Making History Count: A Primer in Quantitative Methods for Historians

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Cambridge University Press, 2002 - Business & Economics - 547 pages
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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.
 

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User Review  - SimonDagut - LibraryThing

Extremely lucid and accessible introduction to statistical methods for historians. Actually covers quite a lot of moderately advanced techniques too. Read full review

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An excellent primer for allowing persons with little or no quantitative background to be able to become competent practitioners of such data, as well as analyse their results. Written in a language that allows arts majors not to be lost in the details but be able to see the bigger picture. A faithful companion by my side that has not let me down. I only wish there was a second and third level above this for persons who would want to know more and be more up to date.  

Contents

Outliers
71
Simple linear regression
93
Standard errors and confidence intervals
117
Hypothesis testing
149
Nonparametric tests
185
93
225
Multiple relationships
231
explanatory variables
244
Dummy variables and lagged values
280
Violating the assumptions of the classical linear regression model
300
Nonlinear models and functional forms
333
Logit probit and tobit models
384
unemployment in Britain and emigration
437
the Old Poor Law in England and leaving home
463
Appendix to 15 1 1 Derivation of reducedform equations from a simultaneous
491
Appendix B Index numbers
507

The classical linear regression model
258
UNEMP
275

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