Poststratifiointimenetelmät

Poststratifiointimenetelmät, known in English as post-stratification methods, are statistical techniques used to adjust survey data to better reflect the known characteristics of the target population. These methods are applied after the data collection phase, hence the term "post." The core idea is to use auxiliary information about the population, such as demographic data from official sources, to correct for potential biases or under/over-representation that may have occurred during the sampling process.

The most common form of post-stratification involves dividing the sample into subgroups, or strata, based on one or more categorical variables (e.g., age groups, gender, region). The weights of individuals within each post-stratum are then adjusted so that the weighted sum of the sample within that stratum matches the known population total for that stratum. For example, if a survey sample has proportionally fewer women than are known to exist in the target population, post-stratification would increase the weights of the women in the sample to align with the population proportion.

Post-stratification is particularly useful when the sampling frame is imperfect or when non-response is differential across certain population subgroups. By aligning the sample's composition with known population totals, post-stratification can improve the precision and representativeness of survey estimates, leading to more accurate conclusions about the population being studied. However, the effectiveness of post-stratification depends on the availability and accuracy of the auxiliary population data.