File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1177/0146621605282772
- Scopus: eid_2-s2.0-33646178269
- WOS: WOS:000237022100004
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Markov Chain Monte Carlo estimation of item parameters for the generalized graded unfolding model
Title | Markov Chain Monte Carlo estimation of item parameters for the generalized graded unfolding model |
---|---|
Authors | |
Keywords | Estimation |
Issue Date | 2006 |
Citation | Applied Psychological Measurement, 2006, v. 30, n. 3, p. 216-232 How to Cite? |
Abstract | The authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response options, and sample size were manipulated. Results indicate that the two methods are comparable in terms of item parameter estimation accuracy. Although the MML estimates exhibit slightly smaller bias than MCMC estimates, they also show greater variability, which results in larger root mean squared errors. Of the two methods, only MCMC provides reasonable standard error estimates for all items. © 2006 Sage Publications. |
Persistent Identifier | http://hdl.handle.net/10722/228039 |
ISSN | 2023 Impact Factor: 1.0 2023 SCImago Journal Rankings: 1.061 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | De La Torre, Jimmy | - |
dc.contributor.author | Stark, Stephan | - |
dc.contributor.author | Chernyshenko, Oleksandr S. | - |
dc.date.accessioned | 2016-08-01T06:45:02Z | - |
dc.date.available | 2016-08-01T06:45:02Z | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | Applied Psychological Measurement, 2006, v. 30, n. 3, p. 216-232 | - |
dc.identifier.issn | 0146-6216 | - |
dc.identifier.uri | http://hdl.handle.net/10722/228039 | - |
dc.description.abstract | The authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response options, and sample size were manipulated. Results indicate that the two methods are comparable in terms of item parameter estimation accuracy. Although the MML estimates exhibit slightly smaller bias than MCMC estimates, they also show greater variability, which results in larger root mean squared errors. Of the two methods, only MCMC provides reasonable standard error estimates for all items. © 2006 Sage Publications. | - |
dc.language | eng | - |
dc.relation.ispartof | Applied Psychological Measurement | - |
dc.subject | Estimation | - |
dc.title | Markov Chain Monte Carlo estimation of item parameters for the generalized graded unfolding model | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1177/0146621605282772 | - |
dc.identifier.scopus | eid_2-s2.0-33646178269 | - |
dc.identifier.volume | 30 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 216 | - |
dc.identifier.epage | 232 | - |
dc.identifier.eissn | 1552-3497 | - |
dc.identifier.isi | WOS:000237022100004 | - |
dc.identifier.issnl | 0146-6216 | - |