mirt: A Multidimensional Item Response Theory Package for the R Environment

By R. Chalmers. 2012

2,588 citations recorded by the source corpus.

Item response theory (IRT) is widely used in assessment and evaluation research to explain how participants respond to item level stimuli. Several R packages can be used to estimate the parameters in various IRT models, the most flexible being the ltm (Rizopoulos 2006), eRm (Mair and Hatzinger 2007), and MCMCpack (Martin, Quinn, and Park 2011) packages. However these packages have limitations in that ltm and eRm can only analyze unidimensional IRT models effectively and the exploratory multidimensional extensions available in MCMCpack requires prior understanding of Bayesian estimation convergence diagnostics and are computationally intensive. Most importantly, multidimensional confirmatory item factor analysis methods have not been implemented in any R package. The mirt package was created for estimating multidimensional item response theory parameters for exploratory and confirmatory models by using maximum-likelihood meth- ods. The Gauss-Hermite quadrature method used in traditional EM estimation (e.g., Bock and Aitkin'81) is presented for exploratory item response models as well as for confirmatory bifactor models (Gibbons and Hedeker'92). Exploratory and confirmatory models are estimated by a stochastic algorithm described by Cai (2010a,b). Various program comparisons are presented and future directions for the package are discussed.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8b8e3001883fa91bdf0836f84cde17dc22949c01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Performances of LOO and WAIC as IRT Model Selection Methods (2017, methodology): “For the computation of LRT, AIC, and BIC, we use the R package mirt (Chalmers, 2012) that implements MMLE method.”
The Basics of Item Response Theory Using R (2018, methodology): “Rather than using any of the existing R packages developed specifically for IRT—e.g., mirt (Chalmers, 2012), ltm (Rizopoulos, 2006)—the tutorials in this book require only those functions that are available in base R.”
Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods (2020, methodology): “…for Frontiers in Education | www.frontiersin.org 2 October 2020 | Volume 5 | Article 589965 estimation by default (e.g., in the R packagemirt; Chalmers, 2012) while in standard SEM software, limited information methods, such as DWLS are employed (e.g., in the R package lavaan; Rosseel, 2012).”
Variational Approximations for Generalized Linear Latent Variable Models (2017, positive): “Both methods 5 and 6 are available in the mirt function in the R package mirt (Chalmers, 2012), with their respective default settings used.”
Adaptation of the Fear of COVID-19 Scale: Its Association with Psychological Distress and Life Satisfaction in Turkey (2020, methodology): “…structure of the Fear of COVID-19 Scale, the discrimination, difficulty, and informativeness of the scale was examined using the Item Response Theory (IRT; Chalmers 2012), which allows us to have more detailed and reliable information at the individual and item level than classical test theory.”
Bayesian Item Response Modeling in R with brms and Stan (2019, background): “2021), mirt (Chalmers 2012), sirt (Robitzsch 2021), lme4 (Bates et al.”
The WHO-5 well-being index – validation based on item response theory and the analysis of measurement invariance across 35 countries (2020, methodology): “Item response analyses were done with the mirt Chalmers, 2012 ), nonnest2 ( Merkle et al., 2020 ), and irtplay ( Lim and ells, 2020 ) packages.”
Variational Item Response Theory: Fast, Accurate, and Expressive (2020, methodology): “ute VIBO = L recon + D ability + D item A public implementation of VIBO will be available in PyTorch and Pyro [4] along with a Python package for more practical uses modeled after the R package, MIRT [9]. 4. DATASETS We explore one synthetic dataset, to build intuition and conrm parameter recovery, ”
What makes mathematical word problem solving challenging? Exploring the roles of word problem characteristics, text comprehension, and arithmetic skills (2019, background): “However, item difficulty index derived from CTT is often criticized because it is dependent on the sample (Chalmers 2012; Stage 2003).”
Psychometric Analyses of Stalking Measures from the National Intimate Partner and Sexual Violence Survey (2019, methodology): “Estimates of item difficulty and discrimination were computed using two-parameter logistic item response models, using the “mirt” package in R (Chalmers 2012).”
MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory (2025, unknown): “Most of the terms and explanations were derived from Chalmers (2012).”
MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory (2026, unknown): “Most of the terms and explanations were derived from Chalmers (2012).”