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A guide to improve your causal inferences from observational data.

Koen Raymaekers1,2, Koen Luyckx1,3, Philip Moons4,5,6

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Summary

Observational studies can approximate causality by tracking constructs over time, examining temporal effects, and separating within-person from between-person influences. The random intercepts cross-lagged panel model offers a statistical approach for this analysis.

Keywords:
Research methodscausalitynursing researchquantitativerepeated measures

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Area of Science:

  • Social Sciences
  • Psychology
  • Health Sciences

Background:

  • Establishing true causality is challenging in observational research.
  • Researchers often rely on observational data to explore causal relationships.

Purpose of the Study:

  • To outline an optimal three-step approach for answering causal questions using observational studies.
  • To demonstrate the utility of the random intercepts cross-lagged panel model (RI-CLPM) for causal inference.
  • To illustrate the practical application of RI-CLPM with a real-world example.

Main Methods:

  • Longitudinal data collection with repeated assessments of constructs over time.
  • Analysis of the temporal sequence of effects between variables.
  • Employing statistical methods that differentiate within-person and between-person effects.
  • Utilizing the random intercepts cross-lagged panel model (RI-CLPM).

Main Results:

  • The RI-CLPM is presented as a viable statistical technique for causal inference within observational studies.
  • The study provides a practical demonstration of RI-CLPM implementation.
  • The example highlights the relationship between loneliness and quality of life in adolescents with congenital heart disease.

Conclusions:

  • While true causality remains elusive in observational research, a structured approach enhances causal inference.
  • The RI-CLPM is a valuable tool for analyzing longitudinal data to understand dynamic relationships.
  • This methodology can be applied to various research areas, including adolescent health and well-being.