SavedAdd your journal

Journals / İstanbul Ticaret Üniversitesi Girişimcilik Dergisi / 2026 / Cilt: 9 Sayı: 20

The Causality Fallacy in Mediation Analyses: A Methodological Review of Cross-Sectional Designs

ARACILIK ANALİZLERİNDE NEDENSELLİK YANILGISI: KESİTSEL TASARIMLAR ÜZERİNE YÖNTEMSEL BİR İNCELEME

Burak Nedim Aktaş(BEYKOZ ÜNİVERSİTESİ)0000-0003-3011-4706
Pages
192–208
Publication Date
2026-05-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The article highlights the need to place careful limits on causal interpretations in cross-sectional mediation studies and emphasizes the importance of adopting stronger methodological designs and more cautious reporting language.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This article examines the limitations of cross-sectional mediation models, which are widely used in social science research, with respect to causal claims.
📊
Key Findings & Data:In addition, the advantages and limitations of alternative approaches, such as time-lagged designs, longitudinal models, experimental and quasi-experimental designs, and multilevel and multi-source measurement strategies, are evaluated.
💡
Conclusion & Impact:The article highlights the need to place careful limits on causal interpretations in cross-sectional mediation studies and emphasizes the importance of adopting stronger methodological designs and more cautious reporting language.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kesitsel Tasarım · Aracılık Modeli · Nedensel Çıkarım

Abstract

This article examines the limitations of cross-sectional mediation models, which are widely used in social science research, with respect to causal claims. First, the conceptual distinction between correlation and causation is clarified, and the conditions under which correlations cannot be taken as evidence of causality are discussed. The article then addresses the main methodological limitations of cross-sectional designs, including common method bias, reverse causality, the lack of temporal precedence, and unmeasured confounders. In particular, it emphasizes that the X→M→Y mechanism implied in mediation models requires temporal ordering and therefore that mediation estimates derived from cross-sectional data may produce substantial bias. In addition, the advantages and limitations of alternative approaches, such as time-lagged designs, longitudinal models, experimental and quasi-experimental designs, and multilevel and multi-source measurement strategies, are evaluated. The article highlights the need to place careful limits on causal interpretations in cross-sectional mediation studies and emphasizes the importance of adopting stronger methodological designs and more cautious reporting language.

Özet

Bu makalede, sosyal bilim araştırmalarında yaygın biçimde kullanılan kesitsel (cross-sectional) aracılık (mediation) modellerinin nedensellik iddiaları bakımından taşıdığı sınırlılıklar incelenmektedir. Öncelikle korelasyon ile nedensellik arasındaki kavramsal fark açıklanmış ve korelasyonların hangi koşullarda nedensellik kanıtı sunamayacağı tartışılmıştır. Ardından kesitsel tasarımların ortak yöntem yanlılığı (common method bias), ters nedensellik (reverse causality), zamansal öncelik kurulamaması (lack of temporal precedence) ve ölçülmemiş karıştırıcı değişkenler (unmeasured confounders) gibi temel metodolojik sınırlılıkları ele alınmıştır. Özellikle aracılık modellerinde, X→M→Y biçimindeki mekanizmanın zamansal sıralama gerektirdiği vurgulanmış ve bu nedenle kesitsel verilerden elde edilen aracılık tahminlerinin ciddi yanlılıklar üretebileceği ortaya konmuştur. Ayrıca zaman gecikmeli tasarımlar (time-lagged designs), boylamsal (longitudinal) modeller, deneysel (experimental) ve yarı-deneysel (quasi-experimental) tasarımlar ile çok düzeyli (multilevel) ve çok kaynaklı (multi-source) ölçüm yaklaşımlarının avantajları ve sınırlılıkları değerlendirilmiştir. Makale, kesitsel aracılık çalışmalarında nedensel yorumların dikkatle sınırlandırılması gerektiğini ve daha güçlü metodolojik tasarımlar ile daha temkinli raporlama dilinin benimsenmesinin önemini vurgulamaktadır.

🔖

Cite This Article

Aktaş, B. N. (2026). The Causality Fallacy in Mediation Analyses: A Methodological Review of Cross-Sectional Designs. İstanbul Ticaret Üniversitesi Girişimcilik Dergisi, Cilt: 9 Sayı: 20, 192–208. https://doi.org/10.55830/tje.1938950

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (34)

