|
||||||||||||||||||||
|
||||||||||||||||||||
|
||||||||||||||||||||
| |||||||||||||||
Produktinformation
Möchten Sie die Produktinformationen aktualisieren oder Feedback zu den Produktabbildungen geben?
Ist der Verkauf dieses Produkts für Sie nicht akzeptabel? |
Mehr über den Autor
Entdecken Sie Bücher, lesen Sie über Autoren und mehr
› Besuchen Sie die Seite von Ita G. G. Kreft auf Amazon
› Besuchen Sie die Seite von Ita G. G. Kreft auf Amazon
Produktbeschreibungen
Pressestimmen
'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 subject... 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. - Journal of the American Statistical Association
Kurzbeschreibung
This is the first accessible and practical guide to using multilevel models in social research. 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. 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. Other key features include the use of worked examples using real data sets, analyzed using the leading computer package for multilevel modeling - MLn. Discussion site at: http:\\www.stat.ucla.edu\phplib\w-agora\w-agora.phtml?bn=Sagebook Data files mentioned in the book are available from: http:\\www.stat.ucla.edu\~deleeuw\sagebook
In diesem Buch
(Mehr dazu)
Einleitungssatz
This chapter is a short overview of multilevel modeling, its use, its history and its implementation in different software packages. Lesen Sie die erste Seite
Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis | Rückseite
This chapter is a short overview of multilevel modeling, its use, its history and its implementation in different software packages. Lesen Sie die erste Seite
Mehr entdecken
Wortanzeiger
Ausgewählte Seiten ansehenWortanzeiger
Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis | Rückseite
Tags(Was ist das?)Bei einem Tag handelt es sich um ein Schlagwort, das zum Produkt passt.
Tags erleichtern allen Kunden die Suche und die Sortierung ihrer Lieblingsprodukte. |
Eine digitale Version dieses Buchs im Kindle-Shop verkaufen
Wenn Sie ein Verleger oder Autor sind und die digitalen Rechte an einem Buch haben, können Sie die digitale Version des Buchs in unserem Kindle-Shop verkaufen.
Weitere Informationen
Kundenrezensionen
Die hilfreichsten Kundenrezensionen
1 von 1 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch
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 within schools, correlations between schools within school districts). In other words, multilevel modeling techniques attempt to model the hierarchical relationships that are found in the real world. In the last 10 years or so there has been a growing number of books and software packages concerned with multilevel analyses. Introducing Multilevel Modeling is shorter and slightly less 'mathematical' than most and gives quite a good introduction to the subject. The book makes reference to the British MLn (MLWiN) computer program in its examples, whereas an introductory text arguably should have used the HLM program, for which a cutdown student version is available free. Taking group dependencies into account is extremely important, but unfortunately many researchers will be discouraged by the dry and heavy-going feel of these texts, which is so often the case with anything involving statistical theory. A highly approachable and readable book remains to be written, but Introducing Multilevel Modeling is probably the best of the current crop.
Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
Amazon.com:
5 Rezensionen
29 von 31 Kunden fanden die folgende Rezension hilfreich
reasonable overview of a burgeoning technique
6. April 2000
Format:Taschenbuch
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 within schools, correlations between schools within school districts). In other words, multilevel modeling techniques attempt to model the hierarchical relationships that are found in the real world. In the last 10 years or so there has been a growing number of books and software packages concerned with multilevel analyses. Introducing Multilevel Modeling is shorter and slightly less 'mathematical' than most and gives quite a good introduction to the subject. The book makes reference to the British MLn (MLWiN) computer program in its examples, whereas an introductory text arguably should have used the HLM program, for which a cutdown student version is available free. Taking group dependencies into account is extremely important, but unfortunately many researchers will be discouraged by the dry and heavy-going feel of these texts, which is so often the case with anything involving statistical theory. A highly approachable and readable book remains to be written, but Introducing Multilevel Modeling is probably the best of the current crop.
13 von 13 Kunden fanden die folgende Rezension hilfreich
User friendly!
11. Mai 2001
Format:Taschenbuch
This book is the best intro to the subject that I've seen. The authors minimize the use of notation, mathematics and the like, and invoke the reader's intuition by developing some good, concrete examples. They present those examples as datasets (accessible on the web), as "run" regressions (i.e. with parameter estimates and standard errors), and graphically.
They also demo how to "run" each of the examples on a PC, using the program MLn. If you don't use MLn (and I had never heard of it); this part of the book is less helpful. It would be great if, in an accompanying website perhaps, they were to demo the same analyses using other packages (e.g. SYSTAT, SAS and the like). But this book is really quite good, and a terrific addition to any applied social scientist's library.
3 von 3 Kunden fanden die folgende Rezension hilfreich
An excellent theoretical introduction to Hierchical Linear Models
9. September 2005
Format:Taschenbuch
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 and use for those in the behavioral sciences. On that same note, formulae are interjected only when necessary and more often clarify than confuse a given point or concept.
The only drawback of this text might be imparting exactly how one performs HLM. There are other books on the market that, though more technical and/or less clear, provide the heavy emphasis on analysis that this book does not quite have.
Even so, I am firmly convinced that there is no better book for HLM in terms of laying out the theoretical foundation. An added bonus for me was the fact that ANOVA and regression are richly elaborated upon--a feature that many graduate students might appreciate as a refresher.
In short, I highly recommend this book.
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 and use for those in the behavioral sciences. On that same note, formulae are interjected only when necessary and more often clarify than confuse a given point or concept.
The only drawback of this text might be imparting exactly how one performs HLM. There are other books on the market that, though more technical and/or less clear, provide the heavy emphasis on analysis that this book does not quite have.
Even so, I am firmly convinced that there is no better book for HLM in terms of laying out the theoretical foundation. An added bonus for me was the fact that ANOVA and regression are richly elaborated upon--a feature that many graduate students might appreciate as a refresher.
In short, I highly recommend this book.
Waren diese Rezensionen hilfreich?
Wir wollen von Ihnen hören.
Kunden diskutieren
|
Das Forum zu diesem Produkt
Fragen stellen, Meinungen austauschen, Einblicke gewinnen Aktive Diskussionen in ähnlichen Foren
Kundendiskussionen durchsuchen
|
Ähnliche Foren
|
||||||||||||||||||||||||||||||||||
Lieblingslisten
Ähnliche Artikel finden
Anhand des Sachgebietes nach ähnlichen Produkten suchen:
Ihr Kommentar
Möchten Sie die Produktinformationen aktualisieren oder Feedback zu den Produktabbildungen geben?
|
