Structural equation models are systems of simultaneous linear equations that are generalizations of linear regression, and have many applications in the social, behavioural and biological sciences. A serious barrier to applications is that it is easy to specify models for which the parameter vector is not identifiable from the distribution of the observable data, and it is often difficult to tell whether a model is identified or not. In this thesis, we study the most straightforward method to check for identification – solving a system of simultaneous equations. However, the calculations can easily get very complex. Grobner basis is introduced to simplify the process. The main idea of checking identification is to solve a set of finitely many simultaneous equations, called identifying equations, which can be transformed into polynomials. If a unique solution is found, the model is identified. Grobner basis reduces the polynomials into simpler forms making them easier to solve. Also, it allows us to investigate the model-induced constraints on the covariances, even when the model is not identified. With the explicit solution to the identifying equations, including the constraints on the covariances, we can (1) locate points in the parameter space where the model is not identified, (2) find the maximum likelihood estimators, (3) study the effects of mis-specified models, (4) obtain a set of method of moments estimators, and (5) build customized parametric and distribution free tests, including inference for non-identified models.

Structural equation models are systems of simultaneous linear equations that are generalizations of linear regression, and have many applications in the social, behavioural and biological sciences. A serious barrier to applications is that it is easy to specify models for which the parameter vector is not identifiable from the distribution of the observable data, and it is often difficult to tell whether a model is identified or not. In this thesis, we study the most straightforward method to check for identification – solving a system of simultaneous equations. However, the calculations can easily get very complex. Grobner basis is introduced to simplify the process. The main idea of checking identification is to solve a set of finitely many simultaneous equations, called identifying equations, which can be transformed into polynomials. If a unique solution is found, the model is identified. Grobner basis reduces the polynomials into simpler forms making them easier to solve. Also, it allows us to investigate the model-induced constraints on the covariances, even when the model is not identified. With the explicit solution to the identifying equations, including the constraints on the covariances, we can (1) locate points in the parameter space where the model is not identified, (2) find the maximum likelihood estimators, (3) study the effects of mis-specified models, (4) obtain a set of method of moments estimators, and (5) build customized parametric and distribution free tests, including inference for non-identified models.

Problem: Many individuals with chronic illnesses such as stroke and ongoing activity limitations report self-perceived health related quality of life (HRQL) that is similar to that of healthy individuals. This phenomenon is termed response shift (RS). RS describes how people change: internal standards in assessing HRQL (recalibration), values (reprioritization), or how they define HRQL (reconceptualization), after an event such as stroke. Changes in HRQL post stroke may be inaccurate if RS is not taken into account. Increased knowledge of RS may affect the way in which HRQL measures are used, both clinically and in research. The overall objective was to assess RS in construct specific HRQL models post stroke: physical function, mental health, and participation. Methods: Data were analysed from the longitudinal study “Understanding Quality of Life Post-Stroke: A Study of Individuals and their Caregivers”. Six-hundred and seventy- eight persons with stroke at 1, 3, 6, and 12 months post stroke participated. Generic and stroke specific HRQL measures were collected. Descriptive analysis was completed with SAS, and identification of RS utilized structural equation modeling with LISREL. Results: Mean age of participants was 67 years (SD 14.8), and 45% were female. RS was identified in mental health using a framework which was developed for identifying RS statistically with multiple time points. RS was also identified in physical function where it had not been expected, possibly due to the self perceived nature of the response options. The effect size of change in physical function was affected by the presence of RS. The timing of RS in mental health and physical function was primarily around the 12 month time period, and predominantly recalibration RS. RS was also identified in participation. Conclusions: The framework that was developed was useful in identifying RS and incorporated important issues such as multiple testing and validation of the model. The presence of RS affects measurement of HRQL constructs post stroke; recalibration RS can be measured clinically with specific methods to account for RS. RS should also be measured in research studies to ensure accurate measurement of change. Future research should evaluate additional models in stroke and other populations. / October 2008

Population aging has been an increasing in many societies during the last century, andespecially in China this issue has become one of the most urgent social phenomenonin the recent twenty years. Meanwhile, being healthy matters to the senior populationthe most. The main purpose of this paper is to investigate how to measure Chineseelderly health condition, and what the main factors are influencing their health. Thedata of this paper is from the China Health and Retirement Longitudinal Study(CHARLS). A structural equation model(SEM) was established to verify therelationship between different influencing factors and the elderly health. The latentvariables in this model were pre-studied by both exploratory factor analysis andconfirmatory factor analysis. The conclusion based on this data is elderly health canbe measured in four aspects physical condition, emotional condition, body functionand pain. The significant influencing effects of each aspects of health are time sharing,exercise, family environment and lifestyle.

