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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Implementing the analysis of two-level structural equation models in LISREL and Mx.

January 2006 (has links)
Bai Yun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 34-36). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- The Analysis of a Two-Level SEM with Group Specific Variables in LISREL --- p.4 / Chapter 2.1 --- The Model --- p.4 / Chapter 2.2 --- An Augmented Model --- p.5 / Chapter 2.3 --- Implementation in LISREL --- p.7 / Chapter 2.4 --- Simulation --- p.9 / Chapter 2.4.1 --- The Simulation Design --- p.9 / Chapter 2.4.2 --- Methods of Evaluation --- p.10 / Chapter 2.4.3 --- Simulation Results --- p.12 / Chapter 2.5 --- A Comparison to Mplus --- p.14 / Chapter 2.6 --- Empirical Demonstration: Multi-source Performance Appraisals --- p.14 / Chapter 3 --- Implementing Two level SEM with Cross-level Covariance Structures in Mx --- p.16 / Chapter 3.1 --- Two level Model Specifications with a Cross-level Covariance Structure --- p.17 / Chapter 3.2 --- An Illustrative Example --- p.20 / Chapter 3.3 --- Mx Simulation Design --- p.22 / Chapter 3.4 --- Simulation Results --- p.23 / Chapter 3.4.1 --- Accuracy of Parameter Estimates --- p.23 / Chapter 3.4.2 --- Accuracy of Standard Error Estimates --- p.24 / Chapter 3.4.3 --- Distribution of Goodness-of-fit Statistics --- p.24 / Chapter 3.5 --- Enlarged Mx Model --- p.24 / Chapter 3.5.1 --- Mx Model with Enlarged Xgi --- p.25 / Chapter 3.5.2 --- Mx Model with Enlarged Ng --- p.26 / Chapter 4 --- LISREL Sampling --- p.27 / Chapter 4.1 --- LISREL Sampling Simulation Design --- p.27 / Chapter 4.2 --- Simulation Results --- p.28 / Chapter 4.2.1 --- Accuracy of Parameter Estimates --- p.29 / Chapter 4.2.2 --- Accuracy of Standard Error Estimates --- p.30 / Chapter 4.2.3 --- Distribution of Goodness-of-fit Statistics --- p.30 / Chapter 5 --- Discussion --- p.31 / Appendices --- p.37 / Appendix 1 LISREL Sample Program --- p.37 / Appendix 2 LISREL Syntax for an ALL-Y Model --- p.38 / Appendix 3 LISREL Data Set Up --- p.39 / Appendix 4 Mx Sample Program --- p.40 / List of Figures / Chapter 1 --- The Augmented Two-level Model --- p.41 / Chapter 2 --- Results of the Performance Appraisal Example --- p.42 / Chapter 3 --- Two-level Model with a Cross-level Structure --- p.43 / Chapter 4 --- QQ-plot for P1-P6 --- p.44 / Chapter 5 --- QQ-plot for M1-M6 --- p.45 / List of Tables / Chapter 1 --- Simulation Conditions Associated with Each Pattern --- p.46 / Chapter 2 --- Simulation Results: Accuracy of Parameter Estimates --- p.47 / Chapter 3 --- Simulation Results: Precision of Standard Error Estimates --- p.48 / Chapter 4 --- Simulation Results: The Goodness-of-fit(GOF) Statistics --- p.49 / Chapter 5 --- Analysis of the Performance Appraisal Example --- p.49 / Chapter 6 --- Simulation Results: Mplus vs. LISREL-Parameter Estimates(l) --- p.50 / Chapter 7 --- Simulation Results: Mplus vs. LISREL-Parameter Estimates(2) --- p.51 / Chapter 8 --- Simulation Results: Mplus vs. LISREL-SE Estimates (Ratio) --- p.52 / Chapter 9 --- Simulation Results: Mplus vs. LISREL-GOF Statistics --- p.53 / Chapter 10 --- Mx Illustrative Example Results --- p.53 / Chapter 11 --- Mx Simulation Patterns --- p.53 / Chapter 12 --- Mx Simulation Results: Accuracy of Parameter Estimates --- p.54 / Chapter 13 --- Mx Simulation Results: MARB for Parameter and S.E. Estimates --- p.54 / Chapter 14 --- Mx Simulation Results: Goodness-of-fit Statistics --- p.55 / Chapter 15 --- Mx Simulation Results for M5 --- p.55 / Chapter 16 --- Mx Simulation Results for M5 and M6: Goodness-of-fit Statistics --- p.56 / Chapter 17 --- Mx Simulation Results for M6 --- p.56 / Chapter 18 --- LISREL Sampling: Simulation Patterns --- p.56 / Chapter 19 --- LISREL Sampling: Simulation Results for LI to L3 --- p.57 / Chapter 20 --- LISREL Sampling: Simulation Results for L4 to L6 --- p.58 / Chapter 21 --- LISREL Sampling: MARB for Parameter and S.E. Estimates --- p.59 / Chapter 22 --- LISREL Sampling: Goodness-of-fit Statistics --- p.59
2

