Development and validation of the peptic ulcer scale under the system of quality of life instruments for chronic disease
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RESEARCH ARTICLE
Open Access
Development and validation of the peptic ulcer scale under the system of quality of life instruments for chronic diseases based on classical test theory and generalizability theory Chonghua Wan1*† , Ying Chen2†, Li Gao3, Qingqing Zhang4, Peng Quan1 and Xiaoyuan Sun1
Abstract Background: Quality of life (QOL) for patients with Peptic ulcer disease (PUD) is of interest worldwide and diseasespecific instruments are needed for clinical research and practice. This paper focus on the development and validation of the PUD scale under the system of quality of life instruments for chronic diseases (QLICD-PU) by the modular approach and both classical test theory and Generalizability Theory. Methods: The QLICD-PU is developed based on programmatic decision-making procedures, including multiple nominal and focus group discussions, in-depth interviews, and quantitative statistical procedures. Based on the data of 153 PUD inpatients, correlation analysis, factor analysis, t-test, and Generalizability Theory analysis (including generalizability study and decision study, ie. G-study and D-study) were used to assess the validity, reliability, and responsiveness of the scale. Results: When the popular scale health survey short form (SF-36) was used as the standard, correlation and factor analysis confirmed good construct validity and criterion-related validity of QLICD-PU. Except for the social domain (0.62), the internal consistency α of all domains is higher than 0.70. The overall score and the test–retest reliability coefficients (Pearson r and intra-class correlation ICC) in all domains are higher than 0.80 (0.77 in the social domain). After treatments, the overall score and scores of all domains have statistically significant changes (P 1, there were 8 principal components extracted from 30 items of the general module (QLICD-GM), accounting for 63.88% of the cumulative variance. By using the Varimax rotation method, it can be seen that the 8 principal components reflected 8 different facets under three domains of the general module with the first, fourth and fifth principals components mainly representing the psychological domain with higher loadings on PS1-PS11; the second and seventh principal components largely reflecting the physical domain with higher loadings on PH1-PH8; the third, sixth and eighth principal components generally depicting the social domain with higher loadings on SO1-SO11. Similarly, the principal component factor analysis extracted 6 principal components from the 14 items of the specific module with the cumulative variance of 65.88%, reflecting 6 facets. Criterion‑related validity
The correlation coefficients between the QLICD-PU and SF-36 domain scores were listed in Table 2, indicating that the correlation between the same and similar domains (bold in the table) is usually higher than different and dissimilar domains. For example, the coefficient between the physical domain of QLICD-PU and the physical function of SF-36 is 0.67, which is higher than any other coeff
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