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Author and presenter Meuleman, Bart; Katholieke Universiteit Leuven, Belgium Title A Monte Carlo sample size study: how many countries are needed for accurate multilevel SEM? Abstract Thanks to the increasing availability of international survey data (e.g. the European Values Study and the European Social Survey), there exists growing scientific interest for cross-national comparisons of values, attitudes and opinions. Various scholars have used international surveys to link individual characteristics to aspects of the national context. Often, multilevel techniques are applied to explain individual-level variables by means of country-level features. However, the application of multilevel models in the field of cross-national research is far from unproblematic. Due to budget limitations, the number of participating countries does not exceed 25 for most international surveys. Consequently, the group level sample sizes are often substantially lower than what rules of thumb suggest (at least 50 or 100 units). On the other hand, cross-national surveys typically contain a large number of respondents per country (> 1000). This paper summarizes the results of a Monte Carlo study that was carried out to assess the accuracy of multilevel modeling in the domain of cross-national research. More specifically, the study concentrates on a rather recent but very promising statistical tool, namely multilevel structural equation modeling (SEM). A multilevel SEM, in which a latent factor is explained by a within- and a between-level variable, is simulated. In order to reproduce realistic circumstances as much as possible, the situation of the ESS round 1 (2002- 2003) -22 countries and over 40.000 respondents- is taken as a starting point. The size of the between-level variable effect and the intra-class correlations are manipulated. In order to test whether trade-off effects between individual sample size and group sample size are present, various numbers of countries and respondents per country are simulated. For all conditions, the parameter estimates and their respective standard errors for both the within- and between
model are evaluated. Special attention is given to the power for detecting the effect of the between-level variable.
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Page 11 and 12: KEY NOTE SPEAKER Snijders, Tom A.B.
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Page 15 and 16: Presenter Croon, Marcel; Tilburg Sc
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Page 43 and 44: Presenter Engel, Uwe; Dept. of Soci
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Page 47 and 48: References: Razum, Oliver; Zeeb, Ha
Page 51 and 52: the resulting prevalence estimates
Page 53 and 54: Presenter Kaczmirek, Lars; GESIS -
Page 55 and 56: Presenter Storfinger, Nina; Univers
Page 57 and 58: Presenter Engel, Uwe; Dept. of Soci
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Page 63 and 64: efore retirement. We identify four
Page 65 and 66: provides an alternative interpretat
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Author and presenter Kampen, Jarl;
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Author and presenter Noack, Marcel;
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Presenter Blettner, Daniela P.; Dep
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Presenter Morren, Meike; Tilburg Sc
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Presenter Beuckelaer De, Alain; Rad
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Presenter Kankaraš, Miloš; Dept.
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The ASA Spring Methodology Conferen
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Presenter Busse, Britta; Darmstadt
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Author and presenter Nieuwenhuyze,
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The ASA Spring Methodology Conferen
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Author and presenter Gorter, Rosali
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Author and presenter Mittelhaëuser
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