Harm avoidance and incompleteness core motivations in obsessive-compulsive disorder: Cross-cultural adaptation and validation of the Persian version of the Obsessive- Compulsive Core Dimensions Questionnaire (OC-CDQ) in clinical and nonclinical samples

Harm avoidance and incompleteness core motivations in obsessive-compulsive disorder: Cross-cultural adaptation and validation of the Persian version of the Obsessive- Compulsive Core Dimensions Questionnaire (OC-CDQ) in clinical and nonclinical samples | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Harm avoidance and incompleteness core motivations in obsessive-compulsive disorder: Cross-cultural adaptation and validation of the Persian version of the Obsessive- Compulsive Core Dimensions Questionnaire (OC-CDQ) in clinical and nonclinical samples Mahjoubeh Pourebrahimi, Mehdireza Sarafraz, Habib Hadianfard, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4347513/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2024 Read the published version in BMC Psychology → Version 1 posted 10 You are reading this latest preprint version Abstract Background The Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ) is the first measure created to assess the motivational dimensions of experiential avoidance in individuals with obsessive-compulsive disorder (Harm Avoidance (HA) and Incompleteness (INC)). The OC-CDQ has been translated and validated in several languages, but not in Persian. This study aimed to translate and investigate the factor structure, reliability, and validity of the Persian version of the OC-CDQ in a clinical group with obsessive-compulsive disorder (OCD) and nonclinical group without OCD. Methods The Persian version of the OC-CDQ was translated and culturally adapted according to international guidelines, including translation, back‑translation, pretesting, and expert committee review. A total of 209 outpatients diagnosed with OCD based on the DSM-V completed the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Obsessive-Compulsive Core Dimensions Interview (OC-CDI), Persian version of the OC-CDQ, Obsessive Belief Questionnaire (OBQ-44) and Beck's Anxiety Inventory (BAI). Additionally, 209 participants without OCD completed the Persian version of the OC-CDQ. To investigate the test-retest reliability, 60 people (30 people from each group) completed the Persian version of the OC-CDQ again after a two-week interval. Results Similar to the original version, the confirmatory factor analysis (CFA) indicated a good fit of the two-factor structure. The reliability of the Persian version of the OC-CDQ, as determined by the Cronbach's alpha coefficient, split-half, and retest indicated good reliability (clinical sample: ranging from 0.72 to 0.81, nonclinical sample: ranging from 0.74 to 0.83). Convergent validity was evaluated through the correlation of the OC-CDQ with the Y-BOCS, OC-CDI, and OBQ-44. Divergent validity was evaluated through correlation with BAI. The results supported the validity of the Persian version of the OC-CDQ (p<0.05). The results of hierarchical regression analysis indicated the incremental validity of this scale in predicting the Y-BOCS and BAI compared to the OBQ-44 (p<0.05), and comparing the scores of two groups with and without OCD indicated its discriminant validity (p<0.01). Conclusion The Persian‑OC-CDQ, developed after the translation and cross‑cultural adaptation process, is a valid tool for evaluating the motivational dimensions of harm avoidance and incompleteness in Iranian individuals with and without OCD. Harm avoidance Incompleteness Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ) OCD Persian version Psychometric. Figures Figure 1 Figure 2 Introduction Attention to the heterogeneous manifestations of OCD symptoms observed in clinical populations, along with different treatment responses, has led researchers to the possibility that OCD is a heterogeneous condition with broad manifestations and different causes. This has motivated researchers to identify distinct and homogeneous subgroups of patients with OCD and to improve treatment strategies by eliminating heterogeneity. As a result, in recent decades, significant studies with different methods have investigated these subgroups and their potential differences in terms of cause and treatment response [ 1 , 2 ]. In the first classification of OCD, symptoms, and comorbidities were emphasized, including washing versus checking and the presence versus absence of tics [ 3 ]. However, this approach's conceptual and methodological challenges, including significant overlap between apparently separate "subsets" of symptoms, led to the formation of the second OCD classification scheme based on cognitive models [ 4 ]. This plan considered dysfunctional beliefs to be the basis of the pathology of OCD and emphasized the feeling of personal responsibility, the overestimation of threats, the importance of thoughts, and the need to control them. These beliefs affect the interpretation of events in a way that leads to an increase in anxiety and then an increase in the motivation to try to avoid harm [ 5 ]. Despite the abundance of evidence supporting the role of these obsessive beliefs in creating and maintaining OCD, it was found that these cognitive constructs do not fully explain the variability in OCD symptoms. Some studies, using cluster analysis methods and the Obsessive Beliefs Questionnaire (OBQ-44) - a measure of beliefs related to obsessive-compulsive disorder in all fields - have identified a subgroup of patients with OCD who have low levels of these beliefs. This subgroup does not have beliefs related to harm avoidance playing a central role, but instead has a different motivation to avoid "not just right experiences and incompleteness" which strongly influences its underlying pathology [ 6 ]. Therefore, to more accurately classify symptoms and use an appropriate therapeutic strategy, recent researchers have paid attention to the motivational dimensions of OCD and the functional relationship between symptoms. They consider the “why” of a behavior (e.g., to avoid harm) more important than the form of that behavior (e.g., checking). Understanding the function of symptoms in patients with OCD is very important due to its motivational heterogeneity. In OCD treatment models, knowing "fear" or the main motivation underlying symptoms and avoidance behaviors helps the therapist better manage obsessive behaviors during treatment [ 7 ]. On the other hand, new approaches such as ACT recognize avoidance in patients with OCD as experiential avoidance (EA). EA is defined as a rigid pattern of trying to avoid or escape from unwanted internal experiences such as distressing thoughts, emotions, or physical sensations [ 8 ]. It has received particular attention and is recognized as the most vital functional process in psychopathology related to disorders, including OCD. Studies have shown that while these avoidance behaviors may reduce discomfort in the short term, ultimately, by preventing obsessive thoughts from being encountered and understanding them only as unwanted and disturbing thoughts on the part of the person, they can maintain obsessive thoughts and contribute to the continuation of the disorder as a motivation [ 9 ]. Most of the studies that have investigated this construct and its relationship with OCD symptoms have focused only on its behavioral dimension and examined its role along with the cognitive fusion component (two cognitive and context constructs of ACT treatment) of OCD symptoms. Some of these studies have shown that these constructs, in higher dimensions and beyond obsessive beliefs, can predict and explain the unacceptable symptoms of OCD [ 9 ]. However, some studies have not obtained such ability for the construct of experimental avoidance and have attributed the inconsistency of results to the challenges related to EA measurement criteria [ 10 , 11 ]. Questionnaires such as the Acceptance and Action Questionnaire (AAQ-II) have been criticized for excessive overlap with related constructs such as thought suppression and negative affect [ 12 ] and the Multidimensional Experiential Avoidance Questionnaire (MEAQ), which assesses general measures of EA, may not effectively assess EA in a specific domain such as obsession [ 13 ]. Therefore, in the last two decades, Rasmussen and Eisen developed the main dimensions model based on the OCD motivation model. They aimed to focus on the motivational dimension of EA as the main motivation underlying OCD symptoms. They grouped people with OCD based on two identified motivations: harm avoidance (HA) and incompleteness (INC). HA refers to symptoms that act to avoid harm to oneself or others (such as contracting an illness or unwanted aggression). INC refers to symptoms associated with internalized feelings of inadequacy and incompleteness of actions or intentions [ 14 ]. Summerfeldt et al. further developed and adapted the motivation model to depict motivations more dimensionally rather than categorically. They hypothesized that HA and INC underlie compulsions, sometimes alone and sometimes in combination. It is important to evaluate the motivations of people with OCD before treatment, given the various motivations underlying OCD and their significant role in treatment [ 15 ]. For this purpose, Summerfeldt et al. developed the Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ), a two-dimensional questionnaire used to assess HA and INC [ 16 ]. To date, several studies have evaluated the psychometric properties of the OC-CDQ and validated its English and German versions in both OCD patients and nonclinical participants [ 15 , 17 , 18 ]. Some studies have also examined the relationship between HA and INC motivational dimensions with other OCD measures. Researchers found that HA was significantly associated with doubting/checking, obsessing, and washing, while INC was significantly associated with doubting/checking, ordering, neutralizing, OCPD features (such as perfectionism), and lower quality of life [ 7 , 19 , 20 ]. Bragdon & Coles reported that the subgroup with high HA had greater beliefs about responsibility/overestimation of threat, but the subgroup with high INC had more perfectionistic beliefs and greater intolerance of uncertainty [ 15 ]. Although these studies confirmed the two-factor structure, validity, and reliability of the OC-CDQ and examined the relationships between HA and INC dimensions and other criteria related to obsessions, most of these studies were conducted in Western cultures. However, some studies have shown that there are cultural differences in OCD patients, and the validation of OCD criteria in different cultures is an important issue. For example, individuals in Eastern cultures show greater OC symptom severity and obsessive beliefs than those in Western cultures [ 21 ]. The results of the study by Asadi et al. showed that Iranian patients with OCD are different from patients of other nations in terms of obsessions such as aggression, sexuality, and religion. Additionally, cultural factors such as the family system, values, religion, and cultural norms can affect the content of obsessions and compulsions [ 22 ]. On the other hand, studies have shown that although cultural differences may affect the experience or severity of OCD symptoms, these differences may not cause different OCD symptoms in different cultures [ 23 , 24 ]. Additionally, several studies conducted in various cultures have found the same construct for other OCD measurement tools [ 25 , 26 ]. Therefore, the OC-CDQ in Eastern countries such as Iran may also have a two-factor structure similar to that of Western cultures. Considering that the Persian version of the OC-CDQ, used to assess the level of HA and INC motivations has not yet been published, and that psychometric properties of the OC-CDQ have not been systematically investigated in a clinical and nonclinical peer sample, the present study aimed to investigate the psychometric properties of the Persian translation of the OC-CDQ in two Iranian samples (clinical with OCD to evaluate the two motivational dimensions underlying obsessive-compulsive symptoms (HA and INC) and in a nonclinical peer population to assess these dimensions as traits). Additionally, the relationships between HA and INC dimensions and obsessive symptoms and severity, obsessive beliefs, and other related clinical phenomena were investigated. Methods Participants Clinical sample A total of 209 individuals with OCD who were referred to psychiatric and psychological clinics in Kerman (84 patients), Sirjan (49 patients), Rafsanjan (45 patients), and Zarand (31 patients) in 2023 were purposefully and accessibly selected for investigation. The primary diagnosis of OCD was based on DSM-V criteria, utilizing the Structured Clinical Interview for DSM-5 Disorders (SCID-5-CV) [ 27 ], which was conducted by trained clinicians and clinical psychologists. The participants' OCD symptoms ranged from moderate to severe, as assessed by the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). The mean total score on the Yale-Brown scale was 238.29, with mean subscale scores of 131.49 for obsession and 106.79 for compulsion. Exclusion criteria included comorbidity of other disorders, particularly anxiety disorders as the primary diagnosis, individuals under the age of 18, and those with an education level below a diploma. Nonclinical sample The nonclinical participants were 209 individuals without a history of psychiatric disorders who were selected in an accessible and purposeful manner to match the clinical sample in terms of demographic variables such as age, gender, level of education, and marital status. The demographic characteristics of both the clinical and nonclinical groups are presented in Table 1 . Table 1 Demographic characteristics of the participants (clinical sample = nonclinical sample = 209). Kerman Sirjan Rafsanjan Zarand Total Variable N (%) N (%) N (%) N (%) N (%) Gender Male 20 (9.6%) 9 (4.3%) 7 (3.3%) 6 (2.9%) 42 (20.1%) Female 64 (30.6%) 40 (19.1%) 38 (18.2%) 25 (12%) 167 (79.9%) Age ≤ 25 13 (6.2%) 7 (3.3%) 5 (2.4%) 5 (2.4%) 30 (14.4%) 26–35 39 (18.7%) 30 (14.4%) 29 (13.9%) 17 (8.1%) 115 (55%) 36–45 26 (12.4%) 10 (4.8%) 8 (3.8%) 9 (4.3%) 53 (25.4%) 46≤ 6 (2.9%) 2 (1%) 3 (1.4%) 0 (0%) 11 (5.3%) Educational Level Diploma 7 (3.3%) 7 (3.3%) 12 (5.7%) 6 (2.9%) 32 (15.3%) Associate Degree 14 (6.7%) 9 (4.3%) 13 (6.2%) 19 (9.1%) 55 (26.3%) Bachelor's degree 59 (28.2%) 32 (15.3%) 20 (9.6%) 6 (2.9%) 117 (56%) Master's degree≤ 4 (1.9%) 1 (0.5%) 0 (0%) 0 (0%) 5 (2.4%) Marital status Single 20 (9.6%) 13 (6.2%) 15 (7.2%) 10 (4.8%) 58 (27.8%) Marriage 59 (28.2%) 34 (16.3%) 26 (12.4%) 19 (9.1%) 138 (66%) Divorced 5 (2.4%) 2 (1%) 4 (1.9%) 2 (1%) 13 6.2%) Procedures All participants from both the clinical and nonclinical groups voluntarily participated in the research. Before the study, the