Walkability indices are currently used for a wide range of research and commercial applications. Few studies have examined the relationship between walkability indices and measured pedestrian volume or walking rates, nor explored moderators of pedestrian volume such as weather. With 14 years of traffic study data from the City of Ottawa, a spatial auto-regressive (SAR) multi-level model (MLM) was used to understand the proportion of variance in walking explained by the commercial Walk Score® index and selected weather variables. Modeling revealed that a significant proportion of pedestrian volume at a given location in Ottawa, including its spatial lag, was explained by the corresponding Walk Score® value and its spatial lag (51.45%). Furthermore, weather expressed as a combination of ‘felt’ temperature, presence or absence of precipitation, and percent cloud cover, accounted for 2.79% of the variance in walking. These findings indicate that walkability indices may provide value as cost-effective engineering and urban planning tools.

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The purpose of the simulation was to assess the impact of weight pattern assignment when using the multiple membership random effects model (MMREM). In contrast with most previous methodological research using the MMREM, mobility was not randomly assigned; rather the likelihood of student mobility was generated as a function of the student predictor. Two true weights patterns were used to generate the data (random equal and random unequal). For each set of generated data, the true correct weights and two incorrect fixed weight patterns (fixed equal and fixed unequal) that are similar to those used in practice by applied researchers were used to estimate the model. Several design factors were manipulated including the percent mobility, the ICC, and the true generating values of the level one and level two mobility predictors. To assess parameter recovery, relative parameter bias was calculated for the fixed effects and random effects variance components. Standard error (SE) bias was also calculated for the standard errors estimated for each fixed effect. Substantial relative parameter bias differences between weight patterns used were observed for the level two school mobility predictor across conditions as well as the level two random effects variance component, in some conditions. Substantial SE bias differences between weight patterns used were also found for the school mobility predictor in some conditions. Substantial SE and parameter bias was found for some parameters for which it was not anticipated. The results, discussion, future directions for research, and implications for applied researchers are discussed.

Earnings inequality had been rising in the United States since the late 1970s. However, at the level of individual states earnings inequality has been rising, stable, and even falling in some states at different points in time. States vary in both the degree and character of change in earnings inequality, the extent to which they have experienced various inequality-increasing developments, and their institutional capacity to mediate these developments. I argue in this dissertation that this variation offers a rich opportunity for comparative analysis and an excellent lens for exploring the dynamics of the recent rise in earnings inequality.In this dissertation I utilize multiple methods and a state-level analysis to explore a number of research questions. What are the major factors driving rising state earnings inequality between 1980 and 2007? To what extent have states taken distinct causal paths to higher levels of inequality? How have states differed in terms of the types of wage growth that have result in rising, stable, or falling inequality? Throughout, special attention is paid to the manner in which state institutional arrangements, such as union strength and minimum wage rates, may mediate various inequality-increasing developments. Additionally, there is a focus on the contribution of industry flows, specifically losses of manufacturing employment and increasing employment in financial, technology and health-related occupations, to regional patterns of change in inequality.Overall, the intensity, timing, and number of factors that have converged upon any particular state vary substantially between regions and over time. A broad finding of this dissertation is that the net impact of many inequality-increasing factors is contingent upon a state's economic condition and institutional character. In particular, state institutional arrangements have powerfully mediated the impact of various inequality-increasing developments. Also, these analyses suggest that industry shifts have substantially impacted state earnings distributions and are critical to understanding regional patterns of change in earnings inequality. In closing, I suggest that much research on rising inequality at the national-level does not fully capture the substantial diversity of state experiences with rising inequality or the complexity of the interactions between the various factors producing those distinct experiences.

