immer-package: Item Response Models for Multiple Ratings

In immer: Item Response Models for Multiple Ratings

Item Response Models for Multiple Ratings

Description

Implements some item response models for multiple ratings, including the hierarchical rater model, conditional maximum likelihood estimation of linear logistic partial credit model and a wrapper function to the commercial FACETS program. See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; <doi:10.1111/jedm.12045>) for an overview of modeling alternatives.

Details

The immer package has following features:

• Estimation of the hierarchical rater model (Patz et al., 2002) with immer_hrm and simulation of it with immer_hrm_simulate.

• The linear logistic partial credit model as an extension to the linear logistic test model (LLTM) for dichotomous data can be estimated with conditional maximum likelihood (Andersen, 1995) using immer_cml.

• The linear logistic partial credit model can be estimated with composite conditional maximum likelihood (Varin, Reid & Firth, 2011) using the immer_ccml function.

• The linear logistic partial credit model can be estimated with a bias-corrected joint maximum likelihood method (Bertoli-Barsotti, Lando & Punzo, 2014) using the immer_jml function.

• Wrapper function immer_FACETS to the commercial program FACETS (Linacre, 1999) for analyzing multi-faceted Rasch models.

• ...

Author(s)

Alexander Robitzsch [aut, cre], Jan Steinfeld [aut]

Maintainer: Alexander Robitzsch <[e-mail]>

References

Andersen, E. B. (1995). Polytomous Rasch models and their estimation. In G. H. Fischer & I. W. Molenaar (Eds.). Rasch Models (pp. 39-52). New York: Springer.

Bertoli-Barsotti, L., Lando, T., & Punzo, A. (2014). Estimating a Rasch Model via fuzzy empirical probability functions. In D. Vicari, A. Okada, G. Ragozini & C. Weihs (Eds.). Analysis and Modeling of Complex Data in Behavioral and Social Sciences, Springer.

Linacre, J. M. (1999). FACETS (Version 3.17)[Computer software]. Chicago: MESA.

Patz, R. J., Junker, B. W., Johnson, M. S., & Mariano, L. T. (2002). The hierarchical rater model for rated test items and its application to large-scale educational assessment data. Journal of Educational and Behavioral Statistics, 27(4), 341-384.

Robitzsch, A., & Steinfeld, J. (2018). Item response models for human ratings: Overview, estimation methods, and implementation in R. Psychological Test and Assessment Modeling, 60(1), 101-139.

Varin, C., Reid, N., & Firth, D. (2011). An overview of composite likelihood methods. Statistica Sinica, 21, 5-42.

Wang, W. C., Su, C. M., & Qiu, X. L. (2014). Item response models for local dependence among multiple ratings. Journal of Educational Measurement, 51(3), 260-280.

See Also

For estimating the Rasch multi-facets model with marginal maximum likelihood see also the TAM::tam.mml.mfr and sirt::rm.facets functions.

For estimating the hierarchical rater model based on signal detection theory see sirt::rm.sdt.

For conditional maximum likelihood estimation of linear logistic partial credit models see the eRm (e.g. eRm::LPCM) and the psychotools (e.g. psychotools::pcmodel) packages.

Examples

   
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immer documentation built on Aug. 22, 2022, 5:05 p.m.