Doctor of Philosophy, The Ohio State University, 2019, Psychology

Multilevel structural equation modeling (MSEM) is an emerging statistical framework for the analysis of hierarchically structured data, such as data corresponding to students nested within classrooms or repeated measurements nested within individuals. The MSEM framework provides several advantages over the traditional multilevel modeling (MLM) and structural equation modeling (SEM) frameworks, including the ability to model multivariate responses, level-2 response variables, measurement error via factor models, and structural relations (e.g., regressions) among the random effects/latent variables. Although several formulations of the MSEM have been presented (see, e.g., Liang & Bentler, 2004; Rabe-Hesketh, Skrondal, & Pickles, 2004; Mehta & Neale, 2005), the framework of B. Muthen and Asparouhov (2008) as implemented in Mplus (L. K. Muthen & Muthen, 2017) has the advantage that the relationship between lower-level (i.e., level-1) latent variables can be modeled as randomly varying across upper-level (i.e., level-2) units. Unfortunately, maximum likelihood (ML) estimation of the parameters for such models, as implemented in Mplus, is computationally demanding due to the likelihood function having to be approximated, as the function cannot be computed in closed-form. Mplus numerically integrates over all of the random effects/latent variables using quadrature-based methods. This approach is not feasible for high-dimensional latent variable models, which reduces the potential models that can practically be fit. In this dissertation, I develop a more computationally efficient and accurate ML estimation routine for MSEMs with random slopes for latent variables. The method relies on a reformulation of the likelihood function so that some of the integrals can be computed analytically, reducing the dimension of numerical integration required. Specifically, only the random slopes for latent variables need to be numerically integrated, as the integrals corresponding to the ot (open full item for complete abstract)

Doctor of Philosophy, Miami University, 2020, Psychology

Social anxiety disorder (SAD) is prevalent and debilitating. More research is needed on a) the factors driving SAD symptoms and b) targeted interventions to shift these factors. Experiential avoidance (EA) has been identified as a possible factor driving SAD symptoms. Many empirically-supported treatments focus on reducing EA as a mechanism of symptom change. However, past research on the relation between EA and SAD symptoms has almost exclusively focused on explicit (i.e., consciously controlled) as compared to implicit (i.e., outside of conscious control) measures of EA. Further, little is known about how EA and SAD symptoms bidirectionally interact throughout the course of an intervention. To address these limitations of past research, the present study: 1) evaluated the psychometric properties of two implicit measures of EA, 2) dynamically modeled the week-to-week temporal relations between explicit EA and SAD symptoms, and 3) tested the efficacy and acceptability of a novel, 3-session pilot intervention targeting EA, which included a computerized program and psychoeducation. Participants (N = 78) consisted of undergraduates with elevated explicit EA and SAD symptoms. There was mixed evidence that the implicit measures demonstrated adequate validity and reliability. In partial support of hypotheses, bidirectional models of explicit EA and SAD symptoms suggested that changes in SAD symptoms preceded and predicted changes in explicit EA from week to week, but not vice versa. Further, the pilot intervention was not associated with reductions in EA and SAD symptoms, but generally, was found to be acceptable and credible. These novel findings advance our understanding of the dynamic relationships between EA and SAD symptoms throughout treatment. Given that many empirically-supported treatments target EA as a mechanism of symptom change, this study highlights a need for future work to more clearly delineate the time course by which changes in mechanisms leads to chang (open full item for complete abstract)

PhD, University of Cincinnati, 2020, Medicine: Epidemiology (Environmental Health)