Abstract The purpose of this investigation is based on the revision model proposed by Ferris , Adams, Kolodinsky, Hochwarter, Ammeter,(2002).This research is deeply to examine the influence of perceptions of organizational politics on trust - organizational cynicism as mediator variable .The data were analyzed by SPSS 15.0 software and by structural equation modeling (SEM). The major results of this study are as following¡GThe perceptions of organizational politics were found have a significant negative relationship with trust. The perceptions of organizational politics were constructed from three patterns( general political behavior¡Bgo along to get ahead¡Bpay and promotion).Only general political behavior has a significant effect on trust.The other two patterns have no significant effect on trust. The perceptions of organizational politics were found have a significant positive relationship with organizationl cynicism . Organizationl cynicism has a significant negative relationship with trust.Organizationl cynicism has a mediating effect on the relationship between perceptions of organizational politics and trust. Key word¡Gperceptions of organizational politics , trust , organizationl cynicism , structural equation modeling

This research is based on the framework of revision model proposed by Ferris et al. (2002), discussing the influence organization politics consciousness to staff's from Machiavellianism and type A personality. The sample consisted of 1890 employee selected from 40 organizations covering 9 industrial sectors in Taiwan. The data were analyzed by descriptive statistics, reliability analysis, confirmatory factor analysis, correlation analysis and structural equation modeling and the summarized findings are in the following sections. The major results of this study are as fallowing: 1. Machiavellianism has a significant effect on employees¡¦ general political behavior of organizational politics perceptions. 2. Machiavellianism has a significant effect on employees¡¦ benefits of remaining silence of organizational politics perceptions. 3. Machiavellianism has a significant effect on employees¡¦ salary and promotion policies of organizational politics perceptions. 4. Type A personality has a significant effect on employees¡¦ general political behavior of organizational politics perceptions. 5. Type A personality has a significant effect on employees¡¦ benefits of remaining silence of organizational politics perceptions. 6. Type A personality has a significant effect on employees¡¦ salary and promotion policies of organizational politics perceptions.

The perceptions of organizational politics and organizational citizenship behavior are neither regulated nor indentified in any organization. But, they are exactly existed in organization. This study is based on the revise model of Perceptions of Organizational Politics (POP) proposed by Ferris et al. (2002). We investigate the relationship between the perceptions of organizational politics and the organizational citizenship behavior. Furthermore, we investigate the indirect effect between the perceptions of organizational politics and the subordinates of organizational citizenship behavior. This study used the scales of 35 items collected by Dr. Chin-ming Ho and the other members of the last research team of POP in 2008. The research is based on the revision model of POP and questionnaire survey. The sample consisted of 1,890 employee selected from 40 organizations covering 9 industrial sectors in Taiwan. The method, path analysis with latent variables of the structural equation modeling (SEM) is used to measure the relationship among the constructs. The major result of this study is as following: 1. The perceptions of organizational politics have negative effect on the organizational citizenship behavior. 2. The perceptions of organizational politics have negative effect on the altruism toward colleagues 3. The perceptions of organizational politics have negative effect on the conscientiousness 4. The perceptions of organizational politics have negative effect on the identification with company 5. The perceptions of organizational politics have negative effect on the interpersonal harmony 6. The perceptions of organizational politics have negative effect on the protecting company resources

