How few countries will do? Comparative survey analysis from a Bayesian perspective

Meuleman and Billiet (2009) have carried out a simulation study aimed at the question how many countries are needed for accurate multilevel SEM estimation in comparative studies. The authors concluded that a sample of 50 to 100 countries is needed for accurate estimation. Recently, Bayesian estimation methods have been introduced in structural equation modeling which should work well with much lower sample sizes. The current study reanalyzes the simulation of Meuleman and Billiet using Bayesian estimation to find the lowest number of countries needed when conducting multilevel SEM. The main result of our simulations is that a sample of about 20 countries is sufficient for accurate Bayesian estimation, which makes multilevel SEM practicable for the number of countries commonly available in large scale comparative surveys.

[1]  J. Harkness Survey Methods in Multinational, Multiregional, and Multicultural Contexts , 2010 .

[2]  Brendan Bunting Structural equation modeling: Present and future. A festschrift in honor of Karl Joreskog. , 2001 .

[3]  Jaak Billiet,et al.  A Monte Carlo sample size study: How many countries are needed for accurate multilevel SEM? , 2009 .

[4]  Vic Barnett,et al.  Comparative Statistical Inference , 1974, Technometrics.

[5]  K. G. J8reskoC,et al.  Simultaneous Factor Analysis in Several Populations , 2007 .

[6]  Timothy J. Robinson,et al.  Multilevel Analysis: Techniques and Applications , 2002 .

[7]  Tapabrata Maiti,et al.  Bayesian Data Analysis (2nd ed.) (Book) , 2004 .

[8]  C. Stein,et al.  Structural equation modeling. , 2012, Methods in molecular biology.

[9]  H. Goldstein Multilevel Statistical Models , 2006 .

[10]  Y. Poortinga,et al.  Multilevel analysis of individuals and cultures , 2008 .

[11]  A. Brix Bayesian Data Analysis, 2nd edn , 2005 .

[12]  Scott M. Lynch,et al.  Introduction to Applied Bayesian Statistics and Estimation for Social Scientists , 2007 .

[13]  Robert E. Ployhart,et al.  Hierarchical Linear Models , 2014 .

[14]  G. A. Marcoulides Multilevel Analysis Techniques and Applications , 2002 .

[15]  Sik-Yum Lee Structural Equation Modeling: A Bayesian Approach , 2007 .

[17]  G. Wetherill,et al.  Comparative Statistical Inference , 1974, Technometrics.

[18]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[19]  Bengt Muthén,et al.  Latent variable modeling in heterogeneous populations , 1989 .

[20]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[21]  R. Groves,et al.  Survey Errors and Survey Costs. , 1991 .

[22]  J. Hox,et al.  Sufficient Sample Sizes for Multilevel Modeling , 2005 .

[23]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[24]  Michael C Neale,et al.  People are variables too: multilevel structural equations modeling. , 2005, Psychological methods.

[25]  A. Boomsma,et al.  The robustness of LISREL modeling revisted. , 2001 .