Introduction: Manganese (Mn) is an essential trace element necessary for normal growth and development, that in excess can be neurotoxic. Excess environmental Mn can occur due to industrial emissions, but exposure pathways from environmental sources to biomarker levels, and ultimately to neurological outcomes have not been determined. Objectives: The objectives of this dissertation are to 1) determine ambient air Mn exposure levels in a population living near the longest operating ferromanganese refinery in North America, using atmospheric dispersion modeling, 2) evaluate associations between modeled ambient air, soil, and indoor dust Mn collected from residences in the exposure area, and 3) determine pathways from environmental measures of Mn to blood, hair, and toenail Mn levels in exposed children using structural equation modeling (SEM). Methods: Data are from the Communities Actively Researching Exposure Study (CARES), a cross-sectional study conducted from 2008-2013 in the Marietta, Ohio area to investigate neurological effects of Mn exposure in 7-9 year old children. Emissions from the Mn refinery were modeled using the U.S. Environmental Protection Agency (EPA) regulatory air dispersion model AERMOD. Average annual ambient air Mn concentrations were determined for census blocks within 32 km of the refinery, and for CARES participants' homes and schools. Monthly modeled ambient air concentrations for 2009-2010 were compared to concentrations from a stationary air sampler in Marietta to evaluate accuracy of the model. Exposures by census blocks were determined to estimate population sizes exposed to air Mn levels exceeding 50 ng/m3, the U.S. EPA guideline. SEM was performed to determine pathways of exposure from air, soil, and indoor dust Mn separately for blood, hair, and toenail Mn. Additional data included in the models were heating, ventilation and air conditioning in the home, average hours/week spent outside by the participant, parent education, (open full item for complete abstract)

PhD, University of Cincinnati, 2012, Medicine: Biostatistics (Environmental Health)

Background: Schizophrenia is a highly heterogeneous disorder with positive and negative symp-toms construed as distinct characteristic manifestations of the disease. Current antipsychotics work primarily by relieving positive symptoms; while negative symptoms are thought hard to treat. However, little is known about the heterogeneity and pattern of negative symptom response with respect to its linkage with the change in positive symptoms. This research work examined the temporal interplay between positive- and negative-symptom trajectories over a 1-year period in schizophrenic patients under antipsychotic treatment, and evaluated the potential utility of patient subgroups defined by the combined symptom trajectories. Methods: This post hoc analysis used data from an open-label, randomized, 1-year pragmatic trial of patients with schizophrenia spectrum disorder who were treated with first and second generation antipsychotics in the usual clinical settings. Data from all the medications were pooled with 399 patients having complete data on both the positive- and negative- subscale scores from the Positive and Negative Syndrome Scale (PANSS). Individual-based, growth mixture modeling combined with a interplay matrix was used to identify the latent trajectory subgroups in term of both the negative and positive symptoms. Baseline demographics, clinical and functional characteristics were examined among the above identified trajectory subgroups. Results: The negative- and positive-symptom trajectory interplay matrix suggests changes in negative and positive symptoms occurred mostly in tandem in the individual patient. Three ma-jor clinical subgroups were identified: (1) dramatic and sustained early improvement in both negative and positive symptoms (DSI); (2) mild and sustained improvement in negative and positive symptoms (MSI), with greater early improvement in positive rather than in negative symptoms, and (3) no improvement in negative and/or positive symptoms (NI). (open full item for complete abstract)

Master of Arts, Case Western Reserve University, 2008, Experimental Psychology

The purpose of this study was to: 1) examine the cognitive and temperament correlates of math achievement through structural equation modeling and 2) apply genetic analyses to test for shared variance across intelligence, specific cognitive abilities, temperament, and math achievement. Participants were a subsample of 324 (87 monozygotic, 75 dizygotic) twins from the Western Reserve Twin Project. Structural equation modeling indicated that there is a hierarchical organization to math skill and cognitive abilities mediate the relationship between general intelligence and math achievement. Multivariate genetic analyses indicated that 1) general intelligence accounts for over half the covariance between specific cognitive abilities and math achievement and 2) temperament factors show a genetic pattern distinct from general intelligence or cognitive ability.