researchers provided a brief explanation of the research aims and obtained written informed consent from all participants. Clinical participants completed the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Obsessive Compulsive Core Dimensions Interview (OC-CDI), Obsessive Beliefs Questionnaire (OBQ-44), Beck Anxiety Inventory (BAI), and Persian Translation of Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ) to assess HA and INC as specific motivations for clinical OCD. Nonclinical participants only completed the OC-CDQ to assess HA and INC as stylistic traits in the nonclinical population. Additionally, 60 participants (30 from each group) were randomly selected and asked to complete the OC-CDQ again after a two-week interval to assess its test-retest reliability. Translation and cross-cultural adaptation The translation and cross-cultural adaptation of the questionnaire were preformed according to the recommendations of the international guidelines and considering the different lifestyles and cultures [ 28 ]. To adapt the 20-question version of the OC-CDQ for use in the Iranian population, we initially translated the German version into Persian with the assistance of two German language experts who were knowledgeable in psychology terminology. Subsequently, another expert performed a back-translation of the translated version into German and corrected the any discrepancies. Once the translation process was completed, the translated version was provided to two psychological experts who verified the face validity of the questionnaire. In a preliminary study, the translated questionnaire was administered to a sample of 10 psychology master's and doctoral students and five patients with OCD. After the questionnaires, were collected, any words that were not understandable to them were rewritten and replaced with the closest word. Throughout all these stages, based on the opinions of the experts and the test sample, there was no need to remove or revise any of the items. Finally, the Persian version was created by keeping 20 items from the original version of the OC-CDQ and using a 5-point Likert scale ranging from 1 ("never") to 5 ("always"). This version was then implemented on the main sample of 418 people (male and female). Measures Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ) The OC-CDQ is a 20-item self-report questionnaire that evaluates the suggested motivational dimensions underlying obsessive-compulsive disorder (harm avoidance (HA) and incompleteness (INC)). It is rated on a 5-point Likert scale, ranging from 1 ("never") to 5 ("always") [ 15 , 16 ]. Previous psychometric investigations have confirmed the use of German version [ 18 ], English version [ 15 ], and Swedish version [ 17 ] of this scale. The data from this study also showed good internal consistency for this scale (α = 0.80). Obsessive-Compulsive Core Dimensions Interview (OC-CDI) This interview is used to assess HA and INC motivations in patients with OCD [ 15 ]. In this interview, immediately after completing the checklist of Y-BOCS symptoms, explanations about HA and INC motivations were given to the participants. The subject is told that each of these motivations, or both motivations at the same time, may be related to their obsessive experiences. After ensuring the subject's full understanding, the interviewer asked two standardized questions for each symptom endorsed as a target in the Y-BOCS: "To what extent do you associate this with the fear that something harmful/bad might happen?" and "To what extent do you associate this with the need to have things 'right' and to make sure they are perfect?". Otherwise, you feel incomplete, tense, or upset?". After each question, the respondent selects the best answer for each motivation item on a 0–4 rating scale. This results in two ratings (from 0 to 4) for each target symptom. Higher scores indicate greater involvement of HA and INC in obsessions and compulsions in the past week. Summerfeld et al. and Cervin & Perrin obtained adequate psychometric properties for the OC-CDI [ 15 , 17 ]. The Cronbach's alpha in the present study indicated excellent internal consistency (α = 0.92). The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Y-BOCS has two parts: 1. The Symptom Checklist (SC) was used to identify eight types of obsessions (contamination, aggression, sexual, religious, symmetry, physical, hoarding, and miscellaneous) and seven types of compulsions (washing, controlling, repetition, counting, order, hoarding, and miscellaneous). 2. The Severity scale (SS) was used to measure the intensity of obsession or compulsion, regardless of their type [ 29 ]. Its Persian version has been validated [ 30 , 31 ]. In this sample, Cronbach's alpha showed good internal consistency (α = 0.81). The Obsessive Beliefs Questionnaire ( OBQ-44 ) The OBQ-44 is a self-report scale used to assess beliefs related to OCD. It consists of three subscales: 1. inflated responsibility and overestimation of threat (RH), 2. perfectionism and intolerance of uncertainty (PC), and 3. importance and overcontrol of thoughts (IT) and is rated on a 7-point Likert scale [ 5 ]. Its Persian version has been previously validated [ 32 ]. Beck Anxiety Inventory (BAI) The BAI is a 21-item self-report scale that measures the severity of physical and cognitive symptoms of anxiety in the past week, using a four-point Likert scale (0 to 3) [ 33 ]. Its Persian version has been previously validated [ 34 ]. The Cronbach's alpha for this sample was excellent (α = 0.91). Statistical analysis A descriptive analysis of the items was performed, including the study of univariate normal distributions (skewness and kurtosis). Cronbach's alpha coefficient was calculated by removing items individually to identify inconsistent questions. The correlation of each question with the total score was calculated without including the score of that question to evaluate the discrimination index of the items of the OC-CDQ. Additionally, using Pearson's correlation coefficient, the correlation between the subscales and the total score was evaluated in both clinical and nonclinical samples. Confirmatory factor analysis (CFA) was performed using the maximum likelihood method to investigate whether the OC-CDQ in clinical and nonclinical samples conformed to the factor model of the original version (i.e., 2-factor structure). Model fitting was assessed using the results of the chi-square test (χ2) and χ2 index divided by degrees of freedom (CMIN/DF), root mean square error of approximation (RMSEA), root mean square residual (SRMR), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), normalized fit index (NFI), incremental fit index (IFI), and comparative fit index (CFI). According to Klein (2016), a model fit is considered good if the values of the RMSEA and SRMR indices are less than 0.05, and average if they are between 0.05 and 0.08. A perfect fit is indicated by GFI, AGFI, NFI, IFI, and CFI values above 0.95, while values above 0.90 indicate a good model fit [ 35 ]. Cronbach's alpha coefficients and split-half reliability were used to test the internal consistency of the OC-CDQ. Reliability values greater than 0.7 are considered acceptable [ 36 ]. The test-retest reliability of the OC-CDQ total/subscale scores was estimated using Pearson's correlation coefficient. According to Cohen's classification, a correlation coefficient of r ≥ 0.50 indicates a strong correlation [ 37 ]. The convergent validity of the OC-CDQ was assessed by examining the correlation between the total score and subscales of the OC-CDQ with the total score and subscales of the OC-CDI, Y-BOCS, and OBQ-44. Divergent validity was assessed by examining the correlation between the total score and subscales of the OC-CDQ with the BAI score. The significance threshold was set at p < 0.05, and the strength of the correlation was classified as weak ( 0.70) [ 38 ]. Incremental validity was assessed through hierarchical regression analysis to investigate whether the OC-CDQ score predicts the Y-BOCS score more accurately than the OBQ-44 score. In Step 1, the only independent variable for the Y-BOCS was the OBQ-44, while in Step 2, the OC-CDQ was included alongside the OBQ-44. It was expected that there would be a significant increase in predictive power in Step 2 and that the OC-CDQ would be positively correlated with the Y-BOCS. In addition, we examined the difference between the scores of the clinical and nonclinical groups on the OC-CDQ to determine discriminant validity through an independent t-test. Statistical analyses were performed using IBM SPSS-26 and Amos-24. Results Descriptive analysis of the item In the clinical group, the average of all 20 items was in the highest range of the scale (average greater than 3.34); in the nonclinical group, all 20 items were in the lowest range (average less than 2.09). In both the clinical and nonclinical groups, all 20 items had skewness and kurtosis indices less than one in absolute value, which shows no deviation from the normality of the distribution of univariate items. The use of Cronbach's alpha coefficient with item deletion to identify inconsistent questions on the test in both the clinical and nonclinical groups showed that all the test questions had good internal consistency except for question 20. Its elimination slightly increased the Cronbach's alpha coefficient in both the clinical and nonclinical groups (α = 0.926 and 0.901, respectively). The item-total correlation coefficient was used to test each item. The results showed that the scores of all items had a positive and significant correlation with the scale's total score, but this correlation was lower for item 20 in both groups. Due to the good correlation of the items with the total score in both groups (more than 0.4), all 20 items were included in the reliability and validity analysis (Table 2 ). Additionally, the Pearson correlation results showed a positive and significant relationship between HA and INC subscales together and with the total score in the clinical sample (r = 0.324, r = 0.545 and r = 0.639, p < 0.01, respectively) and the nonclinical sample (r = 0.143, r = 0.421 and r = 0.498, p < 0.01, respectively). Table 2 Item means, standard deviations, ranges, Cronbach's alpha coefficients (α), and corrected item-rest correlations (r). Items Clinical sample Nonclinical sample M SD Range α r M SD Range α r Q1 3.70 0.99 1–5 0.835 0.529 1.86 0.78 1–4 0.796 0.623 Q2 3.34 1.09 1–5 0.819 0.489 1.86 0.76 1–4 0.808 0.472 Q3 3.71 1.06 1–5 0.821 0.569 1.89 0.76 1–4 0.791 0.675 Q4 3.35 1.17 1–5 0.828 0.610 1.82 0.74 1–4 0.814 0.460 Q5 3.84 0.98 1–5 0.792 0.512 1.87 0.75 1–4 0.789 0.642 Q6 3.46 1.01 1–5 0.812 0.534 1.84 0.69 1–3 0.806 0.462 Q7 3.74 0.92 1–5 0.841 0.547 1.83 0.77 1–4 0.788 0.560 Q8 3.44 1.08 1–5 0.825 0.621 1.82 0.64 1–3 0.812 0.467 Q9 3.85 1.07 1–5 0.801 0.502 1.80 0.70 1–4 0.781 0.525 Q10 3.52 1.03 1–5 0.819 0.530 1.81 0.64 1–3 0.809 0.447 Q11 3.77 0.90 1–5 0.799 0.529 1.76 0.67 1–3 0.793 0.513 Q12 3.52 1.12 1–5 0.829 0.608 1.80 0.60 1–3 0.811 0.430 Q13 3.73 1.02 1–5 0.839 0.557 1.75 0.63 1–3 0.790 0.520 Q14 3.49 1.07 1–5 0.831 0.536 1.79 0.60 1–3 0.821 0.514 Q15 3.74 1.06 1–5 0.835 0.553 1.73 0.66 1–3 0.787 0.483 Q16 3.52 1.10 1–5 0.807 0.541 1.86 0.68 1–3 0.809 0.435 Q17 3.85 0.95 1–5 0.833 0.546 1.71 0.70 1–4 0.785 0.586 Q18 3.56 1.09 1–5 0.825 0.592 1.85 0.71 1–4 0.819 0.463 Q19 3.63 1.07 1–5 0.829 0.564 1.91 0.83 1–4 0.794 0.524 Q20 3.66 1.07 1–5 0.901 0.446 2.09 0.56 1–4 0.926 0.401 Factor structure for the OC-CDQ CFA for the OC-CDQ in the clinical sample The results of the CFA confirming the two-factor structure of the OC-CDQ in the clinical sample (n = 209) showed a good fit (Table 3 ). All standardized factor loadings were greater than 0.40 and statistically significant (ranging from 0.54–0.82) (Fig. 1 ). The correlation between the two OC-CDQ subscales was statistically significant (0.32, p < 0.01). CFA for the OC-CDQ in the nonclinical sample CFA results in the nonclinical sample (n = 209) showed a good fit for the two-factor structure (Table 3 ). All standardized factor loadings were greater than 0.40 and statistically significant (ranging from 0.50–0.83) (Fig. 2 ). The correlation between the two OC-CDQ subscales in the nonclinical sample was statistically significant (r = 0.15, p < 0.01). Table 3 Fit indices for the con fi rmatory factor analysis models of the core dimensions. Model CMIN/DF SRMR GFI AGFI NFI IFI CFI RMSEA OC-CDQ self-report data Two factors (revised): trait version, nonclinical sample 1.608 0.000 0.887 0.860 0.875 0.949 0.948 0.054 Two factors (revised): state version, clinical sample 1.940 0.000 0.861 0.828 0.873 0.934 0.933 0.067 Note. OC-CDQ = Obsessive-Compulsive Core Dimensions Questionnaire; CMIN/DF = chi-degree freedom; SRMR = standardized root-mean-square residual; GFI = goodness-of-fi t index; AGFI = adjusted goodness of fit index; NFI = normed fit index; IFI = incremental fit index; CFI = comparative fi t index; RMSEA = root mean square error of approximation. Reliability: internal consistency and temporal consistency In the clinical sample, the Cronbach's alpha coefficients of the Persian version of the OC-CDQ were 0.80, and those of the HA and INC subscales were 0.81 and 0.78, respectively. Its split-half reliability was 0.80, which indicated satisfactory internal consistency of the scale. Additionally, the total scale and the HA and INC subscales showed good test-retest reliability (0.81, 0.78, and 0.72, respectively). In the nonclinical sample, the Cronbach's alpha coefficients of the total scale and the HA and INC subscales were 0.79, 0.79, and 0.83, respectively. Its split-half reliability was 0.80 which indicated satisfactory internal consistency of the scale. Additionally, the total scale and the HA and INC subscales showed good test-retest reliability (0.74, 0.75, and 0.80, respectively). Convergent and divergent, incremental and discriminant validity The relationships between the total score and subscales of the OC-CDQ with the total score and subscales of the OC-CDI, Y-BOCS (SC and SS), and OBQ-44 were investigated in the clinical sample. The total score of the OC-CDQ had a significant positive relationship with other scales and subscales except hoarding (ranging from 0.12 to 0.78, p < 0.05). Convergent validity was also confirmed for the OC-CDQ subscales: the HA-Q had a significant positive relationship with the total score of the OC-CDI and the HA-I subscale, the total score of the Y-BOCS and the contamination, aggression, sexual, religious, physical, washing, and controlling subscales, the severity scale (SS), the total score of the OBQ-44, and the RH and IT subscales (ranging from 0.14 to 0.83, p < 0.05). However, it had a weak and significant negative relationship with the INC-I subscale (r= -0.09, p 0.05). The INC-Q had a meaningful positive relationship with the total score of the OC-CDI and