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Factorial structure and measurement invariance of the Chinese version of the Oral Health Impact Profile-14 among clinical populations and non-clinical populations: an evidence for public oral investigations

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Title: Factorial structure and measurement invariance of the Chinese version of the Oral Health Impact Profile-14 among clinical populations and non-clinical populations: an evidence for public oral investigations
Authors: Guang-Hui Yang, Yao Feng, Lan-Xin Xue, Ze-Yue Ou-Yang, Yi-Fan Yang, Ya-Qiong Zhao, Jie Zhao, Jing Hu, Qin Ye, Xiao-Lin Su, Ning-Xin Chen, Meng-Mei Zhong, Yun-Zhi Feng, Yue Guo
Source: BMC Oral Health, Vol 23, Iss 1, Pp 1-10 (2023)
Publisher Information: BMC, 2023.
Publication Year: 2023
Collection: LCC:Dentistry
Subject Terms: Oral Health Impact Profile-14, Oral health-related quality of life, Measurement invariance, Confirmatory factor analysis, Oral investigations, Dentistry, RK1-715
More Details: Abstract Objective Oral health-related quality of life (OHRQoL) is a multidimensional concept that is commonly used to examine the impact of oral health status on quality of life. The purpose of this study was to examine the optimal factor model of the Chinese version of the Oral Health Impact Profile (OHIP-14) questionnaire in clinical populations, measurement invariance across clinical status and gender cohorts. This would ensure equal validity of the Chinese version of OHIP-14 in different populations and further support public oral investigations. Methods The Chinese version of OHIP-14 was used to investigate 490 dental patients and 919 college students. Confirmatory factor analysis (CFA), item analysis and reliability, measurement invariance, and the t-test were used for data analyses. Results We found that the 7-factor structure had the best-fit index in the sample (CFI = 0.970, TLI = 0.952; SRMR = 0.029, RMSEA = 0.052(0.040,0.063)). The reliability of the scales was satisfactory (Cronbach’s α = 0.942). The error variance invariance fitted the data adequately in measurement invariance, indicating that measurement invariance is acceptable both across the clinical and non-clinical populations (∆CFI=-0.017, ∆RMSEA = 0.010) and across genders in the clinical population (∆CFI = 0.000, ∆RMSEA=-0.003). T-test for scores showed that the clinical populations scored significantly higher than the non-clinical populations, as did the overall score (t = 7.046, p
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1472-6831
Relation: https://doaj.org/toc/1472-6831
DOI: 10.1186/s12903-023-03310-6
Access URL: https://doaj.org/article/336c8112564c432f8a812a7cf67fd8bb
Accession Number: edsdoj.336c8112564c432f8a812a7cf67fd8bb
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  Data: Factorial structure and measurement invariance of the Chinese version of the Oral Health Impact Profile-14 among clinical populations and non-clinical populations: an evidence for public oral investigations
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  Data: BMC Oral Health, Vol 23, Iss 1, Pp 1-10 (2023)
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  Data: Abstract Objective Oral health-related quality of life (OHRQoL) is a multidimensional concept that is commonly used to examine the impact of oral health status on quality of life. The purpose of this study was to examine the optimal factor model of the Chinese version of the Oral Health Impact Profile (OHIP-14) questionnaire in clinical populations, measurement invariance across clinical status and gender cohorts. This would ensure equal validity of the Chinese version of OHIP-14 in different populations and further support public oral investigations. Methods The Chinese version of OHIP-14 was used to investigate 490 dental patients and 919 college students. Confirmatory factor analysis (CFA), item analysis and reliability, measurement invariance, and the t-test were used for data analyses. Results We found that the 7-factor structure had the best-fit index in the sample (CFI = 0.970, TLI = 0.952; SRMR = 0.029, RMSEA = 0.052(0.040,0.063)). The reliability of the scales was satisfactory (Cronbach’s α = 0.942). The error variance invariance fitted the data adequately in measurement invariance, indicating that measurement invariance is acceptable both across the clinical and non-clinical populations (∆CFI=-0.017, ∆RMSEA = 0.010) and across genders in the clinical population (∆CFI = 0.000, ∆RMSEA=-0.003). T-test for scores showed that the clinical populations scored significantly higher than the non-clinical populations, as did the overall score (t = 7.046, p
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