Introducing Multilevel Modeling (Introducing Statistical Methods series)
| Author | : | |
| Rating | : | 4.62 (872 Votes) |
| Asin | : | 0761951415 |
| Format Type | : | paperback |
| Number of Pages | : | 160 Pages |
| Publish Date | : | 2015-04-05 |
| Language | : | English |
DESCRIPTION:
I particularly liked the non-directive feel to the book, which leaves the reader clear that there are some issues about which they will have to make up their own mind based on the evidence presented.' - British Journal of Mathematical and Statistical Psychology `If you teach statistics to students with little patience for Greek letters and formulas and who do not have a matrix algebra and mathematical statistics background, I recommend that you use Introducing Multilevel Modeling. `This book offers an introduction to multilevel modelling that is both clear and accessible. For this reason I strongly recommend it to anyone new to the subj
Other key fe. Multilevel approaches are becoming increasingly important in social, behavioural, and educational research and it is clear from recent developments that such models are seen as being more realistic, and potentially more revealing, than ordinary regression models. This is the first accessible and practical guide to using multilevel models in social research. While other books describe these multilevel models in considerable detail none focuses on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling. The authors' approach is user-oriented and the formal mathematics and statistics are kept to a minimum
"A non-mathematical introduction to simple models" according to not a natural. Kreft and DeLeeuw's Introducing Multilevel Modeling was written to mercifully place only limited mathematical demands on readers. It includes an interesting and readable account of intercepts and slopes as outcomes. However, in common with most other texts, it fails to accessibly develop parallels between multilevel modeling and more widely understood procedures. Instead, the authors' intent seems to be to highlight d. An excellent theoretical introduction to Hierchical Linear Models As a foreword, I am a 2nd year psychology graduate student with ANOVA and multiple regression experience.That said, I've found this work to be clear, precise, and straight-forward in introducing the logic and concepts behind why one would wish to use a hiearchical linear model, as well as the foundation of said statistical design. The lack of emphasis on heavy math-based calculations will undoubtedly expedite training. reasonable overview of a burgeoning technique Dean McKenzie When analysing data, the relationships between people that belong in the same classroom, live in the same street or suburb, are part of the same family or therapy group,etc., are often ignored. Multilevel or hierarchical linear modelling is a statistical technique for taking into account such dependencies, arranged in hierarchies (e.g., correlations between students within classrooms, correlations between classrooms w