Chronic obstructive pulmonary disease (COPD) has a severe impact on quality of life (QOL). Using the Health Promotion Model as a guide, a cross-sectional, correlational design was used to describe relationships among individual characteristics and experiences (age, gender, race, severity of illness, resilience), behavior-specific cognitions and affect (self-efficacy, barriers, social support), behavioral outcomes (health promoting lifestyle), and QOL in this patient population. One hundred and twenty participants were recruited from three clinics at Virginia Commonwealth University Health System. In addition to a demographic survey, participants completed a 151-item questionnaire incorporating measures resilience, severity of illness, self-efficacy, and barriers to a health-promoting lifestyle, social support, lifestyle, and QOL. Spirometric evaluation of lung function and the 6-minute walking test were also completed. Structural equation modeling was used to determine the effect of nine independent variables on QOL.Participants were white (51.2%), female (63.6%), and approximately 60.5 years old. Severity of illness, characterized by symptoms and functional capacity, suggested they were not severely ill (mean = 3.18, S.D.= 2.69). They were somewhat resilient (mean = 136.01, S.D.= 23.01), had adequate social support (mean = 68.10, S.D.= 19.95), were uncertain about their competency (self-efficacy) to manage their health (mean = 24.91, S.D.= 4.92), sometimes experienced barriers (mean = 33.33, S.D.= 9.02), and sometimes included attributes of a healthy lifestyle in their lives (mean = 123.93, S.D.= 25.22). Their QOL was fair to poor (mean = 6.10, S.D.= 2.39).A series of analyses using structural equation modeling was conducted. The first model that was tested did not fit the data χ2(df = 13)= 67.989,p = 0.000, GFI = 0.895, CFI = 0.781, RMSEA = 0.189). Next, modification indices were use to reexamine for fit. Using the recommended modifications, a good fit model was obtained χ2(df = 9)=5.016, p = 0.833, GFI = 0.992, CFI = 1.0, RMSEA = 0.0); however, non-significant paths were present. An alternative model was tested and fit the data very well χ2(df=18)= 10.011, p = 0.932, GFI = 0.981, CFI = 1.0, RMSEA = 0.0). The independent variables explained about 45.1% of the variance in health-promoting lifestyle. All the variables explained 45.3% of variance in QOL. The most significant predictor of a healthy lifestyle was social support (0.383) and the most significant predictor of QOL was self-efficacy (0.364). The findings confirmed the utility of the HPM.

Event history analysis is the most prevalent modeling technique used to model event occurrence with longitudinal data (Cox & Oakes, 1984; Menard, 1991; Singer & Willett, 1993, 2003). An alternative is to model longitudinal data within the SEM framework, known as latent variable growth modeling (McArdle, 1988; Meredith & Tisak, 1990), which can provide a more robust framework. Whether or not a student remains in college presents an appropriate context within which to examine the modeling of event occurrence with longitudinal data. The purpose of the study was to compare event history and latent growth modeling as for predicting change in college student perseverance, with college student persistence literature serving as the framework. Students are defined as having persevered if they have earned hours and the end of the semester rather than if they are enrolled at the beginning of the semester, which is the traditional definition of persistence.The population for the study was the 2001 and 2002 cohorts of first-time, full-time freshmen at a large mid-Atlantic urban research university. Stopouts and transfer students were excluded. Data was analyzed for the first five semesters for each cohort. The results showed that parameter estimates were quite consistent across model type and time frame and were mostly consistent with previous research. No one method outperformed the others in terms of predicting correct classification. Using event history analysis with the structural equation modeling framework, however, appeared to be a very promising alternative to event history analysis with logistic regression since one can model error term and examine the differential effects of predictors at each time period. Finally, while latent growth modeling did not outperform the other methods in predictive classification, the study demonstrated it can be used for event occurrence analysis to test more complex theories.

The purpose of this study was to assess the impact of acquiescence on both positively and negatively worded questions, both when unidimensionality was assumed and when it was not. To accomplish this, undergraduate student responses to a previously validated survey of student engagement were used to compare several models of acquiescence, using a priori goodness-offit statistics as evidence for model fit, in order to develop a model that adequately accounted for acquiescence bias. Using a true experimental design, undergraduate students from a variety of classes at a large, urban university were randomly assigned to one of three versions of the same survey of student engagement (all positively worded items, all negatively worded items, an equal balance of both positively and negatively worded items). Structural equation modeling was used to analyze the results. Although the presence of acquiescence was confirmed for both positively and negatively worded items, it was not consistent by content scale or item polarization. This suggests that there may be an interaction between item polarization and content that may cause acquiescence to be present or absent. The scales that did not show acquiescence on the balanced survey portrayed a split factor loading based upon item polarization. Further, the splitting of factor loadings by item polarization was not due to acquiescence, suggesting that something other than acquiescence is causing the loadings to split. Further research is needed to develop models and/or methods to better assess and control for acquiescence. Although demographic groups were compared by gender and race/ethnicity to assess if different groups acquiesced differently, using multi-group confirmatory factor analysis, many of the models did not converge. The findings of this study were limited by the nature of the sample size. Additional research is needed to determine if acquiescence differs by group membership.

