Estimation of Cronbach’s alpha is extremely easy in SAS. It is done using the CORR procedure. The ALPHA option must be included on the CORR statement to request SAS to output estimates of alpha, and all the variables to be included in the alpha estimate should be listed on the VAR statement (or they can be referenced by a prefix, e.g., EngProb: ). An example of the syntax to produce estimates for the engineering problem-solving items in the engineering data is presented below. It is followed by a screenshot of the output produced by the ALPHA option.

proc corr data=engdata alpha;
   var EngProb:;
run;

As you can see in Figure 11.1 PROC CORR output requested by the ALPHA option, the ALPHA option produces a lot more than a single alpha estimate! The first table provides a raw and standardized estimate of Cronbach’s alpha. You should use the raw alpha if you are inputting your original data into the CORR procedure, and you should use the standardized alpha if you are inputting standardized versions of your variables (e.g., z-scores) into the CORR procedure. You would use standardized versions of your variables if some of the items in the scale contained different numbers of response options (e.g., three dichotomous items and five Likert-type 5-point items). In the current example, we input the unstandardized engineering items into the scale. Thus, we will interpret the raw alpha.

The second table provides item-total correlations and revised estimates of alpha based on removal of items. The item-total correlations are the correlations between each individual item and the total composite score computed as the average or sum of the items. We expect items to be highly correlated with the total score as it represents the construct the item is intended to measure. Items with low item-total correlations can be scrutinized for potential removal. The revised alpha estimates represent the Cronbach’s alpha if a particular item was removed from the scale. A revised alpha estimate that is larger than our alpha indicates that an item contributes unique variance that reduces our overall reliability. This unique variance can in fact be unwanted error associated with an item., In that case you might consider removing the item, or it could be construct-relevant variance that is not captured by the other items, in which case you would want to keep the item. Knowledge of the construct must be used to guide decisions about item removal. Finally, similar to our interpretation of the first table, we would interpret the raw or standardized versions of these estimates based on the format of our data.

The engineering problem-solving scale demonstrates good internal consistency. It has a strong alpha of .95. The item-total correlations range from .80 to .88, and none of the revised alphas exceeds the original estimate. Unless we had a content-based reason for removing an item (e.g., an item is related but does not directly reflect problem solving), we would recommend making no changes to this scale.