Abstract
Confronted with incomplete data due to nonresponse, a researcher may want to impute missing values to estimate latent properties of respondents. In this chapter the results of a simulation study are presented, investigating the performance of several imputation techniques. Some imputation procedures are based on item response theory (IRT) models, which can also be used to estimate latent abilities directly from the incomplete data by using incomplete testing designs when data are missing by design. This strategy has some serious disadvantages in the case of item nonresponse, because nonresponse is assumed to be ignorable and computational problems arise in scales with many items. In a second simulation study, the performance of some imputation techniques is compared to the incomplete design strategy, in the case of item nonresponse. The latter strategy results in slightly better ability estimates, but imputation is almost as good, especially when it is based on IRT models.
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Huisman, M., Molenaar, I.W. (2001). Imputation of Missing Scale Data with Item Response Models. In: Boomsma, A., van Duijn, M.A.J., Snijders, T.A.B. (eds) Essays on Item Response Theory. Lecture Notes in Statistics, vol 157. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0169-1_13
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DOI: https://doi.org/10.1007/978-1-4613-0169-1_13
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