Challenges and Opportunities in the Estimation of Dynamic Models

被引:10
|
作者
Xu, Ran [1 ]
DeShon, Richard P. [2 ]
Dishop, Christopher R. [3 ]
机构
[1] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA
[2] Michigan State Univ, Ind Org Psychol, E Lansing, MI 48824 USA
[3] Michigan State Univ, Org Psychol, E Lansing, MI 48824 USA
关键词
longitudinal; change; dynamics; lagged response variable; hierarchical linear models; multilevel models; estimation; structural equation modeling; bias; PANEL-DATA; EFFICIENT ESTIMATION; TIME; SPECIFICATION; BEHAVIORS; PARADOX;
D O I
10.1177/1094428119842638
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Interest in modeling longitudinal processes is increasing rapidly in organizational science. Organizational scholars often employ multilevel or hierarchical linear models (HLMs) to study such processes given that longitudinal data in organizational science typically consist of observations over a relatively small number of time intervals (T) nested within a relatively large number of units (N; e.g., people, teams, organizations). In this paper, we first distinguishchangeanddynamicsas common research foci when modeling longitudinal processes and then demonstrate that a unique set of inferential hazards exists when investigating change or dynamics using multilevel models. Specifically, multilevel models that include one or more time-lagged values of the dependent variable as predictors often result in substantially biased estimates of the model parameters, inflated Type I error rates, and ultimately inaccurate inference. Using Monte Carlo simulations, we investigate the bias and Type I error rates for the standard centered/uncentered hierarchical linear model (HLM) and compare them with two alternative estimation methods: the Bollen and Brand structural equation modeling (SEM) approach and the Arrelano and Bond generalized method of moments using instrumental variables (GMM-IV) approach. We find that the commonly applied hierarchical linear model performs poorly, whereas the SEM and GMM-IV approaches generally perform well, with the SEM approach yielding slightly better performance in small samples with large autoregressive effects. We recommend the Bollen and Brand SEM approach for general use when studying change or dynamics in organizational science.
引用
收藏
页码:595 / 619
页数:25
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