Pampel's book offers readers the first "nuts and bolts" approach to doing logistic regression through the use of careful explanations and worked-out examples. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic.
Logistic Regression
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About the Author
Fred C. Pampel (Ph.D., University of Illinois) is Professor of Sociology and Research Associate of the Institute of Behavior Science at the University of Colorado, Boulder. With John Williamson, he is the author of Age, Class, Politics, and the Welfare State and Old Age Security in Comparative Perspective. He has published numerous articles on topics relating to social policy, age structure, and pension spending, and is currently doing research on age differences in income inequality and mortality from suicide and homicide.
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Most Helpful Customer Reviews
38 of 41 people found the following review helpful:
5.0 out of 5 stars
Excellent explanation of this esoteric method.,
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This review is from: Logistic Regression: A Primer (Quantitative Applications in the Social Sciences) (Paperback)
This is an excellent book on the subject. It covers the subject in detail. I work in a quantitative group of a large financial institution. My boss asked me recently to make a presentation on logistic regression, a subject I knew nothing about at the time. Thanks to this book, I was able to develop the knowledge and expertise in this esoteric statistical method to make a descent presentation to my group. I was also able to develop basic logistic regression models in Excel. Personally, unlike many of my colleagues (Russians with formidable backgrounds in math) I don't have an advanced knowledge in math or statistic. The book reviewed and taught me what I needed to know to grasp logistic regression. This included an excellent review of logarithms and partial derivatives. If you are interested to learn this subject, and you need the equivalent of a most user friendly "Logistic Regression for Dummies" this is the book for you. By the way, logistic regression is not so well known today outside of academic and quantitatively oriented circle. But, after studying it, I feel like it will take over linear regression big time. It is such a superior method to analyze situations associated with human behavior and human choices. Whether you are a psychologist, social scientist, or developer of financial products you will sooner or later have to grasp logistic regression because your counterparts and competitors will. And, in the statistical evaluation of decision-making, linear regression just can't compete with logistic regression. This book will help you catch the logistic regression wave.
4 of 4 people found the following review helpful:
5.0 out of 5 stars
Excellent Intro,
By Charlotte Booklover (Pittsburgh, PA) - See all my reviews
This review is from: Logistic Regression: A Primer (Quantitative Applications in the Social Sciences) (Paperback)
This book is a good, quick introduction that will allow you to jump right into logistic regression. I found Pampel's writing to be comprehensible and quite suitable for anyone with a good working knowledge of regular linear regression.
All in all, you could do far worse if you need a basic overview into LR. Think of this as a good book to let you know what you need to know. It'd also make a good reference if you need a primer on logs.
1 of 1 people found the following review helpful:
4.0 out of 5 stars
quick intro (as advertised),
By SG "timequaker" (Ann Arbor, MI) - See all my reviews
Amazon Verified Purchase(What's this?)
This review is from: Logistic Regression: A Primer (Quantitative Applications in the Social Sciences) (Paperback)
Well written and easy to understand. In an hour or 2, you can get a quick grasp on this subject. Considering the cost of textbooks nowadays this is sort of a good place to start if you are in a logistic regression class.
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Inside This Book
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First Sentence:
Many social phenomena are discrete or qualitative rather than continuous or quantitative in nature-an event occurs or it does not occur, a person makes one choice but not the other, an individual or group passes from one state to another. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
logged odds, baseline log likelihood, cumulative standard normal curve, dummy variable group, exponentiated coefficient, predicted logit, dummy dependent variable, proportion smoking, cumulative standard normal distribution, probit values, logistic regression programs, logistic regression coefficients, model log likelihood, omitted group, probit coefficients, log likelihood values, logit transformation, dichotomous dependent variable, predicted probabilities, probit analysis, continuous independent variables, probit transformation, standardized coefficients, ordinary regression, predicted probability New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Many social phenomena are discrete or qualitative rather than continuous or quantitative in nature-an event occurs or it does not occur, a person makes one choice but not the other, an individual or group passes from one state to another. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
logged odds, baseline log likelihood, cumulative standard normal curve, dummy variable group, exponentiated coefficient, predicted logit, dummy dependent variable, proportion smoking, cumulative standard normal distribution, probit values, logistic regression programs, logistic regression coefficients, model log likelihood, omitted group, probit coefficients, log likelihood values, logit transformation, dichotomous dependent variable, predicted probabilities, probit analysis, continuous independent variables, probit transformation, standardized coefficients, ordinary regression, predicted probability New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Citations (learn more)
This book cites 65
books:
See all 65 books this book cites
8
books
cite this book:
See all 8 books citing this book
- Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences) by John H. Aldrich in Front Matter, and Back Matter
- Causal Modeling (Quantitative Applications in the Social Sciences) by Herbert B. Asher in Back Matter
- Research Designs (Quantitative Applications in the Social Sciences) by Paul E. Spector in Back Matter
- Stochastic Parameter Regression Models (Quantitative Applications in the Social Sciences) by Paul Newbold in Back Matter
- Nonrecursive Causal Models (Quantitative Applications in the Social Sciences) by William D. Berry in Back Matter
See all 65 books this book cites
- Multiple Time Series Models (Quantitative Applications in the Social Sciences) by Patrick T. Brandt in Front Matter
- Drug Testing in Law Enforcement Agencies: Social Control in the Public Sector by James R. Brunet in Back Matter
- Logit and Probit: Ordered and Multinomial Models (Quantitative Applications in the Social Sciences) by Vani Kant Borooah in Front Matter
- Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences) (v. 106) by Scott Menard in Front Matter
- Causal Analysis with Panel Data (Quantitative Applications in the Social Sciences) by Steven Eric Finkel in Back Matter
See all 8 books citing this book
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