The purpose of this study was to determine if the frequent use of LanguageLinks: Syntax Assessment and Intervention (LL), produced by Laureate Learning Systems, Inc., as a supplemental classroom activity, affected morphosyntax structures (determiners, tense, and complementizers) in participants who are Deaf/Hard of Hearing (DHH) and use American Sign Language (ASL). Twenty-six students from an urban day school for the Deaf participated in this study. Two hierarchical linear modeling (HLM) growth curve analyses were used to examine the influence of LL on the comprehension of morphosyntax based on two dependent variables: 1) the scores from LL’s Optimized Intervention (OI; Wilson, 2003) reports; and 2) the scores from a subset of the Comprehension of Written Grammar (CWG; Easterbrooks, 2010) test. The results of the HLM analyses revealed that time was a statistically significant indicator of progress on both dependent variables: 1) LL, t(25) = 4.510, p < .001, and 2) CWG, t(25) = 4.750, p < .001. Two independent variables served as predictors of where the participants started on the level-1 intercept of the growth curve: 1) Degree of Hearing Loss; and 2) Age. The results indicated that Age, t(23) = 2.182, p = .039, was a statistically significant predictor of the level-1 intercept. A second set of independent variables served as predictors of change over time on the growth curve: 1) Diagnostic Evaluation of Language Variation Norm-Referenced (DELV-NR; Seymour, Roeper, & de Villiers, 2005) pretest scores on the syntax and semantic subtest; and 2) the Basic Reading Inventory (BRI; Johns, 2008) scores. The results indicated that the BRI scores, t(22) = 3.522, p = .002, were statistically significant predictors of change over time on the LL program. A dependent t-test was used to examine the comprehension of morphosyntax based on the third dependent variable of the DELV-NR assessment, and revealed statistically significant results on the syntax subtest, t(25) = -2.394, p = .024. The daily use of LL affected the morphosyntax of the participants in this study and may be an evidence-based practice for students who are DHH and use ASL.

In educational research, students often exist in a multilevel social setting that can be identified by students within classrooms, classrooms nested in schools, schools nested in school districts, school districts nested in school counties, and school counties nested in states. These are considered hierarchical, nested, or multilevel because students are within the same community and share similar experiences which have the potential to influence an outcome. Because students within the same classrooms have similar characteristics, conclusions made on these students cannot be independent. To adapt to the hierarchical, multilevel, or nested data structure, multilevel analysis techniques such as hierarchical linear modeling (HLM) can be used to analyze the data. One purpose of HLM is to specify a model that includes appropriate random effects (Guo, 2005). One method which should be considered for inclusion or exclusion of random effects and to evaluate the goodness of fit of the final model to the data is the comparison of models with different specifications of random effects based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) , or Deviance Information Criterion (DIC) which corrects for bias induced by sample size and number of random effects. AIC, BIC, and DIC are information criteria that measure the statistical fit of a model. There has not been any research conducted in the multilevel literature about the impact of sample size and information criteria. This Monte Carlo Monte Carlo simulation compared the influence of sample size on the ability to select the best model in two-level hierarchical models using the information criteria Akaike Information Criterion, Bayesian Information Criterion, and Deviance Information Criterion. Results of this investigation showed that all three information criteria had very low or nonexistent success in choosing the best hierarchical linear model.

There are two serious methodological problems in the research literature on school effectiveness, the ecological problem in the analysis of aggregate data and the problem of not controlling for important confounding variables. This dissertation corrects these errors by using multilevel modeling procedures, specifically Hierarchical Linear Modeling (HLM), and the Canadian Trends in International Mathematics and Science Study (TIMSS) 2007 data, to evaluate the effect of school variables on the students’ academic achievement when a number of theoretically-relevant student variables have been controlled. In this study, I demonstrate that an aggregate analysis gives the most biased results of the schools’ impact on the students’ academic achievement. I also show that a disaggretate analysis gives better results, but HLM gives the most accurate estimates using this nested data set. Using HLM, I show that the physical resources of schools, which have been evaluated by school principals and classroom teachers, actually have no positive impact on the students’ academic achievement. The results imply that the physical resources are important, but an excessive improvement in the physical conditions of schools is unlikely to improve the students’ achievement. Most of the findings in this study are consistent with the best research literature. I conclude the dissertation by suggesting that aggregate analysis should not be used to infer relationships for individual students. Rather, multilevel analysis should be used whenever possible.

