Multilevel Modelling of Country Effects: A Cautionary Tale
By Mark L. Bryan, S. Jenkins. 2016
817 citations recorded by the source corpus.
Country effects on outcomes for individuals are often analysed using multilevel (hierarchical) models applied to harmonized multi-country data sets such as ESS, EU-SILC, EVS, ISSP, and SHARE. We point out problems with the assessment of country effects that appear not to be widely appreciated, and develop our arguments using Monte Carlo simulation analysis of multilevel linear and logit models. With large sample sizes of individuals within each country but only a small number of countries, analysts can reliably estimate individual-level effects but estimates of parameters summarizing country effects are likely to be unreliable. Multilevel modelling methods are no panacea.
Subfields: Economics and Econometrics, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/f6e287e94f465257ab266814e3b3b39c697744af
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Citations in the corpus
- Why Income Inequality Is Dissatisfying—Perceptions of Social Status and the Inequality-Satisfaction Link in Europe (2019, background): “However, variability in the point estimates of macro-level effects may still be high and lead to inaccurate conclusions (Bryan and Jenkins, 2016).”
- Why You Should Always Include a Random Slope for the Lower-Level Variable Involved in a Cross-Level Interaction (2019, neutral): “, 2017) and for statistical inference (Barr et al., 2013; Bell et al., 2016; Berkhof and Kampen, 2004; Bryan and Jenkins, 2016; Schmidt-Catran and Fairbrother, 2016).”
- The paradox of inequality: income inequality and belief in meritocracy go hand in hand (2019, background): “Estimating random intercept models, instead of country fixed effects, means I treat my sample as randomly drawn from the larger theoretical population of all Western countries at any point in time, rather than just the set of countries and periods that constitute my empirical sample (Snijders and Bo”