Применение многоуровневого регрессионного моделирования к межстрановым данным (на примере генерализованного доверия)

The paper deals with multilevel regression modeling (MLM) as a method preferred to the ordinary least-squares regression in the analysis of comparative data with hierarchical data structure. We present substantive reasons (contextual sources of heterogeneity, causal heterogeneity, and generalizability of results) and statistical reasons (obtaining more precise and reliable estimates) for multilevel modeling. We also provide an overview of MLM implementation in several statistical packages. Using the cross-national World Values Survey (WVS) data, we outline a step-by-step procedure for building and fitting a two-level linear regression model of generalized trust on educational attainment levels (the “null” model, the fixed-intercept model, the random-intercept model, the random-intercept random-slope model, the model with a country-level predictor, and the cross-level interaction model). Then we describe and compare existing goodness-of-fit measures for MLM (AIC, BIC, maximum likelihood functions, and pseudo-R2). We also demonstrate robustness check techniques for multilevel models (visualization, Cook’s distance, and DFBETAs). In the final section, we overview alternative approaches to multilevel modeling when dealing with hierarchical data (cluster robust standard errors, generalized estimating equations, country fixed effects, country means, and aggregation) as currently practiced in comparative crossnational social science research. The replicable R code is attached.