Ulm, Univ., Diss., 2007.

Jena, Univ., Diss., 2005. / Computerdatei im Fernzugriff.

Universiẗat, Habil.-Schr., 2004--Bremen.

Univ., Diss.--München, 2004.

Universiẗat, Diss., 2005--Jena.

Universiẗat, Diss.--Erlangen-Nürnberg, 2007.

Thesis (Doctoral)--Ruhr-Universität Bochum, 1983.

Thesis (doctoral)--Technische Universität Darmstadt, 2005.

Univ., Diss.--München, 2003. / Zsfassung in engl. Sprache.

Missing values are a serious problem in surveys. The literature suggests to replace these with realistic values using imputation methods. This master thesis examines four different imputation techniques concerning their ability for handling missing data. Therefore, mean imputation, conditional mean imputation, Expectation-Maximization algorithm and Markov-Chain-Monte-Carlo method are presented. In addition, the three first mentioned methods were simulated by using a large real data set. To analyse the quality of these techniques a metric variable of the original data set was chosen to generate some missing values considering different percentages of missingness and common missing data mechanism. After the replacement of the simulated missing values, several statistical parameters, like quantiles, arithmetic mean and variance of all completed data sets were calculated in order to compare them with the parameters from the original data set. The results, that have been established by empiric data analysis, show that the Expectation-Maximization algorithm estimates all considered statistical parameters of the complete data set far better than the other analysed imputation methods, although the assumption of a multivariate normal distribution could not be achieved. It is found, that the mean as well as the conditional mean imputation produce statistically significant estimator for the arithmetic mean under the supposition of missing completely at random, whereas other parameters as the variance do not show the estimated effects. Generally, the accuracy of all estimators from the three imputation methods decreases with increasing percentage of missingness. The results lead to the conclusion that the Expectation-Maximization algorithm should be preferred over the mean and the conditional mean imputation.