From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)

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  • Michael J. Zyphur
    Department of Management & Marketing, Business & Economics, University of Melbourne, Melbourne, Australia
  • Paul D. Allison
    Department of Sociology, University of Pennsylvania, PA, USA
  • Louis Tay
    Department of Psychology, Purdue University, IN, USA
  • Manuel C. Voelkle
    Institut für Psychologie, Humboldt University Berlin, Berlin, Germany
  • Kristopher J. Preacher
    Department of Psychology & Human Development, Vanderbilt University, TN, USA
  • Zhen Zhang
    Department of Management, W. P. Carey School of Business, Arizona State University, AZ, USA
  • Ellen L. Hamaker
    Department of Methods and Statistics, Utrecht University, Netherlands
  • Ali Shamsollahi
    ESSEC Business School, Cergy-Pontoise, France
  • Dean C. Pierides
    Department. of Management Work and Organisation, University of Stirling, Stirling, UK
  • Peter Koval
    Department of Psychology, University of Melbourne, Melbourne, Australia
  • Ed Diener
    Department of Psychology, University of Utah, UT, USA

Description

<jats:p>This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs . We conclude with a discussion of issues surrounding causal inference.</jats:p>

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