We collected data from university students in Tehran, Iran, in 2024. We recruited respondents through stratified random sampling technique. We contacted respondents through social media platforms such as Telegram, WhatsApp, and Facebook. This method is most effective when members of the population are difficult to reach, such as people with an addiction and given that cannabis use is illegal in Iran [14] .

We first identified a group of university students who were cannabis users, and after collecting data from them, we asked for help identifying other cannabis users. Participants were young university students in Tehran, Iran (ages 19 - 24). In total, we contacted 1350 students to encourage their participation in this cannabis consumption-related study. After communicating about the study and building trust among the participants, 541 students consented to participate in it. We informed them that their personal information would remain confidential due to the illegality of cannabis use in Iran.

Ethical considerations

Before participating, all participants signed the consent letter. This study uses the research ethics committee certificate code of the Najafabad branch of Islamic Azad University, Iran (IR.IAU.NAJAFABAD.REC.1403.097). Iran’s cannabis ban guarantees the confidentiality of participants’ personal information.

Inclusion criteria:

According to the fifth edition of the Diagnostic and Statistical Mental Health Analysis, Text Revision Criteria (DSM-5-TR), participants must have at least twelve months of experience with marijuana use. They must also avoid stimulant drugs other than cigarettes and marijuana. The participants must be between 18 and 25 years of age and have signed an ethical form indicating their consent.

Exclusion criteria:

Participants who require more advanced care than what an outpatient group can provide, such as those experiencing active mania or psychosis. Any psychiatric or physical condition may find it challenging to communicate effectively. The participant is concurrently receiving another medical or psychological intervention.

Researchers have extensively studied the CUDIT-R and found it to be a reliable and valid screening tool for identifying problematic cannabis use worldwide. It has demonstrated good internal consistency, concurrent validity, and discriminant validity among college students [4] [11] . The CUDIT-R consists of eight self-reported items assessing the past six months of cannabis consumption and consequences. We recorded the responses to the items on a five-point Likert scale from 0 to 4, then summed them to obtain a total score [5] .

First et al. (2016) [15] developed the SCID-5-CT, a structured clinical interview for DSM-5 disorders. It has been widely used in research and clinical settings for evaluation and diagnosis [16] ; making it a valuable tool for mental health professionals. Amirinia (2024) [1] used the Persian version of SCID-5-CT on 165 university students who used cannabis, demonstrating high reliability and validity in Iran. The mean and standard deviation (13.83 ± 5.03) were found to have a high omega coefficient and good Richardson Kadre (ω = 0.85; KR20 = 0.86). The results also showed enough stability in test-retest with a Kappa index of 0.66 (ICC = 0.66, 95 CI% 0.56, 0.76). We conducted 90-minute interviews with all participants who completed the CUDIT-R-Pr, using the Persian version of SCID-5-CT. As specified in the DSM-5, CUD diagnosis is defined as meeting at least two diagnostic criteria out of 11. “Mild” CUD corresponds to subjects meeting levels two or three. In comparison, “moderate” CUD refers to subjects meeting levels four or five, while “severe” CUD refers to subjects meeting levels six or more.

Two separate psychologists formed two groups, Group A (n = 271) and Group B (n = 270), and simultaneously interviewed participants in a free 90-minute session based on the DSM-5 criteria for CUD and the Persian version of SCID-5-CT. In the second step, we organized and coordinated a team of two psychologists (A and B) for the equivalence (parallel) method, as Nunnally (1994) [17] supposed, in two different clinical and social fields to standardize the CUDIT-R. Because a double review by independent reviewers is required to focus on many stages, such as literature search, review process, and quality assessment, to resolve disputes that may arise during the study inclusion process, we also included a third independent reviewer on our team ( Figure 1 ).

Boateng et al. (2018) [18] recommended a general framework to evaluate the psychometric properties of our proposed scale. We assessed the proposed CUDIT-R Persian version using the criteria below for validity and reliability. We conducted item response theory and ROC analysis in STATA 17 [19] , confirmatory factor analysis (CFA) in AMOS 29 [20] , and the remaining analyses in IBM SPSS 29 [21] .

