为研究美国生育率而重建出生历史，1830-1910 年》（Reconstruction of Birth Histories for the Study of Fertility in the United States，1830-1910 年）。

IF 1.6 2区历史学 Q1 HISTORY Historical Methods Pub Date : 2020-01-01 Epub Date: 2019-10-09 DOI:10.1080/01615440.2019.1664357

J David Hacker

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引用次数: 0

摘要

本文介绍了一种重建 1900 年和 1910 年美国人口普查 IPUMS 样本中妇女完整出生史的方法。该方法是 Luther 和 Cho（1988 年）早期方法的延伸。基本方法依赖于曾经出生的孩子数量、存活的孩子数量、与家庭同住的孩子数量以及特定年龄段的人口生育率，以概率方式为已故和未匹配的孩子分配 "年龄"。修改包括增加一个迭代泊松回归模型，以微调特定年龄的生育率输入。通过几个例子说明了完整出生史在研究美国生育率转变方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Reconstruction of Birth Histories for the Study of Fertility in the United States, 1830-1910.

This paper describes a method to reconstruct complete birth histories for women in the 1900 and 1910 U. S. census IPUMS samples. The method is an extension of an earlier method developed by Luther and Cho (1988). The basic method relies on the number of children ever born, number of children surviving, number of children coresident in the household and age-specific fertility rates for the population to probabilistically assign an "age" to deceased and unmatched children. Modifications include the addition of an iterative Poisson regression model to fine-tune age-specific fertility inputs. The potential of complete birth histories for the study of the U.S. fertility transition is illustrated with a few examples.

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来源期刊

Historical Methods Multiple-

CiteScore

3.20

自引率

7.10%

发文量

期刊介绍： Historical Methodsreaches an international audience of social scientists concerned with historical problems. It explores interdisciplinary approaches to new data sources, new approaches to older questions and material, and practical discussions of computer and statistical methodology, data collection, and sampling procedures. The journal includes the following features: “Evidence Matters” emphasizes how to find, decipher, and analyze evidence whether or not that evidence is meant to be quantified. “Database Developments” announces major new public databases or large alterations in older ones, discusses innovative ways to organize them, and explains new ways of categorizing information.