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. 2010 Aug 19;19(3):142–155. doi: 10.1002/mpr.308

Revising the Cannabis Use Disorders Identification Test (CUDIT) by means of Item Response Theory

Beatrice Annaheim 1,✉, Thomas J Scotto 2, Gerhard Gmel 1,3
PMCID: PMC6878503  PMID: 20812291

Abstract

Cannabis use among adolescents and young adults has become a major public health challenge. Several European countries are currently developing short screening instruments to identify ‘problematic’ forms of cannabis use in general population surveys. One such instrument is the Cannabis Use Disorders Identification Test (CUDIT), a 10‐item questionnaire based on the Alcohol Use Disorders Identification Test. Previous research found that some CUDIT items did not perform well psychometrically. In the interests of improving the psychometric properties of the CUDIT, this study replaces the poorly performing items with new items that specifically address cannabis use.

Analyses are based on a sub‐sample of 558 recent cannabis users from a representative population sample of 5722 individuals (aged 13–32) who were surveyed in the 2007 Swiss Cannabis Monitoring Study. Four new items were added to the original CUDIT. Psychometric properties of all 14 items, as well as the dimensionality of the supplemented CUDIT were then examined using Item Response Theory.

Results indicate the unidimensionality of CUDIT and an improvement in its psychometric performance when three original items (usual hours being stoned; injuries; guilt) are replaced by new ones (motives for using cannabis; missing out leisure time activities; difficulties at work/school). However, improvements were limited to cannabis users with a high problem score. For epidemiological purposes, any further revision of CUDIT should therefore include a greater number of ‘easier’ items. Copyright © 2010 John Wiley & Sons, Ltd.

Keywords: cannabis, CUDIT, screening, cannabis abuse, Item Response Theory

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Acknowledgements

The Swiss Cannabis Monitoring Study is commissioned and financed by the Swiss Federal Office of Public Health (SFOPH; contract number 01.001316). The following institutions and researchers contribute to the study: Swiss Institute for the Prevention of Alcohol and Drug Problems (SIPA), Lausanne (G. Gmel, B. Annaheim); Research Institute for Public Health and Addiction (ISGF/RIPHA), Zurich (A. Uchtenhagen, M. Schaub); Institute of Social and Preventive Medicine (IUMSP), Lausanne (F. Dubois‐Arber, S. Arnaud, J. P. Gervasoni); Institute of Criminology and Criminal Law (ICDP) (M. Killias, J. Vuille); Lausanne; Institut fuer Begleit‐ und Sozialforschung (IBSF), Zurich (M. Mueller). The SFOPH approved the publication of the current investigation conducted as part of the Swiss Cannabis Monitoring Study.

Table A1.

Residual correlation and residual variance (diagonal); CUDIT, supplemented

Supplemented CUDIT, 14 items
Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10 Item 11 Item 12 Item 13 Item 14
 1 frequency of use 0.316 – – – – – – – – – – – – –
 2 usual hours being stoned −0.086 0.836 – – – – – – – – – – – –
 3 stoned for six or more hours 0.044 0.291 0.535 – – – – – – – – – – –
 4 not able to stop 0.015 −0.140 −0.080 0.402 – – – – – – – – – –
 5 failed to do what expected −0.174 −0.061 −0.057 −0.046 0.548 – – – – – – – – –
 6 morning use 0.151 −0.095 0.017 0.086 −0.155 0.503 – – – – – – – –
 7 guilt/remorse −0.093 −0.073 −0.098 0.136 −0.017 −0.154 0.703 – – – – – – –
 8 memory problems −0.046 −0.031 −0.012 −0.008 0.115 −0.109 0.014 0.419 – – – – – –
 9 injuries −0.003 −0.091 −0.059 −0.166 −0.006 −0.129 0.045 0.090 0.895 – – – – –
10 concerned others 0.005 0.128 0.065 0.062 −0.135 −0.003 0.052 −0.094 0.042 0.652 – – – –
11 neglected social environment −0.089 −0.150 0.055 −0.013 0.149 −0.070 0.094 −0.013 0.154 −0.040 0.721 – – –
12 missed activities −0.119 −0.033 0.016 −0.116 0.142 −0.147 0.045 0.062 0.083 −0.079 −0.039 0.613 – –
13 difficulties at school/work −0.127 −0.025 −0.135 −0.119 0.161 −0.160 0.002 0.097 0.038 0.011 0.086 0.098 0.494 –
14 motives using cannabis 0.144 −0.135 −0.133 0.064 −0.185 0.041 0.069 −0.065 −0.115 0.011 −0.028 −0.039 −0.079 0.416
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Note: n = 558; estimator = weighted least square, mean‐ and variance‐adjusted (WLSMV; Mplus); bold typeface: residual correlation > 0.20, considered as local dependence (Reeve et al., 2007).

Table A2.

Model fit and internal consistency of CUDIT; unidimensional, bidimensional and tridimensional models according to exploratory factor analyses

Unidimensional Bidimensional Tridimensional
CUDIT, original: 10 items
Goodness of fit
 Comparative Fit Index (CFI) 0.927 0.957 0.988
 Root Mean Square Error of Approximation (RMSEA) 0.090 0.077 0.047
 Chi‐square 137.714, df = 25, p < 00.000 86.235, df = 20, p < 00.000 33.739, df = 15, p = 00.0037
Internal consistency (Cronbach's α)
 First factor 0.759 0.719 0.639
 Second factor – 0.579 0.579
 Third factor – – 0.535
CUDIT, supplemented: 14 items
Goodness of fit
 Comparative Fit Index (CFI) 0.913 0.955 0.991
 Root Mean Square Error of Approximation (RMSEA) 0.079 0.060 0.028
 Chi‐square 204.389, df = 46, p < 0.000 123.790, df = 41, p < 0.000 51.736, df = 36, p = 0.0433
Internal consistency (Cronbach's α)
 First factor 0.805 0.739 0.579
 Second factor – 0.685 0.701
 Third factor – – 0.685
CUDIT, revised: 10 items
Goodness of fit
 Comparative Fit Index (CFI) 0.935 0.992 –
 Root Mean Square Error of Approximation (RMSEA) 0.091 0.034 –
 Chi‐square 140.876, df = 25, p < 0.000 34.641, df = 21, p < 0.0309 –
Internal consistency (Cronbach's α)
 First factor 0.800 0.703 –
 Second factor – 0.662 –
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Note: n = 558; estimator = weighted least square, mean‐ and variance‐adjusted (WLSMV; Mplus); CUDIT, original: bidimensional model: Items 1, 4, 5, 6, 7, 8, 9, 10 (factor one), Items 2, 3 (factor two); tridimensional model: Items 1, 4, 6, 10 (factor one), Items 2, 3 (factor two), Items 5, 7, 8, 9 (factor three); CUDIT, supplemented: bidimensional model: Items 1, 2, 3, 4, 6, 10, 14 (factor one), Items 5, 7, 8, 9, 11, 12, 13 (factor two); tridimensional model: Items 2, 3 (factor one), Items 1, 4, 6, 10, 14 (factor two), Items 5, 7, 8, 9, 11, 12, 13 (factor three); CUDIT, revised: unidimensional model: Items 1, 3, 4, 5, 6, 8, 10, 12, 13, 14; bidimensional model: Items 1, 5, 8, 12 (factor one), Items 3, 4, 6, 10, 13, 14 (factor two); tridimensional model: impossible to estimate the model, no convergence achieved (iterations = 200 000).

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