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Related Concept Videos

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Cross-lagged network models do not prove causality and may be evaluated through triangulation.

Kimmo Sorjonen1, Bo Melin1, Gustav Nilsonne2

  • 1Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.

Acta Psychologica
|September 13, 2025
PubMed
Summary

The cross-lagged panel network (CLPN) model may produce spurious causal findings from correlational data. Researchers should use triangulation to validate CLPN results and avoid over-interpreting effects, especially in mental health research.

Keywords:
CausalityCross-lagged panel network (CLPN) modelsSpurious effectsSymptoms of depression and anxietyTriangulation

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Area of Science:

  • Psychometrics
  • Network Analysis
  • Causal Inference

Background:

  • The cross-lagged panel network (CLPN) model extends the cross-lagged panel model (CLPM) for analyzing numerous effects between variables across two time points.
  • Despite ongoing debate on inferring causality from correlational data, CLPN findings are frequently interpreted causally.

Purpose of the Study:

  • To investigate the potential for spurious findings from CLPN models when applied to non-experimental data.
  • To demonstrate the utility of triangulation as a method to mitigate false positives in CLPN analyses.

Main Methods:

  • Simulations were conducted to assess CLPN performance under conditions with and without direct causal effects.
  • A previously proposed triangulation method was applied to reanalyze existing data on depression and anxiety symptoms.

Main Results:

  • Simulations revealed that CLPN can detect cross-lagged effects even when no direct effects exist, indicating a risk of spurious findings.
  • Reanalysis using triangulation suggested that initially observed positive cross-lagged effects between depression and anxiety symptoms were likely spurious.

Conclusions:

  • CLPN findings from non-experimental data should not be interpreted causally due to the risk of false positives.
  • Triangulation serves as a valuable technique to scrutinize CLPN results and reduce the likelihood of erroneous causal inferences.