PhD, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Counselor Education

This dissertation reviewed the literature on alcohol use disorder (AUD) recovery definitions, prevalence, pathways, and psychometrics, identifying gaps, including a lack of clarity about the dimensionality of recovery and whether its domains have the same meaning between those experiencing who have and have not experienced AUD as well as between those using and not using services (e.g., formal treatment or 12 steps). The predictive validity of AUD recovery domains over more extended periods and across development is also largely unknown. Here, I used public data from the National Longitudinal Study of Adolescent to Adult Health (n = 4,512) to (a) determine the structure of biopsychosocial domains included in definitions and measures of AUD recovery during young adulthood, (b) test this structure for measurement invariance across service utilization and non-service utilization as well as alcohol abuse and non-abuse subgroups, and (c) test whether AUD recovery domains exhibit predictive validity over 6 years. Exploratory structural equation modeling (ESEM) showed an eight-factor solution fit the data well (CFI = .968, RMSEA = .023). Bifactor ESEM showed evidence against a global recovery factor (e.g., common variance [ECVG = .146] and omega hierarchical [?h = .027]). Scalar invariance for the eight-factor model held across alcohol abuse and non-abuse (CFI = .973; RMSEA = .023) as well as service utilization and non-service utilization (CFI = .972; RMSEA = .021) subgroups, suggesting AUD recovery domains have the same meaning across them and scores may be compared. Longitudinal structural equation modeling showed that Wave III daily activities and physical health (βs = -.068 to -.134), parental support and connection (βs = -.059 to -.100), and religiosity and spirituality (βs = -.053 to -.100) significantly forecasted decreases in many AUD indicators over 6 years; whereas risky behavior and violence, psychological illness, self-esteem, and economic deprivation did not. (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, City and Regional Planning

Transportation infrastructure has generated a wide-ranging socioeconomic impact on society. Evaluating this impact is crucial for transportation planning and policymaking. This dissertation contributes to socioeconomic impact assessments of transportation infrastructure by developing an analytical framework based on a set of quantitative approaches, including structural equation modeling, machine learning, and network analysis. The objective is to provide a systematical and holistic examination of transportation infrastructure's effects on city development. Transportation infrastructures examined in this study include high-speed rail (HSR), highways, and aviation systems. City development involves multiple aspects: ranging from economic growth to urban amenities, from individual city development to city interactions. This dissertation consists of three essays. The first essay untangled the influence mechanism through which transportation infrastructure affects city attractiveness using structural equation modeling. The second essay compared the relative significance of multimodal transportation infrastructure in shaping city attractiveness using machine learning models. The third essay investigated the network effects of transportation infrastructure on human mobility and city interaction by network analysis. The results uncover that transportation infrastructure increases city attractiveness through its role in stimulating the economy and increasing amenity accessibility. Despite economic growth, amenities such as housing, education, and technology also play a significant role in enhancing city attractiveness. The analysis also shows that HSR has a higher importance in predicting city attractiveness than highways and aviation, particularly during the rapid development period of HSR from 2008 to 2018 in China. Moreover, the impact of transportation infrastructure on city attractiveness demonstrates a threshold effect, which is consistent with the law of dimin (open full item for complete abstract)

MS, University of Cincinnati, 2024, Engineering and Applied Science: Computer Science

Asthma is a widespread chronic respiratory disease which is poorly understood despite significant research efforts into its etiology. Some of the difficulty involved is due to its heterogeneous nature and the wide variety of factors involved. Developing a predictive model for asthma risk would help us to better model and predict asthma and develop better treatments and protocols for its management. Bayesian networks are a useful tool that have been use to learn relationships embedded in an observed dataset, but causality cannot be inferred from any relationships learned in this way. Bayesian networks also are sensitive to the composition of the given dataset, and relationships present in only subsets of the data may not be identified. We propose a methodology that integrates structural equation modeling with Bayesian networks to facilitate the development of predictive models of asthma to incorporate causal information based on domain knowledge. We also utilize clustering to identify and model unique subsets of the population with potentially unique relationships of various factors relevant towards asthma. We have applied this approach with NHANES data to model the risk of asthma in subpopulations of residents of the United States. We have also validated several steps in our methodology to demonstrate its effectiveness for developing predictive models for asthma risk in specific subpopulations. We believe that this methodology can be extended to many real world complex processes with heterogeneous phenotypes to create robust models and find new insights.