Drost (2011) [22] defines face validity as a subjective judgment about the operationalization of a construct. In order to achieve the desired face validity, we revised the item wordings based on expert opinions. Initially, we translated CUDIT-R to Persian. Then, an English-speaking native speaker performed a back translation. In both stages, we received advice from five experts regarding the face validity of the items. After revising the item wordings to solve ambiguities and discussing the visual and qualitative content of the questionnaire, the authors approved the final Persian version of the instrument (Appendix).

1) Content Validity

Bollen (1989) [23] proposed numerous criteria that were used in this investigation. We ensured that 1) the questionnaire aimed to investigate recognized cannabis use; 2) the study’s target demographic comprised university students in Iran aged 19 to 24 who used cannabis; and 3) the final wording of the questionnaire items is available in Table 1 .

Table 1. Observed distribution of scores for each item of the CUDIT-R, n = 541.

NOTE. Values represent the number of participants with percent (n = 541). ^aResponse options: 0 = never, 1 = monthly or less, 2 = 2 - 4 times a month, 3 = 2 - 3 times a week, 4 = 4 or more times a week. ^bResponse options: 0 ≤ 1, 1 = 1 or 2, 2 = 3 or 4, 3 = 5 or 6, 4 = 7 or more. ^cResponse options: 0 = never, 1 = less than monthly, 2 = monthly, 3 = weekly, 4 = daily or almost daily. ^dResponse options: 0 = never, 2 = yes, but not in the past year, 4 = yes, during the past year.

The construct validity refers to how our translated CUDIT-R can detect cannabis users in a functioning and operational reality [22] . We conducted confirmatory factor analyses (CFA) to assess the internal consistency and dimensions of the CUDIT-R-Pr. For the CFA calibration sample, we used the principal components method with variable rotation (n = 541) to easily pull out one factor from eight CUDIT-R-Pr questions. We employed the Kaiser-Meyer-Olkin (KMO) method to verify the appropriateness of our sample technique for the analysis [24] [25] . According to Sofroniou & Hutcheson (1999) [26] indicated that KMO values exceeding 0.8 were deemed favorable for each item.

Ebadi et al. (2017) [24] stated reliability as the consistency of scores produced by an instrument, indicating that it yields the same measurements at a specific time or across a duration. Sofroniou & Hutcheson (1999) [26] identified two types of errors in an instrument: 1) systematic or biased errors, which arise in a consistent manner and are assessed through internal consistency, test-retest reliability, and inter-rater reliability; and 2) random errors, which are attributable to unpredictable factors.

1) Internal consistency

Terwee et al. (2007) [25] describe internal consistency as a method for assessing reliability, with Cronbach’s alpha serving as a measure of internal consistency that evaluates the extent of inter-correlation among scale items.

a) Cronbach’s alpha

Cortina (1993) [27] indicated that Cronbach’s alpha or McDonald’s omega can be used to assess the reliability of item-specific variance in a one-dimensional experiment, particularly when factor-analytic techniques confirm the experiment’s one-dimensionality. The CUDIT-R-Pr is an effective tool for measuring fundamental aspects such as low question count, multiple response choices, and uni-dimensionality. We attempted to conduct the homogeneity indicators as accurately as possible to avoid using Cronbach’s alpha as a consistency coefficient, which is not a valid measure

b) Split-half

The split-half approach, another method of test reliability, assumes several items to measure behavior and adjusts the correlation between the two halves of the test to obtain a reliability coefficient for the entire test [22] [23] . Therefore, we conduct these two half-tests concurrently. This allows us to control the effect of memory, which interferes with and limits the test-retest process [22] [23] .

Inter-rater reliability, or reproducibility, refers to a test’s time consistency from one measurement session to another, also known as test-retest reliability [22] . In this study, we randomly selected one hundred participants from group B after a month, and psychologist B reassessed them. In this way, the correlation between scores on identical tests at different times operationally defines the reliability of testing and retesting it. ICC is the most suitable and commonly used parameter to assess the reliability of continuous measures after a test-retest by one hundred participants [25] . This period is long enough to prevent recall, and the CUDIT-R is based on the lowest meaningful scale [28] . In other words, we set option 1 to “monthly or less”, making it an excellent point to consider while also ensuring that any changes are unlikely to have occurred. Indeed, we eliminated the individual exhibiting signs of cannabis withdrawal.

