Discriminant analysis: Fisher’s scores (like SPSS)Discriminant analysis: Fisher’s scores (like SPSS)
Statistics and AnalyticsRe: Discriminant analysis: Fisher’s scores (like SPSS)Detail:
Occasionally, R&D creates product category segments from a large number of questions using cluster analysis. The next step is to use stepwise linear discriminant analysis (LDA) to reduce the number of questions needed to accurately predict the respondent segment. From the output, we used to provide a set of "Fisher's Scores" (using the SPSS language) from the LDA model to research suppliers so that they could create an algorithm to classify respondents. (Let n=number of respondents, k=number of questions in the reduced LDA model, and m=number of segments). Using linear algebra, this required multiplying the n x k respondent data matrix times the k x m Fisher Score matrix (and summing over k plus the intercept) to calculate the LDA score for each respondent by segment. The segment with the highest score was assigned the respondent. Could you produce a similar Fisher's Score matrix (k x m, plus the intercept) for the m segments as part of your output?
