From Data to Causes I

Building A General Cross-Lagged Panel Model (GCLM)

Bibliographic Data

ID23345488
AuthorsMichael J Zyphur (0000-0003-3237-7892, The University of Melbourne, corresponding author), P D Allison (0000-0002-0646-5242, University of Pennsylvania), Louis Tay (0000-0002-5522-4728, Department of Psychology, Purdue University, IN, USA), Manuel C Voelkle (0000-0001-5576-8103, Institut für Psychologie, Humboldt University Berlin, Berlin, Germany), Kristopher J Preacher (0000-0003-4099-3636, Vanderbilt University), Zhen Zhang (0000-0003-4317-9701, Arizona State University), Ellen L Hamaker (Utrecht University), Ali Shamsollahi (0000-0002-5156-3116, ESSEC Business School, Cergy-Pontoise, France), Dean Pierides (0000-0003-0876-9909, University of Stirling), Dean C Pierides (University of Stirling), Peter Koval (0000-0002-5461-2278, The University of Melbourne), Ed Diener (0000-0002-7418-4282, University of Utah)
Year2020
Volume23
Issue4
Pages651-687
Publication date2020-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueOrganizational Research Methods (JOURNAL)
Journal identifiersISSN: 1094-4281 • E-ISSN: 1552-7425
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/1094428119847278
OpenAlexW2945530942
LanguageEN
Citations received109
References cited190

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.

Causal inference · Econometrics · Economics · GCLC · GCLM · Inference · Panel data · Artificial Intelligence · Computer Science · Health disparities and outcomes · Intergenerational and Educational Inequality Studies · Psychological Well-being and Life Satisfaction

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