This Master Thesis on Iterative Proportional Fitting (IPF) is basically split into two main sections a theoretical and a practical part. The sooner provides a review on how the IPF procedure can be deduced from several other estimation techniques, from the Horvitz-Thompson estimator via the ratio estimator and the one-dimensional poststratification until the more-dimensional IPF. Moreover, this section includes a numerical example to demonstrate how the procedure works as well as some attempts to prove its convergence and finally a short description of how IPF is applied on the Austrian microcensus by Statistics Austria. The latter then deals with real data from the Austrian microcensus and the application of IPF on it. For this purpose, a new population is simulated from the microcensus which then represents the Austrian population. From this population, samples are drawn simulating different types of non-response behavior. Applying IPF on these samples then illustrates what the IPF procedure does and in which cases it is actually useful in order to enhance precision of certain estimations. Results also underline the necessity of an additional dimension in the calibration process which Statistics Austria introduced in 2014.