1) Inter-rater (observer) reliability

When different raters repeat a measurement under the same conditions and subjects, the degree of agreement is known as inter-rater reliability [29] . In this study, from the beginning, we divided the stages of data collection, implementation, and evaluation of participants between two psychologists: appraiser A for two hundred and seventy-one participants and appraiser B for two hundred and seventy participants. They kept the process a secret from each other until the results analysis was complete. As a result, the psychologist in Group B selected and reassessed one hundred of the two hundred and seventy-one participants. Finally, we used the intra-cluster correlation coefficient (ICC) to measure inter-rater reliability, which is the most suitable and commonly used parameter to assess the reliability of continuous measures [25] .

2) Agreement

The agreement and reliability parameters are different and focus on two different questions; the agreement parameter is “How good is the agreement between repeated measurements?”, which expresses measurement error and evaluates precisely how close the scores of repeated measurements are. However, the reliability question asks, “How reliable is the measurement?” [30] . The standard error of measurement (SEM) adequately expresses the measurement error [25] . The SEM equals the square root of the error variance of an ANOVA analysis, either including systematic differences (SEM_agreement) or excluding them (SEM_consistency) [30] .

Terwee (2007) [25] calculated the standard error of measurement (SEM) by taking the square root of the error variance of the ANOVA analysis, either including systematic differences (SEM_agreement) or excluding them (SEM_consistency). In this study, the SEM_agreement was measured using the SD √ (1-ICC) formula, which was measured for one hundred participants who were individually interviewed by two psychologists (n = 100). The SEM was converted into the slightest. The SEM was converted into the slightest detectable change (SDC) using the formula (1.96 √2 SEM), (1.96) because of the 95% confidence, and (2) because of the difference of two variances, which reflects the slightest within-person change in score that, with p < 0.05, can be interpreted as a real change in one individual (SDC_individual) [31] . We used the minimal important change (MIC) value and an anchor-based approach to analyze the smallest score difference in the area of interest that participants found useful.

Researchers define responsiveness as a questionnaire’s ability to detect clinically significant changes over time, even if they are minor, and test it by relating the SDC to the MIC [25] , recommended an anchor-based method to determine the MIC because distribution-based methods do not provide a good indication of the importance of the observed change [31] . Another adequate measure of responsiveness is the area under the receiver operating characteristics (ROC) curve (AUC) which is a measure of the questionnaire’s ability to differentiate participants who have or have not changed according to an external criterion [30] .

When we can assign qualitative meaning to quantitative scores, it means that the questionnaire has interpretability [30] . For interpretability, we provide the means and standard deviations of the participants’ scores before and after the test [12] . We analyzed the MIC using an anchor-based approach and decided to use the ability item (cannabis use rate) as an anchor question [31] . Students who performed significantly better on the ability item in the test showed a corresponding change. Based on changes in the CUDIT-R-Pr scores, the Pearson correlation coefficient confirmed the usefulness of the anchor question. The correlation coefficient (−0.21) should be considered a favorable anchor.

When measuring cannabis use, one important thing to think about is how sensitive the CUDIT-R-Pr is to changes in cannabis use at the lower and upper ends of the scale. If floor or ceiling effects are present, it’s likely that extreme items are missing at the lower or upper end of the scale, which means the scale doesn’t have a lot of content validity [32] . We assessed the presence of ceiling and floor effects based on the percentage of patients with the highest or lowest CUDIT-R-Pr scores, considering whether this was the case for 15% or more of the patients [25] .

Criteria validity refers to the degree of correspondence between a test measure and one or more external criteria, usually measured by their correlation [22] . Some researchers have described a gold standard based on criterion validity [25] , but related, constructed, and currently available standard CUD questionnaires are unavailable. We also opted to solely utilize the CUDIT-R-Pr, incorporating the DSM-5 criteria for CUD and calculated its sensitivity and specificity. Mean CUDIT-R-Pr scores significantly differed across the four DSM-5 diagnostic severity levels ( Figure 2 ).

