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Related Experiment Video

Updated: Jan 20, 2026

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
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Using Quantile Regression to Estimate Intervention Effects Beyond the Mean.

Spyros Konstantopoulos1, Wei Li2, Shazia Miller3

  • 1Michigan State University, East Lansing, MI, USA.

Educational and Psychological Measurement
|September 7, 2019
PubMed
Summary

Quantile regression offers a powerful alternative to ordinary least squares regression for analyzing educational and social science data. This method reveals how predictors impact outcomes across the entire distribution, not just the average.

Keywords:
OLS regressionachievement gapfield experimentinstrumental variablesinterim assessmentsquantile regression

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Area of Science:

  • Social Sciences
  • Education Research
  • Statistics

Background:

  • Ordinary least squares (OLS) regression provides average effects, potentially masking differential impacts across outcome distributions.
  • Understanding predictor effects at various distribution points (e.g., tails) is crucial in social and educational research.
  • Existing methods may not fully capture the heterogeneity of treatment effects.

Purpose of the Study:

  • To define quantile regression and highlight its advantages over OLS regression.
  • To compare OLS and quantile regression methodologies.
  • To demonstrate the application of quantile regression in estimating intervention effects using empirical data.

Main Methods:

  • Definition and illustration of quantile regression principles.
  • Comparative analysis of OLS and quantile regression.
  • Application of quantile regression to estimate treatment effects in a large-scale educational experiment, including handling dropouts.

Main Results:

  • Quantile regression effectively estimates predictor effects across different quantiles of the outcome distribution.
  • The methodology proves useful for analyzing intervention effects in empirical settings.
  • Quantile regression provides a more nuanced understanding compared to OLS by examining effects at the lower and upper tails of the distribution.

Conclusions:

  • Quantile regression is a valuable statistical tool for social science and education research.
  • It offers a more comprehensive analysis by examining effects across the entire outcome distribution.
  • The method is particularly useful for understanding intervention impacts and predictor effects at various distribution levels.