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.

abstract: An abundance of data has established the links between both pain-related cognitions and relationship attachment qualities in the experience of pain, including long-term functional health in chronic pain patients. However, relatively few studies have explored the dynamic relation between pain and pain-related cognitions within a day, and no studies have tested the moderating role of relationship attachment on the within-day cognition—pain association in chronic pain patients. The objectives of this study were to: 1) assess whether late morning pain flares predicted changes in afternoon positive and negative pain-related cognitive appraisals, and whether these changes in turn predicted end-of-day pain, and 2) explore whether adult attachment anxiety moderated the pain-cognition relation in individuals with chronic pain due to fibromyalgia. One hundred and seventy four partnered individuals with fibromyalgia completed initial assessments of demographics and attachment anxiety, and subsequently completed electronic assessments of pain intensity and positive and negative cognitive pain-related appraisals three times a day for three weeks. Multilevel structural equation modeling established that a latent negative cognitive appraisal factor (encompassing shared variance from catastrophizing, pain irritation, and self-criticism related to pain) mediated the link between late morning and end-of-day pain intensity, in line with the hypothesis. Analyses also provided some support for a mediating role for a positive cognitive appraisal factor (a composite of pain control, pain self-efficacy, and feeling pain without reacting) in the daily course of pain; the mediated effect for positive appraisals was weaker than the mediated effect of negative appraisals, but was sustained in a model that included negative appraisals. Inconsistent with prediction, attachment anxiety did not moderate the within-day links between pain and cognitions. These findings establish the dynamic links within day between pain and pain-related cognitions, and highlight the potential impact of both negative and positive cognitions on daily pain regulation. They point to the value of broadening cognitive-behavioral treatment strategies for chronic pain patients to target not only negative but also positive cognitions. / Dissertation/Thesis / Doctoral Dissertation Psychology 2015

The study of well-being has a long history of investigation from a nomothetic (between-person) perspective that aimed to understand characteristic levels of well-being and individual difference variables that account for stable differences across people. Recent investigations have demonstrated that levels of well-being have the capacity to rapidly fluctuate within people over short intervals and also exhibit slower changes over longer intervals, highlighting the importance of considering the ideographic (within-person) nature of well-being. The aim of this dissertation was to help build on such within-person understanding by demonstrating how theories of well-being may be empirically evaluated using innovative research designs (e.g., intensive repeated measurement designs) and analytic techniques (e.g., multilevel structural equation models [MSEM]) that can fully capture the complexity and dynamic nature of well-being. Three distinct research studies employing intensive repeated measurement designs and an MSEM analytic framework addressed a variety of research questions concerning intra- and inter-individual predictors of well-being. Study one (Chapter 2) simultaneously examined the multilevel moderation and mediation effects of cognitive interference on stress reactivity estimated in a 14-day daily diary design. Study two (Chapter 3) utilized measurement burst data from a large U.S. sample of adults, assessed across multiple time-scales, to examine long-term changes in short-term within-person associations. Random within-person slopes were specified as exogenous predictor variables of individual differences in global levels of psychological well-being. Study three (Chapter 4) used simulation data to examine the conditions where specifying within-person measurement scales as latent variables compared to unit-weighted composite scores optimized detection of within-person effects. This dissertation demonstrates the importance of innovative design and analysis to appropriately model and understand the complex, dynamic associations that operate within and across individuals in predicting their experiences of well-being. / Graduate / 2019-05-14

