More than 8 million of America's middle and high school students are struggling readers. Two-thirds of all eighth graders read below grade level, and the reading scores of high school students have not improved since 1974. Low literacy levels affect learning in all subject areas and impede student opportunity for future success. The No Child Left Behind Act of 2001 and the accountability measures associated with the Act have heightened public awareness of the deficiency in adolescent literacy. School districts are choosing to respond in one of two ways. Some school districts are opting to invest in teacher-proof curricula that negate the effect of the teacher. Other districts are opting to invest in the professional development of their teachers. The goal of this study was to support district efforts to provide strategic professional development opportunities for teachers by investigating the effects of teacher efficacy for instructional strategies, classroom management and student engagement on ninth and tenth grade students' reading achievement. Teachers with high efficacy were hypothesized to impact students' reading gains positively. Student contextual variables of prior achievement, socioeconomic status, ethnicity and grade were controlled for in the study. The participants included 2,061 students in 23 classrooms taught by 110 teachers in two school districts on the west coast of Florida. The results indicated that there was no statistically significant relationship between teacher efficacy and student reading achievement gains. The only variables of statistical significance were race (white vs. Non-white) and grade. As more researchers use the findings and recommendations from this study to inform new investigations of the complex relationship between teacher efficacy and student achievement in reading, teacher educators, policymakers, teachers and administrators will be better informed as they continue to work towards improving the reading achievement scores of and narrowing the achievement gaps in adolescent literacy.

The current study, initially aimed to explore the problem solving skills of the sixth grade students within the four-process problem solving framework including the processes of understanding the problem, devising a plan, carrying out the plan, and looking back and evaluating. Secondly, it aimed to investigate the relationships between student and teacher related characteristics and problem solving skills of the students. In the study, a model was proposed based on the related literature and this proposed model was tested by using hierarchical linear modeling (HLM) technique. A total of 2562 sixth grade students from 37 public elementary schools in the eight central districts of Ankara completed a problem solving skills test developed by the researcher and a student questionnaire. At the same time, mathematics teachers of the students participated in the study completed a teacher questionnaire. The results indicated that in general the sixth grade students displayed low performance in the overall problem solving skills test. Moreover, the students performed best in the process of understanding problem whereas they showed the worst performance in the process of looking back and evaluating. The results of the hierarchical linear modeling technique indicated that the student characteristics significantly and positively related to the problem solving skills of the sixth grade students were socioeconomic status, mathematics self concept, extrinsic motivation, use of control strategies, preference for competitive learning situation, and teacher support, on the other hand student level characteristics significantly and negatively related to the problem solving skills of the sixth grade students were mathematics anxiety, giving homework, activities related with homework, and different types of homework. Furthermore, the teacher level characteristics significantly related to the problem solving skills of the sixth grade students were only teacher gender and perceptions about limitations aroused from students. The characteristic of perceptions about limitations aroused from students was negatively related to problem solving skills of the students on the other hand teacher gender was found to be related to problem solving skills of the students in favor of female teachers. It was also found that there was no teacher level characteristic influencing the relationship that was between student level characteristics and the problem solving skills of the students.

Hierarchical Linear Modeling (HLM) sample size recommendations are mostly made with traditional group-design research in mind, as HLM as been used almost exclusively in group-design studies. Single-case research can benefit from utilizing hierarchical linear growth modeling, but sample size recommendations for growth modeling with HLM are scarce and generally do not consider the sample size combinations typical in single-case research. The purpose of this Monte Carlo simulation study was to expand sample size research in hierarchical linear growth modeling to suit single-case designs by testing larger level-1 sample sizes (N1), ranging from 10 to 80, and smaller level-2 sample sizes (N2), from 5 to 35, under the presence of autocorrelation to investigate bias and power. Estimates for the fixed effects were good for all tested sample-size combinations, irrespective of the strengths of the predictor-outcome correlations or the level of autocorrelation. Such low sample sizes, however, especially in the presence of autocorrelation, produced neither good estimates of the variances nor adequate power rates. Power rates were at least adequate for conditions in which N2 = 20 and N1 = 80 or N2 = 25 and N1 = 50 when the squared autocorrelation was .25.Conditions with lower autocorrelation provided adequate or high power for conditions with N2 = 15 and N1 = 50. In addition, conditions with high autocorrelation produced less than perfect power rates to detect the level-1 variance.

