Psychometrics With R: A Review Of CRAN Packages For Item Response Theory

In this paper we review the current state of R packages for Item Response Theory (IRT). We group the available packages based on their purpose and provide an overview of each package's main functionality. Each of the packages we describe has a peer-reviewed publication associated with it. We also provide a tutorial analysis of data from the 1990 Workplace Industrial Relation Survey to show how the breadth and exibility of IRT packages in R can be leveraged to conduct even challenging item analyses with versatility and ease. These items relate to the type of consultations that are carried out in a firm when major changes are implemented. We first use unidimensional IRT models just to discover that they fit do not fit well. We then use nonparametric IRT to explore the possible causes for the scaling problem. Based on the results from the exploration, we finally use a two-dimensional model on a subset of the original items to achieve a good fit with a sensible interpretation, namely that there are two types of consultations a firm may engage in: consultations with workers/representatives from the firm and with official union representatives. The different items relate mostly to one of these dimensions and firms can be scaled well along these two dimensions.

[1]  Paul K Crane,et al.  lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations. , 2011, Journal of statistical software.

[2]  David Magis,et al.  Random Generation of Response Patterns under Computerized Adaptive Testing with the R Package catR , 2012 .

[3]  David J. Earl,et al.  Monte Carlo simulations. , 2008, Methods in molecular biology.

[4]  Jonathan P. Weeks.  plink: An R Package for Linking Mixed-Format Tests Using IRT-Based Methods , 2010 .

[5]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[6]  Thomas Rusch,et al.  IRT models with relaxed assumptions in eRm: A manual-like instruction , 2009 .

[7]  Douglas M. Bates,et al.  Estimating the Multilevel Rasch Model: With the lme4 Package , 2007 .

[8]  Achim Zeileis,et al.  Flexible Rasch Mixture Models with Package psychomix , 2012 .

[9]  Daniel Pemstein,et al.  The Scythe Statistical Library: An Open Source C++ Library for Statistical Computation , 2011 .

[10]  D. J. Bartholomew,et al.  Scaling unobservable constructs in social science , 2002 .

[11]  Achim Zeileis,et al.  A new method for detecting differential item functioning in the Rasch model , 2011 .

[12]  Brian McGuire.  KernSmoothIRT : An R Package allowing for Kernel Smoothing in Item Response Theory , 2012 .

[13]  Abe D. Hofman,et al.  The estimation of item response models with the lmer function from the lme4 package in R , 2011 .

[14]  William N. Venables,et al.  An Introduction To R , 2004 .

[15]  Andrew D. Martin,et al.  MCMCpack: Markov chain Monte Carlo in R , 2011 .

[16]  Jeroen K. Vermunt,et al.  Estimation of Models in a Rasch Family for Polytomous Items and Multiple Latent Variables , 2007 .

[17]  Peter Müller,et al.  DPpackage: Bayesian Semi- and Nonparametric Modeling in R , 2011 .

[18]  David Preinerstorfer,et al.  Parameter recovery and model selection in mixed Rasch models. , 2012, The British journal of mathematical and statistical psychology.

[19]  Marie Wiberg,et al.  Performing the Kernel Method of Test Equating with the Package kequate , 2013 .

[20]  John Fox,et al.  GETTING STARTED WITH THE R COMMANDER: A BASIC-STATISTICS GRAPHICAL USER INTERFACE TO R , 2005 .

[21]  Antonio Punzo,et al.  KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory , 2012, 1211.1183.

[22]  Thomas Rusch,et al.  Linear Logistic Models with Relaxed Assumptions in R , 2013, Algorithms from and for Nature and Life.

[23]  Dimitrios Rizopoulos.  ltm: An R Package for Latent Variable Modeling and Item Response Theory Analyses , 2006 .

[24]  Klaas Sijtsma,et al.  Comparing Optimization Algorithms for Item Selection in Mokken Scale Analysis , 2013, J. Classif..

[25]  Achim Zeileis,et al.  psychotree - Recursive partitioning based on psychometric models: Version 0.12-1 , 2011 .

[26]  P. Mair,et al.  Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R , 2007 .

[27]  van der Ark,et al.  New Developments in Mokken Scale Analysis in R , 2012 .

[28]  R. Philip Chalmers,et al.  mirt: A Multidimensional Item Response Theory Package for the R Environment , 2012 .

[29]  van der Ark,et al.  Mokken Scale Analysis in R , 2007 .

[30]  T. Yanagida,et al.  R you ready for R?: the CRAN psychometrics task view. , 2011, The British journal of mathematical and statistical psychology.

[31]  P. Boeck,et al.  A general framework and an R package for the detection of dichotomous differential item functioning , 2010, Behavior research methods.

[32]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[33]  L. V. D. Ark.  Getting started with Mokken Scale Analysis in R , 2011 .

[34]  Michael J. Kolen,et al.  The kernel method of test equating , 2006 .