Data, Methods and Theory in the Organizational Sciences
portes grátis
Data, Methods and Theory in the Organizational Sciences
A New Synthesis
Murphy, Kevin R.
Taylor & Francis Ltd
02/2022
384
Dura
Inglês
9780367857707
15 a 20 dias
453
Descrição não disponível.
Part 1: Data 1. Organizational Data and its Implications for Research and Theory 2. Using Other Peoples' Data: Implications of Reliance on Meta-analysis and Archival Data 3. Data sharing and Data Integrity 4. Using Data in Organizations Part 2: Methods 5. Evaluating data 6. Organic Data and the Design of Studies 7. Surviving the Statistical Arms Race Part 3: Theory 8. How do Theories in the Behavioral and Social Sciences Emerge, Develop and Decline?: The Evolution of Politics Perceptions Theory 9. The Data Revolution and the Interplay Between Theory and Data 10. Scholarly Course Corrections Needed to Advance Organizational Science: Field Tests of Theory-based Deductions are Long Overdue Part 4: Implications for Organizational Science 11. The Research Environment: Opportunities and Obstacles for Advancing Organizational Science 12. Training (and Retraining) in Data, Methods, and Theory in the Organizational Sciences 13. Rebuilding Relationships between Data, Method, and Theories: How the Scientific Method Can Help
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Null Hypothesis Significance Tests;Big Data;theory;SIOP;data;Organizational Science;data analysis;Organizational Science Research;data collection;Machine Learning Algorithms;organizational sciences;Counterproductive Work Behaviors;research methods;Meta-analytic Datasets;Traditional NHST;organizational psychology;Data Generation Process;I/O psychology;Open Science Movement;Society for Industrial and Organizational Psychology;Organic Dataset;organizations;Supervised Machine Learning;organizational frontiers;Open Science Practices;SIOP Organizational Frontiers Series;Validity Threats;SIOP Organizational Frontiers;Wearable Sensors;Random Forests;organizational research;Evidence Based Practice Recommendations;Kevin Murphy;Voluntary Turnover;Open Science;Stem Field;Meta-analytic Structural Equation Modeling;Algorithmic Errors;OLS Regression;Nontenure Track Faculty
Part 1: Data 1. Organizational Data and its Implications for Research and Theory 2. Using Other Peoples' Data: Implications of Reliance on Meta-analysis and Archival Data 3. Data sharing and Data Integrity 4. Using Data in Organizations Part 2: Methods 5. Evaluating data 6. Organic Data and the Design of Studies 7. Surviving the Statistical Arms Race Part 3: Theory 8. How do Theories in the Behavioral and Social Sciences Emerge, Develop and Decline?: The Evolution of Politics Perceptions Theory 9. The Data Revolution and the Interplay Between Theory and Data 10. Scholarly Course Corrections Needed to Advance Organizational Science: Field Tests of Theory-based Deductions are Long Overdue Part 4: Implications for Organizational Science 11. The Research Environment: Opportunities and Obstacles for Advancing Organizational Science 12. Training (and Retraining) in Data, Methods, and Theory in the Organizational Sciences 13. Rebuilding Relationships between Data, Method, and Theories: How the Scientific Method Can Help
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Null Hypothesis Significance Tests;Big Data;theory;SIOP;data;Organizational Science;data analysis;Organizational Science Research;data collection;Machine Learning Algorithms;organizational sciences;Counterproductive Work Behaviors;research methods;Meta-analytic Datasets;Traditional NHST;organizational psychology;Data Generation Process;I/O psychology;Open Science Movement;Society for Industrial and Organizational Psychology;Organic Dataset;organizations;Supervised Machine Learning;organizational frontiers;Open Science Practices;SIOP Organizational Frontiers Series;Validity Threats;SIOP Organizational Frontiers;Wearable Sensors;Random Forests;organizational research;Evidence Based Practice Recommendations;Kevin Murphy;Voluntary Turnover;Open Science;Stem Field;Meta-analytic Structural Equation Modeling;Algorithmic Errors;OLS Regression;Nontenure Track Faculty
