Self-reported symptoms of cannabis use disorder: Psychometric testing and validation

Abstract

Cannabis use disorder (CUD) is commonly assessed using self-report items based on its symptoms as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM) that are scored on a dichotomous scale (yes/no) to indicate symptom presence; however, scoring items on a dichotomy may result in relatively limited sensitivity for research. Thus, we developed a 13-item measure of CUD, the Self-Reported Symptoms of CUD (SRSCUD), based on the 11 symptoms described in the DSM-5 that is scored on a 4-point response scale indicating degree of severity. In the present study, we conduct an initial evaluation of the psychometric properties of the SRSCUD. Past-month cannabis users (N = 2,077) were recruited from nine universities in nine states throughout the U.S. Each item of the SRSCUD loaded saliently onto a single factor of CUD symptoms in both exploratory and confirmatory factor analyses. The SRSCUD was strongly correlated with measures of cannabis use, dependence severity, and cannabis-related problems. In addition, the SRSCUD was moderately positively correlated with a well-known risk factor for CUD (coping motives) and moderately negatively correlated with a well-known protective factor for CUD (protective behavioral strategies). These correlations mostly remained statistically significant while controlling for CUD symptom count and diagnosis using dichotomized SRSCUD items. Receiver operator characteristic curve analyses revealed that the SRSCUD had excellent sensitivity/specificity for predicting probable CUD. Although more research evaluating performance of the SRSCUD compared to a clinical diagnosis is needed, we found preliminary evidence for construct validity of this measure. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Figure 1.

Scree plot of the eigenvalues of the exploratory factor analysis of the Self-Reported Symptoms of Cannabis Use Disorder and eigenvalues of the accompanying parallel analysis.