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SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients.

Bruce Weaver¹, Karl L Wuensch

¹Human Sciences Division, Northern Ontario School of Medicine, Thunder Bay, ON, Canada, P7B 5E1. bweaver@lakeheadu.ca

Behavior Research Methods

|January 25, 2013

Summary

This study details common statistical tests for Pearson correlations and regression coefficients using summary data. It provides SPSS and SAS programs for these analyses, enhancing statistical research accessibility.

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Area of Science:

Statistics
Biostatistics
Quantitative Psychology

Background:

Hypothesis testing for Pearson correlations and ordinary least squares (OLS) regression coefficients is crucial in statistical analysis.
Existing literature and resources often lack a comprehensive overview of common hypothesis testing procedures.
Many established statistical tests are not readily available in widely used software packages like SPSS and SAS.

Purpose of the Study:

To consolidate and describe the most common hypothesis testing procedures for Pearson correlations and OLS regression coefficients using summary data.
To provide practical SPSS and SAS programming code for implementing these statistical tests.
To offer methods for testing hypotheses about independent regression coefficients using both summary and raw data.

Main Methods:

Systematic review and description of established statistical procedures for hypothesis testing on correlation and regression coefficients.
Development and validation of SPSS and SAS code to execute these tests on summary data.
Implementation of Potthoff analysis for hypothesis testing on regression coefficients using raw data.

Main Results:

A comprehensive description of common hypothesis tests for Pearson correlations and OLS regression coefficients is presented.
Functional SPSS and SAS code is provided, enabling users to perform these tests and compute confidence intervals.
Both summary data and raw data (Potthoff analysis) methods for regression coefficient hypothesis testing are demonstrated.

Conclusions:

The article serves as a unified resource for common statistical tests on correlations and regression coefficients.
The provided code facilitates the application of these tests in SPSS and SAS, improving research efficiency.
For regression coefficient hypothesis testing, raw data analysis (Potthoff analysis) is recommended over summary data when available due to higher precision.