The estimation of power in two-level models used to analyze data that are hierarchically
structured is particularly complex because the outcome contains variance at two levels that …

In small sample contexts, Bayesian estimation is often suggested as a viable alternative to
frequentist estimation, such as maximum likelihood estimation. Our systematic literature …

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 …

More comprehensive than other texts, this new book covers the classic and cutting edge
multivariate techniques used in today's research. Ideal for courses on multivariate …

Researchers in comparative research increasingly use multilevel models to test effects of
country‐level factors on individual behavior and preferences. However, the asymptotic …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

Applauded for its clarity, this accessible introduction helps readers apply multilevel
techniques to their research. The book also includes advanced extensions, making it useful …

A new edition of the definitive guide to logistic regression modeling for health science and
other applications This thoroughly expanded Third Edition provides an easily accessible …

Multilevel analysis is a methodology for the analysis of data with complex patterns of
variability, with a focus on nested sources of such variability–pupils in classes, employees in …

An important problem in multilevel modeling is what constitutes a sufficient sample size for
accurate estimation. In multilevel analysis, the major restriction is often the higher-level …