Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R

Item response theory models (IRT) are increasingly becoming established in social science research, particularly in the analysis of performance or attitudinal data in psychology, education, medicine, marketing and other fields where testing is relevant. We propose the R package eRm (extended Rasch modeling) for computing Rasch models and several extensions. A main characteristic of some IRT models, the Rasch model being the most prominent, concerns the separation of two kinds of parameters, one that describes qualities of the subject under investigation, and the other relates to qualities of the situation under which the response of a subject is observed. Using conditional maximum likelihood (CML) estimation both types of parameters may be estimated independently from each other. IRT models are well suited to cope with dichotomous and polytomous responses, where the response categories may be unordered as well as ordered. The incorporation of linear structures allows for modeling the effects of covariates and enables the analysis of repeated categorical measurements. The eRm package fits the following models: the Rasch model, the rating scale model (RSM), and the partial credit model (PCM) as well as linear reparameterizations through covariate structures like the linear logistic test model (LLTM), the linear rating scale model (LRSM), and the linear partial credit model (LPCM). We use an unitary, efficient CML approach to estimate the item parameters and their standard errors. Graphical and numeric tools for assessing goodness-of-fit are provided.

Modeling heterogeneity in ranked responses by nonparametric maximum likelihood:How do Europeans get their scientific knowledge?

This paper is motivated by a Eurobarometer survey on science knowledge. As part of the survey, respondents were asked to rank sources of science information in order of importance. The official statistical analysis of these data however failed to use the complete ranking information. We instead propose a method which treats ranked data as a set of paired comparisons which places the problem in the standard framework of generalized linear models and also allows respondent covariates to be incorporated. An extension is proposed to allow for heterogeneity in the ranked responses. The resulting model uses a nonparametric formulation of the random effects structure, fitted using the EM algorithm. Each mass point is multivalued, with a parameter for each item. The resultant model is equivalent to a covariate latent class model, where the latent class profiles are provided by the mass point components and the covariates act on the class profiles. This provides an alternative interpretation of the fitted model. The approach is also suitable for paired comparison data

The Rasch Sampler

The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It is a Markov chain Monte Carlo (MCMC) algorithm. The program can handle matrices of up to 1024 rows and 64 columns. A special option allows to sample square matrices with given marginals and fixed main diagonal, a problem prominent in social network analysis. In all cases the stationary distribution is uniform. The user has control on the serial dependency.

Modelling dependency in multivariate paired comparisons:a log-linear approach.

A log-linear representation of the Bradley-Terry model is presented for multivariate paired comparison data, where judges are asked to compare pairs of objects on more than one attribute. By converting such data to multiple binomial responses, dependencies between the decisions of the judges as well as possible association structures between the attributes can be incorporated in the model, providing an advantage over parallel univariate analyses of individual attributes. The approach outlined gives parameters which can be interpreted as (conditional) log–odds and log–odds ratios. As the model is a generalised linear model, parameter estimation can use standard software and the GLM framework can be used to test hypotheses on these parameters

prefmod: An R Package for Modeling Preferences Based on Paired Comparisons, Rankings, or Ratings

The first part of this paper describes a series of loglinear preference models based on paired comparisons, a method of measurement whose aim is to order a set of objects according to an attribute of interest by asking subjects to compare pairs of objects. Based on the basic Bradley-Terry specification, two types of models, the loglinear Bradley-Terry model and a pattern approach are presented. Both methods are extended to include subject and object-specific covariates and some further structural effects. In addition, models for derived paired comparisons (based on rankings and ratings) are also included. Latent classes and missing values can be included. The second part of the paper describes the package prefmod that implements the above models in R. Illustrational applications are provided in the last part of the paper

A Microanalytical Simulation Model to Predict the Long-Term Evolution of Employment Biographies in Austria: The Demographics Module

The well-known problems of decreasing birth rates and population ageing represent a major challenge for the Austrian pension system. It is expected that the group of pensioners will grow steadily in the future, while the proportion of people that support them - the taxpayers - will shrink. In this regard, microsimulation provides a valuable tool to identify the impact of various policy measures. With microsimulation, it is not only possible to predict cross-sectional data (e.g., the distribution of age groups in 2050), but also to simulate lifecourses of people, providing longitudinal outcomes. The demographics module is the first in a series of modules that are part of a microsimulation prototype. This prototype is being developed in order to predict the long-term evolution of Employment Biographies in Austria

The Rasch Sampler

The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It is a Markov chain Monte Carlo (MCMC) algorithm. The program can handle matrices of up to 1024 rows and 64 columns. A special option allows to sample square matrices with given marginals and fixed main diagonal, a problem prominent in social network analysis. In all cases the stationary distribution is uniform. The user has control on the serial dependency. (authors' abstract

Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R

Item response theory models (IRT) are increasingly becoming established in social science research, particularly in the analysis of performance or attitudinal data in psychology, education, medicine, marketing and other fields where testing is relevant. We propose the R package eRm (extended Rasch modeling) for computing Rasch models and several extensions. A main characteristic of some IRT models, the Rasch model being the most prominent, concerns the separation of two kinds of parameters, one that describes qualities of the subject under investigation, and the other relates to qualities of the situation under which the response of a subject is observed. Using conditional maximum likelihood (CML) estimation both types of parameters may be estimated independently from each other. IRT models are well suited to cope with dichotomous and polytomous responses, where the response categories may be unordered as well as ordered. The incorporation of linear structures allows for modeling the effects of covariates and enables the analysis of repeated categorical measurements. The eRm package fits the following models: the Rasch model, the rating scale model (RSM), and the partial credit model (PCM) as well as linear reparameterizations through covariate structures like the linear logistic test model (LLTM), the linear rating scale model (LRSM), and the linear partial credit model (LPCM). We use an unitary, efficient CML approach to estimate the item parameters and their standard errors. Graphical and numeric tools for assessing goodness-of-fit are provided. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematic

IRT models with relaxed assumptions in eRm: A manual-like instruction

Linear logistic models with relaxed assumptions (LLRA) as introduced by Fischer (1974) are a flexible tool for the measurement of change for dichotomous or polytomous responses. As opposed to the Rasch model, assumptions on dimensionality of items, their mutual dependencies and the distribution of the latent trait in the population of subjects are relaxed. Conditional maximum likelihood estimation allows for inference about treatment, covariate or trend effect parameters without taking the subjects' latent trait values into account. In this paper we will show how LLRAs based on the LLTM, LRSM and LPCM can be used to answer various questions about the measurement of change and how they can be fitted in R using the eRm package. A number of small didactic examples is provided that can easily be used as templates for real data sets. All datafiles used in this paper are available from http://eRm.R-Forge.R-project.org/