An evaluation of k-nearest neighbour imputation using Likert data

摘要: Studies in many different fields of research suffer from the problem missing data. With data, statistical tests will lose power, results may be biased, or analysis not feasible at all. There are several ways to handle problem, for example through imputation. imputation, values replaced with estimated according an imputation method model. In k-nearest neighbour (k-NN) method, a case is imputed using k most similar cases. this paper, we present evaluation k-NN Likert data software engineering context. We simulate and percentages Our findings indicate that it use suggest suitable value approximately square root number complete also show by relaxing rules respect selecting neighbours, ability remains high large amounts without affecting quality