Testing a Comprehensive Model of Muscle Dysmorphia Symptomatology in a Nonclinical Sample of Men

Woodruff, Elissa J. 08 1900 (has links)
As increasing emphases are placed on the importance of a muscular male physique in Westernized culture, more men are experiencing eating, exercise, and body image (EEBI) disturbances. Clinician-researchers have identified a syndrome, termed muscle dysmorphia (MD), in which individuals, usually men, are pathologically preoccupied with their perceived lack of muscularity. The current study tested a modified version of an extant theoretical model of MD symptomatology as well as an alternative model of MD symptomatology. Over 700 adult men completed a demographic questionnaire, a symptom inventory, a self-esteem questionnaire, a measure of perfectionism, a measure of the media’s influence on EEBI disturbances, and measures of body dissatisfaction and MD symptoms. Structural equation modeling (SEM) was used to examine the goodness of fit of the proposed models. Overall, the first model demonstrated poor fit with the data. Conversely, the alternative model fit the data adequately. The alternative model was cross validated with a second sample, and also fit this data adequately.
3

A Study of Korean Students' Creativity in Science Using Structural Equation Modeling

JO, SON MI January 2009 (has links)
Through the review of creativity research I have found that studies lack certain crucial parts: a) a theoretical framework for the study of creativity in science, b) studies considering the unique components related to scientific creativity, and c) studies of the interactions among key components through simultaneous analyses. The primary purpose of this study is to explore the dynamic interactions among four components (scientific proficiency, intrinsic motivation, creative competence, context supporting creativity) related to scientific creativity under the framework of scientific creativity. A total of 295 Korean middle school students participated. Well-known and commonly used measurements were selected and developed. Two scientific achievement scores and one score measured by performance-based assessment were used to measure student scientific knowledge/inquiry skills. Six items selected from the study of Lederman, Abd-El-Khalick, Bell, and Schwartz (2002) were used to assess how well students understand the nature of science. Five items were selected from the subscale of the scientific attitude inventory version II (Moore & Foy, 1997) to assess student attitude toward science. The Test of Creative Thinking-Drawing Production (Urban & Jellen, 1996) was used to measure creative competence. Eight items chosen from the 15 items of the Work Preference Inventory (1994) were applied to measure students' intrinsic motivation. To assess the level of context supporting creativity, eight items were adapted from measurement of the work environment (Amabile, Conti, Coon, Lazenby, and Herron, 1996). To assess scientific creativity, one open-ended science problem was used and three raters rated the level of scientific creativity through the Consensual Assessment Technique (Amabile, 1996). The results show that scientific proficiency and creative competence correlates with scientific creativity. Intrinsic motivation and context components do not predict scientific creativity. The strength of relationships between scientific proficiency and scientific creativity (estimate parameter=0.43) and creative competence and scientific creativity (estimate parameter=0.17) are similar [Δx²(.05)(1)=0.670, P > .05]. In specific analysis of structural model, I found that creative competence and scientific proficiency play a role of partial mediators among three components (general creativity, scientific proficiency, and scientific creativity). The moderate effects of intrinsic motivation and context component were investigated, but the moderation effects were not found.
4