INC-I subscale; the total score of the Y-BOCS and the subscales of contamination, symmetry, washing, controlling, repetition, counting, and order; the severity scale (SS); the total score of OBQ-44; the PC subscale, and the BAI (ranging from 0.16 to 0.77, p < 0.05). However, it had a weak and significant negative relationship with the HA-I subscale (r= -0.11, p 0.05) (Table 4 ). Additionally, the results of Table 4 show a positive and moderate correlation between the OC-CDQ total score and the HA and INC subscales with the BAI (0.47, 0.49, and 0.43, respectively), indicating the divergent validity of this scale. Hierarchical regression analysis was conducted to examine the incremental validity of the OC-CDQ. We investigated whether the OC-CDQ is better at explaining the incremental variance of the Y-BOCS and BAI than the OBQ-44. As shown in Table 5 , the OC-CDQ and its subscales accounted for a significant amount of additional variance (5–19%) in the Y-BOCS and BAI. The results indicated that even after controlling for the effects of the OBQ-44 on the dependent variables, the effects of the OC-CDQ and its subscales (ΔR2) on the Y-BOCS and BAI remained significant. Specifically, the OC-CDQ and its subscales were found to be significant independent explanatory variables for the Y-BOCS and BAI. To compare the average scores obtained in the clinical and nonclinical groups, a t-test for two independent groups was conducted after checking for homogeneity of variance. The results indicated a significant difference between the means of the two groups in the HA and INC subscales, as well as the total score of the OC-CDQ (p < 0.01). The mean scores of the clinical group were greater than those of the nonclinical group (Table 6 ). Table 4 Pearson's correlation coefficient between the total score and subscales of the OC-TCDQ with the total scores and subscales of the OC-CDI, Y-BOCS, OBQ-44, and BAI in the clinical sample. OC-CDQ HA-Q INC-Q OC-CDI 0.76 ** 0.81 ** 0.70 ** HA-I 0.78 ** 0.83 ** -0.11 * INC-I 0.67 ** -0.09 * 0.77 ** Y-BOCS SC 0.69 ** 0.73 ** 0.63 ** contamination 0.44 ** 0.49 ** 0.43 * aggression 0.19 * 0.31 * 0.11 sexual 0.09 * 0.14 * 0.06 religious 0.24 * 0.35 * 0.09 symmetry 0.44 * 0.12 0.67 ** physical 0.17 * 0.21 * 0.10 hoarding 0.11 0.07 0.13 w ashing 0.41 ** 0.46 ** 0.38 * controlling 0.30 ** 0.30 ** 0.28 ** repetition 0.15 * 0.09 0.23 * counting 0.12 * 0.07 0.16 * order 0.42 * 0.22 0.57 ** hoarding 0.15 0.08 0.19 SS 0.64 ** 0.60 ** 0.63 ** OBQ-44 0.67 * 0.70 * 0.62 * RH 0.38 * 0.62 ** 0.08 PC 0.34 * 0.11 0.68 ** IT 0.21 * 0.40 * 0.12 BAI 0.47 ** 0.49 ** 0.43 ** Note. OC-CDQ: Obsessive-Compulsive Core Dimensions Questionnaire; HA; Harm avoidance; INC: Incompleteness; OC-CDI: Obsessive-Compulsive Core Dimensions Interview; Y-BOCS: Yale-Brown Obsessive Compulsive Scale; SC: Symptom Checklist; SS: Severity Scale; OBQ-44: Obsessive Beliefs Questionnaire; RH: inflated responsibility and overestimation of threat; PC: perfectionism and intolerance of uncertainty; IT: importance and overcontrol of thoughts; BAI: Beck Anxiety Inventory. ** p < 0.01, * p < 0.05 Table 5 Incremental validity of the OC-CDQ above the OBQ-44 Variables B SE β T R 2 ΔR 2 Y-BOCS Step1 0.16 *** OBQ-44 0.47 0.07 0.40 *** 6.36 Step2 0.29 *** 0.13 *** OBQ-44 0.27 0.07 0.18 * 3.27 Total scores of OC-CDQ 1.56 0.20 0.49 *** 6.72 Step1 0.16 *** OBQ-44 0.47 0.07 0.40 * 6.36 Step2 0.35 *** 0.19 *** OBQ-44 0.17 0.06 0.11 * 1.30 HA 3.56 0.27 0.68 *** 8.93 INC 2.41 0.26 0.59 ** 5.09 BAI Step1 0.07 ** OBQ-44 0.04 0.01 0.19 * 2.40 Step2 0.12 ** 0.05 ** OBQ-44 0.01 0.01 0.07 * 1.03 Total scores of OC-CDQ 0.11 0.04 0.21 ** 2.69 Step1 0.05 ** OBQ-44 0.04 0.01 0.19 * 2.40 Step2 0.14 ** 0.09 ** OBQ-44 0.01 0.01 0.05 * 0.98 HA 0.15 0.07 0.23 ** 2.94 INC 0.09 0.03 0.16 * 1.07 Note. Y-BOCS: Yale-Brown Obsessive Compulsive Scale; OBQ-44: Obsessive Beliefs Questionnaire; OC-CDQ: Obsessive-Compulsive Core Dimensions Questionnaire; HA; Harm avoidance; INC: Incompleteness; BAI: Beck Anxiety Inventory. *** p < 0.001, ** p < 0.01, * p < 0.05 Table 6 Comparison of the total score and subscales of the OC-CDQ between the clinical and nonclinical groups M (SD.) Clinical sample M (SD.) Nonclinical sample F df P t-test P OC-CDQ 71.06 (10.18) 49.96 (6.95) 6.99 416 0.182 27.13 *** 0.001 HA 32.53 (5.73) 22.38 (3.74) 5.54 416 0.222 18.46 *** 0.001 INC 38.52 (6.01) 25.57 (4.73) 8.49 416 0.164 19.18 *** 0.001 Note. OC-CDQ: Obsessive-Compulsive Core Dimensions Questionnaire; HA; Harm avoidance; INC: Incompleteness; BAI: Beck Anxiety Inventory. *** p < 0.001 Discussion Similar to the original OC-CDQ, the CFA results in this study showed that the OC-CDQ has the same two-factor structure in both clinical and nonclinical populations, and this result was consistent with previous studies [ 15 , 18 ]. The analysis of the items in both the clinical and nonclinical groups showed that the removal of any of the items had no significant effect on increasing Cronbach's alpha. Only the removal of item 20 in the clinical group to a small extent (α = 0.90) and in the nonclinical group to a greater extent (α = 0.92) increased the Cronbach's alpha coefficient. Further examination of this item confirms the above hypothesis because the examination of the discrimination index of the items shows that item 20 has a lower correlation with the modified total-item correlation than other items in both groups (r = 0.44 and r = 0.40, respectively). The inconsistency of this item may indicate that the above item is ambiguous and should be further investigated in terms of the content of the translation. A relatively moderate positive correlation (r = 0.32) was obtained between the HA and INC dimensions in the clinical population, indicating that these dimensions measure distinct motives but also capture commonalities. This relationship suggests that in the clinical population, compulsion is often caused by both dimensions, but the effectiveness of these two dimensions may differ among individuals. Previous studies have also reported similar results [ 14 , 15 , 18 ]. This finding, along with the next findings, shows that both the HA and INC dimensions had a significant positive relationship with the total score, but the INC dimension had a greater correlation with the total score than the HA dimension, which is consistent with the findings of previous studies [ 15 , 20 , 39 , 40 ]. This may indicate how these two dimensions functionally affect this disorder. These studies have suggested that while HA may be the key to the initiation of obsessive rituals, INC is the key to their continuation. In the nonclinical group, a relatively weak positive correlation (r = 0.15) was obtained between the two dimensions of HA and INC, which is in accordance with the findings of Summerfeldt et al. [ 15 ]. They suggested that in the nonclinical population, the HA dimension is associated with constructs such as trait anxiety, which form the personality substratum of anxiety disorders. However, they consider INC to be a type of "sensory perfectionism" that is a precursor to obsessive-compulsive personality disorder (OCPD) and consider it to be two independent dimensions. Consistent with Summerfeldt's model [ 41 ], and other previous studies [ 18 ], that assumed that HA plays a greater role than INC in other anxiety disorders, in this study, the level of clinical anxiety in the clinical group was more related to HA than to INC (r = 0.49 and r = 0.43, respectively). The reliability of the Persian version of the OC-CDQ in both the clinical and nonclinical groups was excellent, as indicated by the Cronbach's alpha coefficient (α > 0.75) and the split-half method. It also demonstrated good test-retest reliability (> 0.70), supporting the temporal stability of the scale. These results were consistent with those of previous studies [ 15 , 17 , 42 ]. To assess the validity of the OC-CDQ, the present study revealed that the total score of the OC-CDQ showed excellent convergent validity, with strong positive correlations with the OC-CDI, Y-BOCS, and OBQ-44 (r = 0.76, r = 0.69 and r = 0.67, respectively). It also demonstrated good divergent validity, with a moderate positive correlation with the BAI (r = 0.47). Furthermore, the strong correlation between HA and INC in the OC-CDQ with HA and INC in the OC-CDI (r = 0.83 and r = 0.77, respectively), as well as the weak and negative correlation between HA and INC in the OC–CDQ with INC and HA in the OC-CDI (r=-0.09 and r=-0.11, respectively), further support the construct validity of the OC-CDQ and are consistent with previous research [ 15 ]. This study, in accordance with studies conducted in other cultures, showed that HA and INC are related to contamination, washing, and controll [ 43 , 44 , 45 ]. Additionally, INC is uniquely related to symmetry, ordering [ 15 , 20 ], counting, and repetition [ 42 ], while hoarding is not related to any HA or INC motives [ 7 ]. Furthermore, this study revealed that HA is uniquely related to aggression, sexual, religious, and physical obsessions. In a systematic review of cultural issues related to OCD in Iran, Rezazadeh and Zarani found that an ineffective belief-value system can lead to misinterpretations and incorrect attitudes in people with OCD [ 46 ]. These attitudes can be seen as related to the HA dimension, including setting strict moral standards and morbid guilt, extreme responsibility, increased rumination, fear of being harmed, fear of making mistakes and injuring others. Factors that, according to the content of Y-BOCS items in aggressive, sexual, religious, and physical obsessions, can determine their role in these obsessions. In this study, the INC group had strong beliefs about PC. In contrast, the HA group showed a traditional OCD profile characterized by increased beliefs related to RH and IT. The findings of the present study agree with those of previous studies [ 14 , 47 ]. The mean values of the two subscales and the total score indicated a more intense experience of HA and INC in the OCD group than in the nonclinical group, with a significant difference between these two groups (p < 0.01). In accordance with previous studies [ 15 , 18 ], this finding shows that harm avoidance and incompleteness are common phenomena in patients with OCD, and the OC-CDQ can distinguish between them. Despite the existence of cultural differences, repeating the results of previous studies in Iran confirms the psychometric properties of the Persian version of the OC-CDQ. This questionnaire can provide valuable information on the motivations underlying obsession and its relationship with beliefs, obsessions, and compulsions in Iranian adulthood. Although our sample's characteristics are consistent with those of other extensive OCD studies, this study was conducted in adults. The findings cannot be generalized to a larger OCD population. Future research could validate the OC CDQ for other age groups. Additionally, this sample was not epidemiologic, and the relatively low representation of racial/ethnic minorities limits generalizability. Second, due to the bias of the participants in answering the self-report questions, future researchers can explore alternative methods, such as structural and functional neuroimaging methods, to expand their understanding of the underlying neural substrate of HA and INC. Third, nearly half of the sample had high or low levels of both motivations. Therefore, it is essential to understand the nature of the "low" and "high" groups when considering a motivation-based classification system. Conclusion The present study showed that the OC-CDQ has good psychometric properties, similar to studies in Western cultures, and is a reliable tool to be used in Iranian patients with OCD to investigate the underlying motivations of OCD, facilitate the conceptualization of clinical heterogeneity in OCD, and guide subsequent treatment protocols. It can also be a valid scale to be used in the nonclinical Iranian population to examine the traits of harm avoidance and incompleteness as underlying traits of clinical disorders, including anxiety disorders and OCPD. Declarations Acknowledgments The authors would like to thank all the study participants Author contributions MP and MRS conceptualized the study, adapted the instrument, collected and analyzed data, and drafted and wrote the article. HH and NM supervised the data analysis, editing, and final manuscript preparation. All the authors have read and approved the final manuscript. Funding No external funding was received for the initiation or completion of this study. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethical approval and consent to participate This article was derived from the first author’s doctoral dissertation in Clinical Psychology from Shiraz University. All questionnaires and methodology for this study were approved by the Research Ethics Committee of Shiraz University of Medical Sciences, Iran (ethical code: IR.US.PSYEDU.REC.1402.013). Informed consent was obtained from all participants included in the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Details 1 Department of Clinical Psychology, Faculty of Educational Sciences and Psychology, Shiraz University, Shiraz, Iran References Abramovitch A, Abramowitz JS, Mittelman A. The neuropsychology of adult obsessive-compulsive disorder: A meta-analysis. Clin Psychol Rev. 2013;33(8):1163-71. https://doi.org/10. 1016/ j.cpr.2013.09.004 Farris SG, McLean CP, Van Meter PE, Simpson HB, Foa EB. Treatment response, symptom remission, and wellness in obsessive-compulsive disorder. J Clin Psychiatry. 2013;74(7):685–90. https://doi.org/10.4088/JCP.12m07789 McKay D, Abramowitz JS, Calamari JE, Kyrios M, Radomsky A, Sookman D, … Wilhelm S. A critical evaluation of obsessive-compulsive disorder subtypes: symptoms versus mechanisms Clin Psychol Rev. 2004;24(3):283–313. https://doi.org/10.1016/j.cpr.2004.04.003 Clark DA. Cognitive-behavioral therapy for OCD. New York: Guilford Press; 2004. Obsessive Compulsive Cognitions Working Group. Psychometrics Validation of the Obsessive Beliefs Questionnaire and the Interpretation of Intrusions Inventory— Part 2: Factor Analysis and Testing of a Brief Version. Behav Res Ther. 2005;43:1527-42. http://dx.doi.org/ 10.1016/j.brat.2004.07.010 Polman A, O'Connor KP, Huisman M. Dysfunctional belief-based subgroups and inferential confusion in obsessive-compulsive disorder. Pers Individ Dif. 2011;50(2):153–8. https://doi.org/10.1016/j.paid.2010.09.017 Schreck M, Georgiadis Ch, Garcia A, Benito K, Case B, Herren J, Walther M, Freeman J. Core Motivations of Childhood Obsessive‑Compulsive Disorder: The Role of Harm Avoidance and Incompleteness. Child Psychiatry Hum Dev. 2020;52(5):957-65. https://doi.org/ 10.1007/s10578- 020- 01075-5 Hayes SC, Wilson KG, Gifford EV, Follette VM, Strosahl K. Experiential avoidance and behavioral disorders: A functional dimensional approach to diagnosis and treatment. J Consult Clin Psychol. 