Reliability and validity of the Japanese version of the Obsessive-Compulsive Inventory-Revised (OCI-R)

Koike, Haruna; Tsuchiyagaito, Aki; Hirano, Yoshiyuki; Oshima, Fumiyo; Asano, Kenichi; Sugiura, Yoshinori; Kobori, Osamu; Ishikawa, Rie; Nishinaka, Hirofumi; Shimizu, Eiji; Nakagawa, Akiko

doi:10.1007/s12144-017-9741-2

Cited by 10 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To investigate convergent validity, we used the Japanese Version of the OCI-R ( Koike et al., 2020 ), which consists of 18 items. Participants rated each item on a 5-point Likert scale ranging from 0 ( not at all ) to 4 ( extremely ).…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, in the Padua Inventory, low mean scores for ordering and impulse were observed in addition to a distorted distribution: ordering ( M = 2.99, range = 0–16, skewness = 1.36) and impulse ( M = 6.97, range = 0–37, skewness = 1.46) ( Sugiura and Tanno, 2000 ). Furthermore, in the Japanese version of the OCI-R ( Koike et al., 2020 ), the floor effect was observed in the neutralizing and cleaning dimensions. Hence, established scales have been ineffective in measuring OC tendencies among university students in Japan due to problems with the factor structure and discrimination.…”

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

Development of a scale to assess obsessive-compulsive tendencies among Japanese university students

Sashikata

Ozawa

2022

Heliyon

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Development of a scale to assess obsessive-compulsive tendencies among Japanese university students

Sashikata

Ozawa

2022

Heliyon

View full text Add to dashboard Cite

“…Obsessive compulsive symptoms: We used the Japanese version [ 20 ] of the Obsessive Compulsive Inventory-Revised (OCI-R; [ 21 ]). This consists of 18 items with a 7-point scale.…”

Section: Methodsmentioning

confidence: 99%

Development of a Scale for COVID-19 Stigma and Its Psychometric Properties: A Study among Pregnant Japanese Women

Kitamura

Matsunaga

Hada

et al. 2022

Behavioral Sciences

View full text Add to dashboard Cite

Background: Stigma towards COVID-19 may negatively impact people who suffer from it and those supporting and treating them. Objective: To develop and validate a scale to assess 11-item COVID-19–related stigma. Methods: A total of 696 pregnant women at a gestational age of 12 to 15 weeks were surveyed using an online survey with a newly developed scale for COVID-19 stigma and other variables. The internal consistency of the scale was calculated using omega indices. We also examined the measurement invariance of the scale. Results: Exploratory factor analyses (EFAs) of the scale items were conducted using a halved sample (n = 350). Confirmatory factor analyses (CFAs) among the other halved sample (n = 346) compared the single-, two-, three-, and four-factor structure models derived from the EFAs. The best model included the following three-factor structure (χ2/df = 2.718, CFI = 0.960, RMSEA = 0.071): Omnidirectional Avoidance, Attributional Avoidance, and Hostility. Its internal consistency was excellent (all omega indices > 0.70). The three-factor structure model showed configuration, measurement, and structural invariances between primiparas and multiparas, and between younger (less than 32 years) and older women (32 years or older). Fear of childbirth, mother–fetal bonding, obsessive compulsive symptoms, depression, adult attachment self-model, and borderline personality traits were not significantly correlated with the Omnidirectional Avoidance subscale but correlated with the Attributional Avoidance and Hostility subscales (p < 0.001). Conclusion: The findings suggested that our scale for COVID-19 stigma was robust in its factor structure, as well as in construct validity.

show abstract

“…This 18-item measure assesses participants’ obsessive and compulsive symptoms using a 5-point Likert scale from 1 (not at all) to 5 (very much). Koike et al [ 71 ] showed that the Japanese version has the following six-factor structure: i) hoarding (3 items; e.g., “I have saved up so many things that they get in the way”), ii) checking (3 items; e.g., “I check things more often than necessary”), iii) ordering (3 items; e.g., “I get upset if objects are not arranged properly”), iv) neutralizing (3 items; e.g., “I feel I have to repeat certain numbers”), v) washing (3 items; e.g., “I sometimes have to wash or clean myself simply because I feel contaminated”), and vi) obsessing (3 items; e.g., “I find it difficult to control my own thoughts”).…”

Section: Methodsmentioning

confidence: 99%

Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering

et al. 2021

View full text Add to dashboard Cite

Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (Mage = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.

show abstract

Reliability and validity of the Japanese version of the Obsessive-Compulsive Inventory-Revised (OCI-R)

Cited by 10 publications

References 22 publications

Development of a scale to assess obsessive-compulsive tendencies among Japanese university students

Development of a scale to assess obsessive-compulsive tendencies among Japanese university students

Development of a Scale for COVID-19 Stigma and Its Psychometric Properties: A Study among Pregnant Japanese Women

Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering

Contact Info

Product

Resources

About