Extensions of the General Linear Model into Methods within Partial Least Squares Structural Equation Modeling

George, Benjamin Thomas 08 1900 (has links)
The current generation of structural equation modeling (SEM) is loosely split in two divergent groups - covariance-based and variance-based structural equation modeling. The relative newness of variance-based SEM has limited the development of techniques that extend its applicability to non-metric data. This study focuses upon the extension of general linear model techniques within the variance-based platform of partial least squares structural equation modeling (PLS-SEM). This modeling procedure receives it name through the iterative PLS‑SEM algorithm's estimates of the coefficients for the partial ordinary least squares regression models in both the measurement model and the overall structural model. This research addresses the following research questions: (1) What are the appropriate measures for data segmentation within PLS‑SEM? (2) What are the appropriate steps for the analysis of rank-ordered path coefficients within PLS‑SEM? and (3) What is an appropriate model selection index for PLS‑SEM? The limited type of data to which PLS-SEM is applicable suggests an opportunity to extend the method for use with different data and as a result a broader number of applications. This study develops and tests several methodologies that are prevalent in the general linear model (GLM). The proposed data segmentation approaches posited and tested through post hoc analysis of structural model. Monte Carlo simulation allows demonstrating the improvement of the proposed model fit indices in comparison to the established indices found within the SEM literature. These posited PLS methods, that are logical transfers of GLM methods, are tested using examples. These tests enable demonstrating the methods and recommending reporting requirements.
5

A Monte Carlo Investigation of Three Different Estimation Methods in Multilevel Structural Equation Modeling Under Conditions of Data Nonnormality and Varied Sample Sizes

Byrd, Jimmy 14 January 2010 (has links)
The purpose of the study was to examine multilevel regression models in the context of multilevel structural equation modeling (SEM) in terms of accuracy of parameter estimates, standard errors, and fit indices in normal and nonnormal data under various sample sizes and differing estimators (maximum likelihood, generalized least squares, and weighted least squares). The finding revealed that the regression coefficients were estimated with little to no bias among the study design conditions investigated. However, the number of clusters (group level) appeared to have the greatest impact on bias among the parameter estimate standard errors at both level-1 and level-2. In small sample sizes (i.e., 300 and 500) the standard errors were negatively biased. When the number of clusters was 30 and cluster size was held at 10, the level-1 standard errors were biased downward by approximately 20% for the maximum likelihood and generalized least squares estimators, while the weighted least squares estimator produced level-1 standard errors that were negatively biased by 25%. Regarding the level-2 standard errors, the level-2 standard errors were biased downward by approximately 24% in nonnormal data, especially when the correlation among variables was fixed at .5 and kurtosis was held constant at 7. In this same setting (30 clusters with cluster size fixed at 10), when kurtosis was fixed at 4 and the correlation among variables was held at .7, both the maximum likelihood and generalized least squares estimators resulted in standard errors that were biased downward by approximately 11%. Regarding fit statistics, negative bias was noted among each of the fit indices investigated when the number of clusters ranged from 30 to 50 and cluster size was fixed at 10. The least amount of bias was associated with the maximum likelihood estimator in each of the data normality conditions examined. As sample size increased, bias decreased to near zero when the sample size was equal to or greater than 1,500 with similar results reported across estimation methods. Recommendations for the substantive researcher are presented and areas of future research are presented.
6

The nature of socioeconomic status among young adults, and its effect on health : a multi-group SEM analysis by gender and race/ethnicity

