An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data
Some of the most influential theories in organizational sciences explicitly describe a dynamic, multilevel process. Yet the inherent complexity of such theories makes them difficult to test. These theories often describe multiple subprocesses that interact reciprocally over time at different levels...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
SAGE PUBLICATIONS INC
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/78407 |
| _version_ | 1848763961984417792 |
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| author | Ballard, T. Palada, H. Griffin, Mark Neal, A. |
| author_facet | Ballard, T. Palada, H. Griffin, Mark Neal, A. |
| author_sort | Ballard, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Some of the most influential theories in organizational sciences explicitly describe a dynamic, multilevel process. Yet the inherent complexity of such theories makes them difficult to test. These theories often describe multiple subprocesses that interact reciprocally over time at different levels of analysis and over different time scales. Computational (i.e., mathematical) modeling is increasingly advocated as a method for developing and testing theories of this type. In organizational sciences, however, efforts that have been made to test models empirically are often indirect. We argue that the full potential of computational modeling as a tool for testing dynamic, multilevel theory is yet to be realized. In this article, we demonstrate an approach to testing dynamic, multilevel theory using computational modeling. The approach uses simulations to generate model predictions and Bayesian parameter estimation to fit models to empirical data and facilitate model comparisons. This approach enables a direct integration between theory, model, and data that we believe enables a more rigorous test of theory. |
| first_indexed | 2025-11-14T11:11:47Z |
| format | Journal Article |
| id | curtin-20.500.11937-78407 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:11:47Z |
| publishDate | 2019 |
| publisher | SAGE PUBLICATIONS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-784072020-06-15T03:50:01Z An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data Ballard, T. Palada, H. Griffin, Mark Neal, A. Social Sciences Psychology, Applied Management Psychology Business & Economics dynamic theory computational modeling multilevel research Bayesian parameter estimation self-regulation ORGANIZATIONAL ROUTINES INDIVIDUAL-DIFFERENCES SELF-REGULATION GOAL REVISION FORMAL MODEL PERFORMANCE SIMULATION MOTIVATION FRAMEWORK TIME Some of the most influential theories in organizational sciences explicitly describe a dynamic, multilevel process. Yet the inherent complexity of such theories makes them difficult to test. These theories often describe multiple subprocesses that interact reciprocally over time at different levels of analysis and over different time scales. Computational (i.e., mathematical) modeling is increasingly advocated as a method for developing and testing theories of this type. In organizational sciences, however, efforts that have been made to test models empirically are often indirect. We argue that the full potential of computational modeling as a tool for testing dynamic, multilevel theory is yet to be realized. In this article, we demonstrate an approach to testing dynamic, multilevel theory using computational modeling. The approach uses simulations to generate model predictions and Bayesian parameter estimation to fit models to empirical data and facilitate model comparisons. This approach enables a direct integration between theory, model, and data that we believe enables a more rigorous test of theory. 2019 Journal Article http://hdl.handle.net/20.500.11937/78407 10.1177/1094428119881209 English SAGE PUBLICATIONS INC fulltext |
| spellingShingle | Social Sciences Psychology, Applied Management Psychology Business & Economics dynamic theory computational modeling multilevel research Bayesian parameter estimation self-regulation ORGANIZATIONAL ROUTINES INDIVIDUAL-DIFFERENCES SELF-REGULATION GOAL REVISION FORMAL MODEL PERFORMANCE SIMULATION MOTIVATION FRAMEWORK TIME Ballard, T. Palada, H. Griffin, Mark Neal, A. An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data |
| title | An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data |
| title_full | An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data |
| title_fullStr | An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data |
| title_full_unstemmed | An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data |
| title_short | An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data |
| title_sort | integrated approach to testing dynamic, multilevel theory: using computational models to connect theory, model, and data |
| topic | Social Sciences Psychology, Applied Management Psychology Business & Economics dynamic theory computational modeling multilevel research Bayesian parameter estimation self-regulation ORGANIZATIONAL ROUTINES INDIVIDUAL-DIFFERENCES SELF-REGULATION GOAL REVISION FORMAL MODEL PERFORMANCE SIMULATION MOTIVATION FRAMEWORK TIME |
| url | http://hdl.handle.net/20.500.11937/78407 |