Abstract
This chapter provides the bootstrap method as a tool to evaluate the precision of multilevel estimates in situations with a small sample size at the group level. Cross-cultural research often deals with hierarchical data structures, either due to the sampling procedure, or to characteristics of sampled units that are related to a grouping variable. In addition to the more traditional technique to deal with hierarchical or nested data, which is multilevel regression analysis, both multigroup structural equation models (SEM) and multilevel SEM are available to analyze such data. Multilevel SEM assumes that a given set of countries is a sample from a larger population. The bootstrap procedure is less dependent on the assumptions of the central limit theory and provides an alternative for the estimation of standard errors. In addition to generating bootstrapped standard errors, the bootstrap procedure can also generate other measures of accuracy, such as bias of estimates.
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