Literature DB >> 29795864

Partially Compensatory Multidimensional Item Response Theory Models: Two Alternate Model Forms.

Christine E DeMars1.   

Abstract

Partially compensatory models may capture the cognitive skills needed to answer test items more realistically than compensatory models, but estimating the model parameters may be a challenge. Data were simulated to follow two different partially compensatory models, a model with an interaction term and a product model. The model parameters were then estimated for both models and for the compensatory model. Either the model used to simulate the data or the compensatory model generally had the best fit, as indexed by information criteria. Interfactor correlations were estimated well by both the correct model and the compensatory model. The predicted response probabilities were most accurate from the model used to simulate the data. Regarding item parameters, root mean square errors seemed reasonable for the interaction model but were quite large for some items for the product model. Thetas were recovered similarly by all models, regardless of the model used to simulate the data.

Keywords:  MIRT; noncompensatory; partially compensatory

Year:  2015        PMID: 29795864      PMCID: PMC5965584          DOI: 10.1177/0013164415589595

Source DB:  PubMed          Journal:  Educ Psychol Meas        ISSN: 0013-1644            Impact factor:   2.821


  2 in total

1.  Multicomponent latent trait models for complex tasks.

Authors:  Susan E Embretson; Xiangdong Yang
Journal:  J Appl Meas       Date:  2006

2.  Generalized latent variable models with non-linear effects.

Authors:  Dimitris Rizopoulos; Irini Moustaki
Journal:  Br J Math Stat Psychol       Date:  2007-05-24       Impact factor: 3.380

  2 in total
  3 in total

1.  Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality.

Authors:  Dubravka Svetina; Arturo Valdivia; Stephanie Underhill; Shenghai Dai; Xiaolin Wang
Journal:  Appl Psychol Meas       Date:  2017-05-11

2.  Gibbs Samplers for Logistic Item Response Models via the Pólya-Gamma Distribution: A Computationally Efficient Data-Augmentation Strategy.

Authors:  Zhehan Jiang; Jonathan Templin
Journal:  Psychometrika       Date:  2018-10-31       Impact factor: 2.500

3.  Noncompensatory MIRT For Passage-Based Tests.

Authors:  Nana Kim; Daniel M Bolt; James Wollack
Journal:  Psychometrika       Date:  2022-01-21       Impact factor: 2.290

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.