MATCHING THE LIMITS OF CLINICAL INFERENCE TO THE LIMITS OF QUANTITATIVE METHODS - A FORMAL APPEAL TO PRACTICE WHAT WE CONSISTENTLY PREACH

Valid clinical inferences may be said to emanate directly from both su bstantive and quantitative considerations. To the extent that either c omponent houses deficiencies, clinical inferences will be undermined. The quantitative literature has reiterated the need to strive for homo geneity in the ''objective-analysis-inference'' chain. However, in spi te of its importance to valid clinical inference, this reminder has be en overlooked with alarming frequency. This paper outlines three of th e most common cases where quantitative errors have undermined data-bas ed inferences. These cases include precomputational data aggregation, data residualization of one form or another, and post hoc significance testing. In each instance, errors may be corrected so as to redress v iolation of the homogeneity of quantitative research components. Solut ions in each case are discussed and illustrated with reference to publ ished and unpublished examples. It is suggested that deployment of the se solutions will enhance the quality of quantitative information and thus, the quality of clinical inferences.