Missing Data and its Impact on the CCCTB Determination

The impact of missing data on quantitative research can be serious. It could lead to biased estimates of parameters, loss of information, increased standard errors or decreased statistical power and weakened results of findings. The aim of the paper is to discuss three missing data methods: regression, imputation and multiple imputation; and their impact on the CCCTB determination and based on the results to identify the most suitable method which will lead to the least distortion. The results gained with the application of those methods are compared with those obtained from the complete data set.