irtQ - Unidimensional Item Response Theory Modeling

Fits unidimensional item response theory (IRT) models to test data that include dichotomous and polytomous items, calibrates pretest items with fixed item and fixed ability parameter calibration, performs multiple-group calibration, estimates the abilities of examinees, and evaluates model-data fit at the item level. It also provides tools for detecting differential item functioning and item parameter drift, for computing classification accuracy and consistency, and for evaluating and simulating multistage-adaptive test panels, together with classical test theory functions for item- and test-level statistics (e.g., item difficulty, item-total correlation, and coefficient alpha) and for scoring selected-response item data. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2020) <doi:10.1177/0146621620929431>). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>). The bisection() function was written by modifying the bisection() function (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve scoring in the est_score() function was written by modifying the irt.eq.tse() function (Gonzalez (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions of the 'catR' package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).