Development of a New Instrument for Depression With Cognitive Diagnosis Models

Abstract

Most existing instruments for depression are developed based on classical test theory, factor analysis, or sometimes, item response theory, and focus on the accurate measurement of the severity of depressive disorder. Nevertheless, they tend to be less useful in supporting the decision based on ICD-10 or DSM-5 because of the lack of detailed information for symptoms. To gain rich and valid information at the symptom level, this article developed a depression test under the framework of cognitive diagnosis models (CDMs), referred to as CDMs-D. A total of 1,181 individuals were finally recruited and their responses were used to examine the psychometric properties of CDMs-D. After excluding poor items for statistical reasons (e.g., low discrimination, poor model-fit or having DIF), 56 items were included in the CDMs-D. The CDMs-D measures all ten symptom criteria for depression defined in ICD-10 and covers five domains of depression defined by Gibbons et al. (2012) . Comparing with the existing self-report measures (such as PHQ-9, SDS, CES-D and so on), a distinguishing feature of the CDMs-D is that it can provide both overall information about the severity of depressive disorder and the assessment information about specific symptoms, which could be useful for diagnostic and interventional purposes.

Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach

“…Information obtained from diagnostic assessments can be used to make a variety of inferences; in psychological settings, for instance, diagnostic assessments can aid in the measurement and identification of psychological disorders (e.g., de la Torre, van der Ark, & Rossi, 2018; Templin & Henson, 2006;Wang et al, 2019). In educational contexts, diagnostic assessments are routinely used to provide teachers with a detailed account of their students overall performance along with a comprehensive profile that outlines the student's strengths and weaknesses, as well as recommendations for improvement in areas of deficiency (Leighton & Gierl, 2007;Ravand & Baghaei, 2020).…”

Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach

“…The process of classifying individuals based on responses obtained from a diagnostic assessment is an invaluable aspect of research in the social, behavioral, and educational sciences. Information obtained from diagnostic assessments can be used to make a variety of inferences; in psychological settings, for instance, diagnostic assessments can aid in the measurement and identification of psychological disorders (e.g., [1][2][3]). In educational contexts, diagnostic assessments are routinely used to provide teachers with a detailed account of their students' overall performance along with a comprehensive profile that outlines the student's strengths and weaknesses, as well as recommendations for improvement in areas of deficiency [4,5].…”

Assessing the Performance of Diagnostic Classification Models in Small Sample Contexts with Different Estimation Methods

“…Owing to these advantages of DCMs, many applied researchers have used diagnostic classification modeling in such domain fields as education and clinical psychology (e.g., Jang et al, 2019;Ranjbaran et al, 2017;Wang et al, 2019). However, most DCM applications deal with large sample sizes.…”