1996;64:1152–68. https://doi/10.1037/0022-006X.64.6.1152 Ojalehto HJ, Hellberg SN, Butcher MW, Buchholz JL, Timpano KR, Abramowitz JS. Experiential avoidance and the misinterpretation of intrusions as prospective predictors of postpartum obsessive-compulsive symptoms in first-time parents. J Contextual Behav Sci. 2021;20:137–43. https://doi.org/10.1016/j.jcbs.2021.04.003 Reuman L, Buchholz J, Abramowitz JS. Obsessive beliefs, experiential avoidance, and cognitive fusion as predictors of obsessive-compulsive disorder symptom dimensions. J Contextual Behav Sci. 2018;9:15-20. https://doi.org/10.1016/j.jcbs.2018.06.001 Akbari M, Seydavi M, Hosseini ZS, Krafft J, Levin, ME. Experiential avoidance in depression, anxiety, obsessive-compulsive related, and posttraumatic stress disorders: A comprehensive systematic review and meta-analysis. J Contextual Behav Sci. 2022;24:65-78. https://doi.org/ 10.1016/j.jcbs.2022.03.007 Tyndall I, Waldeck D, Pancani L, Whelan R, Roche B, Dawson DL. The Acceptance and Action Questionnaire-II (AAQ-II) as a measure of experiential avoidance: Concerns over discriminant validity. J Contextual Behav Sci. 2019;12:278–84. https://doi.org/10.1016/j.jcbs. 2018.09.005 Ong CW, Lee EB, Levin ME, Twohig MP. A review of AAQ. variants and other context-specific measures of psychological flexibility. J Contextual Behav Sci. 2019;12:329–46. https://doi.org/10.1016/j.jcbs.2019.02.007 Bragdon LB, Coles ME. Examining heterogeneity of obsessive-compulsive disorder: Evidence for subgroups based on motivations. J Anxiety Dis. 2017;45:64–71. https://doi.org/10. 1016/j.janxdis.2016.12. 002 Summerfeldt LJ, Kloosterman PH, Antony MM, Swinson RP. Examining an obsessive-compulsive core dimensions model: Structural validity of harm avoidance and incompleteness. J Obsessive-Compulsive Relat Dis. 2014;3(2):83-94. https://doi.org/10.1016/ j. jocrd.2014.01.003 Summerfeldt LJ, Kloosterman PH, Parker JDA, Antony MM, Swinson RP. Assessing and validating the obsessive-compulsive-related construct of incompleteness. Poster presented at the 62nd Annual Convention of the Canadian Psychological Association: Ste-Foy, Quebec; 2001. Cervin M, Perrin S. Measuring harm avoidance, incompleteness, and disgust in youth with obsessive-compulsive disorder and anxiety disorders. J Obsessive-Compulsive Relat Dis. 2019;22:100442. https://doi.org/10.1016/j.jocrd.2019.100442 Ecker W, Gönner S. The measurement of motivational dimensions of OCD: incompleteness and harm avoidance. Psychother Psychosom Med Psychol. 2011;61(2):62-9. https://doi.org/10. 1055/s-0030-1251972 Cervin M, Perrin S, Olsson E, Claesdotter-Knutsson E, Lindvall M. Incompleteness, harm avoidance, and disgust: a comparison of youth with OCD, anxiety disorders, and no psychiatric disorder. J Anxiety Dis. 2020;69:102175. https://doi.org/10.1016/j.janxdis.2019. 102175 Sibrava NJ, Boisseau ChL, Eisen JL, Mancebo MC, Rasmussen SA. An Empirical Investigation of Incompleteness in a Large Clinical Sample of Obsessive Compulsive Disorder. J Anxiety Dis. 2016;42:45-51. https://doi.org/10.1016/j.janxdis.2016.05.005 Wheaton MG, Berman NC, Fabricant LE, Abramowitz JS. Differences in obsessive-compulsive symptoms and obsessive beliefs: A comparison between African Americans, Asian Americans, Latino Americans, and European Americans. Cog Behav Ther. 2013;42(1):9–20. https:// doi.org/10.1080/16506073.2012.701663 Asadi S, Daraeian A, Rahmani B, Kargari A, Ahmadian A, Shams J. Exploring Yale-Brown Obsessive-Compulsive Scale symptom structure in Iranian OCD patients using item-based factor analysis. Psychiatry Res. 2016;245:416-22. http://dx.doi.org/10.1016/j Nicolini H, Salin-Pascual R, Cabrera B, Lanzagorta N. Influence of culture in obsessive-compulsive disorder and its treatment. Curr Psychiatry Rev. 2017;13(4):285–92. https://doi.org/10.2174/221155600766618011510593 Ghassemzadeh H, Mojtabai R, Khamseh A, Ebrahimkhani N, Issazadegan AA, Saif-Nobakht Z. Symptoms of obsessive-compulsive disorder in a sample of Iranian patients. Inter J Soc Psychiatry. 2002;48(1):20–8. https://doi.org/10.1177/002076402128783055 Khosravani V, Abramowitz JS, Ardestani SMS, Bastan FSh, Kamali Z. The Persian version of the Dimensional Obsessive-Compulsive Scale (P-DOCS): A psychometric evaluation. J Obsessive-Compulsive Relat Dis. 2020;25:100522. https://doi.org/10.1016/j.jocrd.2020. 100522 Algin S, Nahar JS, Sajib MWH, Arafat SY. Validation of the Bangla version of the dimensional obsessive-compulsive scale. Asian J Psychiatry. 2018;37:136–39. https://doi.org/10.1016/j. ajp.2018.09.001 First MB, Williams JBW, Karg RS, Spitzer RL. SCID-5-CV: Structured Clinical Interview for DSM-5 Disorders, Clinician Version. Arlington: American Psychiatric Association Publishing; 2015. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross‑cultural adaptation of self‑report measures. Spine. 2000;25(24):3186–91. https://doi.org/10.1097/00007632-200012150-00014 Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, Heninger GR, Charney DS. The Yale-Brown obsessive compulsive scale: I. Development, use, and reliability. Arch Gen Psychiatry. 1989;46(11):1006–11. https://doi.org/10.1001/archpsyc. 1989.018101100480 07 Khosravani V, Ardestani SMS, Bastan FSh, Malayeri S. Difficulties in emotion regulation and symptom dimensions in patients with obsessive-compulsive disorder. Curr Psychol. 2018;39(2):1578-88. https://doi.org/10.1007/s12144-018-9859-x Esfahani SR, Motaghipour Y, Kamkari K, Zahiredin A, Janbozorgi M. Reliability and validity of the Persian version of the Yale-Brown obsessive-compulsive scale (Y-BOCS). Iranian J Psychiatry Clin Psychol. 2013;17(4):297–303. http://ijpcp.iums.ac.ir/article-1-1453-en.html Shams G, Karam Ghadiri N, Esmaeli Torkanbori Y, Ebrahimkhani N. Validation and reliability assessment of the Persian version of obsessive beliefs questionnaire-44. Adv Cog Sci. 2004;6(1):23-36. https://acta.tums.ac.ir/index.php/acta/article/view/4729 Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988;56(6):893–7. https://doi.org/10. 1037//0022-006x.56.6.893 Kaviani H, Mousavi A. Psychometric properties of the Persian version of Beck Anxiety Inventory (BAI). Tehran Univ Med J. 2008;66:136–40. http://tumj.tums.ac.ir/article-1-641-en.html Kline RB. Principles and practice of structural equation modeling. 2nd ed. New York: Guilford Press; 2016. Zhang C, Wang T, Zeng P, Zhao M, Zhang G, Zhai S, Meng L, Wang Y, Liu D. Reliability, validity, and measurement invariance of the general anxiety disorder scale among Chinese medical university students. Front. Psychiatry. 2021;12: 648755. https://doi.org/10.3389/fpsyt.2021.648755 Cohen J. Statistical power analysis for the behavioral sciences. New York: Academic Press; 1977. Gerstman BB. Basic biostatistics: Statistics for public health practice. 2nd ed. Burlington, MA: Jones and Bartlett Learning; 2015. Apa F, Tumkaya S, Yucens B, Kashyap H. Are "not just-right experiences" traits and/or state markers for obsessive-compulsive disorder? Eur J Psychiatry. 2022;36:51-9. https://doi.org/ 10.1016/j.ejpsy.2021.09.003 Belloch A, Fornés G, Carrasco A, López-Solá C, Alonso P, Menchón JM. Incompleteness and not just right experiences in the explanation of Obsessive-Compulsive Disorder. Psychiatry Res. 2016;236:1-8. https://doi.org/10.1002/cpp.1842 Summerfeldt LJ, Kloosterman PH, Antony MM, Richter MA, Swinson RP. The relationship between miscellaneous symptoms and major symptom factors in obsessive-compulsive disorder. Behav Res Ther. 2004;42:1453-67. https://doi.org/10.1016/j.brat.2003.09.006 Ecker W, Kupfer J, Gonner S. Incompleteness as a link between obsessive-compulsive personality traits and specific symptom dimensions of obsessive-compulsive disorder. Clin Psychol Psychother. 2014;21(5):394-402. https://doi.org/10.1016/j.jocrd.2013.12.001 Ólafsson RP, Emmelkamp P, Olason DT, Kristjansson A. Disgust and contamination concerns: the mediating role of harm avoidance and incompleteness. Inter J Cog Ther. 2020;13(3): 251-70. https://doi.org/10.1007/s41811-020-00076-5 Sica C, Caudek C, Belloch A, Bottesi G, Ghisi M, Melli G, García-Soriano G, Olatunji BO. Not just right experiences, disgust proneness and their associations to obsessive-compulsive symptoms: a stringent test with structural equation modeling analysis. Cog Ther Res. 2019;43(6):1086–96. https://doi.org/10.1007/s10608-019-10029-8 Mathes BM, Kennedy GA, Wilver NL, Carlton CN, Cougle JR. A multi-method analysis of incompleteness in behavioral treatment of contamination-based OCD. Behav Res Ther. 2019;114:1–6. https://doi.org/10.1016/j.brat.2018.12.008 Rezazadeh Z, Zarani F. Obsessive-compulsive disorder and related cultural issues in Iran: a Systematic Review. Rooyesh. 2022;11(2):45-58. http://frooyesh.ir/article-1-3035-fa.html Pietrefesa AS, Coles ME. Moving beyond an exclusive focus on harm avoidance in obsessive-compulsive disorder: Considering the role of incompleteness. Behav Ther. 2008; 39 (3):224–31. https://doi.org/10.1016/j.beth.2007.08.004 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Oct, 2024 Read the published version in BMC Psychology → Version 1 posted Editorial decision: Revision requested 08 Jun, 2024 Reviews received at journal 02 Jun, 2024 Reviewers agreed at journal 02 Jun, 2024 Reviews received at journal 30 May, 2024 Reviewers agreed at journal 16 May, 2024 Reviewers agreed at journal 12 May, 2024 Reviewers invited by journal 10 May, 2024 Editor assigned by journal 06 May, 2024 Submission checks completed at journal 04 May, 2024 First submitted to journal 30 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4347513","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299384691,"identity":"17d9293d-7e0b-431f-b884-75467960b862","order_by":0,"name":"Mahjoubeh Pourebrahimi","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Mahjoubeh","middleName":"","lastName":"Pourebrahimi","suffix":""},{"id":299384693,"identity":"a6735c69-c050-45bd-b892-02e58d8b5515","order_by":1,"name":"Mehdireza Sarafraz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYNACGyBmb2BjSICLGBDSkgbEPAdI1iKRwEack3T7Dz/78CPhnrz8zOfPHjzcY2dvcID54QeGgns4tZjdSDOe2ZNQbNg4O8fcIOFZMrPBATZjCQaDYjxaGIwZeH8kMDZL57BJJBxgZjM4wGAG9EsCbi3nj39m/JOQYN8mefwZUEs9j8EB9m/4tRzIMWbmSUhI7JFgMANqOSxhcICHgC03coqZZRISkmfw5IC0HDeQPMxTLJGA32GbGd8kJNjObz/+TPLHgWp7vuPtGz98+INbCxbADMQkaRgFo2AUjIJRgAEANu1Oc1w/9VgAAAAASUVORK5CYII=","orcid":"","institution":"Shiraz University","correspondingAuthor":true,"prefix":"","firstName":"Mehdireza","middleName":"","lastName":"Sarafraz","suffix":""},{"id":299384696,"identity":"2e56a735-1870-47c4-81fc-10e3231116be","order_by":2,"name":"Habib Hadianfard","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Habib","middleName":"","lastName":"Hadianfard","suffix":""},{"id":299384699,"identity":"e93ec4ec-ffee-45f6-b3a1-ed8ed3064e48","order_by":3,"name":"Nurallah Mohammadi","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Nurallah","middleName":"","lastName":"Mohammadi","suffix":""}],"badges":[],"createdAt":"2024-04-30 08:36:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4347513/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4347513/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-024-02058-0","type":"published","date":"2024-10-15T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56398125,"identity":"485cea26-e477-4942-99ba-036eadb4584c","added_by":"auto","created_at":"2024-05-13 15:53:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102060,"visible":true,"origin":"","legend":"\u003cp\u003eCFA for the OC-CDQ in clinical sample\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4347513/v1/3d3c87a4e03b17c6a07752cd.png"},{"id":56398124,"identity":"df2dcfe3-f04c-4f47-a944-669a38157d09","added_by":"auto","created_at":"2024-05-13 15:53:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95188,"visible":true,"origin":"","legend":"\u003cp\u003eCFA for the OC-TCDQ in nonclinical sample\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4347513/v1/0d82ee1eacd42d10168786d1.png"},{"id":67149584,"identity":"0fed33b9-48e8-4531-b8f1-59d689759102","added_by":"auto","created_at":"2024-10-21 16:13:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1308928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4347513/v1/4ff5c8ed-c39a-45b0-8bd8-f669bb93cdaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Harm avoidance and incompleteness core motivations in obsessive-compulsive disorder: Cross-cultural adaptation and validation of the Persian version of the Obsessive- Compulsive Core Dimensions Questionnaire (OC-CDQ) in clinical and nonclinical samples","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAttention to the heterogeneous manifestations of OCD symptoms observed in clinical populations, along with different treatment responses, has led researchers to the possibility that OCD is a heterogeneous condition with broad manifestations and different causes. This has motivated researchers to identify distinct and homogeneous subgroups of patients with OCD and to improve treatment strategies by eliminating heterogeneity. As a result, in recent decades, significant studies with different methods have investigated these subgroups and their potential differences in terms of cause and treatment response [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the first classification of OCD, symptoms, and comorbidities were emphasized, including washing versus checking and the presence versus absence of tics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, this approach's conceptual and methodological challenges, including significant overlap between apparently separate \"subsets\" of symptoms, led to the formation of the second OCD classification scheme based on cognitive models [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This plan considered dysfunctional beliefs to be the basis of the pathology of OCD and emphasized the feeling of personal responsibility, the overestimation of threats, the importance of thoughts, and the need to control them. These beliefs affect the interpretation of events in a way that leads to an increase in anxiety and then an increase in the motivation to try to avoid harm [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the abundance of evidence supporting the role of these obsessive beliefs in creating and maintaining OCD, it was found that these cognitive constructs do not fully explain the variability in OCD symptoms. Some studies, using cluster analysis methods and the Obsessive Beliefs Questionnaire (OBQ-44) - a measure of beliefs related to obsessive-compulsive disorder in all fields - have identified a subgroup of patients with OCD who have low levels of these beliefs. This subgroup does not have beliefs related to harm avoidance playing a central role, but instead has a different motivation to avoid \"not just right experiences and incompleteness\" which strongly influences its underlying pathology [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, to more accurately classify symptoms and use an appropriate therapeutic strategy, recent researchers have paid attention to the motivational dimensions of OCD and the functional relationship between symptoms. They consider the \u0026ldquo;why\u0026rdquo; of a behavior (e.g., to avoid harm) more important than the form of that behavior (e.g., checking). Understanding the function of symptoms in patients with OCD is very important due to its motivational heterogeneity. In OCD treatment models, knowing \"fear\" or the main motivation underlying symptoms and avoidance behaviors helps the therapist better manage obsessive behaviors during treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, new approaches such as ACT recognize avoidance in patients with OCD as experiential avoidance (EA). EA is defined as a rigid pattern of trying to avoid or escape from unwanted internal experiences such as distressing thoughts, emotions, or physical sensations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It has received particular attention and is recognized as the most vital functional process in psychopathology related to disorders, including OCD. Studies have shown that while these avoidance behaviors may reduce discomfort in the short term, ultimately, by preventing obsessive thoughts from being encountered and understanding them only as unwanted and disturbing thoughts on the part of the person, they can maintain obsessive thoughts and contribute to the continuation of the disorder as a motivation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost of the studies that have investigated this construct and its relationship with OCD symptoms have focused only on its behavioral dimension and examined its role along with the cognitive fusion component (two cognitive and context constructs of ACT treatment) of OCD symptoms. Some of these studies have shown that these constructs, in higher dimensions and beyond obsessive beliefs, can predict and explain the unacceptable symptoms of OCD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, some studies have not obtained such ability for the construct of experimental avoidance and have attributed the inconsistency of results to the challenges related to EA measurement criteria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Questionnaires such as the Acceptance and Action Questionnaire (AAQ-II) have been criticized for excessive overlap with related constructs such as thought suppression and negative affect [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and the Multidimensional Experiential Avoidance Questionnaire (MEAQ), which assesses general measures of EA, may not effectively assess EA in a specific domain such as obsession [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, in the last two decades, Rasmussen and Eisen developed the main dimensions model based on the OCD motivation model. They aimed to focus on the motivational dimension of EA as the main motivation underlying OCD symptoms. They grouped people with OCD based on two identified motivations: harm avoidance (HA) and incompleteness (INC). HA refers to symptoms that act to avoid harm to oneself or others (such as contracting an illness or unwanted aggression). INC refers to symptoms associated with internalized feelings of inadequacy and incompleteness of actions or intentions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Summerfeldt et al. further developed and adapted the motivation model to depict motivations more dimensionally rather than categorically. They hypothesized that HA and INC underlie compulsions, sometimes alone and sometimes in combination. It is important to evaluate the motivations of people with OCD before treatment, given the various motivations underlying OCD and their significant role in treatment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For this purpose, Summerfeldt et al. developed the Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ), a two-dimensional questionnaire used to assess HA and INC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, several studies have evaluated the psychometric properties of the OC-CDQ and validated its English and German versions in both OCD patients and nonclinical participants [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Some studies have also examined the relationship between HA and INC motivational dimensions with other OCD measures. Researchers found that HA was significantly associated with doubting/checking, obsessing, and washing, while INC was significantly associated with doubting/checking, ordering, neutralizing, OCPD features (such as perfectionism), and lower quality of life [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Bragdon \u0026amp; Coles reported that the subgroup with high HA had greater beliefs about responsibility/overestimation of threat, but the subgroup with high INC had more perfectionistic beliefs and greater intolerance of uncertainty [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough these studies confirmed the two-factor structure, validity, and reliability of the OC-CDQ and examined the relationships between HA and INC dimensions and other criteria related to obsessions, most of these studies were conducted in Western cultures. However, some studies have shown that there are cultural differences in OCD patients, and the validation of OCD criteria in different cultures is an important issue. For example, individuals in Eastern cultures show greater OC symptom severity and obsessive beliefs than those in Western cultures [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The results of the study by Asadi et al. showed that Iranian patients with OCD are different from patients of other nations in terms of obsessions such as aggression, sexuality, and religion. Additionally, cultural factors such as the family system, values, religion, and cultural norms can affect the content of obsessions and compulsions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, studies have shown that although cultural differences may affect the experience or severity of OCD symptoms, these differences may not cause different OCD symptoms in different cultures [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, several studies conducted in various cultures have found the same construct for other OCD measurement tools [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, the OC-CDQ in Eastern countries such as Iran may also have a two-factor structure similar to that of Western cultures.\u003c/p\u003e \u003cp\u003eConsidering that the Persian version of the OC-CDQ, used to assess the level of HA and INC motivations has not yet been published, and that psychometric properties of the OC-CDQ have not been systematically investigated in a clinical and nonclinical peer sample, the present study aimed to investigate the psychometric properties of the Persian translation of the OC-CDQ in two Iranian samples (clinical with OCD to evaluate the two motivational dimensions underlying obsessive-compulsive symptoms (HA and INC) and in a nonclinical peer population to assess these dimensions as traits). Additionally, the relationships between HA and INC dimensions and obsessive symptoms and severity, obsessive beliefs, and other related clinical phenomena were investigated.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eClinical sample\u003c/h2\u003e \u003cp\u003eA total of 209 individuals with OCD who were referred to psychiatric and psychological clinics in Kerman (84 patients), Sirjan (49 patients), Rafsanjan (45 patients), and Zarand (31 patients) in 2023 were purposefully and accessibly selected for investigation. The primary diagnosis of OCD was based on DSM-V criteria, utilizing the Structured Clinical Interview for DSM-5 Disorders (SCID-5-CV) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which was conducted by trained clinicians and clinical psychologists. The participants' OCD symptoms ranged from moderate to severe, as assessed by the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). The mean total score on the Yale-Brown scale was 238.29, with mean subscale scores of 131.49 for obsession and 106.79 for compulsion. Exclusion criteria included comorbidity of other disorders, particularly anxiety disorders as the primary diagnosis, individuals under the age of 18, and those with an education level below a diploma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eNonclinical sample\u003c/h2\u003e \u003cp\u003eThe nonclinical participants were 209 individuals without a history of psychiatric disorders who were selected in an accessible and purposeful manner to match the clinical sample in terms of demographic variables such as age, gender, level of education, and marital status. The demographic characteristics of both the clinical and nonclinical groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the participants (clinical sample\u0026thinsp;=\u0026thinsp;nonclinical sample\u0026thinsp;=\u0026thinsp;209).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKerman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSirjan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRafsanjan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZarand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e167 (79.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115 (55%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117 (56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster's degree\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138 (66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 6.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eProcedures\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll participants from both the clinical and nonclinical groups voluntarily participated in the research. Before the study, the researchers provided a brief explanation of the research aims and obtained written informed consent from all participants. Clinical participants completed the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Obsessive Compulsive Core Dimensions Interview (OC-CDI), Obsessive Beliefs Questionnaire (OBQ-44), Beck Anxiety Inventory (BAI), and Persian Translation of Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ) to assess HA and INC as specific motivations for clinical OCD. Nonclinical participants only completed the OC-CDQ to assess HA and INC as stylistic traits in the nonclinical population. Additionally, 60 participants (30 from each group) were randomly selected and asked to complete the OC-CDQ again after a two-week interval to assess its test-retest reliability.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTranslation and cross-cultural adaptation\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe translation and cross-cultural adaptation of the questionnaire were preformed according to the recommendations of the international guidelines and considering the different lifestyles and cultures [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo adapt the 20-question version of the OC-CDQ for use in the Iranian population, we initially translated the German version into Persian with the assistance of two German language experts who were knowledgeable in psychology terminology. Subsequently, another expert performed a back-translation of the translated version into German and corrected the any discrepancies. Once the translation process was completed, the translated version was provided to two psychological experts who verified the face validity of the questionnaire.\u003c/p\u003e\u003cp\u003eIn a preliminary study, the translated questionnaire was administered to a sample of 10 psychology master's and doctoral students and five patients with OCD. After the questionnaires, were collected, any words that were not understandable to them were rewritten and replaced with the closest word. Throughout all these stages, based on the opinions of the experts and the test sample, there was no need to remove or revise any of the items. Finally, the Persian version was created by keeping 20 items from the original version of the OC-CDQ and using a 5-point Likert scale ranging from 1 (\"never\") to 5 (\"always\"). This version was then implemented on the main sample of 418 people (male and female).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eObsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe OC-CDQ is a 20-item self-report questionnaire that evaluates the suggested motivational dimensions underlying obsessive-compulsive disorder (harm avoidance (HA) and incompleteness (INC)). It is rated on a 5-point Likert scale, ranging from 1 (\"never\") to 5 (\"always\") [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous psychometric investigations have confirmed the use of German version [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], English version [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and Swedish version [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] of this scale. The data from this study also showed good internal consistency for this scale (α\u0026thinsp;=\u0026thinsp;0.80).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eObsessive-Compulsive Core Dimensions Interview (OC-CDI)\u003c/h2\u003e \u003cp\u003eThis interview is used to assess HA and INC motivations in patients with OCD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this interview, immediately after completing the checklist of Y-BOCS symptoms, explanations about HA and INC motivations were given to the participants. The subject is told that each of these motivations, or both motivations at the same time, may be related to their obsessive experiences. After ensuring the subject's full understanding, the interviewer asked two standardized questions for each symptom endorsed as a target in the Y-BOCS: \"To what extent do you associate this with the fear that something harmful/bad might happen?\" and \"To what extent do you associate this with the need to have things 'right' and to make sure they are perfect?\". Otherwise, you feel incomplete, tense, or upset?\". After each question, the respondent selects the best answer for each motivation item on a 0\u0026ndash;4 rating scale. This results in two ratings (from 0 to 4) for each target symptom. Higher scores indicate greater involvement of HA and INC in obsessions and compulsions in the past week. Summerfeld et al. and Cervin \u0026amp; Perrin obtained adequate psychometric properties for the OC-CDI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Cronbach's alpha in the present study indicated excellent internal consistency (α\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe Yale-Brown Obsessive Compulsive Scale (Y-BOCS)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eY-BOCS has two parts: 1. The Symptom Checklist (SC) was used to identify eight types of obsessions (contamination, aggression, sexual, religious, symmetry, physical, hoarding, and miscellaneous) and seven types of compulsions (washing, controlling, repetition, counting, order, hoarding, and miscellaneous). 