Surveyanalyse

Surveyanalyse, or survey analysis, is the systematic process of analyzing data collected through surveys to draw inferences about populations, opinions, behaviors, or processes. It combines data cleaning, weighting for the sampling design, and statistical analysis to answer predefined research questions in a transparent and reproducible way.

Data preparation includes checking data quality, handling missing values, recoding variables, and aligning survey weights with the sampling design. In complex surveys, weights, strata, clustering, and finite population corrections are used to obtain unbiased estimates and valid standard errors. Proper preparation helps mitigate biases stemming from nonresponse and measurement error.

Common methods encompass descriptive statistics to summarize responses, cross-tabulations to explore relationships, and inferential techniques such as regression models (linear, logistic, multinomial) that account for survey design. Design-based or model-based approaches are employed, with standard errors computed using methods like Taylor series linearization, bootstrap, or jackknife. For longitudinal surveys, panel data techniques, fixed or random effects, and growth models may be applied.

Reporting yields estimates with confidence intervals, effect sizes, and, where appropriate, p-values and margins of interpretation. Outputs should reflect the survey design, and accompanying documentation should detail instrument design, weighting schemes, and analysis syntax to support reproducibility.

Quality considerations include handling nonresponse bias, measurement error, and coverage errors. Privacy and ethical considerations guide data handling, and the field emphasizes transparent methods, clear reporting, and adherence to data protection standards.