Unidimensional Item Response Theory Modeling
Bind Fill
The Bisection Method to Find a Root
Import Item and Ability Parameters from IRT Software
Classification Accuracy and Consistency Using Lee's (2010) Approach
Classification Accuracy and Consistency Based on Rudner's (2001, 2005)...
CATSIB DIF Detection Procedure
Asymptotic Variance-Covariance Matrices of Item Parameter Estimates
Residual-Based DIF Detection Framework Using Categorical Residuals (RD...
Dichotomous Response Model (DRM) Probabilities
Item parameter estimation using MMLE-EM algorithm
Fixed ability parameter calibration
Multiple-group item calibration using MMLE-EM algorithm
Estimate examinees' ability (proficiency) parameters
Generate Weights
Extract Components from 'est_irt', 'est_mg', or 'est_item' Objects
Generalized IRT residual-based DIF detection framework for multiple gr...
Item and Test Information Function
Traditional IRT Item Fit Statistics
irtQ: Unidimensional Item Response Theory Modeling
Log-Likelihood of Ability Parameters
Lord-Wingersky Recursion Formula
Pseudo-count D2 method
Plot Item and Test Information Functions
Draw Raw and Standardized Residual Plots
Plot Item and Test Characteristic Curves
Polytomous Response Model (PRM) Probabilities (GRM and GPCM)
IRT Residual-Based Differential Item Functioning (RDIF) Detection Fram...
Recursion-based MST evaluation method
Run flexMIRT from Within R
Combine fixed and new item metadata for fixed-item parameter calibrati...
Create a Data Frame of Item Metadata
Simulated Response Data
Summary of Item Calibration Results
S-X2 Fit Statistic
Compute Item/Test Characteristic Functions
Write a "-prm.txt" File for flexMIRT
Fit unidimensional item response theory (IRT) models to test data, which includes both dichotomous and polytomous items, calibrate pretest item parameters, estimate examinees' abilities, and examine the IRT model-data fit on item-level in different ways as well as provide useful functions related to IRT analyses such as IRT model-data fit evaluation and differential item functioning analysis. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2022) <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 (González (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>).