Yarnell, Lisa Marie 19 September 2011 (has links)
This dissertation focuses on results of multi-group SEM models estimated using data from the National Longitudinal Study of Adolescent Health (Add Health) in order to determine appropriate measurement and structural models for the relationship between socioeconomic status (SES) and health among six young adult U.S. social groups. Examining the links between SES and health during young adulthood is important because while there is a strong, documented link between lower SES and poorer health (Adler & Snibbe, 2003), young adults can exercise a considerable amount of agency with regard to their own SES and health. Young adults make critical decisions about pursuing post-secondary education, entering the workforce, and practicing healthy behaviors--activities which differ in their immediate and long-term economic and health payoff (Mirowsky & Ross, 2003; Elder, 1985; 1994). Yet, the nature of SES and its links with health for members of various gender and racial/ethnic groups is not entirely clear. Literature suggests that occupation, education, and income are neither defined nor linked among women in the same ways that they are for men (APA, 2007). Self-assessment of health is also thought to differ by gender and ethnicity (Krause & Jay, 1994). Moreover, limited research has addressed the unique mediating pathways by which aspects of SES affect health for specific social groups (Matthews, Gallo, & Taylor, 2010). In this work, I estimate measurement models for several aspects of SES among African American, Latina, and White men and women, then link aspects of SES with each other and with health using structural equation modeling. I also examine the unique mediating pathways by which aspects of SES are linked with health for these groups. / text
7

Structural equation modeling compared with ordinary least squares in simulations and life insurers’ data

Xiao, Xuan, active 2013 04 December 2013 (has links)
Structural equation model (SEM) is a general approach to analyze multivariate data. It is a relatively comprehensive model and combines useful characteristics from many statistical approaches, thus enjoys a variety of advantages when dealing complex relationships. This report gives a brief introduction to SEM, focusing especially the comparison of SEM and OLS regression. A simple tutorial of how to apply SEM is also included with the introduction and comparison. SEM can be roughly seen as OLS regression added with features such as simultaneous estimation, latent factors and autocorrelation. Therefore, SEM enjoys a variety of advantages over OLS regression. However, it is not always the case that SEM will be the optimal choice. The biggest concern is the complexity of SEM, for simpler model will be preferable for researchers when the fitness is similar. Two simulation cases, one requires special features of SEM and one satisfies assumptions of OLS regression, are applied to illustrate the choice between SEM and OLS regression. A study using data from US life insurers in the year 1994 serves as a further illustration. The conclusion is when special features of SEM is required, SEM fits better and will be the better choice, while when OLS regression assumptions are satisfied, SEM and OLS regression will fit equally well, considering the complexity of SEM, OLS regression will be the better choice. / text
8

The Integrative Neuropsychological Theory of Executive-Related Abilities and Component Transactions (INTERACT): a novel validation study.

Frazer, Jeff 25 June 2012 (has links)
The Integrative Neuropsychological Theory of Executive-Related Abilities and Component Transactions (INTERACT; Garcia-Barrera, 2011) is a novel perspective on executive function(s), and the functional interactions among those neural systems thought to underlie them. INTERACT was examined in this validation study using structural equation modeling. A novel battery of computerized tasks was implemented in a sample of 218 healthy, adult, university students. Each of the derived indicator variables represented a specific aspect of performance, and corresponded with one of the five distinct executive components of INTERACT. After eliminating tasks that demonstrated poor psychometric properties, overall model fit was excellent, χ2 = 36.38, df = 44, p = .786; CFI = 1.00; RMSEA = .000. Further, INTERACT was superior to six alternative measurement models, which were theoretically-based. Although the structural model of INTERACT was too complex to be tested here, a novel analysis of the data was introduced to test the interactions among INTERACT’s components. This analysis demonstrated the significant utility of INTERACT’s fundamental theoretical predictions. Given the outcome of this initial validation study, the predictive power of INTERACT should continue to be exploited in future studies of executive function(s), and should be extended to explore executive systems in unique populations. / Graduate
9

Using Structural Equation Modeling to study the relationship between the sea anemone Phymanthus strandesi and ecological factors in the seagrass bed of Hsiao-Liuchiu Island

