Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of panels
- Preface
- Part I Elementary statistical analysis
- Part II Samples and inductive statistics
- Part III Multiple linear regression
- Part IV Further topics in regression analysis
- Part V Specifying and interpreting models: four case studies
- Appendix A The four data sets
- Appendix B Index numbers
- Bibliography
- Index of subjects
- Index of names
Preface
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- List of figures
- List of tables
- List of panels
- Preface
- Part I Elementary statistical analysis
- Part II Samples and inductive statistics
- Part III Multiple linear regression
- Part IV Further topics in regression analysis
- Part V Specifying and interpreting models: four case studies
- Appendix A The four data sets
- Appendix B Index numbers
- Bibliography
- Index of subjects
- Index of names
Summary
We would like to express our deep appreciation to Daniel Benjamin, Levis Kochin, Timothy Hatton, Jeffrey Williamson, George Boyer, and Richard Steckel for agreeing to our use of their work for the case studies, which are a central feature of the approach we have adopted for this book. We are particularly grateful to Boyer, Hatton, and Steckel for providing us with the unpublished data that we have used throughout the text and exercises to illustrate quantitative procedures, and for allowing us to reproduce their data sets on our web site. We have also received very helpful comments from a number of our colleagues, notably James Foreman-Peck, Jane Humphries, Sharon Murphy, and Richard Smith. We owe a special debt of gratitude to Joachim Voth, who read the entire text with great care and sent us many thoughtful criticisms and suggestions. None of these scholars has any responsibility for any errors of procedure or interpretation that remain.
We particularly wish to acknowledge the contributions of generations of students who took our courses in quantitative methods in Oxford and Charlottesville. They showed that intuitive appreciation of the importance and limitations of quantification in history could be developed by undergraduates and graduates from a wide range of intellectual and disciplinary backgrounds. The approach adopted in the present text was originally designed primarily for students who had no prior knowledge of statistics, and who were in many cases initially hostile to, or intimidated by, quantitative procedures.
- Type
- Chapter
- Information
- Making History CountA Primer in Quantitative Methods for Historians, pp. xxi - xxiiPublisher: Cambridge University PressPrint publication year: 2002