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

Gerhard Kling; Charles Harvey; Mairi Maclean

doi:10.1177/1094428115618760

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

Gerhard Kling^*, Charles Harvey, Mairi Maclean

^*Corresponding author for this work

Accountancy & Finance, Finance

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

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.

Original language	English
Pages (from-to)	770-799
Number of pages	30
Journal	Organizational Research Methods
Volume	20
Issue number	4
Early online date	30 Nov 2015
DOIs	https://doi.org/10.1177/1094428115618760
Publication status	Published - 1 Oct 2017

Bibliographical note

Acknowledgments
We wish to thank the Editor, James LeBreton, the Associate Editor, Brian Boyd, and two anonymous referees for their most helpful comments. Without their efforts, our qualitative short panel vector autoregression (QSP-VAR) would not have reached its full potential. We had the benefit of advice on our OpenBUGS code from Dave Lunn, one of the developers of WinBUGS/OpenBUGS, for which we are grateful. Sadly, when we sought advice from Michael Dueker, who devised the QVAR and whose work inspired our own, we learned that he had passed away on January 29, 2015. Dueker’s research has had a significant impact in economics, econometrics, and related disciplines. We dedicate this paper to him in recognition of his many pioneering achievements.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.

Keywords

Bayesian statistics
binary dependent variables
causality
longitudinal research
vector autoregression

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http://eprints.soas.ac.uk/21140/

Gerhard Kling
Person: Academic

Cite this

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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.",

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note = "Acknowledgments We wish to thank the Editor, James LeBreton, the Associate Editor, Brian Boyd, and two anonymous referees for their most helpful comments. Without their efforts, our qualitative short panel vector autoregression (QSP-VAR) would not have reached its full potential. We had the benefit of advice on our OpenBUGS code from Dave Lunn, one of the developers of WinBUGS/OpenBUGS, for which we are grateful. Sadly, when we sought advice from Michael Dueker, who devised the QVAR and whose work inspired our own, we learned that he had passed away on January 29, 2015. Dueker{\textquoteright}s research has had a significant impact in economics, econometrics, and related disciplines. We dedicate this paper to him in recognition of his many pioneering achievements. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.",

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N2 - 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.

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KW - vector autoregression

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

Abstract

Bibliographical note

Keywords

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Gerhard Kling

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