poststratifiointimenetelmiä

Poststratifiointimenetelmät, often translated as post-stratification methods, are statistical techniques used to adjust survey data to improve its representativeness. This adjustment is performed after the data has been collected, hence the term "post." The core idea is to make the sample statistics more closely align with known population characteristics.

The process begins with dividing the survey population into mutually exclusive subgroups, known as strata. These strata are typically defined based on demographic variables like age, gender, ethnicity, or geographic location. Information about the distribution of these variables in the total population, usually from census data or other reliable sources, must be available.

Once the survey data is collected, it is compared to the known population proportions for each stratum. If the sample proportions do not match the population proportions, a weighting adjustment is applied. This adjustment increases the weight of observations from underrepresented strata and decreases the weight of observations from overrepresented strata. The goal is to ensure that the weighted sample accurately reflects the known population distribution across the defined strata.

Post-stratification is a powerful tool for correcting for non-response bias or sampling frame deficiencies. By adjusting the sample to match known population totals, it can lead to more accurate and reliable estimates of population parameters, such as means, proportions, and totals. However, the effectiveness of post-stratification depends on the availability of accurate population control totals and the appropriateness of the chosen stratification variables.