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Fitting item response unfolding models to Likert-scale data using mirt in R.

ABSTRACT: While a large family of unfolding models for Likert-scale response data have been developed for decades, very few applications of these models have been witnessed in practice. There may be several reasons why these have not appeared more widely in published research, however one obvious limitation appears to be the absence of suitable software for model estimation. In this article, the authors demonstrate how the mirt package can be adopted to estimate parameters from various unidimensional and multidimensional unfolding models. To concretely demonstrate the concepts and recommendations, a tutorial and examples of R syntax are provided for practical guidelines. Finally, the performance of mirt is evaluated via parameter-recovery simulation studies to demonstrate its potential effectiveness. The authors argue that, armed with the mirt package, applying unfolding models to Likert-scale data is now not only possible but can be estimated to real-datasets with little difficulty.

SUBMITTER: Liu CW

PROVIDER: S-EPMC5933773 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Fitting item response unfolding models to Likert-scale data using mirt in R.

Liu Chen-Wei CW Chalmers R Philip RP

PloS one 20180503 5

While a large family of unfolding models for Likert-scale response data have been developed for decades, very few applications of these models have been witnessed in practice. There may be several reasons why these have not appeared more widely in published research, however one obvious limitation appears to be the absence of suitable software for model estimation. In this article, the authors demonstrate how the mirt package can be adopted to estimate parameters from various unidimensional and ...[more]

PMID: 29723217

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