  1. Baron, R. M. ve Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
  2. Bernal Turnes, P. ve Ernst, R. (2016). The use of longitudinal mediation models for testing causal effects and measuring direct and indirect effects. China-USA Business Review, 15(1), 1–9. https://doi.org/10.17265/1537-1514/2016.01.001
  3. Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In K. J. Klein ve S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 349–381). Jossey-Bass.
  4. Bozkurt, S., Celik, F. ve Gligor, D. (2026). Assessing the current state of mediation analysis in marketing research: Pitfalls and recommendations. Journal of Business Research, 206, Article 115934. https://doi.org/10.1016/j.jbusres.2025.115934
  5. Cole, D. A. ve Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577. https://doi.org/10.1037/0021-843X.112.4.558
  6. Coleman, R., Thorson, E. ve Chen, W. (2025). But is that mediator really the cause? An experiment manipulating persuasion knowledge as a mediator for how adaptive frames create positive responses to climate change news. Science Communication, 47(6), 828-858. https://doi.org/10.1177/10755470241310822
  7. Çelik, F. (2022). Davranışsal iletişim araştırmalarında aracılık testine genel bir bakış. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 49, 392–410. https://doi.org/10.52642/susbed.1158738
  8. Çizel, B., Selçuk, O. ve Atabay, E. (2020). Ortak yöntem yanlılığı üzerine sistematik bir yazın taraması. Anatolia: Turizm Araştırmaları Dergisi, 31(1), 7–18. https://doi.org/10.17123/atad.713552
  9. Fairchild, A. J. ve McDaniel, H. L. (2017). Best (but oft-forgotten) practices: Mediation analysis. The American Journal of Clinical Nutrition, 105(6), 1259–1271. https://doi.org/10.3945/ajcn.117.152546
  10. Hamaker, E. L., Kuiper, R. M. ve Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
  11. Imai, K., Keele, L. ve Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334. https://doi.org/10.1037/a0020761
  12. Judd, C. M. ve Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5(5), 602–619. https://doi.org/10.1177/0193841X8100500502
  13. Kock, F., Berbekova, A. ve Assaf, A. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330. https://doi.org/10.1016/j.tourman.2021.104330
  14. Ledermann, T., Rudaz, M. ve Fritz, M. S. (2025). On partial versus full mediation and the importance of effect sizes. Advances in Methods and Practices in Psychological Science, 8(3). https://doi.org/10.1177/25152459251355585
  15. Lucas, R. E. (2023). Why the cross-lagged panel model is almost never the right choice. Advances in Methods and Practices in Psychological Science, 6(1). https://doi.org/10.1177/25152459231158378
  16. MacKinnon, D. P., Fairchild, A. J. ve Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. https://doi.org/10.1146/annurev.psych.58.110405.085542
  17. Maier, C., Thatcher, J. B., Grover, V. ve Dwivedi, Y. K. (2023). Cross-sectional research: A critical perspective, use cases, and recommendations for IS research. International Journal of Information Management, 70, 102625. https://doi.org/10.1016/j.ijinfomgt.2023.102625
  18. Maxwell, S. E., Cole, D. A. ve Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46(5), 816–841. https://doi.org/10.1080/00273171.2011.606716
  19. Maxwell, S. E. ve Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23
  20. O’Laughlin, K. D., Martin, M. J. ve Ferrer, E. (2018). Cross-sectional analysis of longitudinal mediation processes. Multivariate Behavioral Research, 53(3), 375–402. https://doi.org/10.1080/00273171.2018.1454822
  21. Orth, U., Clark, D. A., Donnellan, M. B. ve Robins, R. W. (2021). Testing prospective effects in longitudinal research: Comparing seven competing cross-lagged models. Journal of Personality and Social Psychology, 120(4), 1013–1034. https://doi.org/10.1037/pspp0000358
  22. Park, S., Lee, N. ve Quintana, R. (2026). Causal decomposition analysis with time-varying mediators: Designing individualized interventions to reduce social disparities. Sociological Methods ve Research, 55(1), 51-84. https://doi.org/10.1177/00491241241264562
  23. Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161
  24. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y. ve Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  25. Podsakoff, P. M. ve Podsakoff, N. P. (2019). Experimental designs in management and leadership research: Strengths, limitations, and recommendations for improving publishability. The Leadership Quarterly, 30(1), 11–33. https://doi.org/10.1016/j.leaqua.2018.11.002
  26. Qin, X. (2024). An introduction to causal mediation analysis. Asia Pacific Education Review, 25, 703–717. https://doi.org/10.1007/s12564-024-09962-5
  27. Raudenbush, S. W. ve Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage.
  28. Rizzo, R. R. N., Cashin, A. G., Bagg, M. K., Gustin, S. M., Lee, H. ve McAuley, J. H. (2022). A systematic review of the reporting quality of observational studies that use mediation analyses. Prevention Science, 23, 1041–1052. https://doi.org/10.1007/s11121-022-01349-5
  29. Rohrer, J. M. (2024). Causal inference for psychologists who think that causal inference is not for them. Social and Personality Psychology Compass, 18(3), e12948. https://doi.org/10.1111/spc3.12948
  30. Schuler, M. S., Coffman, D. L., Stuart, E. A., Nguyen, T. Q., Vegetabile, B. ve McCaffrey, D. F. (2025). Practical challenges in mediation analysis: A guide for applied researchers. Health Services and Outcomes Research Methodology, 25(1), 57–84. https://doi.org/10.1007/s10742-024-00327-4
  31. Selig, J. P. ve Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6(2–3), 144–164. https://doi.org/10.1080/15427600902911247
  32. Shadish, W. R. Cook, T. D., ve Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
  33. Snijders, T. A. B., ve Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage.
  34. Stone-Romero, E. F. ve Rosopa, P. J. (2008). The relative validity of inferences about mediation as a function of research design characteristics. Organizational Research Methods, 11(2), 326–352. https://doi.org/10.1177/1094428107300342