InDisc: an R Package

A unified approach for obtaining and estimating unidimensional Item Response Theory (IRT) Dual Models (DMs) has been proposed by Ferrando (2019). DMs are intended for personality and attitude measures, are based on a Thurstonian response process, and are, essentially, extended standard IRT models with an extra person parameter that models the discriminating power of the individual. So, both items and individuals are considered as sources of measurement error in DMs.

InDisc is based on the procedure proposed by Ferrando (2019) for estimating unidimensional Item Response Theory (IRT) Dual Models (DMs).Estimation is based on a two stage (calibration and scoring) random-regressors approach (McDonald, 1982). Item calibration at the first stage is the same as in the corresponding standard IRT models, is based on a factor-analytic Underlying-Variables approach, and uses an unweighted least squares, (ULS) minimum-residual criterion as implemented in the psych R package (Revelle, 2018). Individual trait scores and individual discriminations are obtained at the second stage using Expected a Posteriori (EAP) Bayes estimation. Overall, the combined ULS-EAP estimation procedure is simple, robust, and can handle large datasets, both in terms of sample size and test length.

References

Ferrando, P. J. (2019). A Comprehensive IRT Approach for Modeling Binary, Graded, and Continuous Responses With Error in Persons and Items. Applied Psychological Measurement, 43(5), 339-359. https://doi.org/10.1177/0146621618817779

McDonald, R. P. (1982). Linear versus models in item response theory. Applied Psychological Measurement, 6, 379-396. https://doi.org/10.1177/014662168200600402

Revelle, W. (2018) psych: P Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12.