Structure and measurement of depression in youths: applying item response theory to clinical data

Abstract

Our goals in this article were to use item response theory (IRT) to assess the relation of depressive symptoms to the underlying dimension of depression and to demonstrate how IRT-based measurement strategies can yield more reliable data about depression severity than conventional symptom counts. Participants were 3,403 children and adolescents from 12 contributing clinical and nonclinical samples; all participants had received the Kiddie Schedule of Affective Disorders and Schizophrenia for School-Aged Children. Results revealed that some symptoms reflected higher levels of depression and were more discriminating than others. Furthermore, use of IRT-based information about symptom severity and discriminability in the measurement of depression severity was shown to reduce measurement error and increase measurement fidelity.

Figure 1

Distributions (probability density functions) of the contributing data sets on the latent depression variable.

Figure 2

Standard error of measurement and Fisher information curves for all contributing studies.

Figure 3

Hypothetical item response curves, depicting symptoms with low vs. high severity and low vs. high discriminability (1 = symptom is absent, 2 = symptom is present at a subclinical level, and 3 = symptom is present at a clinical level)

Figure 4

Item response curves for each symptom, where 1 = symptom is absent, 2 = symptom is present at a subclinical level, and 3 = symptom is present at a clinical level.

Figure 5

SEM curves for four indices of depression severity, as a function of the latent depression variable.

Figure 6

Histograms of latent depression levels at each level of KSADS-based symptom count index of depression