2. The Severity scale (SS) was used to measure the intensity of obsession or compulsion, regardless of their type [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Its Persian version has been validated [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this sample, Cronbach's alpha showed good internal consistency (α\u0026thinsp;=\u0026thinsp;0.81).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eThe Obsessive Beliefs Questionnaire (\u003c/em\u003eOBQ-44\u003cem\u003e)\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe OBQ-44 is a self-report scale used to assess beliefs related to OCD. It consists of three subscales: 1. inflated responsibility and overestimation of threat (RH), 2. perfectionism and intolerance of uncertainty (PC), and 3. importance and overcontrol of thoughts (IT) and is rated on a 7-point Likert scale [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its Persian version has been previously validated [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBeck Anxiety Inventory (BAI)\u003c/h2\u003e \u003cp\u003eThe BAI is a 21-item self-report scale that measures the severity of physical and cognitive symptoms of anxiety in the past week, using a four-point Likert scale (0 to 3) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Its Persian version has been previously validated [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The Cronbach's alpha for this sample was excellent (α\u0026thinsp;=\u0026thinsp;0.91).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA descriptive analysis of the items was performed, including the study of univariate normal distributions (skewness and kurtosis). Cronbach's alpha coefficient was calculated by removing items individually to identify inconsistent questions. The correlation of each question with the total score was calculated without including the score of that question to evaluate the discrimination index of the items of the OC-CDQ. Additionally, using Pearson's correlation coefficient, the correlation between the subscales and the total score was evaluated in both clinical and nonclinical samples.\u003c/p\u003e \u003cp\u003eConfirmatory factor analysis (CFA) was performed using the maximum likelihood method to investigate whether the OC-CDQ in clinical and nonclinical samples conformed to the factor model of the original version (i.e., 2-factor structure). Model fitting was assessed using the results of the chi-square test (χ2) and χ2 index divided by degrees of freedom (CMIN/DF), root mean square error of approximation (RMSEA), root mean square residual (SRMR), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), normalized fit index (NFI), incremental fit index (IFI), and comparative fit index (CFI). According to Klein (2016), a model fit is considered good if the values of the RMSEA and SRMR indices are less than 0.05, and average if they are between 0.05 and 0.08. A perfect fit is indicated by GFI, AGFI, NFI, IFI, and CFI values above 0.95, while values above 0.90 indicate a good model fit [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCronbach's alpha coefficients and split-half reliability were used to test the internal consistency of the OC-CDQ. Reliability values greater than 0.7 are considered acceptable [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The test-retest reliability of the OC-CDQ total/subscale scores was estimated using Pearson's correlation coefficient. According to Cohen's classification, a correlation coefficient of r\u0026thinsp;\u0026ge;\u0026thinsp;0.50 indicates a strong correlation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe convergent validity of the OC-CDQ was assessed by examining the correlation between the total score and subscales of the OC-CDQ with the total score and subscales of the OC-CDI, Y-BOCS, and OBQ-44. Divergent validity was assessed by examining the correlation between the total score and subscales of the OC-CDQ with the BAI score. The significance threshold was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the strength of the correlation was classified as weak (\u0026lt;\u0026thinsp;0.30), moderate (0.30 to 0.70), and strong (\u0026gt;\u0026thinsp;0.70) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncremental validity was assessed through hierarchical regression analysis to investigate whether the OC-CDQ score predicts the Y-BOCS score more accurately than the OBQ-44 score. In Step 1, the only independent variable for the Y-BOCS was the OBQ-44, while in Step 2, the OC-CDQ was included alongside the OBQ-44. It was expected that there would be a significant increase in predictive power in Step 2 and that the OC-CDQ would be positively correlated with the Y-BOCS.\u003c/p\u003e \u003cp\u003eIn addition, we examined the difference between the scores of the clinical and nonclinical groups on the OC-CDQ to determine discriminant validity through an independent t-test. Statistical analyses were performed using IBM SPSS-26 and Amos-24.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis of the item\u003c/h2\u003e \u003cp\u003eIn the clinical group, the average of all 20 items was in the highest range of the scale (average greater than 3.34); in the nonclinical group, all 20 items were in the lowest range (average less than 2.09). In both the clinical and nonclinical groups, all 20 items had skewness and kurtosis indices less than one in absolute value, which shows no deviation from the normality of the distribution of univariate items. The use of Cronbach's alpha coefficient with item deletion to identify inconsistent questions on the test in both the clinical and nonclinical groups showed that all the test questions had good internal consistency except for question 20. Its elimination slightly increased the Cronbach's alpha coefficient in both the clinical and nonclinical groups (α\u0026thinsp;=\u0026thinsp;0.926 and 0.901, respectively). The item-total correlation coefficient was used to test each item. The results showed that the scores of all items had a positive and significant correlation with the scale's total score, but this correlation was lower for item 20 in both groups. Due to the good correlation of the items with the total score in both groups (more than 0.4), all 20 items were included in the reliability and validity analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the Pearson correlation results showed a positive and significant relationship between HA and INC subscales together and with the total score in the clinical sample (r\u0026thinsp;=\u0026thinsp;0.324, r\u0026thinsp;=\u0026thinsp;0.545 and r\u0026thinsp;=\u0026thinsp;0.639, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively) and the nonclinical sample (r\u0026thinsp;=\u0026thinsp;0.143, r\u0026thinsp;=\u0026thinsp;0.421 and r\u0026thinsp;=\u0026thinsp;0.498, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItem means, standard deviations, ranges, Cronbach's alpha coefficients (α), and corrected item-rest correlations (r).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eClinical sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eNonclinical sample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFactor structure for the OC-CDQ\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eCFA for the OC-CDQ in the clinical sample\u003c/h2\u003e \u003cp\u003eThe results of the CFA confirming the two-factor structure of the OC-CDQ in the clinical sample (n\u0026thinsp;=\u0026thinsp;209) showed a good fit (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All standardized factor loadings were greater than 0.40 and statistically significant (ranging from 0.54\u0026ndash;0.82) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The correlation between the two OC-CDQ subscales was statistically significant (0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eCFA for the\u003c/em\u003e OC-CDQ in the nonclinical sample\u003c/h2\u003e \u003cp\u003eCFA results in the nonclinical sample (n\u0026thinsp;=\u0026thinsp;209) showed a good fit for the two-factor structure (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All standardized factor loadings were greater than 0.40 and statistically significant (ranging from 0.50\u0026ndash;0.83) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The correlation between the two OC-CDQ subscales in the nonclinical sample was statistically significant (r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit indices for the con\u003cem\u003efi\u003c/em\u003ermatory factor analysis models of the core dimensions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC-CDQ self-report data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo factors (revised): trait version, nonclinical sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo factors (revised): state version, clinical sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. OC-CDQ\u0026thinsp;=\u0026thinsp;Obsessive-Compulsive Core Dimensions Questionnaire; CMIN/DF\u0026thinsp;=\u0026thinsp;chi-degree freedom; SRMR\u0026thinsp;=\u0026thinsp;standardized root-mean-square residual; GFI\u0026thinsp;=\u0026thinsp;goodness-of-fi\u003cem\u003et\u003c/em\u003e index; AGFI\u0026thinsp;=\u0026thinsp;adjusted goodness of fit index; NFI\u0026thinsp;=\u0026thinsp;normed fit index; IFI\u0026thinsp;=\u0026thinsp;incremental fit index; CFI\u0026thinsp;=\u0026thinsp;comparative fi\u003cem\u003et\u003c/em\u003e index; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eReliability: internal consistency and temporal consistency\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the clinical sample, the Cronbach's alpha coefficients of the Persian version of the OC-CDQ were 0.80, and those of the HA and INC subscales were 0.81 and 0.78, respectively. Its split-half reliability was 0.80, which indicated satisfactory internal consistency of the scale. Additionally, the total scale and the HA and INC subscales showed good test-retest reliability (0.81, 0.78, and 0.72, respectively).\u003c/p\u003e \u003cp\u003eIn the nonclinical sample, the Cronbach's alpha coefficients of the total scale and the HA and INC subscales were 0.79, 0.79, and 0.83, respectively. Its split-half reliability was 0.80 which indicated satisfactory internal consistency of the scale. Additionally, the total scale and the HA and INC subscales showed good test-retest reliability (0.74, 0.75, and 0.80, respectively).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConvergent and divergent, incremental and discriminant validity\u003c/h2\u003e \u003cp\u003eThe relationships between the total score and subscales of the OC-CDQ with the total score and subscales of the OC-CDI, Y-BOCS (SC and SS), and OBQ-44 were investigated in the clinical sample. The total score of the OC-CDQ had a significant positive relationship with other scales and subscales except hoarding (ranging from 0.12 to 0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Convergent validity was also confirmed for the OC-CDQ subscales: the HA-Q had a significant positive relationship with the total score of the OC-CDI and the HA-I subscale, the total score of the Y-BOCS and the contamination, aggression, sexual, religious, physical, washing, and controlling subscales, the severity scale (SS), the total score of the OBQ-44, and the RH and IT subscales (ranging from 0.14 to 0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, it had a weak and significant negative relationship with the INC-I subscale (r= -0.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and had no significant association with other subscales (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The INC-Q had a meaningful positive relationship with the total score of the OC-CDI and INC-I subscale; the total score of the Y-BOCS and the subscales of contamination, symmetry, washing, controlling, repetition, counting, and order; the severity scale (SS); the total score of OBQ-44; the PC subscale, and the BAI (ranging from 0.16 to 0.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, it had a weak and significant negative relationship with the HA-I subscale (r= -0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and no significant relationship with the other subscales (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, the results of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show a positive and moderate correlation between the OC-CDQ total score and the HA and INC subscales with the BAI (0.47, 0.49, and 0.43, respectively), indicating the divergent validity of this scale.\u003c/p\u003e \u003cp\u003eHierarchical regression analysis was conducted to examine the incremental validity of the OC-CDQ. We investigated whether the OC-CDQ is better at explaining the incremental variance of the Y-BOCS and BAI than the OBQ-44. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the OC-CDQ and its subscales accounted for a significant amount of additional variance (5\u0026ndash;19%) in the Y-BOCS and BAI. The results indicated that even after controlling for the effects of the OBQ-44 on the dependent variables, the effects of the OC-CDQ and its subscales (ΔR2) on the Y-BOCS and BAI remained significant. Specifically, the OC-CDQ and its subscales were found to be significant independent explanatory variables for the Y-BOCS and BAI.\u003c/p\u003e \u003cp\u003eTo compare the average scores obtained in the clinical and nonclinical groups, a t-test for two independent groups was conducted after checking for homogeneity of variance. The results indicated a significant difference between the means of the two groups in the HA and INC subscales, as well as the total score of the OC-CDQ (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The mean scores of the clinical group were greater than those of the nonclinical group (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson's correlation coefficient between the total score and subscales of the OC-TCDQ with the total scores and subscales of the OC-CDI, Y-BOCS, OBQ-44, and BAI in the clinical sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOC-CDQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA-Q\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINC-Q\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC-CDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHA-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINC-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY-BOCS\u003c/p\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaggression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereligious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ephysical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehoarding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ew\u003c/em\u003eashing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrolling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erepetition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehoarding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote.