Statistical Methods for Social Scientists / Edition 1

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Overview

The aspects of this text which we believe are novel, at least in degree, include: an effort to motivate different sections with practical examples and an empirical orientation; an effort to intersperse several easily motivated examples throughout the book and to maintain some continuity in these examples; and the extensive use of Monte Carlo simulations to demonstrate particular aspects of the problems and estimators being considered. In terms of material being presented, the unique aspects include the first chapter which attempts to address the use of empirical methods in the social sciences, the seventh chapter which considers models with discrete dependent variables and unobserved variables. Clearly these last two topics in particular are quite advanced--more advanced than material that is currently available on the subject. These last two topics are also currently experiencing rapid development and are not adequately described in most other texts.

Audience: Undergraduate, and graduate students who have an understanding of statistical methods.

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Product Details

  • ISBN-13: 9780123243508
  • Publisher: Elsevier Science
  • Publication date: 2/11/1977
  • Series: Quantitative Studies in Social Relations Series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 374
  • Product dimensions: 1.00 (w) x 6.00 (h) x 9.00 (d)

Meet the Author

Eric Hanushek is the Paul and Jean Hanna Senior Fellow at the Hoover Institution of Stanford University. He is also chairman of the Executive Committee for the Texas Schools Project at the University of Texas at Dallas, a research associate of the National Bureau of Economic Research, and a member of the Koret Task Force on K-12 Education. He serves as a member of the Board of Directors of the National Board for Education Sciences and of the Governor's Advisory Committee on Education Excellence (California).
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Table of Contents

Empirical Analyses in the Social Science.
Estimation with Simple Linear Models.
Least Squares Estimators: Statistical Properties and Hypothesis Testing.
Ordinary Least Squares in Practice.
Multivariate Estimation in Matrix Form.
Generalized Least Squares.
Models with Discrete Dependent Variables.
Introduction to Multiequation Models.
Structural Equations: Simultaneous Models.
Estimating Models with Erroneous and Unobserved Variables.
Statistical Review.
Matrix Algebra.
Statistical Tables.
References.
Index.
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