Troubleshooting
Problem
Bejamini & Hochberg introduced a simple method of handling the multiplicity problem in statistical testing that controls the false discovery rate rather than the familywise error rate. The reference is 1995, Journal of the Royal Statistical Society, Series B, Volume 57, No. 1, pp. 289-300. Does SPSS offer this method?
Resolving The Problem
The STATS PADJUST extension, available on the Extension Hub through Extensions>Extension Hub in the SPSS Statistics menus, offers both the Benjamini-Hochberg and Benjamini-Yekutieli methods for controlling the false discovery rate.
The following commands will also compute the desired results according to Benjamini & Hochberg's method. They assume that you have an SPSS file containing one case per p value, with a variable named p holding the p value or significance level of interest for each comparison. Make sure that there are no other cases in the data file, as the number of cases in the file is used to count the number of comparisons involved.
The primary result of interest is the variable test, which will have a value of 1 for all significant comparisons, and 0 for nonsignificant comparisons. These commands use a .05 level for the false discovery rate. You can change that by changing the value of q in the second COMPUTE command.
sort cases by p (a).
compute i=$casenum.
sort cases by i (d).
compute q=.05.
compute m=max(i,lag(m)).
compute crit=q*i/m.
compute test=(p le crit).
compute test=max(test,lag(test)).
execute.
formats i m test(f8.0) q (f8.2) crit(f8.6).
value labels test 1 'Significant' 0 'Not Significant'.
The primary result of interest is the variable test, which will have a value of 1 for all significant comparisons, and 0 for nonsignificant comparisons. These commands use a .05 level for the false discovery rate. You can change that by changing the value of q in the second COMPUTE command.
Related Information
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Historical Number
70073
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Document Information
Modified date:
11 May 2022
UID
swg21476447