\u003c/b\u003e OC-CDQ: Obsessive-Compulsive Core Dimensions Questionnaire; HA; Harm avoidance; INC: Incompleteness; OC-CDI: Obsessive-Compulsive Core Dimensions Interview; Y-BOCS: Yale-Brown Obsessive Compulsive Scale; SC: Symptom Checklist; SS: Severity Scale; OBQ-44: Obsessive Beliefs Questionnaire; RH: inflated responsibility and overestimation of threat; PC: perfectionism and intolerance of uncertainty; IT: importance and overcontrol of thoughts; BAI: Beck Anxiety Inventory.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e**\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e*\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncremental validity of the OC-CDQ above the OBQ-44\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΔR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eY-BOCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal scores of OC-CDQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal scores of OC-CDQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBQ-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote.\u003c/b\u003e Y-BOCS: Yale-Brown Obsessive Compulsive Scale; OBQ-44: Obsessive Beliefs Questionnaire; OC-CDQ: Obsessive-Compulsive Core Dimensions Questionnaire; HA; Harm avoidance; INC: Incompleteness; BAI: Beck Anxiety Inventory.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003csup\u003e**\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e*\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the total score and subscales of the OC-CDQ between the clinical and nonclinical groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (SD.)\u003c/p\u003e \u003cp\u003eClinical sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM (SD.)\u003c/p\u003e \u003cp\u003eNonclinical sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC-CDQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.06 (10.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.96 (6.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.53 (5.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.38 (3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.52 (6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.57 (4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote.\u003c/b\u003e OC-CDQ: Obsessive-Compulsive Core Dimensions Questionnaire; HA; Harm avoidance; INC: Incompleteness; BAI: Beck Anxiety Inventory.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSimilar to the original OC-CDQ, the CFA results in this study showed that the OC-CDQ has the same two-factor structure in both clinical and nonclinical populations, and this result was consistent with previous studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The analysis of the items in both the clinical and nonclinical groups showed that the removal of any of the items had no significant effect on increasing Cronbach's alpha. Only the removal of item 20 in the clinical group to a small extent (α\u0026thinsp;=\u0026thinsp;0.90) and in the nonclinical group to a greater extent (α\u0026thinsp;=\u0026thinsp;0.92) increased the Cronbach's alpha coefficient. Further examination of this item confirms the above hypothesis because the examination of the discrimination index of the items shows that item 20 has a lower correlation with the modified total-item correlation than other items in both groups (r\u0026thinsp;=\u0026thinsp;0.44 and r\u0026thinsp;=\u0026thinsp;0.40, respectively). The inconsistency of this item may indicate that the above item is ambiguous and should be further investigated in terms of the content of the translation.\u003c/p\u003e \u003cp\u003eA relatively moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.32) was obtained between the HA and INC dimensions in the clinical population, indicating that these dimensions measure distinct motives but also capture commonalities. This relationship suggests that in the clinical population, compulsion is often caused by both dimensions, but the effectiveness of these two dimensions may differ among individuals. Previous studies have also reported similar results [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This finding, along with the next findings, shows that both the HA and INC dimensions had a significant positive relationship with the total score, but the INC dimension had a greater correlation with the total score than the HA dimension, which is consistent with the findings of previous studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This may indicate how these two dimensions functionally affect this disorder. These studies have suggested that while HA may be the key to the initiation of obsessive rituals, INC is the key to their continuation. In the nonclinical group, a relatively weak positive correlation (r\u0026thinsp;=\u0026thinsp;0.15) was obtained between the two dimensions of HA and INC, which is in accordance with the findings of Summerfeldt et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. They suggested that in the nonclinical population, the HA dimension is associated with constructs such as trait anxiety, which form the personality substratum of anxiety disorders. However, they consider INC to be a type of \"sensory perfectionism\" that is a precursor to obsessive-compulsive personality disorder (OCPD) and consider it to be two independent dimensions. Consistent with Summerfeldt's model [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and other previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], that assumed that HA plays a greater role than INC in other anxiety disorders, in this study, the level of clinical anxiety in the clinical group was more related to HA than to INC (r\u0026thinsp;=\u0026thinsp;0.49 and r\u0026thinsp;=\u0026thinsp;0.43, respectively).\u003c/p\u003e \u003cp\u003eThe reliability of the Persian version of the OC-CDQ in both the clinical and nonclinical groups was excellent, as indicated by the Cronbach's alpha coefficient (α\u0026thinsp;\u0026gt;\u0026thinsp;0.75) and the split-half method. It also demonstrated good test-retest reliability (\u0026gt;\u0026thinsp;0.70), supporting the temporal stability of the scale. These results were consistent with those of previous studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. To assess the validity of the OC-CDQ, the present study revealed that the total score of the OC-CDQ showed excellent convergent validity, with strong positive correlations with the OC-CDI, Y-BOCS, and OBQ-44 (r\u0026thinsp;=\u0026thinsp;0.76, r\u0026thinsp;=\u0026thinsp;0.69 and r\u0026thinsp;=\u0026thinsp;0.67, respectively). It also demonstrated good divergent validity, with a moderate positive correlation with the BAI (r\u0026thinsp;=\u0026thinsp;0.47). Furthermore, the strong correlation between HA and INC in the OC-CDQ with HA and INC in the OC-CDI (r\u0026thinsp;=\u0026thinsp;0.83 and r\u0026thinsp;=\u0026thinsp;0.77, respectively), as well as the weak and negative correlation between HA and INC in the OC\u0026ndash;CDQ with INC and HA in the OC-CDI (r=-0.09 and r=-0.11, respectively), further support the construct validity of the OC-CDQ and are consistent with previous research [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study, in accordance with studies conducted in other cultures, showed that HA and INC are related to contamination, washing, and controll [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, INC is uniquely related to symmetry, ordering [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], counting, and repetition [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], while hoarding is not related to any HA or INC motives [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, this study revealed that HA is uniquely related to aggression, sexual, religious, and physical obsessions. In a systematic review of cultural issues related to OCD in Iran, Rezazadeh and Zarani found that an ineffective belief-value system can lead to misinterpretations and incorrect attitudes in people with OCD [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. These attitudes can be seen as related to the HA dimension, including setting strict moral standards and morbid guilt, extreme responsibility, increased rumination, fear of being harmed, fear of making mistakes and injuring others. Factors that, according to the content of Y-BOCS items in aggressive, sexual, religious, and physical obsessions, can determine their role in these obsessions.\u003c/p\u003e \u003cp\u003eIn this study, the INC group had strong beliefs about PC. In contrast, the HA group showed a traditional OCD profile characterized by increased beliefs related to RH and IT. The findings of the present study agree with those of previous studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The mean values of the two subscales and the total score indicated a more intense experience of HA and INC in the OCD group than in the nonclinical group, with a significant difference between these two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In accordance with previous studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], this finding shows that harm avoidance and incompleteness are common phenomena in patients with OCD, and the OC-CDQ can distinguish between them.\u003c/p\u003e \u003cp\u003eDespite the existence of cultural differences, repeating the results of previous studies in Iran confirms the psychometric properties of the Persian version of the OC-CDQ. This questionnaire can provide valuable information on the motivations underlying obsession and its relationship with beliefs, obsessions, and compulsions in Iranian adulthood. Although our sample's characteristics are consistent with those of other extensive OCD studies, this study was conducted in adults. The findings cannot be generalized to a larger OCD population. Future research could validate the OC CDQ for other age groups. Additionally, this sample was not epidemiologic, and the relatively low representation of racial/ethnic minorities limits generalizability. Second, due to the bias of the participants in answering the self-report questions, future researchers can explore alternative methods, such as structural and functional neuroimaging methods, to expand their understanding of the underlying neural substrate of HA and INC. Third, nearly half of the sample had high or low levels of both motivations. Therefore, it is essential to understand the nature of the \"low\" and \"high\" groups when considering a motivation-based classification system.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe present study showed that the OC-CDQ has good psychometric properties, similar to studies in Western cultures, and is a reliable tool to be used in Iranian patients with OCD to investigate the underlying motivations of OCD, facilitate the conceptualization of clinical heterogeneity in OCD, and guide subsequent treatment protocols. It can also be a valid scale to be used in the nonclinical Iranian population to examine the traits of harm avoidance and incompleteness as underlying traits of clinical disorders, including anxiety disorders and OCPD.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the study participants\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMP and MRS conceptualized the study, adapted the instrument, collected and analyzed data, and drafted and wrote the article. HH and NM supervised the data analysis, editing, and final manuscript preparation. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for the initiation or completion of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This article was derived from the first author\u0026rsquo;s doctoral dissertation in Clinical Psychology from Shiraz University. All questionnaires and methodology for this study were approved by the Research Ethics Committee of Shiraz University of Medical Sciences, Iran (ethical code: IR.US.PSYEDU.REC.1402.013). Informed consent was obtained from all participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Clinical Psychology, Faculty of Educational Sciences and Psychology, Shiraz University, Shiraz, Iran\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbramovitch A, Abramowitz JS, Mittelman A. The neuropsychology of adult obsessive-compulsive disorder: A meta-analysis. Clin Psychol Rev. 2013;33(8):1163-71. https://doi.org/10. 1016/ j.cpr.2013.09.004\u003c/li\u003e\n \u003cli\u003eFarris SG, McLean CP, Van Meter PE, Simpson HB, Foa EB. Treatment response, symptom remission, and wellness in obsessive-compulsive disorder. J Clin Psychiatry. 2013;74(7):685\u0026ndash;90. https://doi.org/10.4088/JCP.12m07789\u003c/li\u003e\n \u003cli\u003eMcKay D, Abramowitz JS, Calamari JE, Kyrios M, Radomsky A, Sookman D, \u0026hellip; Wilhelm S. A critical evaluation of obsessive-compulsive disorder subtypes: symptoms versus mechanisms Clin Psychol Rev. 2004;24(3):283\u0026ndash;313. https://doi.org/10.1016/j.cpr.2004.04.003\u003c/li\u003e\n \u003cli\u003eClark DA. Cognitive-behavioral therapy for OCD. New York: Guilford Press; 2004.\u003c/li\u003e\n \u003cli\u003eObsessive Compulsive Cognitions Working Group. Psychometrics Validation of the Obsessive Beliefs Questionnaire and the Interpretation of Intrusions Inventory\u0026mdash; Part 2: Factor Analysis and Testing of a Brief Version. Behav Res Ther. 2005;43:1527-42. http://dx.doi.org/ 10.1016/j.brat.2004.07.010\u003c/li\u003e\n \u003cli\u003ePolman A, O\u0026apos;Connor KP, Huisman M. Dysfunctional belief-based subgroups and inferential confusion in obsessive-compulsive disorder. Pers Individ Dif. 2011;50(2):153\u0026ndash;8. https://doi.org/10.1016/j.paid.2010.09.017\u003c/li\u003e\n \u003cli\u003eSchreck M, Georgiadis Ch, Garcia A, Benito K, Case B, Herren J, Walther M, Freeman J. Core Motivations of Childhood Obsessive‑Compulsive Disorder: The Role of Harm Avoidance and Incompleteness. Child Psychiatry Hum Dev. 2020;52(5):957-65. https://doi.org/ 10.1007/s10578- 020- 01075-5\u003c/li\u003e\n \u003cli\u003eHayes SC, Wilson KG, Gifford EV, Follette VM, Strosahl K. Experiential avoidance and behavioral disorders: A functional dimensional approach to diagnosis and treatment. J Consult Clin Psychol. 