xv, 111 p. : ill. / Despite numerous legislative and programmatic efforts, individuals with disabilities continue to experience greater difficulties gaining employment and poorer outcomes of employment than individuals without disabilities. These disparities negatively impact society. My review of the U.S. empirical research literature suggests, however, that self-employment could improve employment opportunities and outcomes for individuals with disabilities, and their success is most influenced by individual characteristics, level of supports, and accountability systems. In this dissertation study, I used a nonexperimental research design to investigate six research questions with Hierarchical Linear Modeling (HLM) and Structural Equation Modeling (SEM) statistical analyses. Extant data on more than a million clients of vocational rehabilitation (VR) agencies from the 50 states and District of Columbia for fiscal years 2003 to 2007 were obtained from the Rehabilitation Services Administration. Results of the HLM analysis indicated that among the significant (<italic>p</italic><.001) predictors of self-employment closure across the fiscal years, ethnicity had the strongest effect. The initial SEM analysis produced an inadmissible solution; the respecified model of individual characteristics, level of supports, and accountability systems produced a reasonable model fit in each fiscal year. The model invariance testing across the four U.S. Census Regions indicated a reasonable fit in each fiscal year when model parameters were freely estimated for each region, but very poor fit and significant differences were indicated when some parameters were fixed to be equal across the regions. The major limitations of this dissertation study are model misspecification in HLM and SEM and the small number of RSA fiscal years that were analyzed; causal inferences cannot be made. The primary implication of this study for researchers is using the results of the statistical analyses to develop and test theories about self-employment of individuals with disabilities through VR. The primary implication for VR is using the results to make decisions about services and agency policies. Recommendations for further research include (a) using Laplace estimation in HLM, (b) analyzing other HLM random effects and predictors, (c) testing a SEM model of different indicators and factor structure with Bayesian estimation, and (d) conducting empirical longitudinal studies given the complex developmental processes of self-employment. / Committee in charge: Richard Albin Chair; Deanne Unruh, Member; Deborah Olson, Member; Lauren Lindstrom, Member; Patricia Gwartney, Outside Member

Doctor of Philosophy / Department of Chemical Engineering / Liang T. Fan / Walter P. Walawender Jr / Carbon adsorbents, namely, activated carbons and carbon molecular sieves, can be variously applied in the purification and separation of gaseous and liquid mixtures, e.g., in the separation of nitrogen or oxygen from air; often, carbon adsorbents also serve as catalysts or catalyst supports. The formation of carbon adsorbents entails the modification of the original internal surfaces of carbonaceous substrates by resorting to a variety of chemical or physical methods, thereby augmenting the carbonaceous substrates' adsorbing capacity. The formation of carbon adsorbents proceeds randomly, which is mainly attributable to the discrete nature, mesoscopic sizes, and irregular shapes of the substrates utilized as well as to their intricate internal surface configuration. Moreover, any process of carbon-adsorbent formation may fluctuate increasingly severely with time. It is desirable that such a process involving discrete and mesoscopic entities undergoing complex motion and behavior be explored by means of the statistical framework or a probabilistic paradigm. This work aims at probabilistic analysis, modeling, and simulation of the formation of carbon adsorbents on the basis of mechanistic rate expressions. Specifically, the current work has formulated a set of linear and non-linear models of varied complexity; derived the governing equations of the models formulated; obtained the analytical solutions of the governing equations whenever possible; simulated one of the models by the Monte Carlo method; and validated the results of solution and simulation in light of the available experimental data for carbon-adsorbent formation from carbonaceous substrates, e.g., biomass or coal, or simulated data obtained by sampling them from a probability distribution. It is expected that the results from this work will be useful in establishing manufacturing processes for carbon adsorbents. For instance, they can be adopted in planning bench-scale or pilot-scale experiments; preliminary design and economic analysis of production facilities; and devising the strategies for operating and controlling such facilities.

Multilevel datasets are commonly used and increasingly popular in research in the organizational and other social sciences. These models are complex and have many elements beyond those found in more traditional linear models. However, research on how multilevel models perform is lacking. The current paper examined the impact of common factors (average cluster size, cluster size distribution, average number of clusters, strength of the intraclass correlation coefficient, and effect sizes of individual and cluster level variables, and their interaction) in multilevel datasets. Monte Carlo data simulation was used across 6,144 factor-combination conditions. The results of study factors on observed intraclass correlation coefficients, calculated design effect, and empirical design effect are discussed. The results of this study have implications for both researchers in both academic and applied fields. The scale of the simulation variables allow it to be germane to datasets from across the social sciences. However, the nature of data simulation and analysis is such that there are still many elements that can and should be accounted for in future research.

