Establishing Causal Order in Longitudinal Studies Combining Binary and Continuous Dependent Variables. (October 2017)

• Record Type: Journal Article
• Authors: Kling, Gerhard; Harvey, Charles; Maclean, Mairi
• Is Part Of: Organizational research methods. Volume 20:Number 4(2017:Oct.)
• Page Start: 770
• Page End: 799
• Publication Date: 2017-10
• Subjects: Bayesian statistics -- binary dependent variables -- causality -- longitudinal research -- vector autoregression; Organization -- Research -- Methodology -- Periodicals; Organizational sociology -- Research -- Methodology -- Periodicals; Management -- Research -- Methodology -- Periodicals; 302.350721
• DOI: 10.1177/1094428115618760
• Languages: English
• ISSNs: 1094-4281

Abstract

Longitudinal studies with a mix of binary outcomes and continuous variables are common in organizational research. Selecting the dependent variable is often difficult due to conflicting theories and contradictory empirical studies. In addition, organizational researchers are confronted with methodological challenges posed by latent variables relating to observed binary outcomes and within-subject correlation. We draw on Dueker's qualitative vector autoregression (QVAR) and Lunn, Osorio, and Whittaker's multivariate probit model to develop a solution to these problems in the form of a qualitative short panel vector autoregression (QSP-VAR). The QSP-VAR combines binary and continuous variables into a single vector of dependent variables, making every variable endogenous a priori. The QSP-VAR identifies causal order, reveals within-subject correlation, and accounts for latent variables. Using a Bayesian approach, the QSP-VAR provides reliable inference for short time dimension longitudinal research. This is demonstrated through analysis of the durability of elite corporate agents, social networks, and firm performance in France. We provide our OpenBUGS code to enable implementation of the QSP-VAR by other researchers.