My research concerns the language of Alzheimer's patients. As the disease progresses, their language becomes more concrete and less abstract - they seem to 'lose' their abstract vocabulary more quickly. Tracking that change over the course of the disease might have clinical benefits.
I have identified a number of factors that measure (to an accuracy of about 85%) the relative concreteness of nouns, within an SPSS binary logistic regression (BLR) model, comprising a constant and four independent variables. The BLR model produces a 'score' for each individual noun: low or negative for abstract nouns, higher and positive for concrete nouns. The objective is not simply to classify the nouns as abstract or concrete, but rather to rank them along a gradient.
To obtain a 'concreteness rating' for a text, I simply calculate the mean of the scores of all the nouns in the text. Although this has given good results in testing, it has been suggested that this is not a legitimate application of BLR (my knowledge of which has been gleaned from YouTube videos).
So - is there a fundamental flaw in my method? And if so, what might be an alternative?
Any help and advice would be very gratefully received.