In spite of the wealth of research in IS security, there is very little understanding of what actually makes an IS security program successful within an organization. Success has been treated generally as a separate entity from IS security altogether; a great deal of research has been conducted on the "means to the end", while limited research has been focused on truly understanding what the end actually is. The problem compelling this research is that previous studies within the IS security domain do not adequately consider what factors contribute towards IS security success within the organizational context, and how the factors interact. This study built upon Shannon and Weaver (1949) and Mason (1978) to develop a model for predicting IS security success within an organization. A considerable body of information systems security literature was organized based on their findings. Core dimensions of information system security success were identified and operationalized within a model for predicting success with IS security initiatives. The model was empirically validated in a three-phase approach using survey methodology. First, the survey was tested for validity and reliability using an expert panel and pilot study. Next, the survey was administered to a sample, with the results analyzed using Confirmatory Factor Analysis and Structural Equation Modeling techniques. Initial analysis of the measurement model generated through Confirmatory Factor Analysis showed mixed fit. Factor loadings and average variance extracted calculations resulted in the selection of low performing items for removal; after revision, the revised measurement model showed improved fit for all measures. Structural Equation Modeling analysis was conducted on three structural models with varying levels of mediation. Based on the analysis of fit and comparison indices, the model depicting partial mediation was determined to be the best variation of the IS security success model. This study is the first known instance of an empirically tested IS security success model and should provide many avenues for future study, as well as providing practitioners a fundamental roadmap for success within their organizational IS security programs.

Structural equation models combine factor analysis models and multivariate regression models to estimate associations between observed variables and unobserved variables. The main achievement of this Capstone Project is the understanding of structural equation models and application of the models to real-world data. In this report, we reviewed structural equation models and several prerequisite topics. We performed a simulation study to compare maximum likelihood structural equation model estimation versus two-stage sequential estimation using multiple linear regression and maximum likelihood factor analysis. The simulation study confirmed that confidence intervals produced by structural equation models are valid and those obtained by two-stage sequential estimation are largely inaccurate. We applied structural equation models to an educational data comparing the efficacy of teaching conditions on learning scientific inquiry skills among 177 middle school students in Fitchburg, Massachusetts using a computer simulated science microworld. Application of structural equation models to the educational data showed that there were no significant differences in test score gains between three learning conditions, while controlling for latent factors measured by survey responses.

Dissertation submitted to the Department of Psychology, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Arts. / The aim of the present study was to develop and evaluate a process model linking stressful environmental change, perceived job insecurity and stress reactions, A review of the literature revealed that stressful changes in the environment are linked to individual stress reactions, Stress theory has also recognised that appraisal of a stressful situation leads to stress reactions, Moreover, the literature has identified job Insecurity as a form of appraisal in that it is an internal event reflecting a transformation of beliefs about what 's happening in the organisation and environment. Job insecurity in turn has been shown to result in various stress reactions in individuals, Based on such research and theorising, a causal model was developed and tested using structural equation modeling techniques, It was assessed whether: stressful environmental change impacted upon stress reactions and job insecurity; job insecurity impacted upon stress reactions; and whether Job insecurity operated as a form of appraisal in mediating the relationship between stressful environmental change and stress reactions, The Independent variable, stressful environmental change, was specified as a common factor of the measured variables, political change, social change and organisational change, The proposed mediator variable, job insecurity, was specified as a common factor of the measured variables perceived threat to total job multiplied by powerlessness, and perceived threat to job features multiplied by powerlessness, The dependent variable, stress reactions, was specified as a common factor of the measured variables psychological distress, job dissatisfaction and reduced organisational commitment. The model was tested empirically using a combined sample of 267 subjects from three organisations, Results indicated that all relationships In the proposed model were confirmed, and that a reasonable fit was demonstrated between the empirical data and the theoretical model. Stressful environmental change was causally related to both stress reactions and Job insecurity, Job Insecurity was causally related to stress reactions, and in addition operated as a partial mediator between stressful environmental change and stress reacdons. Conceptual and methodological reasons for the findings are discussed, as well as some theoretical and practical implications, Limitations in the methodology are identified and future considerations of research are suggested, / AC2017

This thesis aims to provide clarity on which factors within an organization positively affect its performance in terms of innovation.  Innovation is seen as a critical component of a company’s strategy in achieving market differentiation and profitability, yet for many, it remains a frustrating pursuit.  This study aims to empirically model the relationship between a firm’s investment in innovation and the effect of this investment on its performance.  The method used is Structural Equation Modeling with data gathered from our online survey of 128 respondents from firms within the EU.  This work addresses two research questions, the first being to confirm that a firm’s innovation performance is influenced by both a commitment to human factors focusing on softer values in combination with strong R&D and technical capability.  Secondly, whether the presence of innovation inhibitors influences this relationship.  The findings showed that a firm’s innovation performance is improved when it prioritizes creating an environment and culture that nurtures innovation only when activated through a strong commitment to technical and R&D excellence, but not without this technical capacity.  Secondly, the introduction of innovation inhibitors reconfirmed the first finding, and the relationship between both the human factors within a company and its technical capability, as well as the relationship between this technical capability and its performance was stronger in their presence.