1) Diagnostic test analysis

In order to compare the performance of CUDIT-R with other diagnostic tests, we applied the ROC curve to interpret the undersurface area (AUC) and determined the optimal cutting point, which is a critical component of these analyses. Over the past decade, significant research on CUDIT-R has focused on determining the sensitivity and specificity of this questionnaire, as well as determining its cutoff point, using AUC as a measure of discriminatory ability [4] - [7] [9] - [11] [33] . We determined the sensitivity and specificity of the CUDIT-R to distinguish between individuals who met the criteria for CUD as positive cases and those who did not as negative cases, based on the measurements made using the SCID-5-CT [11] [28] by STATA 17.0 [19] , We achieved a cutoff of ten and less for CUDIT-R-Pr to predict any DSM-5, based on the exact maximum correctly classified value (0.887) with high levels of sensitivity (0.96), specificity (0.69), and Youden value (0.65). Similarly, we achieved a cutoff of twelve and less for CUDIT-R-Pr to predict moderate DSM-5, based on the exact maximum Youden index (0.72) with high levels of sensitivity (0.82), specificity (0.89), and correctly classified (0.84) [11] .

2) Discriminant validity

We also examined the psychometric properties of individual CUDIT-R items through an item response theory (IRT) analysis [34] [35] . IRT is a statistical method that models the interaction between a subject’s ability and the difficulty of test items. It is particularly useful in developing and scoring multi-item scales [34] . IRT estimates a parameter for each item and participant, and can be used to measure underlying latent traits [35] . In addition to implementing IRT for CUDIT-R-Pr, we compared the results and outputs of IRT with similar research in CUDIT-R [4] [11] . Descriptions of items in the GR model are described by an alpha discrimination parameter (α) and a category threshold or location parameter (β) [11] .

The median (M) and mean standard deviation (SD) describe age, sex, the number of DSM-5 criteria, and the CUDIT-R-Pr item distributions, while the number and percentage (n%) are as follows.

We selected 541 single and undergraduate participants (ages 19 to 24), with a gender distribution of men (53.6%) and females (46.4%). One hundred seventy-two participants (21.6%) did not meet the DSM-5 criteria for CUD; 82 participants (15.2%) met two or three criteria for CUD (mild CUD); 124 participants (22.9%) met four or five criteria for CUD (moderate CUD); and 280 participants (40.3%) met six or more criteria for CUD, which was consistent with severe CUD ( Table 2 ).

Table 2. Distribution, means, and standard deviation of sample characteristics, n = 541.

Regarding the Persian translation of the CUDIT-R (CUDIT-R-Pr), the one-factor solution presented a satisfactory fit, in line with the initial validation of the scale in English. CUDIT-R-Pr scores increased with the severity of the DSM-5 diagnostic levels, with mean scores ranging from 8.80 to 18.86. The mean CUDIT-R-Pr scores significantly differed across the four DSM-5 diagnostic severity levels.

The mean and standard deviation numbers of the DSM-5 criteria were 2.82 and 1.18, respectively. The mean and standard deviation of the CUDIT-R-Pr total score were, respectively, 13.82 and 5.03 (SD = 9.05). The CUD rate varied depending on the instrument used. 70.4 percent met the DSM-5 criteria for CUD (n = 150); 78.4 percent of participants (n = 424) were screened positive based on the CUDIT-R-Pr with adequate ICC in our sample (KR20 = 0.86; ω = 0.85) ( Table 3 ).

Table 3. Descriptive statistics and reliability coefficient among study variables, n = 541.

NOTE. 0 = no diagnosis (0 - 1 symptoms), 2 = mild (2 - 3 symptoms), 3 = moderate (4 - 5 symptoms), and 4 = severe (6 or more symptoms).

We performed CFA to test the proposed CUDIT-Pr scale’s uni-dimensionality. We specified a latent variable model in which all eight indicators (items) were loaded onto a single latent factor. The model showed an acceptable fit to the data (χ² (df) = 84.829 (20), CFI = 0.947, TLI = 0.926, RMSEA [90% CI] = 0.077 [0.061, 0.095]) and all standardized factor loadings ranged between 0.52 - 0.73 ( Figure 3 ).

The Cronbach’s alpha was calculated for each explicit question, ranging from 0.72 to 0.81, and for the total CUDIT-R-Pr, it was 0.76 (ranging from 0.72 to 0.81), as shown in Table 4 . Additionally, McDonald’s Omega was 0.82 [36] .

