On the value of homogeneous constructs for construct validation, theory testing, and the description of psychopathology.
The authors argue for a significant shift in how clinical psychology researchers conduct construct validation and theory validation tests. They argue that sound theory and validation tests can best be conducted on measures of unidimensional or homogeneous constructs. Hierarchical organizations of such constructs are useful descriptively and theoretically, but higher order composites do not refer to definable psychological processes. Application of this perspective to the approach of the Diagnostic and Statistical Manual of Mental Disorders to describing psychopathology calls into doubt the traditional use of the syndromal approach, in which single scores reflect the presence of multidimensional disorders. For many forms of psychological dysfunction, this approach does not appear optimal and may need to be discarded. The authors note that their perspective represents a straightforward application of existing psychometric theory, they demonstrate the practical value of adopting this perspective, and they provide evidence that this shift is already under way among clinical researchers. Description in terms of homogeneous dimensions provides improved validity, utility, and parsimony. In contrast, the use of composite diagnoses can retard scientific progress and hamper clinicians' efforts to understand and treat dysfunction.
Smith
Gregory T
GT
Department of Psychology, University of Kentucky, Lexington, KY 40506-0044, USA. gsmith@email.uky.edu
McCarthy
Denis M
DM
Zapolski
Tamika C B
TC
D035843:Biomedical Research; D003661:Decision Support Techniques; D005163:Factor Analysis, Statistical; D006801:Humans; D001523:Mental Disorders / Q000175:diagnosis* / Q000523:psychology; D008960:Models, Psychological; D010552:Personality Assessment; D010554:Personality Disorders / Q000175:diagnosis / Q000523:psychology; D011582:Psychological Theory; D011586:Psychology, Clinical / Q000379:methods*; D011594:Psychometrics; D015203:Reproducibility of Results; D012107:Research Design