Applied Multivariate Data Analysis
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Author Description
This book describes the exploratory use of data analysis. The importance of searching for patterns in data is stressed through many worked examples. The interpretation of the output from statistical software is also emphasized. This up-to-date text includes modern techniques such as multidimensional scaling, cluster analysis, generalized linear models and structural equation models. Part I explains data types and the statistical knowledge required to use the book. Part II explains which methods to use. The final parts cover the analysis of variables in more detail. This outstanding volume will prove useful as a reference for statisticians, and valuable to advanced undergraduates and graduate students in fields such as psychology, sociology, economics, and medicine, or in other areas where quantitative methods are central.
Menrva's Summary
THE THESIS
This text investigates the practical application of multivariate statistical methods to identify and interpret complex patterns within diverse datasets. Authors Brian S. Everitt and Graham Dunn provide a structured framework for researchers and students to navigate the transition from raw data to meaningful statistical inference. By emphasizing the interpretation of output from modern statistical software, the authors bridge the gap between theoretical mathematical models and their real-world utility in fields such as psychology, economics, and medicine.
THE SCOPE MAP
What You Will Find
- Comprehensive coverage of multidimensional scaling, cluster analysis, and generalized linear models.
- Methodological guidance on selecting appropriate statistical techniques based on data types.
- Extensive use of worked examples to demonstrate the application of structural equation models.
- Foundational explanations of statistical prerequisites and variable analysis techniques.
THE AUTHORITY PERSPECTIVE
Experts and academics frequently cite this volume as a reliable reference for practitioners needing to apply multivariate techniques in social and medical sciences. Readers often note the clarity of the worked examples, which effectively demystify the output generated by standard statistical software packages.
Page Count:
320
Publication Date:
1992-02-27
Publisher:
Oxford University Press
ISBN-10:
0195209370
ISBN-13:
9780195209372
Science
Data Analysis
Reference
Mathematics
Multivariate analysis
Statistics
