On the Imputation of Missing Data in Surveys with Likert-Type Scales

Starting from the problem of missing data in surveys with Likert-type scales, the aim of this paper is to evaluate a possible improvement for the imputation procedure proposed by Lavori, Dawson, and Shera (1995) here called Approximate Bayesian bootstrap with Propensity score (ABP). We propose an imputation procedure named Approximate Bayesian bootstrap with Propensity score and Nearest neighbour (ABPN), which, after the “propensity score step” of ABP, randomly selects a donor in the nonrespondent’s neighbourhood, which includes cases with response patterns similar to the one of the nonrespondent to be imputed. A preliminary simulation study with single imputation on missing data in two Likerttype scales from a real data set shows that ABPN: (a) performed better than the ABP imputation, and (b) can be considered as a serious competitor of other procedures used in this context.