InDisc (version 1.1.0)

InDisc-package: A unified approach for obtaining and estimating unidimensional Item Response Theory (IRT) Dual Models (DMs)

Description

Package consisting on a main function (InDisc) that calls all the sub-functions that implement the procedures described in Ferrando (2019) for fitting binary, graded, and continuous response 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.

Value

InDisc

Performs the procedure proposed in Ferrando (2019).

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. 10.1177/0146621618817779

McDonald, R. P. (1982). Linear versus models in item response theory. Applied Psychological Measurement, 6, 379-396. 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.

Examples

# NOT RUN {
## For speeding purposes, the number of observations and quadrature nodes have been
## reduced. For a proper use of InDisc, use the full dataset and the default quadrature
## nodes, and consider using the graded model.

InDisc(CTAC35[1:40,], nquad = 10, model = "linear", approp = FALSE, display = TRUE)

# }