Doctor of Philosophy, The Ohio State University, 2023, Educational Studies

As mobile learning (m-learning) becomes increasingly prevalent in education, it is recognized for its potential to enhance the overall quality of teaching and learning. Despite the many benefits, m-learning apps often experience low retention rates, which directly impede learners' benefit from using them and cause a waste of resources in app design, development, and maintenance. To investigate the critical factors influencing learners' acceptance of m-learning outside the classroom, this study introduced a novel model, the Mobile Learning Acceptance Determination (mLAD) Model, based on the Technology Acceptance Model and the updated DeLone and McLean Information System Success Model. Through the mLAD model, the study identified the critical app quality factors that influence learners' acceptance of m-learning. The moderating effects of the type of m-learning apps on learners' acceptance of m-learning were also revealed. An online questionnaire named the m-Learning Acceptance Questionnaire (mLAQ) was developed and disseminated through Amazon Mechanical Turk. A total of seven hundred forty-seven adult learners in the U.S. participated in the study. The descriptive statistical results of the examined factors revealed that m-learning apps available in the market demonstrate high mobility and content quality. Still, their interactivity and service quality could be improved. Furthermore, the results of the structural equation modeling analysis indicated that learners' two beliefs, perceived usefulness, and perceived ease of use, are the two essential determinants of learners' intention to use m-learning apps outside the classroom. Quality factors, such as content quality, interface design, mobility, and service quality, are the antecedents of learners' m-learning acceptance, given that they significantly and directly influence perceived usefulness and ease of use and indirectly impact learners' intention to use m-learning apps through learners' two beliefs. Through (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Social Work

Background: Approximately 50% of individuals seeking treatment for substance use and mental health conditions in behavioral healthcare settings have a lifetime history of TBI affecting their ability to engage in behavioral health treatment. Identifying lifetime history of TBI using validated screening methods can optimize interventions for these individuals, however, TBI screening adoption has failed in these settings. Drawing on the Theory of Planned Behavior and Diffusion of Innovations Theory, this explanatory sequential mixed methods study aimed to improve our understanding about how provider characteristics (attitudes, subjective norms, perceived behavioral control (PBC), intentions), innovation-level factors (acceptability, feasibility, appropriateness), and contextual determinants affect TBI screening adoption in behavioral healthcare settings. Methods: In Phase I, 215 behavioral health providers in the United States completed a training introducing the OSU TBI-ID, followed by a web-based survey assessing attitudes, PBC, subjective norms, and intentions to screen for TBI (Time 1). After one-month, providers completed a second survey assessing the number of TBI screens conducted, and the acceptability, feasibility, and appropriateness of TBI screening (Time 2). Data were analyzed using structural equation modelling with logistic regressions (SEM) and logistic regression with moderation effects. Results informed development of a qualitative interview guide. In Phase II, 20 providers from Phase I participated in interviews to build upon the quantitative results. Data were analyzed thematically and integrated with the quantitative results. Barriers to adoption were also identified and linked to constructs from the Consolidated Framework for Implementation Research (CFIR). Results: Approximately 25% of providers adopted TBI screening, which was driven by motivations to trial the innovation. SEM demonstrated that more favorable attitudes toward TBI screening were (open full item for complete abstract)

PHD, Kent State University, 2021, College of Education, Health and Human Services / School of Foundations, Leadership and Administration

This study examined the relationship between cumulative participation in high-impact practices (HIP) and post-graduation college outcomes using the Educational Longitudinal Study of 2002 (ELS) dataset. The methodological approach proposed and tested by Leite et al., 2019 incorporated the ability to account for self-selection using propensity score (PS) analysis with a multiple-group structural equation model (SEM) design, thereby allowing examination of differences between students who participated in two or more HIPs and those who did not. Results offered evidence of benefit to perceived importance of postsecondary education in preparation for adult life from participation in two or more HIP experiences as a main effect. Additionally, positive and statistically significant differences in perceived importance of postsecondary education in preparation for adult life were identified for female students, students from low SES backgrounds, and students who are members of minoritized racial/ethnic populations. Conversely, no statistically significant main effect in perceived learning and challenge in professional experiences was identified. Similarly, there were no statistically significant differences in perceived learning and challenge in professional experiences identified across groups who participated in two or more HIP experiences and those who did not.

