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    Imputation of missing values within WHODAS 2.0 data collected from low back pain patients using the response function approach
    (Taylor & Francis Ltd, 2023) Siddikoglu, Duygu; Erdogan, Beyza Doganay; Gokmen, Derya; Kutlay, Sehim
    Purpose To investigate the impact of missing data and imputation with the response function (RF) approach on bias and precision of disability estimates as well as reliability of scale of WHO Disability Assessment Schedule 2.0. Material and methods Data were collected by face-to-face interviews and self-report surveys from 284 respondents with low back pain. Hypothetical datasets were created by using person and item parameters of real data. A simulation study was devised to assess the ability parameters and reliability measures on incomplete and imputed datasets. Rasch model was used to evaluate latent trait levels. Imputation was carried out using the response function method. Results Almost the same level of bias and MSE was reached. While the missing rate increases, the Person separation index slightly reduced, still exceeded 0.94 and Cronbach alpha values have similar mean values of 0.99 with larger variations. After deletion of four items of work or school activities in domain 5, reliability measures reduced the lowest. Conclusion Construct validity is preserved. Problems regarding the compliance of the items with the target group still persist. When researchers encounter missingness in data collected with WHODAS 2.0, the response function can be usefully implemented to impute missing values to improve the reliability of disability level estimates.