Combining Design- and Model-based Analyses for Cross-national Survey Data

Abstract

Survey weighting matters when different sampling probabilities between countries, as well as within a country, are related to the response variable conditional on its predictors. This study aims at improving estimation of regression coefficients from a cross-national data set by assessing the relative effectiveness of different techniques for survey weighting to gain in precision. Data came from the Asian subset of the World Values Survey. Using pooled data across five Asian countries, the logistic regression analysis is fitted to examine the relationship between information seeking behaviors as predictor variables and political participation as a response variable in the context of East and Southeast Asia. The results show that, sampling weights have an advantage of reducing standard errors when estimating a structural relationship among the variables, so much so that the population is stratified into different countries where the regression model is likely to vary in explaining the within-country data. Therefore, the unequal sampling probability of the cross-national survey needs to be adjusted for by accounting for different probabilities of selection between countries. Incorporating the between-country survey weighting into a model-based analysis is a remedy for the problem in the estimated regression parameters.