Doctor of Philosophy, The Ohio State University, 2021, Educational Studies

Online learning is one of the fastest growing trends in education. A practical problem faced by instructional designers and online instructors is how to design an interactive learning activity that benefits content mastery without adding technological barriers. The online discussion forum provides quick solutions because it is usually ready for use in mainstream online learning systems and affords peer interaction and online community building in a flexible manner. This dissertation sets the study site to asynchronous argumentative discussion, a type of online forum activity that minimizes the need of communication immediacy and maximizes the quality of communication. Asynchronous argumentative discussions can foster not only purposeful social interaction among online learners but also higher-order cognitive processing of learning content. Previous studies show promising results that learners engage in more cognitive elaboration and acquire argumentation knowledge when the discussion process is well facilitated. However, challenges exist due to the nature of asynchronous communication, the heavy load on reading and writing, and the declined participation before reaching learning objectives. This study applied self-regulated learning theory to explore the possible benefit of using self-regulatory strategies for asynchronous argumentative discussions. Specifically, the study examines how goals, writing, responding, and reflection strategies may influence students' participation performance from both quantity and quality aspects. The four research questions of the study are: (1) How do students set goals for asynchronous argumentative discussions? (2) Can goals predict students' participation quantity? (3) Can goals predict students' use of self-regulatory strategies (i.e., writing, responding, reflection strategies)? (4) What are the relationships between goals, self-regulatory strategies, and post quality? This study recruited 203 college students as participa (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2021, Educational Studies

This dissertation is a collection of three papers. The first is a conceptual paper, followed by two data analysis papers. All three papers examine the connection between structural equation models and regression models, and how one may better learn, research and apply structural equation models when structural equation models are thought of as regression models. Each paper contains unique contributions. In the first paper, I focus on conceptual issues related to estimating structural equation models (SEMs) as Bayesian multilevel regression models. I review prevailing views on the equivalence of the two model classes (SEM and multilevel regression), and show how a Bayesian approach allows for the unity of both model classes. Adopting a Bayesian approach introduces additional considerations for estimating SEMs which I review. Additionally, I lay out linear regression model specifications that are directly equivalent to commonplace SEMs. Finally, the paper ends with a discussion of open issues in SEMs that a Bayesian multilevel regression approach to SEMs more readily solves. The goal of the second paper is to frame structural equation models (SEMs) as Bayesian multilevel regression models using the example of a unidimensional confirmation factor model. Framing SEMs as Bayesian regression models provides an alternative approach to understanding SEMs that can improve model transparency and enhance innovation during modeling. For demonstration, I analyze six indicators of living standards data from 101 countries. I show how the unidimensional confirmatory factor analysis (CFA) with congeneric indicators is a nonlinear multilevel regression model. I fit this model using Bayesian estimation and conduct model diagnostics from the regression perspective. The model diagnostics identify misspecification that standard SEM misfit statistics are unable to detect and I extend the congeneric model to accommodate the unique features of the data under study. I also provide exte (open full item for complete abstract)

Doctor of Business Administration, Cleveland State University, 2021, Monte Ahuja College of Business

This research aims to shed light on the impact of various antecedents on sustainable supply chain management (SSCM) practices and strategies and their indirect effect on the triple bottom line (TBL) performance. This dissertation presents a comprehensive meta-analysis of correlations to articulate the relationships between institutional pressures and the most researched and implemented SSCM practices. The meta-analysis aggregates the results of 42 studies, 23,147 relationships analyzed based on 8,412 observations. Further, building on a solid theoretical background, findings from extant literature, and the meta-analytic investigation results, we collect data from companies that operate in the United States to understand various aspects of SSCM. Two structural models are studied by analyzing the primary data. The first model explores the effect of isomorphic institutional pressures on symbolic and substantive sustainability strategies, as well as those pressures' indirect effect on the triple bottom line (TBL). The second model offers a unique perspective on the detrimental effects of barriers on supply chain sustainable collaboration (SCSC) and the indirect effect of barriers on the TBL in the United States. Based on the empirical results, this study proposes recommendations to policymakers, practitioners, and various stakeholder groups to transition from reactive or compliant sustainability practices to techniques and approaches that create value for the company and its stakeholders through the commitment to society and the environment employing sustainable operations.

Doctor of Philosophy in Urban Studies and Public Affairs, Cleveland State University, 2021, Maxine Goodman Levin College of Urban Affairs

