Methods of analysis of large numbers of audiograms.
The analysis of large numbers of audiograms raises the question if and how we can reduce the amount of data without discarding essential information. The present paper compares two ways of data reduction: principal-component analysis and curve fitting. The methods are tested on the audiograms of a large family suffering from a dominant hereditary, progressive hearing loss, beginning in the high frequencies. It is shown that principal-component analysis rejects information on the shape of the audiogram, as do all methods generally referred to as factor analysis. The information concerned is essential for our understanding of the patient's ability to discriminate speech. Curve-fitting procedures are shown to be effective in data reduction.
D001299:Audiometry; D003201:Computers; D005163:Factor Analysis, Statistical; D006319:Hearing Loss, Sensorineural / Q000175:diagnosis; D006801:Humans; D013223:Statistics as Topic