Establishing causal order in longitudinal studies combining binary and continuous dependent variables

Kling, Gerhard; Harvey, Charles; Maclean, Mairi

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 (2005) qualitative vector autoregression (QVAR) and Lunn et al.’s (2014) 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.

Citation

Kling, G., Harvey, C., & Maclean, M. (2017). Establishing causal order in longitudinal studies combining binary and continuous dependent variables. Organizational Research Methods, 20(4), 770-799. https://doi.org/10.1177/1094428115618760

• Journal Article Type: Article
• Online Publication Date: Nov 30, 2015
• Publication Date: Oct 1, 2017
• Deposit Date: Oct 23, 2015
• Publicly Available Date: Jun 6, 2019
• Journal: Organizational Research Methods
• Print ISSN: 1094-4281
• Electronic ISSN: 1552-7425
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 4
• Pages: 770-799
• DOI: https://doi.org/10.1177/1094428115618760
• Keywords: Bayesian statistics, binary dependent variables, causality, longitudinal research, vector autoregression