Literature DB >> 29881118

A Note on N in Bayesian Information Criterion for Item Response Models.

Sun-Joo Cho1, Paul De Boeck2,3.   

Abstract

This brief report derives the N in the penalty term of the Schwarz's (1978) Bayesian information criterion (BIC) for two-parameter logistic item response models. The results in this study show that the N is the number of persons for fixed item models, whereas it is the number of observations (the Number of Persons times the Number of Items) for random item models. Given these results, the authors recommend researchers to calculate the BIC or to validate the BIC value that shows in the output of software instead of accepting the output value without a further check of implicit assumptions made for the software.

Entities:  

Keywords:  Bayesian information criterion; information function; item response theory

Year:  2017        PMID: 29881118      PMCID: PMC5978647          DOI: 10.1177/0146621617726791

Source DB:  PubMed          Journal:  Appl Psychol Meas        ISSN: 0146-6216


  1 in total

1.  Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model.

Authors:  Sun-Joo Cho; Sarah Brown-Schmidt; Paul De Boeck; Jianhong Shen
Journal:  Psychometrika       Date:  2020-02-21       Impact factor: 2.500

  1 in total

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