Split-half

The Cronbach’s alpha, calculated using the split-half method, was 0.80 for part 1 and 0.59 for part 2. The Spearman-Brown coefficient, both at the same equal and unequal length, was 0.91, while the Gutman coefficient was 0.76 [26] .

Inter-observer reliability

The A two-way random-effects model calculated the ICC for the total scores on the questionnaire, which ranged from a minimum score of 0 to a maximum score of 32. Researchers recommend an ICC higher than 0.70 as a minimum standard for reliability [30] . At this point, a psychologist from group B simultaneously reassessed 100 participants from group A using the DSM5 for CUD and the CUDIT-R-Pr test (n = 100, M difference = −0.81, SD difference = 2.48). The CUDIT-R-Pr’s ICC was high (ICC = 0.86.95, CI 0.79 - 0.91), indicating a high degree of reliability and higher than the threshold of 0.7. If Kendall’s tau-b, which measures the relationship between the difference in the sum of the scores and the difference in their means, was not significant (p value = 0.08 > 0.05), we knew that there was no systematic error [29] . The measure of agreement with the Kappa index was 78%, which was substantial agreement [37] .

Inter-rater (observer) reliability

Additionally, two psychologists conducted a parallel measurement of Cronbach’s alpha between groups A (n = 271) and B (n = 270), yielding an adequate coefficient of 0.84. We calculated the ICC for the total scores on the questionnaire, which ranged from a minimum score of 0 to a maximum score of 32, using a one-way random-effects model. The ICC was high (0.84, 95% CI = 0.80, 0.88), indicating a high degree of reliability and higher than the threshold of 0.7 [30] .

Reproducibility

The same psychologist randomly selected 100 participants from group B for this study, and after a month, they reassessed and completed the CUDIT-R-Pr. We calculated the ICC for the total scores on the questionnaire, which ranged from a minimum score of 0 to a maximum score of 32, using a two-way random-effects model [25] : (n = 100, M difference = −0.17, SD difference = 1.70). The CUDIT-R-Pr’s ICC was high (ICC = 0.91, 95 CI 0.86 - 0.94), indicating high reliability and higher than the threshold of 0.7. We found no systematic error because Kendall’s tau-b, which measures the relationship between the difference in the sum of the scores and the difference in their means, was not significant (p value = 0.51 > 0.05). The agreement estimate is subjective and does not provide an objective index such as the ICC. The agreement estimate is subjective and does not provide an objective index such as the ICC [24] [37] . So, we have to do more evaluation.

Agreement

We calculated the SDC_individual = 2.57 (n = 100) and found that the SEM_agreement was 0.93. Additionally, we can measure SDC in a group of individuals, known as the SDC _group, by dividing the SDC_individual by √n (SDC_group = 0.26; n = 100) [12] [25] .

Responsiveness

The SDC_individual and SDC_group were 2.57 and 0.25 smaller than the MIC (3), indicating a responsive CUDIT-R-Pr [31] .

Interpretability

Students who scored “much better” or “better” on the satisfaction statement saw improvements from a mean score of 12 to 15 and 13.40 to 16.40, respectively (3). For students who were neither “better” nor “worse”, there was some improvement; this was from 11.13 to 11.35 (0.22). For students who were “worse” or “much worse”, the mean scores decreased from 14.40 to 11 (a decrease of 3.40) and from 14.50 to 11.50 (a decrease of 3) ( Table 5 ).

Table 5. Distribution of based on an anchor question, n = 100.

Floor and Ceiling Effects

We observed floor effects in less than 7 percent of cases at the CUDIT-R-Pr test (valid n = 100) and less than 2 percent at the retest (valid n = 100). We observed ceiling effects in less than 8 percent of cases at the CUDIT-R-Pr test and 2 percent at the retest. Ceiling or floor effects were considered not present, as the percentages did not exceed 15 percent.

We conducted measurement invariance testing on two groups of respondents (i.e., females, n = 251 and males, n = 290) to assess whether the measurement model’s psychometric properties can be generalized by sex [38] . As presented in Table 6 , results indicated configural, factorial, and scalar invariance of our measurement model between the female and male sub-samples. First, we estimated the unconstrained measurement models simultaneously across the two sub-samples. The goodness of fit indices displayed a good fit of the models to the data and supported configural invariance ( Table 6 ). The fit statistics of the unconstrained models served as the baseline for comparing the fit of the following constrained models, where, in each step, we increasingly imposed model constraints where parameters were set to be equal across female and male sub-samples.