Individuals' residential location strongly affects their personal access to opportunity, such as obtaining sufficient public goods and services. In addition, the neighborhood environment shapes the outcomes of their children when they reach adulthood. One explanation for these neighborhood effects on children is social capital. This study reconceptualizes social capital based on Pierre Bourdieu's Capital theory (1984; 2011) to resolve unexplained gaps in existing social capital theory and aims to analyze empirically the impact of various forms of neighborhood social capital in childhood on adult outcomes. This study categorizes social capital into two types: relation-based social capital (relationships within a neighborhood) and descriptive neighborhood social capital (the neighborhood location and its resources). This research quantitatively measures these two types of childhood social capital and examines its effects on adult outcomes, showing how a lack of cumulative resources creates unequal access to opportunities. This study uses Structural Equation Modeling (SEM) and data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to determine the role neighborhood social capital plays in unequal access to neighborhood resources. This approach shows both direct and indirect effects of each form of neighborhood social capital on adult outcomes. Also, how childhood neighborhood social capital mitigate or promote its effects on adult outcomes. Results indicate that a lack of cumulative resources creates unequal access to opportunities. It also shows the ways in which childhood neighborhood attachment acts as a mediator of that relationship. Even if residents have access to neighborhood resources, the impact of neighborhood social capital can vary depending on whether they experience relationships within a neighborhood or not. This research contributes to the literature in two ways, by showing: 1) how the embeddedness of social capital create (open full item for complete abstract)

Doctor of Philosophy (PhD), Ohio University, 2022, Clinical Psychology (Arts and Sciences)

Social anxiety disorder (SAD) is associated with diffuse impairment and constitutes a substantial public health burden. To better understand how social anxiety develops, it is crucial to identify how risk factors contribute to social anxiety. Anxiety sensitivity social concerns (ASSC), defined as the fear of publicly observable anxiety symptoms, and fear of negative evaluation (FNE), defined as distress arising from concerns about negative judgment, are risk factors that may amplify anxiety following social stressors. However, it is unclear how ASSC and FNE influence acute anxiety following stressors in naturalistic settings. In the current study, the impact of ASSC and FNE on anxious arousal (panic symptoms) and anxious apprehension (worry symptoms) following stressors was examined in a sample of community adults (N = 83; M age = 29.66 years, SD = 12.49, 59.0% female) who completed questionnaires five times per day over a two-week period. Dynamic structural equation modeling was used to examine predictors of overall levels of anxiety as well as anxiety following social and nonsocial stressors. ASSC interacted with the presence of social stressors, such that ASSC positively predicted anxious arousal following social stressors. FNE interacted with the presence of nonsocial stressors to predict anxious arousal and anxious apprehension, such that FNE positively predicted anxiety following nonsocial stressors. These findings suggest ASSC may specifically amplify anxious arousal following social stressors, whereas FNE may broadly amplify anxiety following nonsocial stressors.

Doctor of Philosophy, The Ohio State University, 2021, Educational Studies

Mediation analysis occupies a unique place in the social science literature. It allows researchers to go beyond testing whether an independent variable has an effect on a dependent variable; rather, it allows the researcher to examine the process or mechanism that enables that effect to occur. In its simplest form, a mediation model tests the effect that an independent variable, X, has on a dependent variable, Y, through its effect on an intervening variable, M. In this way, it provides a more complete and nuanced description of the relationship between X and Y. Researchers working within the field of education are often required to work with data that are not independent due to how the data are typically structured. The lack of independence inherent in the majority of educational data is caused by the multilevel structure of observations (e.g., students nested within classrooms, teachers nested within schools, and/or longitudinal observations, to name a few). This requires researchers to adjust their methods of analysis in order to properly model this lack of independence at the lowest level of the hierarchy. The estimation of mediated relationships becomes complicated when working in a multilevel context. Traditional approaches to multilevel modeling (MLM) can be used to model some mediated relationships, but it carries multiple limitations. A latent variable approach using multilevel structural equation modeling (MSEM) overcomes many of the limitations found in the MLM approach. This Monte Carlo simulation study examined the performance of MSEM when estimating two specific types of multilevel mediated relationships (the 2-1-1 and 2-1-2 mediation design configurations). Simulations were performed to investigate how well MSEM was able to estimate the indirect effect of X on Y through M in these two configurations under various ICCs, indirect effect sizes, number of clusters, and levels of cluster size imbalance. Model performance was assessed by mode (open full item for complete abstract)

