Making History Count
A Primer in Quantitative Methods for Historians

Authoritative guide to using quantitative methods in history; clearly illustrated and accompanied by website material.

Charles H. Feinstein (Author), Mark Thomas (Author)

9780521001373, Cambridge University Press

Paperback, published 29 August 2002

572 pages, 1 table
25.6 x 17.8 x 4 cm, 1.258 kg

'… no competitor text is, to my knowledge, as effective in taking the student from the basics of descriptive statistics through to the intricacies of multiple linear regression … I wish this text had been available when I was trying to teach quantitative methods to numerically challenged historians…'. The Times Higher Education Supplement

Making History Count introduces the main quantitative methods used in historical research. The emphasis is on intuitive understanding and application of the concepts, rather than formal statistics; no knowledge of mathematics beyond simple arithmetic is required. The techniques are illustrated by applications in social, political, demographic and economic history. Students will learn to read and evaluate the application of the quantitative methods used in many books and articles, and to assess the historical conclusions drawn from them. They will also see how quantitative techniques can open up new aspects of an enquiry, and supplement and strengthen other methods of research. This textbook will encourage students to recognize the benefits of using quantitative methods in their own research projects. The text is clearly illustrated with tables, graphs and diagrams, leading the student through key topics. Additional support includes five specific historical data-sets, available from the Cambridge website.

Part I. Elementary Statistical Analysis: 1. Introduction
2. Descriptive statistics
3. Correlation
4. Simple linear regression
Part II. Samples and Inductive Statistics: 5. Standard errors and confidence intervals
6. Hypothesis testing
7. Non-parametric tests
Part III. Multiple Linear Regression: 8. Multiple relationships
9. The classical linear regression model
10. Dummy variables and lagged values
Part IV. Further Topics in Regression Analysis: 11. Violating the assumptions of the classical model
12. Non-linear models and functional forms
13. Logit, probit, and tobit models
Part V. Specifying and Interpreting Models: Four Case Studies: 14. Case studies 1 and 2: unemployment in Britain and emigration from Ireland
15. Case studies 3 and 4: the Old Poor Law in England and leaving home in the United States, 1850–60
Appendix A. The four data sets
Appendix B. Index numbers
Index.

Subject Areas: Economic history [KCZ], Social & cultural history [HBTB], History: theory & methods [HBA], Research methods: general [GPS]