1996;64:1152\u0026ndash;68. https://doi/10.1037/0022-006X.64.6.1152\u003c/li\u003e\n \u003cli\u003eOjalehto HJ, Hellberg SN, Butcher MW, Buchholz JL, Timpano KR, Abramowitz JS. Experiential avoidance and the misinterpretation of intrusions as prospective predictors of postpartum obsessive-compulsive symptoms in first-time parents. J Contextual Behav Sci. 2021;20:137\u0026ndash;43. https://doi.org/10.1016/j.jcbs.2021.04.003\u003c/li\u003e\n \u003cli\u003eReuman L, Buchholz J, Abramowitz JS. Obsessive beliefs, experiential avoidance, and cognitive fusion as predictors of obsessive-compulsive disorder symptom dimensions. J Contextual Behav Sci. 2018;9:15-20. https://doi.org/10.1016/j.jcbs.2018.06.001\u003c/li\u003e\n \u003cli\u003eAkbari M, Seydavi M, Hosseini ZS, Krafft J, Levin, ME. Experiential avoidance in depression, anxiety, obsessive-compulsive related, and posttraumatic stress disorders: A comprehensive systematic review and meta-analysis. J Contextual Behav Sci. 2022;24:65-78. https://doi.org/ 10.1016/j.jcbs.2022.03.007\u003c/li\u003e\n \u003cli\u003eTyndall I, Waldeck D, Pancani L, Whelan R, Roche B, Dawson DL. The Acceptance and Action Questionnaire-II (AAQ-II) as a measure of experiential avoidance: Concerns over discriminant validity. J Contextual Behav Sci. 2019;12:278\u0026ndash;84. https://doi.org/10.1016/j.jcbs. 2018.09.005\u003c/li\u003e\n \u003cli\u003eOng CW, Lee EB, Levin ME, Twohig MP. A review of AAQ. variants and other context-specific measures of psychological flexibility. J Contextual Behav Sci. 2019;12:329\u0026ndash;46. https://doi.org/10.1016/j.jcbs.2019.02.007\u003c/li\u003e\n \u003cli\u003eBragdon LB, Coles ME. Examining heterogeneity of obsessive-compulsive disorder: Evidence for subgroups based on motivations. J Anxiety Dis. 2017;45:64\u0026ndash;71. https://doi.org/10. 1016/j.janxdis.2016.12. 002\u003c/li\u003e\n \u003cli\u003eSummerfeldt LJ, Kloosterman PH, Antony MM, Swinson RP. Examining an obsessive-compulsive core dimensions model: Structural validity of harm avoidance and incompleteness. J Obsessive-Compulsive Relat Dis. 2014;3(2):83-94. https://doi.org/10.1016/ j. jocrd.2014.01.003\u003c/li\u003e\n \u003cli\u003eSummerfeldt LJ, Kloosterman PH, Parker JDA, Antony MM, Swinson RP. Assessing and validating the obsessive-compulsive-related construct of incompleteness. Poster presented at the 62nd Annual Convention of the Canadian Psychological Association: Ste-Foy, Quebec; 2001.\u003c/li\u003e\n \u003cli\u003eCervin M, Perrin S. Measuring harm avoidance, incompleteness, and disgust in youth with obsessive-compulsive disorder and anxiety disorders. J Obsessive-Compulsive Relat Dis. 2019;22:100442. https://doi.org/10.1016/j.jocrd.2019.100442\u003c/li\u003e\n \u003cli\u003eEcker W, G\u0026ouml;nner S. The measurement of motivational dimensions of OCD: incompleteness and harm avoidance. Psychother Psychosom Med Psychol. 2011;61(2):62-9. https://doi.org/10. 1055/s-0030-1251972\u003c/li\u003e\n \u003cli\u003eCervin M, Perrin S, Olsson E, Claesdotter-Knutsson E, Lindvall M. Incompleteness, harm avoidance, and disgust: a comparison of youth with OCD, anxiety disorders, and no psychiatric disorder. J Anxiety Dis. 2020;69:102175. https://doi.org/10.1016/j.janxdis.2019. 102175\u003c/li\u003e\n \u003cli\u003eSibrava NJ, Boisseau ChL, Eisen JL, Mancebo MC, Rasmussen SA. An Empirical Investigation of Incompleteness in a Large Clinical Sample of Obsessive Compulsive Disorder. J Anxiety Dis. 2016;42:45-51. https://doi.org/10.1016/j.janxdis.2016.05.005\u003c/li\u003e\n \u003cli\u003eWheaton MG, Berman NC, Fabricant LE, Abramowitz JS. Differences in obsessive-compulsive symptoms and obsessive beliefs: A comparison between African Americans, Asian Americans, Latino Americans, and European Americans. Cog Behav Ther. 2013;42(1):9\u0026ndash;20. https:// doi.org/10.1080/16506073.2012.701663\u003c/li\u003e\n \u003cli\u003eAsadi S, Daraeian A, Rahmani B, Kargari A, Ahmadian A, Shams J. Exploring Yale-Brown Obsessive-Compulsive Scale symptom structure in Iranian OCD patients using item-based factor analysis. Psychiatry Res. 2016;245:416-22. http://dx.doi.org/10.1016/j\u003c/li\u003e\n \u003cli\u003eNicolini H, Salin-Pascual R, Cabrera B, Lanzagorta N. Influence of culture\u003cbr\u003ein obsessive-compulsive disorder and its treatment. Curr Psychiatry Rev. 2017;13(4):285\u0026ndash;92. https://doi.org/10.2174/221155600766618011510593\u003c/li\u003e\n \u003cli\u003eGhassemzadeh H, Mojtabai R, Khamseh A, Ebrahimkhani N, Issazadegan AA, Saif-Nobakht Z. Symptoms of obsessive-compulsive disorder in a sample of Iranian patients. Inter J Soc Psychiatry. 2002;48(1):20\u0026ndash;8. https://doi.org/10.1177/002076402128783055\u003c/li\u003e\n \u003cli\u003eKhosravani V, Abramowitz JS, Ardestani SMS, Bastan FSh, Kamali Z. \u003cem\u003eThe Persian version of the Dimensional Obsessive-Compulsive Scale (P-DOCS): A psychometric evaluation.\u003c/em\u003e J Obsessive-Compulsive Relat Dis. 2020;25:100522. https://doi.org/10.1016/j.jocrd.2020. 100522\u003c/li\u003e\n \u003cli\u003eAlgin S, Nahar JS, Sajib MWH, Arafat SY. Validation of the Bangla version of the dimensional obsessive-compulsive scale. Asian J Psychiatry. 2018;37:136\u0026ndash;39. https://doi.org/10.1016/j. ajp.2018.09.001\u003c/li\u003e\n \u003cli\u003eFirst MB, Williams JBW, Karg RS, Spitzer RL. SCID-5-CV: Structured Clinical Interview for DSM-5 Disorders, Clinician Version. Arlington: American Psychiatric Association Publishing; 2015.\u003c/li\u003e\n \u003cli\u003eBeaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross‑cultural adaptation of self‑report measures. Spine. 2000;25(24):3186\u0026ndash;91. https://doi.org/10.1097/00007632-200012150-00014\u003c/li\u003e\n \u003cli\u003eGoodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, Heninger GR, Charney DS. The Yale-Brown obsessive compulsive scale: I. Development, use, and reliability. Arch Gen Psychiatry. 1989;46(11):1006\u0026ndash;11. https://doi.org/10.1001/archpsyc. 1989.018101100480 07\u003c/li\u003e\n \u003cli\u003eKhosravani V, Ardestani SMS, Bastan FSh, Malayeri S. Difficulties in emotion regulation and symptom dimensions in patients with obsessive-compulsive disorder. Curr Psychol. 2018;39(2):1578-88. https://doi.org/10.1007/s12144-018-9859-x\u003c/li\u003e\n \u003cli\u003eEsfahani SR, Motaghipour Y, Kamkari K, Zahiredin A, Janbozorgi M. Reliability and validity of the Persian version of the Yale-Brown obsessive-compulsive scale (Y-BOCS). Iranian J Psychiatry Clin Psychol. 2013;17(4):297\u0026ndash;303. http://ijpcp.iums.ac.ir/article-1-1453-en.html\u003c/li\u003e\n \u003cli\u003eShams G, Karam Ghadiri N, Esmaeli Torkanbori Y, Ebrahimkhani N. Validation and reliability assessment of the Persian version of obsessive beliefs questionnaire-44. Adv Cog Sci. 2004;6(1):23-36. https://acta.tums.ac.ir/index.php/acta/article/view/4729\u003c/li\u003e\n \u003cli\u003eBeck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988;56(6):893\u0026ndash;7. https://doi.org/10. 1037//0022-006x.56.6.893\u003c/li\u003e\n \u003cli\u003eKaviani H, Mousavi A. Psychometric properties of the Persian version of Beck Anxiety Inventory (BAI). Tehran Univ Med J. 2008;66:136\u0026ndash;40. http://tumj.tums.ac.ir/article-1-641-en.html\u003c/li\u003e\n \u003cli\u003eKline RB. Principles and practice of structural equation modeling. 2nd ed. New York: Guilford Press; 2016.\u003c/li\u003e\n \u003cli\u003eZhang C, Wang T, Zeng P, Zhao M, Zhang G, Zhai S, Meng L, Wang Y, Liu D.\u003cbr\u003eReliability, validity, and measurement invariance of the general anxiety\u003cbr\u003edisorder scale among Chinese medical university students. Front. Psychiatry. 2021;12: 648755. https://doi.org/10.3389/fpsyt.2021.648755\u003c/li\u003e\n \u003cli\u003eCohen J. Statistical power analysis for the behavioral sciences. New York: Academic Press; 1977.\u003c/li\u003e\n \u003cli\u003eGerstman BB. Basic biostatistics: Statistics for public health practice. 2nd ed.\u003cbr\u003eBurlington, MA: Jones and Bartlett Learning; 2015.\u003c/li\u003e\n \u003cli\u003eApa F, Tumkaya S, Yucens B, Kashyap H. Are \u0026quot;not just-right experiences\u0026quot; traits and/or state markers for obsessive-compulsive disorder? Eur J Psychiatry. 2022;36:51-9. https://doi.org/ 10.1016/j.ejpsy.2021.09.003\u003c/li\u003e\n \u003cli\u003eBelloch A, Forn\u0026eacute;s G, Carrasco A, L\u0026oacute;pez-Sol\u0026aacute; C, Alonso P, Mench\u0026oacute;n JM. Incompleteness and not just right experiences in the explanation of Obsessive-Compulsive Disorder. Psychiatry Res. 2016;236:1-8. https://doi.org/10.1002/cpp.1842\u003c/li\u003e\n \u003cli\u003eSummerfeldt LJ, Kloosterman PH, Antony MM, Richter MA, Swinson RP. The relationship between miscellaneous symptoms and major symptom factors in obsessive-compulsive disorder. Behav Res Ther. 2004;42:1453-67. https://doi.org/10.1016/j.brat.2003.09.006\u003c/li\u003e\n \u003cli\u003eEcker W, Kupfer J, Gonner S. Incompleteness as a link between obsessive-compulsive personality traits and specific symptom dimensions of obsessive-compulsive disorder. Clin Psychol Psychother. 2014;21(5):394-402. https://doi.org/10.1016/j.jocrd.2013.12.001\u003c/li\u003e\n \u003cli\u003e\u0026Oacute;lafsson RP, Emmelkamp P, Olason DT, Kristjansson A. Disgust and contamination concerns: the mediating role of harm avoidance and incompleteness. Inter J Cog Ther. 2020;13(3): 251-70. https://doi.org/10.1007/s41811-020-00076-5\u003c/li\u003e\n \u003cli\u003eSica C, Caudek C, Belloch A, Bottesi G, Ghisi M, Melli G, Garc\u0026iacute;a-Soriano G, Olatunji BO. Not just right experiences, disgust proneness and their associations to obsessive-compulsive symptoms: a stringent test with structural equation modeling analysis. Cog Ther Res. 2019;43(6):1086\u0026ndash;96. https://doi.org/10.1007/s10608-019-10029-8\u003c/li\u003e\n \u003cli\u003eMathes BM, Kennedy GA, Wilver NL, Carlton CN, Cougle JR. A multi-method analysis of incompleteness in behavioral treatment of contamination-based OCD. Behav Res Ther. 2019;114:1\u0026ndash;6. https://doi.org/10.1016/j.brat.2018.12.008\u003c/li\u003e\n \u003cli\u003eRezazadeh Z, Zarani F. Obsessive-compulsive disorder and related cultural issues in Iran: a Systematic Review. Rooyesh. 2022;11(2):45-58. http://frooyesh.ir/article-1-3035-fa.html\u003c/li\u003e\n \u003cli\u003ePietrefesa AS, Coles ME. Moving beyond an exclusive focus on harm avoidance in obsessive-compulsive disorder: Considering the role of incompleteness. \u003cem\u003eBehav Ther.\u0026nbsp;\u003c/em\u003e2008;\u003cem\u003e39\u003c/em\u003e(3):224\u0026ndash;31. https://doi.org/10.1016/j.beth.2007.08.004\u003cspan dir=\"RTL\"\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Harm avoidance, Incompleteness, Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ), OCD, Persian version, Psychometric. ","lastPublishedDoi":"10.21203/rs.3.rs-4347513/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4347513/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e The Obsessive-Compulsive Core Dimensions Questionnaire (OC-CDQ) is the first measure created to assess the motivational dimensions of experiential avoidance in individuals with obsessive-compulsive disorder (Harm Avoidance (HA) and Incompleteness (INC)). The OC-CDQ has been translated and validated in several languages, but not in Persian. This study aimed to translate and investigate the factor structure, reliability, and validity of the Persian version of the OC-CDQ in a clinical group with obsessive-compulsive disorder (OCD) and nonclinical group without OCD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThe Persian version of the OC-CDQ was translated and culturally adapted according to international guidelines, including translation, back‑translation, pretesting, and expert committee review. A total of 209 outpatients diagnosed with OCD based on the DSM-V completed the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Obsessive-Compulsive Core Dimensions Interview (OC-CDI), Persian version of the OC-CDQ, Obsessive Belief Questionnaire (OBQ-44) and Beck's Anxiety Inventory (BAI). Additionally, 209 participants without OCD completed the Persian version of the OC-CDQ. To investigate the test-retest reliability, 60 people (30 people from each group) completed the Persian version of the OC-CDQ again after a two-week interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Similar to the original version, the confirmatory factor analysis (CFA) indicated a good fit of the two-factor structure. The reliability of the Persian version of the OC-CDQ, as determined by the Cronbach's alpha coefficient, split-half, and retest indicated good reliability (clinical sample: ranging from 0.72 to 0.81, nonclinical sample: ranging from 0.74 to 0.83). Convergent validity was evaluated through the correlation of the OC-CDQ with the Y-BOCS, OC-CDI, and OBQ-44. Divergent validity was evaluated through correlation with BAI. The results supported the validity of the Persian version of the OC-CDQ (p\u0026lt;0.05). The results of hierarchical regression analysis indicated the incremental validity of this scale in predicting the Y-BOCS and BAI compared to the OBQ-44 (p\u0026lt;0.05), and comparing the scores of two groups with and without OCD indicated its discriminant validity (p\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The Persian‑OC-CDQ, developed after the translation and cross‑cultural adaptation process, is a valid tool for evaluating the motivational dimensions of harm avoidance and incompleteness in Iranian individuals with and without OCD.\u003c/p\u003e","manuscriptTitle":"Harm avoidance and incompleteness core motivations in obsessive-compulsive disorder: Cross-cultural adaptation and validation of the Persian version of the Obsessive- Compulsive Core Dimensions Questionnaire (OC-CDQ) in clinical and nonclinical samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 15:53:18","doi":"10.21203/rs.3.rs-4347513/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-08T14:43:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-02T21:38:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216184442902961826793798959676407828251","date":"2024-06-02T17:13:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-30T06:44:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277960931050197048668737846871165257664","date":"2024-05-16T20:46:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306029209502130241421310198300216351024","date":"2024-05-12T16:47:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-10T15:55:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-06T13:37:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-04T22:08:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2024-04-30T08:34:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dfaa22ab-313a-43ec-b7ae-4638259e052d","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T16:09:16+00:00","versionOfRecord":{"articleIdentity":"rs-4347513","link":"https://doi.org/10.1186/s40359-024-02058-0","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2024-10-15 15:58:06","publishedOnDateReadable":"October 15th, 2024"},"versionCreatedAt":"2024-05-13 15:53:18","video":"","vorDoi":"10.1186/s40359-024-02058-0","vorDoiUrl":"https://doi.org/10.1186/s40359-024-02058-0","workflowStages":[]},"version":"v1","identity":"rs-4347513","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4347513","identity":"rs-4347513","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}