-Understand why designs like stratified sampling are often more informative than simple random sampling.
-Learn to recognize the opportunities to apply more advanced sampling designs in practical situations.
-Learn to implement different sampling designs, and process their outcomes.

Marketing researchers often draw samples in order to infer the opinions of a client population. This is very practical, but since only a small part of the population is observed, the results are subject to inaccuracy. Of course, we want the sample results to be as close as possible to the true values. The most straightforward way to draw a sample is simple random sampling, where every population member has the same chance of being sampled. However, sometimes more complex sampling designs (e.g. stratified samples) are much better. "Better" in the sense of value-for-money: they allow us to make more accurate statements at the same cost, or to reach a desired level of accuracy at lower cost, than simple random sampling. This course focuses on the statistical aspects of various sampling designs. The objective is to construct appropriate sampling designs in real life situations. During the first half of the course, some theory is introduced, and tested by small numerical assignments. During the second half, a sampling design is developed for a practical situation and the actual sampling is conducted. The samples are drawn from a given data set which serves as population: the course does not involve the construction of a questionnaire and the actual gathering of data.

A translated and adapted version of Theorie en Praktijk van het Steekproefonderzoek by J.G. Bethlehem, 1st ed., CBS, Voorburg (to be made available through Canvas ).

This course is in transition for the bachelor Fiscal Economics.
See the Bachelor Education and Examination Regulations for more information.

The following rule applies to bachelor Fiscal Economics students.
TRANSITIONAL REGULATION (EBS2037):
The bachelor Fiscal Economics has been discontinued.

There will be repeat education for year 1 and year 2 courses up to and including academic year 2025-2026 and exam-only up to and including academic year 2026-2027. For year 3 courses there will be repeat education up to and including 2026-2027 and exam-only up to and including academic year 2027-2028.

Whether a course is in transition, cancelled, or replaced may depend on the cohort you are in. Sometimes there are additional criteria. It is therefore very important to carefully read the EER and any addendum so you can apply the rules to your individual situation.


PREREQUISITES:
Basic principles from inferential statistics as discussed in typical first-year Quantitative Methods courses such as QM1 (code EBC1005/1006/1007) and QM2 (code EBC1033/1034/1035): basic probability theory, population versus sample, sampling distribution, point estimation, confidence intervals, type I error, regression analysis.
An advanced level of English.