Clustering and multiple imputation of missing data

Abstract

The present work specifically focuses on the data analysis as the objective is to deal with the missing values in cluster analysis. Two-Step Cluster Analysis is applied in which each participant is classified into one of the identified pattern and the optimal number of classes is determined using SPSS Statistics/IBM. Any observation with missing data is excluded in the Cluster Analysis because like multi-variable statistical techniques. Therefore, before performing the cluster analysis, missing values will be imputed using multiple imputations (SPSS Statistics/IBM). The clustering results will be displayed in tables. Furthermore, goal of analysis is to reduce biases arising from the fact that non-respondents may be different from those who participate and to bring sample data up to the dimensions of the target population totals.