Average Reviews:
(More customer reviews)This book gives a detailed description of the use of an advanced method to deal with nested data sets.
At a general level the constructs and ideas are well written and can be followed reasonably easily.
However the mathematics is often written very dense, which makes reading and understanding complex.
My main problem with the book, is that in many of the examples they provide, the given formula's, and data skip rapidly to the solution. Thus it is often not insightfull at all, how the data led to the numerical outcome (and I and several of my colleagues could not reproduce all of the example outcomes). A more extensive discussion and a more step-by-step construction of the examples would have been helpful there.
So in short: Conceptually this book is fine, but for practical use mathematics are too dense, and examples are too hard to follow
Click Here to see more reviews about: Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences)
Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3* New section on multivariate growth models in Chapter 6 * A discussion of research synthesis or meta-analysis applications in Chapter 7* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators
