Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 23 for “"causal modeling"”.
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Causal Modeling of Organizational Commitment
… of organizational commitment by establishing a causal network among three individual characteristics--tenure, work motivation, and job satisfaction--two organizational/structural variables--decentralization and formalization,--and two job facets--the job characteristic model and job stress--as …
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Dynamic Causal Modeling Across Network Topologies
Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing strategy for a given cognitive task. The logical network topology of the model is specified by a combination of prior knowledge and statistical analysis of the neuro-imaging signals. Parameters of …
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Policy information and policymaking in governmental bureaucracies: A causal modeling of processes and impacts
… this study attempts to empirically investigate causal relationships among the factors involved in the ""impact stage"" of information utilization in bureaucratic decisionmaking. This study is conducted on the basis of a comprehensive conceptual framework, which combines past theories and/or …
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Causal Programming
Causality is central to scientific inquiry. There is broad agreement on the meaning of causal statements, such as “Smoking causes cancer”, or, “Applying pesticides affects crop yields”. However, formalizing the intuition underlying such statements and conducting rigorous inference is difficult in …
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Applying natural language models and causal models to project management systems
… work on creating an easy-to-use, extensible causal modeling framework, a Python package called CEModels. This package allows users to create causal inference models using input data. We tested this framework on project management data as well.
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Discovering Heterogeneous Causal Effects in Relational Data
… data. There is growing interest in deriving causal insights from such data which is inherently relational in nature. Causal inference in relational settings has to account for interference, where a unit's outcome may be influenced by the treatments or outcomes of other units. Despite recent …
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Growth Breaks: A Qualitative Study of Exceptional Growth
… This dissertation explores the challenges of causal modeling in economic development. It revisits pioneering attempts to estimate the economic policies that advance growth, using twenty-six years of additional data and different econometric modeling techniques. Levine and Renelt’s (1992) …
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A Confirmatory Multivariate Study of the Nature of Second Language Proficiency and Its Relationship to Learner Variables (Acquisition, Covariance, Analysis, Structural Equation Models, Lisrel, Foreign, Teaching English as A Foreign Language, Tefl)
… learner variables were investigated using causal modeling techniques. Regarding the nature of second language proficiency, a correlated traits hypothesis was tested against a second-order hypothesis. As for the relationships between proficiency and learner variables, four hypotheses were …
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Antecedents and Continuity of Compliance in Preschoolers
… of preschool compliance behavior through causal modeling utilizing a large and diverse longitudinal dataset from the NICHD Study of Early Child Care and Youth Development. We aimed to predict compliance and delay of gratification performance in children across 2, 3 and 4.5 years of age by …
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Does Cultural Heterogeneity Lead to Lower Levels of Regime Respect for Basic Human Rights?
… bivariate, linear multivariate regression, and causal modeling techniques to test whether higher levels of ethnolinguistic and religious diversity are associated with less regime respect for subsistence and security rights. The analysis reveals that higher levels of cultural diversity do appear …
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Three essays on online word-of-mouth and user behavior in online environments
… media. By combining vision-language models with causal inference techniques, it reveals that certain discrete emotions, such as anger and surprise, significantly boost engagement, while joy has no discernible effect. The second essay introduces emotional ambiguity, uncertainty in emotional …
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Effects of parental involvement on Mexican-American eighth grade students' academic achievement: a structural equations analysis
… of Mexican-American students. For this research, causal modeling (path analysis) was used to investigate the influence of parental involvement on overall academic achievement, and the reading, math, science, and social studies achievement on 1,714 eighth grade Mexican-American children. This …
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Brain Activation and Connectivity In Non-Disabled Multiple Sclerosis Patients
… the 2-back working memory task. Using dynamic causal modeling, we tested whether increased cognitive control recruitment is associated with alterations in connectivity in the working memory functional network. Patients exhibited similar network connectivity to that of control subjects when …
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Conceptual clarification of the structure of social support.
… A descriptive correlational design with a causal modeling approach was used to assess a four-stage conceptual framework: Help-Seeking in Adversity Model. Model predictions were that the individual's innate drive leads to self-appraisal and social comparison which are negatively associated …
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Development and Initial Validation of the African American Workplace Authenticity Scale
… thus far and has the potential to facilitate causal modeling in the area of workplace authenticity for Blacks with further validity evidence.
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Virtual reality visual feedback and its effect on brain excitability
… with improvement in clinical scores. Dynamic causal modeling (DCM) shows that interhemispheric coupling between the bilateral motor cortices tends to decrease after training and to negatively correlate with improvement in scores for clinical scales, and with the amount of re-lateralization. …
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Hybrid Causal Logic Methodology for Risk Assessment
… identification of initiating events, scenario modeling, quantification, uncertainty analysis, sensitivity analysis, importance ranking, and data analysis. Fault trees and event trees are widely used tools for risk scenario analysis in PRAs of technological systems. This methodology is most …
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