Comparative Analyses of Mirt Models and Software (BMIRT and flexMirt)

Bibliographic Data

ID20284128
AuthorsGuler Yavuz (0000-0001-9995-6034, Adiyaman University, Adiyaman, Turkey, corresponding author), Ronald K Hambleton (0000-0002-7412-2188, University of Massachusetts Amherst, MA, USA)
Year2017
Volume77
Issue2
Pages263-274
Publication date2017-04-01
Open AccessYes
TypeARTICLE
VenueEducational and Psychological Measurement (JOURNAL)
Journal identifiersISSN: 0013-1644 • E-ISSN: 1552-3888
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/0013164416661220
PMID29795913
OpenAlexW2492055944
LanguageEN
References cited23

Application of MIRT modeling procedures is dependent on the quality of parameter estimates provided by the estimation software and techniques used. This study investigated model parameter recovery of two popular MIRT packages, BMIRT and flexMIRT, under some common measurement conditions. These packages were specifically selected to investigate the model parameter recovery of three item parameter estimation techniques, namely, Bock–Aitkin EM (BA-EM), Markov chain Monte Carlo (MCMC), and Metropolis–Hastings Robbins–Monro (MH-RM) algorithms. The results demonstrated that all estimation techniques had similar root mean square error values when larger sample size and higher test length were used. Depending on the number of dimensions, sample size, and test length, each estimation technique exhibited some strengths and weaknesses. Overall, the BA-EM technique was found to have shorter estimation time with all test specifications

Advanced Causal Inference Techniques · Psychology · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference

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