Table 6. Measurement invariance testing results between female (n = 251) and male (n = 290) sub-samples.

Next, we imposed constraints on measurement weights between the two sub-samples to assess factorial invariance, which resulted in no significant decline in model fit compared to the unconstrained model (∆χ² (∆df) = 7.757 (7), p-value = 0.645). In the last step, we constrained the measurement intercepts to be equal between female and male sub-samples for testing scalar invariance. This constraint did not cause a significant decline in model fit compared to the baseline model (∆χ² (∆df) = 15.065 (15), p-value = 0.553). Thus, we concluded invariance of the CUDIT-Pr models between the female and male sub-samples suggesting that the performance of the CUDIT-Pr scale does not vary by sex.

ROC Analyses

All pairwise comparisons were significant (p < 0.001), with a Pearson correlation of 0.81. Looking at the ROC curve with a self-reported CUD as the criterion showed a decent fit (AUC = 0.95, CI: 0.92 - 0.96, p < 0.001) and a standard error of 0.01 ( Figure 4 ).

Sensitivity and Specificity

We found that a cut-point of ten or less was best for predicting any DSM-5, with sensitivity, specificity, and the Youden index at 96.17 percent, 69.13 percent, and 0.65 percent, respectively. This was based on the exact highest correctly classified value (88.72%), with LR+ (3.11) for a positive result and LR− (0.05) for a negative result. In addition, we calculated the optimal cut-point of twelve and less, taking into account the exact maximum Youden index (0.72), the likelihood ratio for a positive result (LR+), and the likelihood ratio for a negative result (LR−), which are respectively 7.72 and 0.19. The ROC curve at each CUDIT-R-Pr cut point is shown in Table 7 .

Table 7. Identification of current self-reported CUD by Persian version of CUDIT-R (CUDIT-R-Pr), n = 541.

^aOptimal cutoff point for CUDIT-R-Pr to predict any DSM-5 based on the exact maximum correctly classified value. ^bOptimal cutoff point for CUDIT-R-Pr to predict moderate DSM-5 based on exact maximum Youden Index.

The IRT result demonstrated an S-shaped curve that described the relationship between the probability of a correct response to an item and the ability scale, known as the item characteristic curve [31] . In this study, the probability was slightly higher than the 10 score ( Figure 5 ).

The item parameters α and β estimated from the GR model demonstrated that all questions had a high discrimination in the range of 1.26 to 2.20 in the alpha parameter. Of course, Item 1 was the highest (α = 2.20), and Item 8 was the lowest (α = 1.26). Location parameters spanned the broadest range for item 7 (β = −5.35 - 3.29), item 6 (β = −5.05 - 3.16), and item 5 (β = −4.39 - 3.03), in that order. These parameters provided information on both low and high levels of the latent hazardous use construct: Item 4 (β = −3.78 - 2.70); item 2 (β = −3.55 - 2.61); and item 3 (β = −3.64 - 2.10). In order to provide information at both low and high levels of the latent hazardous use construct, item 8 (β = 0.66 - 1.61) spanned the least range of the latent hazardous use construct. Item 1 (β = −077 - 1.75) exhibited relatively low item difficulty in the sample, although it provided narrower coverage relative to item 8. Table 8 and Figure 6 display all estimated item parameters from the GR model and the characteristic curves for Items 1 to 8.

NOTE. α represents the discrimination parameter for each item, or a slope indicating an item’s ability to discriminate between individuals with different levels of the latent hazardous use construct. Each β represents a location parameter, or the point along the latent hazardous use construct at which the probability of a response lying at or above a given category threshold is 0.50. The 95% confidence intervals (CI) represent the higher slope parameter α, the better the item discriminates the latent trait and between-category threshold of the parameters β represent the points along the latent trait at which the probability of responding to a certain category passes 0.50. The greater the variety of threshold parameters β (negative to positive), the more descriptive the item is. Descriptions adapted from [4] [7] [11] .