There are two serious methodological problems in the research literature on school effectiveness, the ecological problem in the analysis of aggregate data and the problem of not controlling for important confounding variables. This dissertation corrects these errors by using multilevel modeling procedures, specifically Hierarchical Linear Modeling (HLM), and the Canadian Trends in International Mathematics and Science Study (TIMSS) 2007 data, to evaluate the effect of school variables on the students’ academic achievement when a number of theoretically-relevant student variables have been controlled. In this study, I demonstrate that an aggregate analysis gives the most biased results of the schools’ impact on the students’ academic achievement. I also show that a disaggretate analysis gives better results, but HLM gives the most accurate estimates using this nested data set. Using HLM, I show that the physical resources of schools, which have been evaluated by school principals and classroom teachers, actually have no positive impact on the students’ academic achievement. The results imply that the physical resources are important, but an excessive improvement in the physical conditions of schools is unlikely to improve the students’ achievement. Most of the findings in this study are consistent with the best research literature. I conclude the dissertation by suggesting that aggregate analysis should not be used to infer relationships for individual students. Rather, multilevel analysis should be used whenever possible.

Au Québec, les nouveaux arrivants sont de plus en plus représentés dans les habitations à loyer modique (HLM), particulièrement dans les habitations destinées aux familles. En plus d’avoir pour mission d’offrir des logements sécuritaires et de qualité aux ménages à faible revenu, les offices municipaux d’habitation (OMH) soutiennent aussi l’établissement d’interventions sociocommunautaires favorisant le mieux-être des résidents en HLM. Or, mobiliser la participation des parents aux différentes activités offertes, et plus spécifiquement la participation des parents issus de l’immigration, demeure un défi. Cette observation soulève donc des questions quant à l’expérience spécifique des parents immigrants dans ce milieu de vie. Ainsi, en se basant sur un cadre conceptuel interactionniste symbolique, l’objectif général de la thèse est de mieux comprendre le vécu de la parentalité pour les immigrants qui résident en HLM par le biais de leur propre point de vue. Pour ce faire, lors d’entrevues semi-structurées individuelles auprès de sept parents résidant dans trois HLM pour familles de la ville de Sherbrooke, deux thèmes sont explorés : un premier lié à la signification de l’expérience de la parentalité chez ces parents, et un second lié aux éléments de l’environnement physique et social ayant pu exercer une influence sur l’expérience de la parentalité. L’utilisation d’une méthode d’analyse thématique permet d’identifier les thèmes saillants qui ressortent du discours des parents. En lien avec la première question de recherche, deux dimensions principales de la parentalité ressortent, soit une dimension conceptuelle et une dimension expérientielle (affective et comportementale). Puis, à titre de facteurs d’influence sur l’expérience de la parentalité, plusieurs éléments de l’environnement résidentiel, autant sur le plan physique (p. ex., les caractéristiques de la construction des infrastructures, la localisation), que social (p. ex., les interventions sur place, les interactions avec le voisinage) se démarquent. Qui plus est, l’histoire personnelle et migratoire des parents, ainsi que des éléments contextuels du pays d’accueil, apparaissent également à titre d’éléments influençant la parentalité. Ainsi, par la mise en valeur de l’importance de l’environnement et de la culture sur l’expérience de la parentalité, les résultats de la thèse appuient la pertinence de prendre en compte le point de vue et l’expérience des parents immigrants dans le développement de services qui leur sont destinés, et ce, particulièrement dans leur milieu de vie.

Leadership has been traditionally conceptualized as individual-centered and top-down. However, the fast growing environment creates demands beyond the capabilities of an individual leader and requires flexible and adaptive leadership structures in response. The current study built upon research in leadership and teams and employed an iterative experimental design to investigate the emergence of shared leadership through its antecedents (i.e., power structure and role assignment) and examine its impact on team outcomes (i.e., productivity, adaptability and innovation). The relationship between shared leadership and other team properties (i.e., collective efficacy and team cohesiveness) were also scrutinized. Social network analysis (SNA) and growth modeling techniques were utilized. Support for hypotheses was not found. Methodological and analytical limitations and future research directions were discussed. / Master of Science