Law enforcement administrators are concerned about the levels of public satisfaction with the police as a key to successful policing. Citizens who are satisfied with the police are more willing to provide cooperation with the police that is essential for the organization to reduce crime and serve the community effectively. Existing empirical studies have shown that citizen demographic characteristics and police performance are predictors of satisfaction with the police. The limitation of the previous studies, however, is that they did not consider what police agencies can do, specifically how they change or determine police performance. Without the organizational-level consideration, we may falsely blame individual police officers and their policing activities for the current elevated tension between the public and the police. This research attempts to address the void in the existing literature by introducing an extended theoretical framework that is structured with organizational-level predictors built upon already identified individual-level relationships with public satisfaction with the police.Using the Law Enforcement Organizations (LEO) survey A and Police-Community Interaction (PCI) survey of the National Police Research Platform Phase II, 2013–2015, at the organizational level, the current research examines the indirect associations between organizational characteristics (i.e., transformational leadership and organizational justice) of police agencies and public satisfaction with the police. Police job satisfaction and the proxy measures of police job performance (i.e., satisfaction with the specific police contact and perception of neighborhood safety) are the intervening variables in the relationship. In the current research, the merged data, including 16,547 citizens from 52 police agencies, are used for the analyses. The primary statistical approaches for the examination include factor analyses for the measurement model, bivariate analyses, and Multilevel Structural Equation Modeling (MSEM). The major finding of this research is that organizational justice, which is about the fairness of organizational behaviors, has an indirect association with public satisfaction with the police through police job satisfaction and citizen perceptions of neighborhood safety. This finding indicates that not only are individual police officers who encounter citizens and provide services able to shape citizen perceptions of the police, but police agencies and their administrators are able to actively improve the levels of satisfaction with the police overall.

The purpose of the study was threefold: (1) to determine the factor structure of mathematical thinking at the within-classroom and at the between-classroom level / (2) to investigate the extent of variation in the relationships among different mathematical thinking constructs at the within- and between-classroom levels / and (3) to examine the cross-level interactions among different types of mathematical thinking. Previous research was extended by investigating the factor structure of mathematical thinking in derivative at the within- and between-classroom levels, and further examining the direct, indirect, and cross-level relations among different types of mathematical thinking. Multilevel analyses of a cross-sectional dataset containing two independent samples of undergraduate students nested within classrooms showed that the within-structure of mathematical thinking includes enactive, iconic, algorithmic, algebraic, formal, and axiomatic thinking, whereas the between-structure contains formal-axiomatic, proceptual-symbolic, and conceptual-embodied thinking. Major findings from the two-level mathematical thinking model revealed that: (1) enactive, iconic, algebraic, and axiomatic thinking varied primarily as a function of formal and algorithmic thinking / (2) the strongest direct effect of formal-axiomatic thinking was on proceptual-symbolic thinking / (3) the nature of the relationships was cyclic at the between-classroom level / (4) the within-classroom mathematical thinking constructs significantly moderate the relationships among conceptual-embodied, proceptual-symbolic, and formal-axiomatic thinking / and (5) the between-classroom mathematical thinking constructs moderate the relationships among enactive, iconic, algorithmic, algebraic, formal, and axiomatic thinking. The challenges when using multilevel exploratory factor analysis, multilevel confirmatory factor analysis, and multilevel structural equation modeling with categorical variables are emphasized. Methodological and educational implications of findings are discussed.

