Customer Reviews

Applied Multilevel Analysis: A Practical Guide for Medical Researchers (Practical Guides to Biostatistics and Epidemiology)

5 star:		(3)
4 star:		(0)
3 star:		(1)
2 star:		(0)
1 star:		(0)

 

 

The book is easy to understand and presents a series of situations where multilevel models are applied, in the context of epidemiological analysis. The examples are mostly very simple, and I don't think the book will help if you want to fit models. If you want to understand them better, then it may be of use. I have some other restrictions as well - some concepts presented in chapter 2 (section 2.8.1) are in direct contradiction with MLwiN user's guide (version 2, 2005, section 7.5, p.86). My understanding is that the latter is correct. Also, a model fitted in chapter 3 with a dichotomous variable random at level 2 includes the covariance parameter, what looks wrong to me. All in all, I think there are better options for an introductory text, e. g. Snijders & Bosker (1999).

This book accomplishes its goal. It does not seek to describe the statistical details of multilevel analysis (hierarchical models/mixed models). It clearly motivates when these analyses should be used. The author cites several other texts that go over details, but believes that the literature lacks a descriptive overview of these methods. The book served this purpose for me.

Twisk's work is very good for those who are not experts in mathematics and would like to apply this statistic method for investigating cluster effect. Through the tutorial journey mainly illustrated by MLwiN, readers can easily catch the general picture of multilevel analysis and make sense of interpretation and application. The comparison of different statistic software used in multilevel analysis and the concise text for power analysis were another two precious gifts the author gave us.

A good introduction to multilevel analysis that addresses the practical issues that confront researchers.