survey-analysis-polisci

Weighted political survey analysis is performed by applying frequency-weighted estimation and iterative proportional fitting using pandas, statsmodels, numpy, and scipy to compute weight-aware crosstabs and regression models that respect complex sampling designs.

Rao-Scott chi-square adjustment is a design-based correction technique applied to weighted cross-tabulations in complex survey data, adjusting standard chi-square tests to account for clustering and stratification effects.

Yes, you can fit a frequency-weighted ordered logit model for Likert or ordinal outcomes using ANES survey weights, applying statsmodels to estimate regression coefficients while respecting the complex sampling design.

Survey weights are calibrated via post-stratification raking by applying iterative proportional fitting to adjust sample weights until they match known target population margins across demographic variables.

Yes, cross-national equivalence workflows are supported by structuring analysis around configural, metric, and scalar invariance steps, enabling country-level comparisons using ESS weights and design-based adjustments.

Unweighted descriptive statistics should be avoided when your political survey data involves complex sampling designs, because ignoring survey weights and design effects leads to biased estimates and invalid inference.