Bachelor of Science (BS), Ohio University, 2021, Biological Sciences

1. Offspring growth, and condition are positively correlated with fledging success, recruitment, adult aerobic capacity, and flight performance, and are affected by environmental conditions and parental care behavior. If offspring face tradeoffs between growth and condition when adverse environmental conditions such as low food availability and presence of parasites, physiological processes including activation of the Hypothalamic-Pituitary-Adrenal axis can prioritize allocation of resources toward survival at a cost to growth or condition. However, parents may alter behaviors to mitigate the impacts of poor environmental conditions on their young. 2. We monitored 69 active Eastern Bluebird nests over two years in Athens County, Ohio to determine how environmental conditions including food availability and parasites influence parental care behaviors and nestling condition. We used general linear mixed models to investigate direct effects of environmental conditions and parental care on offspring physiology, growth and hematocrit. We also used piecewise structural equation modeling (SEM) to evaluate the direct effects of the environment on offspring and indirect effects of the environment on offspring via parental care behaviors. 3. None of the environmental conditions or parental care variables we measured were correlated to nestling corticosterone levels. 4. Our piecewise SEM indicated that parents had greater provisioning and nest attendance in habitats with greater arthropod biomass and larger amounts of small prey items but did not alter behavior in response to parasitism by blow flies. While parents altered provisioning rate and nest attendance in response to the environment, parental care behaviors were not correlated with offspring condition, thus behavioral responses did not override the direct effects of environmental conditions on offspring growth and hematocrit. 5. Our work highlights the importance of the environment on offspring developme (open full item for complete abstract)

MS, University of Cincinnati, 2020, Engineering and Applied Science: Civil Engineering

The research aims to evaluate the behavior of high strength structural steel (HS3) gravity columns (with yield stresses 80 ksi and greater) at elevated temperature in an effort to promote the more widespread use of this material in the United States and to evaluate the potential benefits of HS3 gravity columns over conventional steel gravity columns. Steel structures may be highly susceptible to fire. This research investigates the buckling capacity and critical temperature of HS3 gravity columns at elevated temperatures using the finite element package, ABAQUS. Finally, a case study is done to evaluate a potential material saving in using the HS3 gravity column over conventional or mild steel columns. A detailed parametric study was conducted on two wide-flange sections, W12x58 and W14x90, at elevated temperature of up to 700 oC for three high yield strengths of 550 MPa (80 ksi), 690 MPa (100 ksi), and 890 MPa (130 ksi). The numerical analysis accounted for column slenderness, uniform temperature, local and global geometric imperfections, and residual stresses. The load-carrying capacity of HS3 columns was compared with 350 MPa (50 ksi) steel columns to access its effectiveness over mild steel gravity columns. Applicability of AISC current equation for elevated temperature as well as the equations for ambient temperature was also evaluated for HS3 gravity columns. It is found that HS3 gravity columns show high load carrying capacity in comparison to mild steel gravity for all slenderness at every temperature under consideration. The temperature resistance of HS3 gravity columns is also compared with the temperature resistance of mild steel gravity columns. It is found that HS3 columns of higher slenderness are more resistant to the higher temperature in comparison to the corresponding mild steel columns. However, stub columns of both, HS3 and mild steel showed equivalent temperature resistance. Failure temperature of HS3 gravity columns was also investigated to (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2020, Educational Studies

Researchers almost unequivocally agree that school leadership matters because school leaders occupy formal positions within highly bureaucratic systems and have a vast influence on the organization of schools. Because of the elusive and complex nature of leadership, however, it has been challenging to identify how principals become effective and through which mechanisms they impact teaching and learning. Since 1980, researchers have conceptualized 14 leadership models. One of those models is instructional leadership, which is generally defined as the school leader's ability to collectively and strategically utilize her or his influence to improve the core technology of schools—teaching and learning. There is an additional consensus among researchers that the effects of instructional leadership on student achievement are indirect and that various other mediating factors exist. Therefore, within educational research there remains a desire to develop and test theoretical frameworks to decipher the black box of instructional leadership. This study was designed to address this mission by developing a complex theoretical model to examine how instructional leadership influences instructional quality, which is directly associated with student achievement. More specifically, this study investigated the interrelations between instructional leadership, teachers' perception of school climate, teacher collaboration, and instructional support by using a large-scale, complex survey data known as the Teacher and Learning International Survey (TALIS) 2018. Multi-level structural equation modeling (SEM) was used to analyze data from a nationally representative sample of 164 principals and 2548 teachers who instruct in grades seven through nine. The study results show that teachers' perception of school climate was a statistically significant predictor of school climate and that school climate was positively and significantly associated with instructional support. However, inst (open full item for complete abstract)