Recursive partitioning methods for data imputation in the context of item response theory: a Monte Carlo simulation

Julianne M. Edwards ^[1] ; W. Holmes Finch ^[2]
1. [1] Azusa Pacific University
  
  Azusa Pacific University
  
  Estados Unidos
2. [2] Ball State University
  
  Ball State University
  
  Township of Center, Estados Unidos
Localización: Psicológica: Revista de metodología y psicología experimental, ISSN-e 1576-8597, ISSN 0211-2159, Vol. 39, Nº 1, 2018, págs. 88-117
Idioma: inglés
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Resumen
- Missing data is a common problem faced by psychometricians and measurement professionals. To address this issue, there are a number of techniques that have been proposed to handle missing data regarding Item Response Theory. These methods include several types of data imputation methods - corrected item mean substitution imputation, response function imputation, multiple imputation, and the EM algorithm, as well as approaches that do not rely on the imputation of missing values - treating the item as not presented, coding missing responses as incorrect, or as fractionally correct. Of these methods, even though multiple imputation has demonstrated the best performance in prior research, higher MAE was still present. Given this higher model parameter estimation MAE for even the best performing missing data methods, this simulation study’s goal was to explore the performance of a set of potentially promising data imputation methods based on recursive partitioning. Results of this study demonstrated that approaches that combine multivariate imputation by chained equations and recursive partitioning algorithms yield data with relatively low estimation MAE for both item difficulty and item discrimination. Implications of these findings are discussed.