How do you use sampling weights and adjustments in your survey research analysis?

1 What are sampling weights and adjustments?

Sampling weights and adjustments are factors that are applied to each survey respondent or observation to reflect their relative importance or representativeness in the population of interest. Sampling weights are usually derived from the inverse of the probability of selection, which depends on the sampling design and method. Adjustments are additional corrections that account for nonresponse, poststratification, calibration, or other sources of discrepancy between the sample and the population.

2 Why are sampling weights and adjustments needed?

Sampling weights and adjustments are needed to ensure that the survey estimates are unbiased and accurate, and that they reflect the characteristics and variability of the population. Without weighting and adjustment, the survey results may be distorted by overrepresentation or underrepresentation of certain groups or regions, or by differential response rates or measurement errors. Weighting and adjustment also help to improve the precision and reliability of the survey estimates, and to make them comparable across different surveys or waves.

3 How to calculate sampling weights and adjustments?

The calculation of sampling weights and adjustments depends on the sampling design and method, and the availability of auxiliary information. The basic formula for sampling weight is the inverse of the probability of selection, which can be calculated as the product of the probabilities at each stage of sampling. For example, if a simple random sample of 100 households is drawn from a population of 10,000 households, the sampling weight for each household is 10,000/100 = 100. The probability of selection may vary depending on the sampling frame, stratification, clustering, or oversampling. Adjustments are usually applied to the sampling weights to account for nonresponse, poststratification, calibration, or other sources of discrepancy. For example, if the response rate for a group is 80%, the nonresponse adjustment factor is 1/0.8 = 1.25, which is multiplied by the sampling weight. Poststratification adjustment involves aligning the sample distribution with the population distribution on some key variables, such as age, gender, or education. Calibration adjustment involves minimizing the difference between the sample and the population totals on some auxiliary variables, such as income, expenditure, or employment.

4 How to apply sampling weights and adjustments to survey data?

The application of sampling weights and adjustments to survey data involves using them as analytical weights in the statistical analysis. Analytical weights are used to give more or less weight to each observation according to their relative importance or representativeness in the population. Analytical weights can be used in descriptive statistics, such as means, proportions, or frequencies, or in inferential statistics, such as regression, correlation, or hypothesis testing. The use of analytical weights requires appropriate software and methods that can handle weighted data, such as survey commands in Stata, SPSS, R, or SAS. The use of analytical weights also requires careful interpretation and reporting of the results, as they may affect the standard errors, confidence intervals, significance tests, or effect sizes.

6 Here’s what else to consider

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