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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

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Classical and causal inference approaches to statistical mediation analysis.

Manuel Ato García¹, Guillermo Vallejo Seco, Ester Ato Lozano

¹Universidad de Murcia.

|April 24, 2014

Summary

Mediation analysis in psychology is controversial due to ignored assumptions. Violating these, particularly with latent confounders, leads to overestimated mediation effects, necessitating causal inference integration.

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Area of Science:

Psychological Research Methods
Statistical Modeling
Causal Inference

Background:

Mediation analysis interpretation is debated in psychology.
Common statistical procedures often ignore crucial assumptions.
Potential violation of assumptions impacts effect estimation.

Purpose of the Study:

Summarize classical/SEM and causal inference/CI mediation analysis approaches.
Identify statistical assumptions for unbiased mediation effects.
Evaluate the impact of assumption violations on mediation estimation.

Main Methods:

Comparative summary of SEM and CI mediation approaches.
Simulation study to assess effects of assumption violations.
Focus on omitted variables and confounders.

Main Results:

Simulation revealed significant overestimation of mediation effects.
Latent confounders were identified as a key factor in overestimation.

Conclusions:

Recommend integrating causal inference with classical approaches.
CI approach generalizes SEM results and offers tools for assumption evaluation.
Comparison of software (R, SAS, SPSS, STATA, Mplus) for implementation.