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While designed and adopted at the federal level, the Clean Air Act (CAA) and Clean Water Act (CWA) rely on states for implementation. The result of this implementation framework is a disparity in environmental conditions across the nation. The objective of this research is to examine how the implementation stage of the policy process affects program outcomes. The findings indicate that the primary means of shaping program outcomes are the decision-making criterion and subsequent behavior of implementing officials, where their value based actions dictate service delivery. These decisions are, in turn, shaped by the context of the work, where organizations and the socio-political environment influence the basis for decision-making. These findings connect broader organizational and socio-political factors with program outcomes through an indirect relationship, rather than assume a direct relationship as previous authors have done. The findings explain a significant portion of the variance in both air and water program outcomes across the nation. This research indicates the importance of front-line operators in the implementation process, an issue that has been left-out of other work. These conclusions can be used to enhance performance management by practitioners, through a greater understanding of how organizations and individuals affect program outcomes. Finally, the theoretical framework and methodological techniques suggest that previous implementation research has failed to properly specify statistical models, which enhances the literature on the subject.

A general approach for conducting power analysis in two-and three-level hierarchical linear models (HLMs) is described. The method can be used to perform power analysis to detect fixed effects at any level of a HLM with dichotomous or continuous covariates. It can easily be extended to perform power analysis for functions of parameters. Important steps in the derivation of this approach are illustrated and numerical examples are provided. Sample code implementing this approach is provided using the free program R.

This thesis is to study the relationship among transformational leadership, organizational commitment and organizational citizenship behavior within team levels and cross-levels. The analysis demonstrated in this thesis is based on 305 questionnaires collected from 63 leaders and 242 questionnaires from team members. The conclusions are listed as following. 1. The relationship between transformational leadership and organizational commitment (1) Transformational leadership positively impacts organizational commitment. (2) If the team members feel the inspiration from leaders, it will positively impact team members¡¦ value commitment; if they feel leaders¡¦ Idealized Influence, they will be positively impacted in retention commitment. (3) Transformational leadership is not the key factor of influencing team members¡¦ organizational commitment. 2. The relationship between transformational leadership and organizational citizenship behavior (1) If the leaders enhance their transformational leadership, it will be helpful of strengthening team members¡¦ OCB in the aspects of identification with the company, interpersonal harmony, civic virtue, conscientiousness and altruism. (2) In cross level, transformational leadership does effect the correlation to interpersonal harmony.

Both advertising and research and development (R&D) can be viewed as two factors crucial to long-term corporate growth. The purpose of this study is to investigate the effects of the advertising, R&D and interaction between advertising and R&D on shareholder value concerning economic scale and industry concentration. The empirical results show R&D investments may generate innovative products which enhance shareholder value. Moreover, the interaction between advertising and R&D is significantly and positively related to shareholder value. In practice, advertising plays a role to build brand awareness for innovative products. Additionally, we examine how economic scale and industry concentration influence the effects of advertising and R&D on shareholder value individually. With the respect to economic scale, advertising and R&D strategies may increase shareholder value more significantly for firms with high economic scale (large firms). The synergy between advertising and R&D is only significant and positive for firms with low economic scale (small firms). This implies that small firms should invest in advertising to build brand awareness and promote new products while large firms have already developed brand awareness, so the large firms should specialize in core competences. Firms in competitive industry rely more on successful advertising campaigns to increase sales. Moreover, economic scale and industry concentration significantly moderate the effectiveness of advertising and R&D. Under the limited firm sources, managers should decide the appropriate mix of advertising and R&D to maximize shareholder value significantly according to economic scale and industry concentration.

The purpose of this study is to discuss the relationship among company characteristics, brand traits and organizational attractiveness. Most of previous studies about organizational attractiveness are mainly focus on job information, industry and organization performance. Therefore, this study is seeking to understand the influence of company characteristics and brand traits to organizational attractiveness during the job seeking period. This study selects 30 Taiwanese local companies with stock release from the research of Cheers Magazine ¡u2011 The most attractive company for the new generation- Top 100 ¡vand 460 MBA students as questionnaires. By adapting the hierarchical linear model to analyze the data and obtain the result. The study found out that company characteristics and brand traits both have positive influence on organizational attractiveness. Besides, there are also influence between the company characteristics and brand traits.

This work is inspired by Lipsitz (1998) and Allport (1954) because both authors connect micro level processes to social macro level patterns. Allport’s Nature of Prejudice sought to understand patterns of anti-Semitism as connected to a larger social context. From this work, Allport developed the contact hypothesis which is premised on the idea that diversity helps alleviate racial tensions. Lipsitz’ Possessive Investment in Whiteness connects White racial privilege to a history of racial social inequality. In conintuum, I develop the nuances on prejudice formation as it leads to the denial of racial privilege or to the conflation of privileges as oppression. While I focus on White racial privilege, the theoretical contribution of my research develops the framework for individual privilege formation. I then draw upon Bonilla-Silva’s (2013) racial colorblind theory to emphasize the connection between privilege and larger patterns of racial attitudes. The macro level contribution of this dissertation focuses on patterns of overt and colorblind attitudes as affected by racial segregation, social inequality, and respondent characteristics. Data was gathered from the 2000 General Social Survey, 2010 GSS, and U.S. Census county data and applied to a hierarchical linear model. Due to sample selection, this research focuses on racial Whites’ attitudes about the racial Black population. I use measures of racial segregation as proxies for racial contact. I find patterns of racial tolerance through a ‘separate but equal’ storyline among White-Black segregation. When using, social demographics with all minorities included, I find that Whites’ attitudes about racial Blacks are attenuated. This finding supports the literature that non-Black racial minorities act as buffers for White-Black racial relations. Racial diversity is one element in helping alleviate negative racial sentiments, but patterns of segregation and social inequality impact the benefits of this racial diversity.

