Following factor analysis, one usually computes factor scores for use in subsequent analyses. These scores can be computed in multiple ways, each with different features. Below I explore such ways so that the reader can make a better choice for him or herself as to which way to compute them.
Below are 5 "non-refined" or simple methods for computing factor scores
1. Sum or average raw scores (including negative scores, if that's what was produced) corresponding to all items that load on a factor
2. Sum or average raw scores corresponding to all items that load above a certain cutoff point (e.g., .4 or above)
4. Calculate a weighted sum or average of raw scores corresponding to all items that load on a factor or only those that load above a certain cutoff point, using the factor loading as the weight.
5. Calculate a sum, average, or weighted sum or average using the standardized scores - either for all items or those above a certain cutoff point. This involves standardizing raw scores to the same mean and standard deviation. This is most often used when standard deviations vary greatly across the items.
The above five scores are frequently used because of simplicity's sake - the choice as to which to use is guided more by the researcher's decisions than hard science. One additional method that is worth mentioning, however, is the use of regression scores. This is considered a "refined" method, meaning linear combination is used to calculate factor scores versus simple addition. Scores are calculated by weighting the raw scores by their regression coefficients and then standardizing the scores to a mean of 0. These scores can easily be obtained using SPSS or SAS and are often used because these popular programs can calculate them without much additional work needed from the researcher. Researchers often believe that this method produces scores that have greater validity than those produced by "unrefined" methods.
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