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Evaluating Response Shift in Statistical Mediation Analysis.

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Area of Science:

  • Psychology
  • Public Health
  • Biostatistics

Background:

  • Interventions often target mediators, variables influencing outcomes.
  • Self-report measures of mediators can be affected by response shift, altering their meaning post-intervention.
  • Previous research has not fully addressed how response shift impacts mediation analysis of intervention effects.

Purpose of the Study:

  • To investigate the impact of recalibration response shift on statistical mediation analysis.
  • To understand how response shift in mediators biases estimates of intervention effects.
  • To examine the influence of response shift on type I error and statistical power in mediation analyses.

Main Methods:

  • Review of response shift theory and detection methods, including tests of measurement invariance.
  • Utilized simulated datasets to model recalibration response shift in a mediator.
  • Analyzed the effects of simulated response shift on mediated effect estimation.

Main Results:

  • Recalibration response shift in the mediator can significantly bias estimates of the mediated effect.
  • The presence of response shift impacts the accuracy of type I error rates in mediation analysis.
  • Response shift can also affect the statistical power to detect true intervention effects.

Conclusions:

  • Ignoring recalibration response shift in self-report mediators can lead to inaccurate conclusions about intervention effectiveness.
  • Methodological adjustments are needed to account for response shift in mediation analyses.
  • Future research should consider response shift when evaluating interventions relying on self-report mediators.