Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis

These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons... In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-persons associations. Finally, despite not being sufficient, within-persons data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions researchers ask, researchers should start with well-defined theoretical estimands, which in turn determine both study design and data analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Methods and Practices in Psychological Science SAGE

These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis

Download PDF
14 pages

Loading next page...
 
/lp/sage/these-are-not-the-effects-you-are-looking-for-causality-and-the-within-4hmjMB7P5a
Publisher
SAGE
Copyright
© The Author(s) 2023
ISSN
2515-2459
eISSN
2515-2467
DOI
10.1177/25152459221140842
Publisher site
See Article on Publisher Site

Abstract

In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-persons associations. Finally, despite not being sufficient, within-persons data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions researchers ask, researchers should start with well-defined theoretical estimands, which in turn determine both study design and data analysis.

Journal

Advances in Methods and Practices in Psychological Science – SAGE

Published: Feb 1, 2023

Keywords: causal inference; longitudinal data; fixed-effects model, cross-lagged panel model; dynamic panel model

References

  • Hedges, Mottes, and Baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance
    Alvarez-Vargas, D.; Braithwaite, D. W.; Lortie-Forgues, H.; Moore, M. M.; Castro, M.; Wan, S.; Martin, E. A.; Bailey, D. H.
  • Equivalent approaches to dealing with unobserved heterogeneity in cross-lagged panel models? Investigating the benefits and drawbacks of the latent curve model with structured residuals and the random intercept cross-lagged panel model
    Andersen, H. K.
  • marginaleffects: Marginal effects, marginal means, predictions, and contrasts
    Arel-Bundock, V.
  • Has the credibility of the social sciences been credibly destroyed? Reanalyzing the “Many Analysts, One Data Set” project
    Auspurg, K.; Brüderl, J.
  • Autoregressive Latent Trajectory (ALT) Models: A synthesis of two traditions
    Bollen, K. A.; Curran, P. J.
  • Assessing the total effect of time-varying predictors in prevention research
    Bray, B. C.; Almirall, D.; Zimmerman, R. S.; Lynam, D.; Murphy, S. A.
  • The three basic factor-analytic research designs—their interrelations and derivatives
    Cattell, R. B.
  • Constructing inverse probability weights for marginal structural models
    Cole, S. R.; Hernán, M. A.
  • A tutorial on Bollen and Brand’s approach to modeling dynamics while attending to dynamic panel bias
    Dishop, C. R.; DeShon, R. P.
  • Continuous time structural equation modeling with R package ctsem
    Driver, C. C.; Oud, J. H. L.; Voelkle, M. C.
  • Graphical Causal Models
    Elwert, F.; Morgan, S. L.
  • The future strikes back: Using future treatments to detect and reduce hidden bias
    Elwert, F.; Pfeffer, F. T.
  • Causal discovery and the problem of psychological interventions
    Eronen, M. I.
  • Causal inference and developmental psychology
    Foster, E. M.
  • The U-shaped relationship between complexity and usefulness: A commentary
    Foster, E. M.
  • The U Shape of Happiness Across the Life Course: Expanding the Discussion
    Galambos, N. L.; Krahn, H. J.; Johnson, M. D.; Lachman, M. E.
  • Forecasting causal effects of interventions versus predicting future outcomes
    Gische, C.; West, S. G.; Voelkle, M. C.
  • Investigating causal relations by econometric models and cross-spectral methods
    Granger, C. W. J.
  • The Taboo against explicit causal inference in nonexperimental psychology
    Grosz, M. P.; Rohrer, J. M.; Thoemmes, F.
  • Statistical modeling of the individual: Rationale and application of multivariate stationary time series analysis
    Hamaker, E. L.; Dolan, C. V.; Molenaar, P. C. M.
  • A critique of the cross-lagged panel model
    Hamaker, E. L.; Kuiper, R. M.; Grasman, R. P. P. P
  • No time like the present: Discovering the hidden dynamics in intensive longitudinal data
    Hamaker, E. L.; Wichers, M.
  • The fixed versus random effects debate and how it relates to centering in multilevel modeling
    Hamaker, E. L.; Muthén, B.
  • Recovering within-person dynamics from psychological time series
    Haslbeck, J. M. B.; Ryan, O.
  • Does obesity shorten life? The importance of well-defined interventions to answer causal questions
    Hernán, M. A.; Taubman, S. L.
  • Using Big Data to emulate a target trial when a randomized trial is not available
    Hernán, M. A.; Robins, J. M.
  • Causal inference: What If
    Hernán, M. A.; Robins, J. M.
  • Statistics and causal inference
