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Related Experiment Video

Updated: Jul 8, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software

Andrew F Hayes¹, Li Cai

¹School of Communication, Ohio State University, Columbus 43210, USA. hayes.338@osu.edu

Behavior Research Methods

|January 11, 2008

Summary

Heteroskedasticity violates ordinary least squares (OLS) regression assumptions, biasing standard errors. Using heteroskedasticity-consistent standard error estimators is recommended for accurate statistical inference in OLS regression.

Related Experiment Videos

Last Updated: Jul 8, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Statistics
Econometrics
Data Analysis

Background:

Homoskedasticity is a key assumption in ordinary least squares (OLS) regression.
Violating this assumption can lead to biased covariance matrix estimators.
This bias affects the reliability of significance tests and confidence intervals in OLS regression.

Purpose of the Study:

To explain heteroskedasticity and its impact on OLS regression inference.
To introduce a family of heteroskedasticity-consistent standard error estimators.
To advocate for the routine use of these estimators in OLS hypothesis testing.

Main Methods:

Description of heteroskedasticity and its inferential consequences.
Discussion of heteroskedasticity-consistent standard error estimators.
Development of SPSS and SAS macros for practical implementation.

Main Results:

Heteroskedasticity leads to biased standard errors in OLS regression.
Heteroskedasticity-consistent standard errors provide more reliable inference.
Provided macros facilitate the application of these robust methods.

Conclusions:

Investigators should routinely employ heteroskedasticity-consistent standard errors for OLS regression.
These estimators ensure valid hypothesis testing and confidence intervals.
Implementation is simplified through provided SPSS and SAS macros.