SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients.
Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 - α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.
Human Sciences Division, Northern Ontario School of Medicine, Thunder Bay, ON, Canada, P7B 5E1. bweaver@lakeheadu.ca
Bruce
B
Weaver
Karl L
KL
Wuensch
D001827:Body Height; D001835:Body Weight; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D015233:Models, Statistical; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis; D012044:Regression Analysis; D012107:Research Design; D012129:Respiratory Function Tests; D012984:Software