Establishing Causal Order in Longitudinal Studies Combining Binary and Continuous Dependent Variables
针对组织研究中混合二元结果和连续变量的纵向数据，提出定性短面板向量自回归模型，以识别因果顺序、处理潜变量和个体内相关性，并通过法国企业精英代理人的数据验证其有效性。
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.