These procedures, collectively known as discriminant analysis, allow a researcher to study the difference between two or more groups of objects with respect to several variables simultaneously, determining whether meaningful differences exist between the groups and identifying the discriminating power of each variable.
Discriminant Analysis
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5.0 out of 5 stars
Excellent in depth introduction to this complex subject.,
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This review is from: Discriminant Analysis (Quantitative Applications in the Social Sciences) (Paperback)
This is an excellent in depth introduction to Discriminant Analysis. This is the second little green book of the Sage University Paper I have studied. The first one was on Logistic Regression. And, the mileage you will get out of these green books will vary based on the complexity of the subject. Frankly, by comparison Logistic Regression was a cruise. After reading the green book I was able to build an Excel based model longhand replicating most of the methodologies described in the book. Discriminant Analysis is different. Relative to Logistic Regression it is a real piece of work. At first, I thought this green book was not as well written as the one on Logistic Regression. But, that is not the case. It is just that Discriminant Analysis is that much more complex. It is a bit like comparing the clarity of a physics textbook vs. a quantum physics one. The quantum physics one will appear much more obscure. And, it is not because of the writing style, but because of the subject complexity. With Discriminant Analysis, you just have to accept a longer more intense learning curve than with Logistic Regression. Given that, this book is excellent. And, it gives you a pretty good understanding of what it is all about. It is a bit like learning how to drive. You can do it without understanding all the mechanical complexities of a combustion engine. This green book will give you a pretty good understanding on how to "drive" and interpret Discriminant Analysis. That probably meets the goals of 90% of the people who approach this Byzantine subject. If you are among the extreme mathematicians who want to be able to derive all the underlying algorithms, you will need more advanced books. And, the green book gives you ample references if you care to do that.
5.0 out of 5 stars
Comprehensive and Direct,
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This review is from: Discriminant Analysis (Quantitative Applications in the Social Sciences) (Paperback)
Like all of the Sage books I've read, the writing in this book very much gets to the point: the entire text is less than 70 pages long! Mainly, this book covers: canonical discriminant analysis and linear and quadratic discriminant classifiers, though a number of ancillary topics are also covered, such as variable selection and violations of assumptions. A capable programmer should be able to implement the described procedures without difficulty.
I would have preferred the inclusion of more worked examples and computer source code, otherwise this is an excellent book.
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Inside This Book
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First Sentence:
Discriminant analysis is a statistical technique which allows the researcher to study differences between two or more groups of objects with respect to several variables simultaneously. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
total discriminating power, total structure coefficients, grand centroid, group covariance matrices, residual discrimination, canonical discriminant functions, discriminating variables, roll call data, group centroids, discriminating information, discriminant scores, discriminant function coefficients, standardized coefficients, entry criterion, closest group, discriminant analysis, classification functions Key Phrases - Capitalized Phrases (CAPs): (learn more)
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Discriminant analysis is a statistical technique which allows the researcher to study differences between two or more groups of objects with respect to several variables simultaneously. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
total discriminating power, total structure coefficients, grand centroid, group covariance matrices, residual discrimination, canonical discriminant functions, discriminating variables, roll call data, group centroids, discriminating information, discriminant scores, discriminant function coefficients, standardized coefficients, entry criterion, closest group, discriminant analysis, classification functions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Senator Aiken, Senator Bridges New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
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Citations (learn more)
This book cites 55
books:
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100
books
cite this book:
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- Tests of Significance (Quantitative Applications in the Social Sciences) by Ramon E. Henkel in Back Matter (1), and Back Matter (2)
- Analysis of Variance (Quantitative Applications in the Social Sciences) by Gudmund R. Iversen in Back Matter (1), and Back Matter (2)
- Statistical Package for the Social Sciences by N.H. Nie in Back Matter (1), and Back Matter (2)
- Analyzing Panel Data (Quantitative Applications in the Social Sciences) by Gregory B. Markus in Back Matter
- Stochastic Parameter Regression Models (Quantitative Applications in the Social Sciences) by Paul Newbold in Back Matter
See all 55 books this book cites
- Causal Modeling (Quantitative Applications in the Social Sciences) by Herbert B. Asher in Back Matter (1), and Back Matter (2)
- Acquisition of Software Engineering Knowledge: Sweep, an Automatic Programming System Based on Genetic Programming and Cultural Algorithms (Series on Software Engineering and Knowledge Engineering) by George S. M., Jr., M.d. Cowan on page 62, and Back Matter
- Introduction to Factor Analysis: What It Is and How To Do It (Quantitative Applications in the Social Sciences) by Jae-On Kim in Back Matter (1), and Back Matter (2)
- Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences) by John H. Aldrich in Back Matter (1), and Back Matter (2)
- Multiple Regression in Practice (Quantitative Applications in the Social Sciences) by William D. Berry in Back Matter (1), and Back Matter (2)
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