Matching Items (13)

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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

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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Agent

Created

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

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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Agent

Created

Date Created
  • 2013

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Public Organization Adaptation to Extreme Events Evidence from the Public Transportation Sector

Description

This dissertation consists of three essays, each examining distinct aspects about public organization adaptation to extreme events using evidence from public transit agencies under the influence of extreme weather in

This dissertation consists of three essays, each examining distinct aspects about public organization adaptation to extreme events using evidence from public transit agencies under the influence of extreme weather in the United States (U.S.). The first essay focuses on predicting organizational adaptive behavior. Building on extant theories on adaptation and organizational learning, it develops a theoretical framework to uncover the pathways through which extreme events impact public organizations and identify the key learning mechanisms involved in adaptation. Using a structural equation model on data from a 2016 national survey, the study highlights the critical role of risk perception to translate signals from the external environment to organizational adaptive behavior.

The second essay expands on the first one to incorporate the organizational environment and model the adaptive system. Combining an agent-based model and qualitative interviews with key decision makers, the study investigates how adaptation occurs over time in multiplex contexts consisting of the natural hazards, organizations, institutions and social networks. The study ends with a series of refined propositions about the mechanisms involved in public organization adaptation. Specifically, the analysis suggests that risk perception needs to be examined relative to risk tolerance to determine organizational motivation to adapt, and underscore the criticality of coupling between the motivation and opportunities to enable adaptation. The results further show that the coupling can be enhanced through lowering organizational risk perception decay or synchronizing opportunities with extreme event occurrences to promote adaptation.

The third essay shifts the gaze from adaptation mechanisms to organizational outcomes. It uses a stochastic frontier analysis to quantify the impacts of extreme events on public organization performance and, importantly, the role of organizational adaptive capacity in moderating the impacts. The findings confirm that extreme events negatively affect organizational performance and that organizations with higher adaptive capacity are more able to mitigate those effects, thereby lending support to research efforts in the first two essays dedicated to identifying preconditions and mechanisms involved in the adaptation process. Taken together, this dissertation comprehensively advances understanding about public organization adaptation to extreme events.

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Agent

Created

Date Created
  • 2020

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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

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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Agent

Created

Date Created
  • 2012

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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

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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Agent

Created

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

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.

Contributors

Agent

Created

Date Created
  • 2018

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Investigation on fatigue behavior of alloys by various approaches

Description

Fatigue is a degradation process of materials that would lead to failure when materials are subjected to cyclic loadings. During past centuries, various of approaches have been proposed and utilized

Fatigue is a degradation process of materials that would lead to failure when materials are subjected to cyclic loadings. During past centuries, various of approaches have been proposed and utilized to help researchers understand the underlying theories of fatigue behavior of materials, as well as design engineering structures so that catastrophic disasters that arise from fatigue failure could be avoided. The stress-life approach is the most classical way that academia applies to analyze fatigue data, which correlates the fatigue lifetime with stress amplitudes during cyclic loadings. Fracture mechanics approach is another well-established way, by which people regard the cyclic stress intensity factor as the driving force during fatigue crack nucleation and propagation, and numerous models (such as the well-known Paris’ law) are developed by researchers.

The significant drawback of currently widely-used fatigue analysis approaches, nevertheless, is that they are all cycle-based, limiting researchers from digging into sub-cycle regime and acquiring real-time fatigue behavior data. The missing of such data further impedes academia from validating hypotheses that are related to real-time observations of fatigue crack nucleation and growth, thus the existence of various phenomena, such as crack closure, remains controversial.

In this thesis, both classical stress-life approach and fracture-mechanics-based approach are utilized to study the fatigue behavior of alloys. Distinctive material characterization instruments are harnessed to help collect and interpret key data during fatigue crack growth. Specifically, an investigation on the sub-cycle fatigue crack growth behavior is enabled by in-situ SEM mechanical testing, and a non-uniform growth mechanism within one loading cycle is confirmed by direct observation as well as image interpretation. Predictions based on proposed experimental procedure and observations show good match with cycle-based data from references, which indicates the credibility of proposed methodology and model, as well as their capability of being applied to a wide range of materials.

Contributors

Agent

Created

Date Created
  • 2018

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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

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.

Contributors

Agent

Created

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

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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Agent

Created

Date Created
  • 2016

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Assessing measurement invariance and latent mean differences with bifactor multidimensional data in structural equation modeling

Description

Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation

Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses. This study explored the effects of mismatch in dimensionality between data and analysis models with multiple-group analyses at the population and sample levels. Datasets were generated using a bifactor model with different factor structures and were analyzed with bifactor and single-factor models to assess misspecification effects on assessments of MI and latent mean differences. As baseline models, the bifactor models fit data well and had minimal bias in latent mean estimation. However, the low convergence rates of fitting bifactor models to data with complex structures and small sample sizes caused concern. On the other hand, effects of fitting the misspecified single-factor models on the assessments of MI and latent means differed by the bifactor structures underlying data. For data following one general factor and one group factor affecting a small set of indicators, the effects of ignoring the group factor in analysis models on the tests of MI and latent mean differences were mild. In contrast, for data following one general factor and several group factors, oversimplifications of analysis models can lead to inaccurate conclusions regarding MI assessment and latent mean estimation.

Contributors

Agent

Created

Date Created
  • 2018