This dissertation studied the determinants and consequences of corporate reputation. It explored how firm-, industry-, and country-level factors influence the general public’s assessment of a firm’s reputation and how this reputation assessment impacted the firm’s strategic actions and organizational outcomes. The three empirical essays are grounded on separate theoretical paradigms in strategy, organizational theory, and corporate governance. The first essay used signaling theory to investigate firm-, industry-, and country-level determinants of individual-level corporate reputation assessments. Using a hierarchical linear model, it tested the theory based on individual evaluations of the largest companies across countries. Results indicated that variables at multiple analysis levels simultaneously impact individual level reputation assessments. Interactions were also found between industry- and country-level factors. Results confirmed the multi-level nature of signaling influences on reputation assessments. Building on a stakeholder-power approach to corporate governance, the second essay studied how differences in the power and preferences of three stakeholder groups – shareholders, creditors, and workers – across countries influence the general public’s reputation assessments of corporations. Examining the largest companies across countries, the study found that while the influence of stock market return is stronger in societies where shareholders have more power, social performance has a more significant role in shaping reputation evaluations in societies with stronger labor rights. Unexpectedly, when creditors have greater power, the influence of financial stability on reputation assessment becomes weaker. Exploring the consequences of reputation, the third essay investigated the specific effects of intangible assets on strategic actions and organizational outcomes. Particularly, it individually studied the impacts of acquirer acquisition experience, corporate reputation, and approach toward social responsibilities as well as their combined effect on market reactions to acquisition announcements. Using an event study of acquisition announcements, it confirmed the significant impacts of both action-specific (acquisition experience) and general (reputation and social performance) intangible assets on market expectations of acquisition outcomes. Moreover, the analysis demonstrated that reputation magnifies the impact of acquisition experience on market response to acquisition announcements. In conclusion, this dissertation tried to advance and extend the application of management and organizational theories by explaining the mechanisms underlying antecedents and consequences of corporate reputation.

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Mental and emotional health concerns among college students are prevalent and diverse in their symptom presentations. With increasing demands on counseling centers to provide efficient care and to address students with higher acuity or risk for harm, there has been an increased focus on identifying therapeutic targets that underlie a wide breadth of concerns to broaden the scope and impact of mental health services. Psychological inflexibility is one such target and refers to a combination of excessive avoidance of internal experiences coupled with a lack of actions that align with a person’s values. Interventions for psychological inflexibility aim to support people in reducing actions that are mostly about avoiding unwanted thoughts and feelings and actions that involve moving towards chosen values. Such interventions may produce changes in people’s actions in part through helping people notice and label the different roles their actions play in relation to thoughts, feelings, and personal values. However, the skill of noticing and labeling the purposes of one’s actions has not been studied in interventions for psychological inflexibility despite being discussed in theoretical writings. Training this skill may serve as a direct means of reducing psychological inflexibility and as a foundation for other interventions, thus it may be a relevant target in interventions for psychological inflexibility among college students. Given this, the present study developed and tested an intervention focused on noticing and labeling one’s actions as an intervention for psychological inflexibility in a college student sample, as delivered through web and app-based media. The study recruited 106 students with symptoms of depression and anxiety from a medium sized university in the Mountain West of the United States, and then randomly assigned them to either wait for eight weeks or receive a three-week online and app-based training for noticing and labeling avoidant and values-consistent actions. The results of the study indicated short-term effects on symptoms of depression and anxiety for participants who received the online and app-based training as compared with participants who were asked to wait, although both groups showed reductions in symptoms by the end of the study period. Participants did not report changes in the target skill of noticing and labeling their actions although the study did find larger reductions in psychological inflexibility among participants who received the training as compared with those asked to wait. Further, changes in psychological flexibility were related to changes in behavioral activity and life satisfaction, but not life quality. The results raise questions about the necessity of training the ability to notice and label one’s actions as a direct intervention mechanism for psychological inflexibility. The findings also suggest that changing inflexible patterns of behavior may be more important than the capacity to notice such changes. These results are further interpreted in relation to interventions for college student mental and emotional health.

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