Recently, researchers in the organizational behavior have continued their research on the related issues in the perception of organizational politics in the hope to promote organizational trust, and thus achieve the competitive advantages of the organization. Therefore, this study is based on the revised model of the perception of organizational politics, proposed by Ferris et al. (2002), to explore the relation between the perception of organizational politics and organizational trust, and to use SPSS 17.0 and the structural equation model as verification. To re-analyze the research information provided by the research team led by Professor Chin-Ming Ho in the perception of organizational politics, there are discoveries as follows: 1. the dimension of "general political behavior" in the perception of organizational politics has a negative impact to the dimension of "trust manager" in the organizational trust. 2. the dimension of "general political behavior" in the perception of organizational politics has a negative impact to the dimension of "trust organization" in the organizational trust. 3. the dimension of "general political behavior" in the perception of organizational politics has a negative impact to the dimension of "trust colleagues" in the organizational trust. 4. the dimensions of "remain silent, waiting for the benefits" and "difference between policies and practices" in the perception of organizational politics has significantly positive impact to each dimension in the perception of organizational trust.

Abstract The escalating fuel price in Taiwan has prompted the transportation industry to explore renewable energy sources for fuels, but what is more urgent at this stage is to improve transportation efficiency to cut transportation costs. The aim of this study is to implement fuel-efficient rewards to modify driving behavior, thereby improving fuel efficiency. The outcome of this strategy is not only about slowing down greenhouse gas production, but also a reduction of fuel costs of transportation companies. Every year highway bus companies consume millions of liters of fuel, and their fuel costs often exceed hundreds of millions of dollars. Therefore, how to conserve on fuel consumption has become an important issue for all of the bus companies. However so far, besides the eventual fuel savings data, there is still a lack of objective methods to evaluate the execution of such conservation programs. the project will use the ¡§Motivation-Opportunity-Ability Method¡¨ ¡]MOA¡^to develop an analysis model; verification of dynamic data analysis will be conducted using Single or Multilevel Structural Equation Modeling (Multilevel SEM). According to the above research results show that Taken together the investigators suggest those public as well as private transportation companies that have not implemented fuel-efficient related policies to start planning fuel-efficient reward programs and implementing the programs as soon as possible. Furthermore, transportation companies should give courses about environmental driving to teach drivers correct environmental-friendly driving as well as award or publicly praise fuel-efficient drivers. These strategies will bring good outcome for drivers, transportation companies, and environmental protection.

The number of longitudinal studies has increased steadily in various social science disciplines over the last decade. Growth Mixture Modeling (GMM) has emerged among the new approaches for analyzing longitudinal data. It can be viewed as a combination of Hierarchical Linear Modeling, Latent Growth Curve Modeling and Finite Mixture Modeling. The combination of both continuous and categorical latent variables makes GMM a flexible analysis procedure. However, when researchers analyze their data using GMM, some may assume that the units are independent of each other even though it may not always be the case. The purpose of this dissertation was to examine the impact of ignoring a higher nesting structure in Multilevel Growth Mixture Modeling on the accuracy of classification of individuals and the accuracy on tests of significance (i.e., Type I error rate and statistical power) of the parameter estimates for the model in each subpopulation. Two simulation studies were conducted. In the first study, the impact of misspecifying the multilevel mixture model is investigated by ignoring a level of nesting structure in cross-sectional data. In the second study, longitudinal clustered data (e.g., repeated measures nested within units and units nested within clusters) are analyzed correctly and with a misspecification ignoring the highest level of the nesting structure. Results indicate that ignoring a higher level nesting structure results in lower classification accuracy, less accurate fixed effect estimates, inflation of lower-level variance estimates, and less accurate standard error estimates, the latter result which in turn affects the accuracy of tests of significance for the fixed effects. The magnitude of the intra-class correlation (ICC) coefficient has a substantial impact when a higher level nesting structure is ignored; the higher the ICC, the more variance at the highest level is ignored, and the worse the performance of the model. The implication for applied researchers is that it is important to model the multilevel data structure in (growth) mixture modeling. In addition, researchers should be cautious in interpreting their results if ignoring a higher level nesting structure is inevitable. Limitations concerning appropriate use of latent class analysis in growth modeling include unknown effects of incorrect estimation of the number of latent classes, non-normal distribution effects, and different growth patterns within-group and between-group.