Applied Multilevel Analysis: A Practical Guide for Medical Researchers (Practical Guides to Biostatistics and Epidemiology) [Kindle Edition] Author: Jos W. R. Twisk | Language: English | ISBN:
B0014DM4DI | Format: PDF, EPUB
Applied Multilevel Analysis: A Practical Guide for Medical Researchers
Download for free books Applied Multilevel Analysis: A Practical Guide for Medical Researchers from 4shared, mediafire, hotfile, and mirror link This is a practical introduction to multilevel analysis suitable for all those doing research. Most books on multilevel analysis are written by statisticians, and they focus on the mathematical background. These books are difficult for non-mathematical researchers. In contrast, this volume provides an accessible account on the application of multilevel analysis in research. It addresses the practical issues that confront those undertaking research and wanting to find the correct answers to research questions. This book is written for non-mathematical researchers and it explains when and how to use multilevel analysis. Many worked examples, with computer output, are given to illustrate and explain this subject. Datasets of the examples are available on the internet, so the reader can reanalyse the data. This approach will help to bridge the conceptual and communication gap that exists between those undertaking research and statisticians. Books with free ebook downloads available Applied Multilevel Analysis: A Practical Guide for Medical Researchers
- File Size: 2188 KB
- Print Length: 196 pages
- Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
- Publisher: Cambridge University Press; 1 edition (March 28, 2006)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B0014DM4DI
- Text-to-Speech: Enabled
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- Lending: Enabled
- Amazon Best Sellers Rank: #872,550 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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).
By VSOP
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.
By Sunny
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