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date: 15 December 2019

Abstract and Keywords

Theories in organizational science place increased emphasis on dynamic relations among multiple theoretically relevant variables. Variants of the hierarchical linear model provide the primary approach used to evaluate dynamic processes in organizational science. This model is well suited to the analysis of univariate outcomes with recursive relations. However, many theories in organizational science posit cycles of influence among multiple variables. The analysis of multivariate, non-recursive data structures requires a new analytic approach. The vector autoregressive model is presented as a useful approach for the analysis of longitudinal data that may possess dynamic cycles of influence among multiple variables. The implementation and applicability of this data analytic approach to the modeling and evaluation of organizational science theories is demonstrated using multiple examples.

Keywords: Dynamics, multivariate, longitudinal, hierarchical linear model, vector autoregressive model

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