<p>The power of positive attitudes toward science is that they influence science achievement by reinforcing higher performance. Interestingly, there continue to be gender disparities in attitudes toward science across many countries. Males generally have more positive attitudes toward science than females. Although most research related to attitudes toward science have been based on the Trends in International Mathematics and Science Study (TIMSS) Student Questionnaire, there remains a dearth of evidence validating the TIMSS science attitudes items and measurement equivalence across genders. </p><p>The goals of this research were as follows: (1) to build support for the structural validity of the TIMSS items, and (2) to investigate whether the instrument measures the same latent construct (attitudes toward science) across genders. The present study followed two steps of statistical analyses. As a first step, two modeling methods (confirmatory factor analysis and exploratory structural equation modeling) were conducted to identify the best-fitting model for the instrument. Second, after determining the model of choice, we tested several nested invariance models progressively. </p><p>This study found (1) the latent factor structure of the TIMSS items and (2) strong measurement invariance across genders. This result indicated that the instrument is well designed by the <i>a priori</i>specification and measures the same latent variable for both female and male students. This study provides support for the multidimensional approach to measuring science attitudes and shows the flexibility of ESEM over CFA by demonstrating that the ESEM approach provided better representation of the underlying factor structure. </p>

Com a necessidade de analisar dados cada vez mais complexos nas mais diversas áreas, surge também a necessidade de novas técnicas e novas modelagens. Nesse contexto, os Modelos de Equações Estruturais são uma evolução da análise de caminhos proposto por Wright (1921), que por sua vez, consegue captar as relações de causa e efeito entre diferentes tipos de variáveis. Este trabalho tem como objetivo abordar uma revisão em torno dessa técnica, com ênfase aos modelos recursivos que utilizam em sua maioria apenas variáveis observadas. A aplicação deste trabalho está fundamentada em um processo de simulação considerando assim, seis cenários com diferentes tipos de relações numa mesma estrutura. As médias das estimativas dos parâmetros no processo de simulação resultam em valores próximos aos valores verdadeiros. Por meio de figuras e gráficos, pode-se observar o comportamento dos parâmetros por meio do erro quadrático médio e boxplot. A análise foi realizada com a utilização do software R e programas implementados com pacote sem (FOX; WEISBERG, 2012) e programação apresentada no Apêndice deste trabalho. / With the increasingly need to analyze complex data in several areas, comes the need for new techniques and new models. In this context, the Structural Equation Modeling is an evolution of the path analysis proposed by Wright (1921), which, can capture the relations of cause and effect between different types of variables. The present work aims to approach a review about this technique with emphasis on recursive models using mostly observed variables only. The application of this work is based on a simulation process considering six scenarios with different types of relationships within the same structure. The mean estimates of the parameters in the simulation result in values that are close to the true values, and by means of figures and graphs, one can observe the behavior of the parameters by means of the mean squared error and boxplot. The analysis was performed using the software software R and implemented programs as sem package (FOX; WEISBERG, 2012) and the programming is presented in the Appendix of this work.

Keywords: Latent variables, Ordered categorical data, Unordered categorical data, Nonignorable missing data, Maximum likelihood approach, Bayesian approach. / Structural equation models (SEMs) have been widely applied to examine interrelationships among latent and observed variables in behavioral, psychological, and medical research. Motivated by the fact that correlated ordered and unordered categorical variables are frequently encountered in practical applications, a nonlinear SEM that accommodates fixed covariates, mixed continuous, ordered categorical, and unordered categorical variables is proposed. Maximum likelihood methods for estimation and model comparison are discussed. Besides, missing data are frequently encountered in practical researches; a lot of attention has been devoted to analyze various SEMs with missing data. Bayesian analysis, including parameter estimate and model comparison, of a nonlinear SEM with mixed continuous, ordered and unordered categorical variables, and nonignorable missing entries is also considered in the thesis. Simulation studies are conducted to reveal the performance of the proposed methods. Moreover, we apply our methodologies to analyze the real-life data set about cardiovascular disease. As none of the existing SEMs can simultaneously accommodate fixed covariates, mixed continuous, ordered and unordered categorical data, and missing data, this thesis offers a novel SEM to cope with more complex practical problems and develop maximum likelihood and Bayesian methods for obtaining results in estimation and model comparison. / Cai, Jingheng. / Advisers: Sik-Yum Lee; Xin-Yuan Song. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3584. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 76-82). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.