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本研究的主要目的是藉由實徵的PISA資料庫資料將多層次結構方程式模型的方法學介紹到台灣的教育領域。多層次結構方程式模型適合應用在大型且具階層或巢狀結構的資料，可以解決因群集性抽樣設計所導致的樣本點相依的問題。 本研究中包含三個小研究。在研究一中，實徵的資料經由多層次結構方程式模型步驟化的分析，並與傳統的結構方程式模型的分析結果相互比較。一共有五個構念及其測量指標從PISA 2003資料庫中被選取來建構多層次結構方程式模型。樣本包含948個學校共26,884位15歲來自加拿大的學生。研究結果顯示某些結構係數的正負向關係在組內層次與組間層次是十分不同的，這也彰顯出多層次結構方程式模型與傳統結構方程式模型比較下的價值。研究一的發現指出，在數學興趣與數學工具性動機控制的條件下，教師的支持對學生的數學成績及數學自我效能在組間層次並無效果，但教師的支持對學生的數學自我效能在組內層次具有正向顯著的效果。此外，除了在組間層次上數學興趣對數學成績有顯著的負向效果以及數學工具性動機對數學自我效能沒有顯著效果外，數學興趣與數學工具性動機對數學成績及數學自我效能具有顯著的正向效果。另外，數學成績對數學自我效能具有很大的效果，特別是在組間層次。 在研究二中，藉由評估跨越不同層級二樣本大小（即120、240、360、480、600、720、840、948個學校）時的模式適配度及參數估計值的穩定性，來決定一個最小較佳的層級二樣本數相對於層級二估計參數數目的比值。研究結果顯示，該比值大約至少8：1是較可以被接受的結果。在研究三中，藉由多群組多層次結構方程式模型進行跨國家的比較。根據研究二的較佳最小比例以及亞洲國家在PISA 2003資料庫中有限的層級二樣本數，一個將焦點集中在數學興趣對數學成績的不同層次預測關係之新多層次結構方程式模型被提出。由再次隨機取樣的加拿大145所學校作為西方國家的代表樣本，而由只有143所學校的日本樣本作為東方國家的代表。研究結果顯示，跨越加拿大與日本樣本，在任一層級中出現十分不同的預測效果。數學興趣對數學成績的預測效果在加拿大樣本中的兩層級皆是正向地顯著，但在日本樣本中卻都是負向地顯著。這意謂著未來某些重要的教育及心理學變項之間關係的跨國研究應該在被重視。 / The main purpose of this research was to introduce multilevel structural equation modeling methodology to Taiwan education field by applying empirical example from PISA 2003 database. Multilevel structural equation modeling was suitable to be applied to the large-scale and hierarchical or nested data structure. It could solve the problem of dependency among sample units resulted from clustered sampling design. There were three studies in the research. In study one, the empirical data dealt with multilevel structural equation modeling analysis was undertaken step by step and compared with conventional structural equation modeling analysis. There were five constructs and their measurement indicators from PISA 2003 database mapped to form the multilevel structural equation model. The sample was 948 schools with 26884 15-year-old students from Canada. The result showed the valences of some structural coefficients were quite different in between-level and within-level structural equation models, which characterisized the value of multilevel structural equation modeling when compared with the outcomes from conventional structural equation modeling analysis. The findings of study one indicated that teacher support had no effect on students’ mathematics grades and mathematics self-efficacy in between-level part but had a significant positive effect on mathematics self-efficacy in within-level part when both interest in mathematics and instrumental motivation to mathematics grades were considered in the model. Besides, interest in mathematics and instrumental motivation had positive effects on mathematics grades and mathematics self-efficacy except for negative effect from interest in mathematics to mathematics grades and no effect from instrumental motivation to mathematics self-efficacy in between-level part. In addition, mathematics grades had great influences on mathematics self-efficacy, especially in between-level part. In study two, a better minimum ratio of the number of level-2 units relative to the number of parameter estimates in between-level part was searched by evaluating the model-fit and stability of parameter estimates across several Canada samples with 120, 240, 360, 480, 600, 720 ,840, and 948 schools. The result showed that the ratio at least about 8:1 was appreciated. In study three, cross-national comparisons were processed by multiple group multilevel structural equation modeling. Based on the better minimum ratio from study two and limited level-2 sample sizes from Asian countries in PISA 2003, a new multilevel structural equation model was proposed focusing on the structural coefficient of mathematics grades regressed on interest in mathematics in each level. A random resampling Canada sample with 145 schools was served as the representative of the West nations and the Japan sample with only 143 schools was on behalf of the East nations. The result showed that quite different predictive effect in either level across the Canada sample and the Japan sample. The predictive effects of the interest in mathematics to mathematics grades were positively significant in the Canada sample in each level but were negatively significant in the Japan sample in each level, which implied that cross-national studies in some important relationships among educational and psychological variables should be emphasized in the future.