Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing.
Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses shou...
| Publicado en: | Educational & Psychological Measurement Vol. 79; no. 3; pp. 495 - 512 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Sage Publications Inc.
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=136880536&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 136880536 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Jun2019 vid: 79 iid: 3 pid: 344 pub: Sage Publications Inc. artinfo: ui: 136880536 10.1177/0013164418805532 ppf: 495 ppct: 17 formats: tig: atl: Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing. aug: au: Cetin-Berber, Dee Duygu Sari, Halil Ibrahim Huggins-Manley, Anne Corinne affil: University of Florida, Gainesville, FL, USA 7 Aralik University, Kilis, Turkey su: Automatic data collection systems Comparative studies Computer adaptive testing Conceptual structures Statistical correlation High performance computing Statistics Data analysis Content mining Descriptive statistics sug: subj: Automatic data collection systems Comparative studies Computer adaptive testing Conceptual structures Statistical correlation High performance computing Statistics Data analysis Content mining Descriptive statistics keyword: computerized adaptive multistage testing imputation Missing data computerized adaptive multistage testing imputation Missing data ab: Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses should be handled carefully. This study investigated multiple missing data methods in computerized adaptive multistage testing, including two imputation techniques, the use of full information maximum likelihood and the use of scoring missing data as incorrect. These methods were examined under the missing completely at random, missing at random, and missing not at random frameworks, as well as other testing conditions. Comparisons were made to baseline conditions where no missing data were present. The results showed that imputation and the full information maximum likelihood methods outperformed incorrect scoring methods in terms of average bias, average root mean square error, and correlation between estimated and true thetas. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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