McDonald's Omega in SAS

Many sources now recommend use of omega (as developed by McDonald, 1999) as opposed to alpha (Cronbach, 1951) to an estimate the reliability of a multi-item measurement scale.

In 2020, Hayes & Coutts (Communication Methods and Measures, 14(1), pp 1-24) provided macros to estimate omega in SPSS or SAS. However, at least as of SPSS28, SPSS has included an option to get omega, so the macro isn't needed. Does anyone know if there is a method to get omega in recent versions of SAS? It doesn't seem to exist as an option in PROC CORR. I've searched the documentation, and also googled it, but haven't found it.

(The need for this comes from the fact that as far as I know the macro requires the use of the raw data, and I would like to/need to enter the variance-covariance (or correlation) matrix.)

It would not be excessively difficult to get it by running a factor analysis and then calculating omega by hand, but I have to do it for many different scales, so it would be nice to simplify it as much as possible.

Not sure about all of the functions of that macro, but the first simple example call can be re-created from a correlation matrix (at least a SAS correlation dataset that includes the N, MEAN and STD of the variables also).

So first convert their example CSV file into a dataset and run it through the %OMEGA() macro

data blirt ;
  infile 'c:\downloads\omega\blirt8.csv' dsd truncover;
  input var1-var8 ;
run;
%omega(data=blirt,items=var1 var2 var3 var4 var5 var6 var7 var8)

to get the expected output:

This estimate of omega is based on the factor loadings of a forced
single-factor maximum-likelihood factor analysis usings SAS's built-in FACTOR procedure.

                        Reliability:
                               Omega

                               0.785


   Item means, standard deviations, and estimated loadings
               Mean           SD      Loading     Error Var

  VAR1        3.365        1.012        0.663         0.583
  VAR2        3.379        1.037        0.642         0.663
  VAR3        3.156        0.936        0.576         0.544
  VAR4        3.133        1.001        0.711         0.496
  VAR5        2.976        1.071        0.549         0.845
  VAR6        3.237        0.981        0.643         0.549
  VAR7        3.379        1.142        0.376         1.161
  VAR8        2.664        1.003        0.398         0.847

So now let's try to recreate it. So first make a correlation matrix. Then run that through PROC FACTOR. Then transpose the results. Calculate the loading and error terms. And sum them up to be able to calculate the RELIABILITY value.

%let data=blirt ;
%let items= var1 var2 var3 var4 var5 var6 var7 var8;
proc corr data=&data out=&data._corr noprint;
  var &items;
run;
proc factor data=&data._corr nfact=1 method=ml outstat=&data._factor noprint;
  var &items;
run;
proc transpose data=&data._factor out=result name=varname ;
  where _type_ in ('MEAN' 'STD' 'PATTERN');
  var &items;
  id _type_;
run;
data result;
  set result end=eof;
  loading=pattern*std;
  err = (1-(pattern*pattern))*std**2;
  lambda+loading;
  err_sum+err;
  if eof then reliab=(lambda*lambda)/(lambda*lambda+err_sum) ;
  if eof then put reliab=;
  keep varname mean std loading err reliab;
run;
proc print;
run;

Results

Obs    varname      MEAN       STD      loading      err       reliab

 1      var1      3.36493    1.01160    0.66329    0.58338     .
 2      var2      3.37915    1.03663    0.64167    0.66287     .
 3      var3      3.15640    0.93564    0.57578    0.54390     .
 4      var4      3.13270    1.00068    0.71053    0.49650     .
 5      var5      2.97630    1.07101    0.54944    0.84517     .
 6      var6      3.23697    0.98114    0.64293    0.54927     .
 7      var7      3.37915    1.14157    0.37648    1.16144     .
 8      var8      2.66351    1.00264    0.39808    0.84681    0.78504