歴史研究のための量的調査の方法<br>Making History Count : A Primer in Quantitative Methods for Historians

個数:

歴史研究のための量的調査の方法
Making History Count : A Primer in Quantitative Methods for Historians

  • 提携先の海外書籍取次会社に在庫がございます。通常3週間で発送いたします。
    重要ご説明事項
    1. 納期遅延や、ご入手不能となる場合が若干ございます。
    2. 複数冊ご注文の場合、分割発送となる場合がございます。
    3. 美品のご指定は承りかねます。
  • 【重要:入荷遅延について】
    ウクライナ情勢悪化・新型コロナウィルス感染拡大により、洋書・洋古書の入荷が不安定になっています。詳しくはこちらをご確認ください。
    海外からのお取り寄せの場合、弊社サイト内で表示している標準的な納期よりもお届けまでに日数がかかる見込みでございます。
    申し訳ございませんが、あらかじめご了承くださいますようお願い申し上げます。
  • ◆画像の表紙や帯等は実物とは異なる場合があります。
  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 572 p.
  • 言語 ENG
  • 商品コード 9780521001373
  • DDC分類 907.2

基本説明

Written by two senior economic historian with very considerable teaching experience on both sides of the Atlantic, this is the authoritative guide to the use of quantitative methods.

Full Description

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.

Contents

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.