A Further Proposal to Perform Multiple Imputation on a Bunch of Polytomous Items Based on Latent Class Analysis
This work advances an imputation procedure for categorical scales which relays on the results of Latent Class Analysis and Multiple Imputation Analysis. The procedure allows us to use the information stored in the joint multivariate structure of the data set and to take into account the uncertainty related to the true unobserved values. The accuracy of the results is validated in the Item Response Models framework by assessing the accuracy in estimation of key parameters in a data set in which observations are simulated Missing at Random. The sensitivity of the multiple imputation methods is assessed with respect to the following factors: the number of latent classes set up in the Latent Class Model and the rate of missing observations in each variable. The relative accuracy in estimation is assessed with respect to the Multiple Imputation By Chained Equation missing data handling method for categorical variables.
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