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Regression with Dummy Variables (Quantitative Applications in the Social Sciences) Paperback – February 25, 1993

ISBN-13: 978-0803951280 ISBN-10: 0803951280 Edition: 1st

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Product Details

  • Series: Quantitative Applications in the Social Sciences (Book 93)
  • Paperback: 96 pages
  • Publisher: SAGE Publications, Inc; 1 edition (February 25, 1993)
  • Language: English
  • ISBN-10: 0803951280
  • ISBN-13: 978-0803951280
  • Product Dimensions: 8.7 x 5.5 x 0.4 inches
  • Shipping Weight: 4 ounces (View shipping rates and policies)
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #692,375 in Books (See Top 100 in Books)

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About the Author

RESEARCH aND TEACHING INTERESTS Quantitative Analysis Techniques, Longitudinal Methods; Aging & Social Change; Public Policy and Aging. Social Inequality, Social Sciences and Humanities Research Council of Canada. “Workforce aging in the new economy: A comparative study of information technology employment” (Julie A. McMullin, PI; Victor Marshall, Joann Marshall, University of North Carolina; Neil Charness, Florida State University). October 2002 – September 2006. Work and Retirement, including the influence of organizational incentives, pensions, family decision-making, health, job satisfaction; Public Policy, including Social Security, the Age Discrimination and Employment Act, the Employee Retirement Income Security Act, Medicare and Medicaid; Political Attitudes, including analysis of social change and individual aging; Women’s Issues and Aging, including poverty and financial security, caregiving, long-term care, intergenerational assistance, and health; Cognitive Components of Saving and Investment behavior, including planning horizon, discounting, risk tolerance, deferred gratification; Older Workers, including training, displacement, work schedules, job demands, organizational incentive structures.

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3 of 3 people found the following review helpful By not a natural on October 18, 2011
Format: Paperback
Regression with Dummy Variables is a very useful book that includes, for most readers, more than they will ever need to know about incorporation of categorical or dummy variables into a regression equation and interpretation of the results. The book is not mathematically dense, relying heavily on verbal exposition rather than mathematics in developing its presentation. However, the author has a condensed, streamlined, no-frills writing style that enables her to present the material in a compact and rigorous manner.

For readers who have had a solid introduction to basic statistics and multiple regression analysis, the material is accessible, though some discussions, including interpretation of interaction effects involving more than one categorical variable, and categorical variables with more than two categories, require even the well prepared reader to pay very close attention and give careful consideration to the author's explanations. It's best to have a pocket calculator handy to reproduce some of the calculations just to make sure we understand what the author is doing.

It's embarrassing to have to admit, but one of the lessons I learned from Melissa Hardy's book is that, in my own work, in some instances I had been correctly interpreting interaction terms, but incorrectly interpreting corresponding main effects. Perhaps, as Berry and Feldman (1985) observe in their book Multiple Regression in Practice, many others have been making the same mistake. In any case, I've been reporting regression analysis results for nearly thirty years and until I belatedly found a technique using partial derivatives for interpreting interaction terms and their corresponding main effects, I was getting the main effects wrong (See Friedman and Necochea, 1988).
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Format: Paperback Verified Purchase
Like most of the books in the Sage Quantitative Applications in the Social Sciences, this is clearly written and understandable. This is one of those rare statistics texts that is readable and useful. If you need to understand or use dummy variables in regression, this book will save you enormous amounts of time and frustration. Strongly recommended.
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2 of 4 people found the following review helpful By A Customer on June 30, 2003
Format: Paperback
This book saved my life! It provided clear explanations and great examples of how to use and interpret regressions with dummy variables. I don't know how I would have passed without it. An extra bonus for people interested in strat--the examples deal with inequality.
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