    Holland, P. W.
  • When should we use unit fixed effects regression models for causal inference with longitudinal data?: Unit fixed effects models for causal inference
    Imai, K.; Kim, I. S.
  • The trait-state-error model for multiwave data
    Kenny, D. A.; Zautra, A.
  • The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology
    Krieger, N.; Smith, G. D.
  • Commentary regarding the section “Modelling the Effectiveness of Teaching Quality
    Lüdtke, O.; Robitzsch, A.
  • A comparison of different approaches for estimating cross-lagged effects from a causal inference perspective
    Lüdtke, O.; Robitzsch, A.
  • What is your estimand? Defining the target quantity connects statistical evidence to theory
    Lundberg, I.; Johnson, R.; Stewart, B. M.
  • Causal effects based on latent variable models
    Mayer, A.
  • The EffectLiteR approach for analyzing average and conditional effects
    Mayer, A.; Dietzfelbinger, L.; Rosseel, Y.; Steyer, R.
  • Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data
    McArdle, J. J.; Hamagami, F.; Collins, L. M.; Sayer, A. G.
  • Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations
    McNeish, D.; Kelley, K.
  • A general framework for comparing predictions and marginal effects across models
    Mize, T. D.; Doan, L.; Long, J. S.
  • A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever
    Molenaar, P. C. M.
  • The new person-specific paradigm in psychology
    Molenaar, P. C. M.; Campbell, C. G.
  • Within-person analysis in educational psychology: Importance and illustrations
    Murayama, K.; Goetz, T.; Malmberg, L.-E.; Pekrun, R.; Tanaka, A.; Martin, A. J.
  • Does obesity shorten life? Or is it the soda? On non-manipulable causes
    Pearl, J.
  • Marginal structural models and causal inference in epidemiology
    Robins, J. M.; Hernán, M. A.; Brumback, B.
  • Thinking clearly about correlations and causation: Graphical causal models for observational data
    Rohrer, J. M.
  • Causal Effects of Well-Being on Health: It’s Complicated
    Rohrer, J. M.; Lucas, R. E.
  • Unleash your inner stats sparkle with this very non-technical introduction to marginal effects
    Rohrer, J. M.
  • Precise answers to vague questions: Issues with interactions
    Rohrer, J. M.; Arslan, R. C.
  • That’s a lot to process! Pitfalls of popular path models
    Rohrer, J. M.; Hünermund, P.; Arslan, R. C.; Elson, M.
  • Estimating causal effects of treatments in randomized and nonrandomized studies
    Rubin, D. B.
  • Fixed effects individual slopes: Accounting and testing for heterogeneous effects in panel data or other multilevel models
    Rüttenauer, T.; Ludwig, V.
  • Why hypothesis testers should spend less time testing hypotheses
    Scheel, A. M.; Tiokhin, L.; Isager, P. M.; Lakens, D.
  • Experiments in the wild: Introducing the within-person encouragement design
    Schmiedek, F.; Neubauer, A. B.
  • Perceived control, effort, and academic performance: Interindividual, intraindividual, and multivariate time-series analyses
    Schmitz, B.; Skinner, E.
  • Many analysts, one data set: Making transparent how variations in analytic choices affect results
    Silberzahn, R.; Uhlmann, E. L.; Martin, D. P.; Anselmi, P.; Aust, F.; Awtrey, E. E.; Bahník, Š.; Bai, F.; Bannard, C.; Bonnier, E.; Carlsson, R.; Cheung, F.; Christensen, G.; Clay, R.; Craig, M. A.; Dalla Rosa, A.; Dam, L.; Evans, M. H.; Cervantes, I. F.; . . . Nosek, B. A
  • A primer on inverse probability of treatment weighting and marginal structural models
    Thoemmes, F.; Ong, A. D.
  • A unified framework of longitudinal models to examine reciprocal relations
    Usami, S.; Murayama, K.; Hamaker, E. L.
  • Within-person variability score-based causal inference: A two-step semiparametric estimation for joint effects of time-varying treatments
    Usami, S.
  • Outcome-wide longitudinal designs for causal inference: A new template for empirical studies
    VanderWeele, T. J.; Mathur, M. B.; Chen, Y.
  • The role of time in the quest for understanding psychological mechanisms
    Voelkle, M. C.; Gische, C.; Driver, C. C.; Lindenberger, U.
  • Campbell’s and Rubin’s perspectives on causal inference
    West, S. G.; Thoemmes, F.
  • Using the margins command to estimate and interpret adjusted predictions and marginal effects
    Williams, R.
  • Marginal structural models in clinical research: When and how to use them? Nephrology, Dialysis, Transplantation: Official Publication of the European Dialysis and Transplant Association
    Williamson, T.; Ravani, P.
  • The generalizability crisis
    Yarkoni, T.
  • From data to causes II: Comparing approaches to panel data analysis
    Zyphur, M. J.; Voelkle, M. C.; Tay, L.; Allison, P. D.; Preacher, K. J.; Zhang, Z.; Hamaker, E. L.; Shamsollahi, A.; Pierides, D. C.; Koval, P.; Diener, E.