With random samples, there is no guarantee that samples will perfectly represent the population when it comes to various characteristics that may be relevant to the concept we are seeking to understand, explain, and/or predict, so POST-STRATIFICATION WEIGHTING is always needed. With post-stratification weighting, sample data are WEIGHTED (adjusted) so the sample better mirrors the overall population. This corrects for potential biases and makes the sample more representative of the population.
Post-stratification weighting involves the following steps:
- Identify STRATA, or subgroups that share a specific characteristic, such as age, race, gender, income, education level, or other socio-economic and/or demographic factors
- Identify the proportions of the population falling into each STRATUM (singular of “strata”)
- NOTE: To calculate population proportions, population-level data (such as census data) must be available for the characteristics you plan to use to weight your sample data
- NOTE: To calculate population proportions, population-level data (such as census data) must be available for the characteristics you plan to use to weight your sample data
- Calculate the proportion of the sample that falls into each stratum (ex: the percentage of the sample who are male)
- Calculate a weight for each stratum — usually, the ratio of the population proportion for a strata to the sample proportion for a strata
- A weight of “1” means that the proportion of the sample for that stratum matches the proportion of the population
- A weight of greater than/less than “1” means the proportion of the sample for that stratum does not match the proportion of the population
- Apply the weights to sample data in each stratum, which adjusts the survey data to better reflect the distribution of these characteristics in the overall population
- When the weight associated with a stratum = 1, no adjustment is necessary because this stratum is perfectly representative of the population
- When the weight associated with a stratum > 1, that stratum is said to have been UNDERREPRESENTED (i.e., included in the sample in lower proportions than what is found in the population); this adjusts values for this stratum so they count more heavily in the overall analysis
- When the weight associated with a stratum < 1, that stratum is said to have been OVERREPRESENTED (i.e., included in the sample in higher proportions than what is found in the population); this adjusts values for this stratum so they count less heavily in the overall analysis