LD. Two discourse types (storytelling and eventcasts) were significantly stronger indicators of the underlying trait. These findings supplement our understanding regarding the validity of scores generated by different estimation techniques. Further, they enhance our knowledge about how productive vocabulary manifests itself across different types of discourse that impose different cognitive and linguistic demands. They also offer clinicians and researchers a point of reference in terms of techniques that measure the LD of a language sample and little of anything else and also types of discourse that might be the most informative for measuring the LD of individuals.

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Date Created
2011

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Robustness of Latent variable interaction methods to nonnormal exogenous indicators

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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

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.

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Date Created
2010

Coping with dating violence as a function of violence frequency, severity, gender role beliefs and solution attribution: a structural modeling approach

Description

This study presents a structural model of coping with dating violence. The model integrates abuse frequency and solution attribution to determine a college woman's choice of coping strategy. Three hundred, twenty-four undergraduate women reported being targets of some physical abuse

This study presents a structural model of coping with dating violence. The model integrates abuse frequency and solution attribution to determine a college woman's choice of coping strategy. Three hundred, twenty-four undergraduate women reported being targets of some physical abuse from a boyfriend and responded to questions regarding the abuse, their gender role beliefs, their solution attribution and the coping behaviors they executed. Though gender role beliefs and abuse severity were not significant predictors, solution attribution mediated between frequency of the abuse and coping. Abuse frequency had a positive effect on external solution attribution and external solution attribution had a positive effect on the level of use of active coping, utilization of social support, denial and acceptance.

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Date Created
2011

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Determining persistence of community college students in introductory geology classes

Description

Science, Technology, Engineering & Mathematics (STEM) careers have been touted as critical to the success of our nation and also provide important opportunities for access and equity of underrepresented minorities (URM's). Community colleges serve a diverse population and a large

Science, Technology, Engineering & Mathematics (STEM) careers have been touted as critical to the success of our nation and also provide important opportunities for access and equity of underrepresented minorities (URM's). Community colleges serve a diverse population and a large number of undergraduates currently enrolled in college, they are well situated to help address the increasing STEM workforce demands. Geoscience is a discipline that draws great interest, but has very low representation of URM's as majors. What factors influence a student's decision to major in the geosciences and are community college students different from research universities in what factors influence these decisions? Through a survey-design mixed with classroom observations, structural equation model was employed to predict a student's intent to persist in introductory geology based on student expectancy for success in their geology class, math self-concept, and interest in the content. A measure of classroom pedagogy was also used to determine if instructor played a role in predicting student intent to persist. The targeted population was introductory geology students participating in the Geoscience Affective Research NETwork (GARNET) project, a national sampling of students in enrolled in introductory geology courses. Results from SEM analysis indicated that interest was the primary predictor in a students intent to persist in the geosciences for both community college and research university students. In addition, self-efficacy appeared to be mediated by interest within these models. Classroom pedagogy impacted how much interest was needed to predict intent to persist, in which as classrooms became more student centered, less interest was required to predict intent to persist. Lastly, math self-concept did not predict student intent to persist in the geosciences, however, it did share variance with self-efficacy and control of learning beliefs, indicating it may play a moderating effect on student interest and self-efficacy. Implications of this work are that while community college students and research university students are different in demographics and content preparation, student-centered instruction continues to be the best way to support student's interest in the sciences. Future work includes examining how math self-concept may play a role in longitudinal persistence in the geosciences.

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Date Created
2014

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Modeling multifaceted constructs in statistical mediation analysis: a bifactor approach

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

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.

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2016

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Model criticism for growth curve models via posterior predictive model checking

Description

Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit at multiple and interrelated levels of analysis. Using posterior predictive

Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit at multiple and interrelated levels of analysis. Using posterior predictive model checking (PPMC)—a popular Bayesian framework for model criticism—the performance of several discrepancy functions was investigated in a Monte Carlo simulation study. The discrepancy functions of interest included two types of conditional concordance correlation (CCC) functions, two types of R2 functions, two types of standardized generalized dimensionality discrepancy (SGDDM) functions, the likelihood ratio (LR), and the likelihood ratio difference test (LRT). Key outcomes included effect sizes of the design factors on the realized values of discrepancy functions, distributions of posterior predictive p-values (PPP-values), and the proportion of extreme PPP-values.

In terms of the realized values, the behavior of the CCC and R2 functions were generally consistent with prior research. However, as diagnostics, these functions were extremely conservative even when some aspect of the data was unaccounted for. In contrast, the conditional SGDDM (SGDDMC), LR, and LRT were generally sensitive to the underspecifications investigated in this work on all outcomes considered. Although the proportions of extreme PPP-values for these functions tended to increase in null situations for non-normal data, this behavior may have reflected the true misfit that resulted from the specification of normal prior distributions. Importantly, the LR and the SGDDMC to a greater extent exhibited some potential for untangling the sources of data-model misfit. Owing to connections of growth curve models to the more fundamental frameworks of multilevel modeling, structural equation models with a mean structure, and Bayesian hierarchical models, the results of the current work may have broader implications that warrant further research.

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Date Created
2015

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Incorporating social network variables into relational turbulence theory: popping the dyadic bubble

Description

Relational turbulence theory (RTT) has primarily explored the effects of relational uncertainty and partner interdependence on relational outcomes. While robust, the theory fails to account for uncertainties and perceived interdependence stemming from extra-dyadic factors (such as partners’ social networks). Thus,

Relational turbulence theory (RTT) has primarily explored the effects of relational uncertainty and partner interdependence on relational outcomes. While robust, the theory fails to account for uncertainties and perceived interdependence stemming from extra-dyadic factors (such as partners’ social networks). Thus, this dissertation had two primary goals. First, scales indexing measures of social network-based relational uncertainty (i.e., network uncertainty) and social network interdependence are tested for convergent and divergent validity. Second, measurements of network uncertainty and interdependence are tested alongside measures featured in RTT to explore predictive validity. Results confirmed both measurements and demonstrated numerous significant relationships for turbulence variables. Discussions of theoretical applications and future directions are offered.

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Date Created
2018