Chang, Chen-hao 30 August 2010 (has links)
Seagrass bed is a highly productivity ecosystem, it also provides habitats for animals and plays an important role in stabilizing the substrate. The sea anemone Phymanthus strandesi is very abundant in the seagrass bed of Thalassia hemprichii on Hsiao-Liuchiu Island. Structural Equation Modeling (SEM) was used to investigate the relationship between P. strandesi and some environmental factors, which affect the distribution of this species at Tuozaiping tidal flat (N 22¢X20"55' E 120¢X21"49'), Hsiao-Liuchiu Island. Light and temperature were also manipulated in the laboratory to test their effect on the hiding response of P. strandesi. The results of SEM show that the abundance of T. hemprichii showed very weak positive relation with P.strandesi. On the other hand, soil depth on the seagrass bed might be the main factor that affects the distribution of P. strandesi. In high a temperature situation (i.e. over 38¢XC), all the sea anemones in the experimental container hided into the sand. However, only some sea anemones hid when exposed to strong light (i.e. 5030 lum/ft²) after one and half hours.
10

Emotion Dysregulation and Psychopathology: A Structural Exploration of Emotional Factors and Positive and Negative Affect.

Melka, Stephen Edward 01 August 2011 (has links)
Recent epidemiological data from the National Comorbidity Survey (NCS) estimate significant lifetime prevalence rates for anxiety and mood disorders, suggesting nearly one in three people would meet diagnostic criteria for an anxiety and/or mood disorder at some point during their lifetime (NCS, 2007). Comorbidity research has also revealed that people often suffer from these disorders concurrently (Rodriguez et al., 2004). Many have argued that anxiety and mood disorders frequently co-occur because they share similar etiological factors (Barlow, 1991; Clark & Watson, 1991; Watson, 2005). Additional empirical research has suggested that depressive and anxiety disorders share similar genetic diatheses and merely present differently because of variation in environmental stressors (Hettema, Neale, & Kendler, 2001; Rutter Moffit, & Caspi, 2006). As a result, an investigation of shared emotion regulation and affective processes across anxiety and mood disorders may reveal parallel etiological factors and areas for intervention. Research examining emotion and affective dysregulation indicates that mood and anxiety pathology may be characterized by similar emotional control and understanding deficits (Amstadter, 2008; Bradley, 2000; Sandin et al., 1996). Models of emotion dysregulation suggest that individuals suffering from anxiety pathology report decreased understanding of emotions, higher reactivity and sensitivity to emotions, and poor emotional management and mood repair skills (Mennin et al., 2005). Similarly, studies have observed parallel difficulties in those with depression (Liverant, Brown, Barlow, & Roemer, 2008; Rude and McCarthy, 2003). Additionally, research has indicated that efforts to reappraise or suppress emotions may affect the intensity and valence of emotional experiences (Gross & John, 2004). The current study builds off this research by incorporating elements of previous models of emotion dysregulation and anxiety and mood pathology in an effort to develop a comprehensive model of affective process that may underlie both anxious and depressive symptomatology. A total of 526 undergraduate students participated in the present investigation by completing a series of self-report instruments measuring affect and psychopathology. Response patterns were analyzed using AMOS 4.0 in order to examine the structural relationships between negative affectivity, positive affectivity, emotion reappraisal, emotion suppression, negative emotional reactivity, and poor understanding of emotions. Initial tests of a single model of emotion dysregulation suggested that the development of two separate models best represented subject responses. As a result, distinct models for suppression and reappraisal were tested concurrently. Tests of model invariance revealed similar structural qualities across gender, ethnicity, and levels of general distress for both models. Following modification, final fit indices suggested good fit for the reappraisal model (CFI = .99, TLI = .99, RMSEA = .057); however, the suppression model did not appear similarly representative of subject response behavior (CFI = .89, TLI = .85, RMSEA = .073). Findings of the current study suggest that the use of emotional reappraisal may be associated with increased positive affective and decreased negative affective states. Further, attempts to reappraise emotional experiences may influence the relationships poor understanding of emotions and fear of strong affect demonstrated with negative and positive affect. Data support previously articulated psychotherapy treatment strategies (Beck, 1979; Barlow & Cerny, 1988; Linehan, 1993; Hayes, 2004), but also indicate that current cognitive behavioral therapies may benefit from heightened attention to emotions and the incorporation of affective regulation skill building strategies. Future research directions, study strengths and limitations, and additional implications of present results are included.

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