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Rating: 
Summary: Useful, but need solid background in stats
Review: This book describes important advances in statistical analysis of social science data, circa 1992. Much of this data has a natural hierarchical grouping. But traditional statistical methods proved inadequate at coping. The biggest drawback was the failure of the assumption of independence. If at the lowest level, Items I1,...,In are grouped into sets J1,...,Jm, where mTo handle this, Hierarchical Linear Models were developed. The book gives a detailed treatment. A very comprehensive discussion. Including the handling of meta-analysis, where we wish to combine results across different studies. Which then involves using empirical Bayesian estimates. This method has also seen important usage in evaluating medical studies, conducted by different researchers on the same topic. The book also illustrates the essential development of non-trivial computer programs to perform the gruntwork. You will need a solid background in statistics to find this book useful. At a minimum, a year of statistics at the undergraduate level.
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