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Median01:08

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Besides mean, the median is a widely used measure of central tendency. Typically, median is defined as the central or middle value of a data set, measured by arranging the data elements in an increasing or decreasing order. Since this middle value is not affected by the precise numerical values of the outliers or fluctuations, it is insensitive to them. Hence, in cases where a data set may have outliers or the extreme values are not known, the median is a better measure of the central tendency...
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Related Experiment Video

Updated: May 7, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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

Published on: September 11, 2021

Robust mediation analysis based on median regression.

Ying Yuan1, David P Mackinnon2

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center.

Psychological Methods
|October 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces robust mediation analysis using median regression, offering a more accurate and powerful alternative to standard methods when data assumptions are violated. The approach enhances the validity of mediation analysis in psychology and social sciences.

Related Experiment Videos

Last Updated: May 7, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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

Published on: September 11, 2021

Area of Science:

  • Psychology
  • Social Sciences
  • Statistics

Background:

  • Standard mediation analysis methods often rely on assumptions of normality and homoscedasticity.
  • These assumptions are frequently unmet in real-world psychological and social science data, potentially compromising analysis validity.
  • Existing methods lack robustness to common data issues like heavy-tailed, skewed, contaminated, or heteroscedastic distributions.

Purpose of the Study:

  • To propose a robust mediation analysis method addressing limitations of standard approaches.
  • To enhance the accuracy and power of mediation analysis under non-ideal data conditions.
  • To extend robust mediation analysis to multilevel data structures.

Main Methods:

  • Developed a novel robust mediation analysis technique utilizing median regression.
  • Extended the robust median regression approach to multilevel mediation analysis.
  • Employed simulation studies to evaluate the performance of the proposed methods.

Main Results:

  • The proposed robust mediation analysis demonstrated superior efficiency and power compared to standard methods under various distributional violations.
  • Simulations confirmed the effectiveness of the robust method for multilevel mediation analysis, outperforming traditional approaches.
  • The method was successfully applied to real-world data from a job seeker program.

Conclusions:

  • Robust mediation analysis based on median regression offers a more reliable approach when standard assumptions are violated.
  • The extended multilevel robust mediation analysis provides a valuable tool for complex data structures in social sciences.
  • This methodology improves the validity and power of mediation analyses in applied research settings.