This dissertation focuses on estimation and selection methods for a simple linear model with two levels of variation. This model provides a foundation for extensions to more levels. We propose new regularization criteria for model selection, subset selection, and variable selection in this context. Regularization is a penalized-estimation approach that shrinks the estimate and selects variables for structured data. This dissertation introduces a procedure (HM-ALASSO) that extends regularized multilevel-model estimation and selection to enforce principles of fixed heredity (e.g., including main effects when their interactions are included) and random heredity (e.g., including fixed effects when their random terms are included). The goals in developing this method were to create a procedure that provided reasonable estimates of all parameters, adhered to fixed and random heredity principles, resulted in a parsimonious model, was theoretically justifiable, and was able to be implemented and used in available software. The HM-ALASSO incorporates heredity-constrained selection directly into the estimation process. HM-ALASSO is shown to enjoy the properties of consistency, sparsity, and asymptotic normality. The ability of HM-ALASSO to produce quality estimates of the underlying parameters while adhering to heredity principles is demonstrated using simulated data. The performance of HM-ALASSO is illustrated using a subset of the High School and Beyond (HS&B) data set that includes math-achievement outcomes modeled via student- and school-level predictors. The HM-ALASSO framework is flexible enough that it can be adapted for various rule sets and parameterizations. / Statistics

Abstract In a two-level hierarchical linear model(HLM2), the outcome as well as covariates may have missing values at any of the levels. One way to analyze all available data in the model is to estimate a multivariate normal joint distribution of variables, including the outcome, subject to missingness conditional on covariates completely observed by maximum likelihood(ML); draw multiple imputation (MI) of missing values given the estimated joint model; and analyze the hierarchical model given the MI [1,2]. The assumption is data missing at random (MAR). While this method yields efficient estimation of the hierarchical model, it often estimates the model given discrete missing data that is handled under multivariate normality. In this thesis, we evaluate how robust it is to estimate a hierarchical linear model given discrete missing data by the method. We simulate incompletely observed data from a series of hierarchical linear models given discrete covariates MAR, estimate the models by the method, and assess the sensitivity of handling discrete missing data under the multivariate normal joint distribution by computing bias, root mean squared error, standard error, and coverage probability in the estimated hierarchical linear models via a series of simulation studies. We want to achieve the following aim: Evaluate the performance of the method handling binary covariates MAR. We let the missing patterns of level-1 and -2 binary covariates depend on completely observed variables and assess how the method handles binary missing data given different values of success probabilities and missing rates. Based on the simulation results, the missing data analysis is robust under certain parameter settings. Efficient analysis performs very well for estimation of level-1 fixed and random effects across varying success probabilities and missing rates. MAR estimation of level-2 binary covariate is not well estimated when the missing rate in level-2 binary covariate is greater than 10%. The rest of the thesis is organized as follows: Section 1 introduces the background information including conventional methods for hierarchical missing data analysis, different missing data mechanisms, and the innovation and significance of this study. Section 2 explains the efficient missing data method. Section 3 represents the sensitivity analysis of the missing data method and explain how we carry out the simulation study using SAS, software package HLM7, and R. Section 4 illustrates the results and useful recommendations for researchers who want to use the missing data method for binary covariates MAR in HLM2. Section 5 presents an illustrative analysis National Growth of Health Study (NGHS) by the missing data method. The thesis ends with a list of useful references that will guide the future study and simulation codes we used.

This study aimed to investigate the relationship between psychological capital and the work-related attitudes of job satisfaction and organisational commitment recognising the hierarchical nature of the data. This relationship was examined in light of a supportive organisational climate as defined by supervisor support which played the role of a moderator in this relationship. Data was gathered using a number of structured questionnaires which were distributed to employees via an online link. The Psychological Capital Questionnaire (Luthans, Youssef & Avolio, 2007), Organisational Commitment Questionnaire (Mowday, Steers & Porter, 1982), Warr, Cook and Wall’s (1979) measure of job satisfaction and Eisenberger’s (1986) adapted measure of supervisor support were administered. A total of 14 departments participated in the study and 50 employees completed the questionnaires. A Hierarchical Linear Model analysis (HLM) was used to analyse the data along with Pearson product moment correlations and a two-way ANOVA. Results indicated that psychological capital was related moderately and positively to job satisfaction but was not related to organisational commitment. Supervisor support was related to both job satisfaction and organisational commitment. Finally supervisor support moderated the relationship between psychological capital and job satisfaction but no interaction was found for the relationship between psychological capital and organisational commitment as moderated by supervisor support. This paper concludes with a discussion of the results, implications of the findings, limitations and directions for future research.