We have located links that may give you full text access.
COMPARATIVE STUDY
EVALUATION STUDIES
JOURNAL ARTICLE
Comparing Imputation Methods for Trait Estimation Using the Rating Scale Model.
This study examined the performance of four methods of handling missing data for discrete response options on a questionnaire: (1) ignoring the missingness (using only the observed items to estimate trait levels); (2) nearest-neighbor hot deck imputation; (3) multiple hot deck imputation; and (4) semi-parametric multiple imputation. A simulation study examining three questionnaire lengths (41-, 20-, and 10-item) crossed with three levels of missingness (10, 25, and 40 percent) was conducted to see which methods best recovered trait estimates when data were missing completely at random and the polytomous items were scored with Andrich's (1978) rating scale model. The results showed that ignoring the missingness and semi-parametric imputation best recovered known trait levels across all conditions, with the semi-parametric technique providing the most precise trait estimates. This study demonstrates the power of specific objectivity in Rasch measurement, as ignoring the missingness leads to generally unbiased trait estimates.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app