《人口学研究中因果机制的识别》特刊社论

IF 1.5 Q2 DEMOGRAPHY
J. Huinink, J. Brüderl
{"title":"《人口学研究中因果机制的识别》特刊社论","authors":"J. Huinink, J. Brüderl","doi":"10.12765/cpos-2021-17","DOIUrl":null,"url":null,"abstract":"Explaining demographic behaviour and population change means identifying the causal mechanisms which “drive” them over time. Based on theoretical modelling and guided by empirical fi ndings in prior studies, demographic and social research pursues the improvement of knowledge about those mechanisms and the relationships between the involved factors. In demography, as in the social sciences in general, theoretical and methodological advancements over the past 50 years have greatly contributed to accomplishing this goal. Methods of longitudinal data collection as well as individualand multilevel longitudinal data analysis have gained relevance. This trend was paralleled by the development of the life course perspective in the social sciences and conceptual refi nements in cohort analysis in demographic research. Meanwhile, collecting and analysing longitudinal data is a standard procedure in individualand multi-level demographic research. Many studies using this methodological inventory have been conducted, enriching our knowledge on individual decision-making and behaviour considerably. Compared to crosssectional data, longitudinal data signifi cantly improve the conditions for identifying the “true” effects of underlying causal mechanisms. While retrospective information is already of great use, prospective panel designs enable a more appropriate and manifold collection of relevant information, as well as more refi ned statistical modelling of the interdependence between individual behaviour, its dispositional and motivational drivers, its situational conditions, and its outcomes over time. Panel data are also useful for another prominent class of methods, i. e. techniques of event history analysis (Blossfeld/Rohwer 2002; Kreyenfeld 2021). In this Special Issue of Comparative Population Studies, we review the degree to which methodological innovations in panel studies have been useful in properly identifying causal mechanisms in the study of demographic behaviour, and ultimately population change. In the fi rst contribution, methodological issues of panel data analysis are discussed and illustrated by the example of estimating the effect of motherhood on life satisfaction. The next four articles address the core question of the Special Issue with regard to major fi elds of demographic research: Comparative Population Studies Vol. 46 (2021): 487-502 (Date of release: 24.11.2021)","PeriodicalId":44592,"journal":{"name":"Comparative Population Studies","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Editorial on the Special Issue “The identification of causal mechanisms in demographic research”\",\"authors\":\"J. Huinink, J. Brüderl\",\"doi\":\"10.12765/cpos-2021-17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Explaining demographic behaviour and population change means identifying the causal mechanisms which “drive” them over time. Based on theoretical modelling and guided by empirical fi ndings in prior studies, demographic and social research pursues the improvement of knowledge about those mechanisms and the relationships between the involved factors. In demography, as in the social sciences in general, theoretical and methodological advancements over the past 50 years have greatly contributed to accomplishing this goal. Methods of longitudinal data collection as well as individualand multilevel longitudinal data analysis have gained relevance. This trend was paralleled by the development of the life course perspective in the social sciences and conceptual refi nements in cohort analysis in demographic research. Meanwhile, collecting and analysing longitudinal data is a standard procedure in individualand multi-level demographic research. Many studies using this methodological inventory have been conducted, enriching our knowledge on individual decision-making and behaviour considerably. Compared to crosssectional data, longitudinal data signifi cantly improve the conditions for identifying the “true” effects of underlying causal mechanisms. While retrospective information is already of great use, prospective panel designs enable a more appropriate and manifold collection of relevant information, as well as more refi ned statistical modelling of the interdependence between individual behaviour, its dispositional and motivational drivers, its situational conditions, and its outcomes over time. Panel data are also useful for another prominent class of methods, i. e. techniques of event history analysis (Blossfeld/Rohwer 2002; Kreyenfeld 2021). In this Special Issue of Comparative Population Studies, we review the degree to which methodological innovations in panel studies have been useful in properly identifying causal mechanisms in the study of demographic behaviour, and ultimately population change. In the fi rst contribution, methodological issues of panel data analysis are discussed and illustrated by the example of estimating the effect of motherhood on life satisfaction. The next four articles address the core question of the Special Issue with regard to major fi elds of demographic research: Comparative Population Studies Vol. 46 (2021): 487-502 (Date of release: 24.11.2021)\",\"PeriodicalId\":44592,\"journal\":{\"name\":\"Comparative Population Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Comparative Population Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12765/cpos-2021-17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"DEMOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Comparative Population Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12765/cpos-2021-17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DEMOGRAPHY","Score":null,"Total":0}
引用次数: 1

摘要

解释人口行为和人口变化意味着确定随着时间的推移“驱动”它们的因果机制。人口和社会研究以理论建模为基础,以先前研究中的实证结果为指导,致力于提高对这些机制以及相关因素之间关系的认识。在人口学和社会科学领域,过去50年来的理论和方法进步对实现这一目标做出了巨大贡献。纵向数据收集方法以及个体和多层次纵向数据分析方法已经获得了相关性。这一趋势与社会科学中生命历程视角的发展以及人口统计学研究中队列分析的概念修正相平行。同时,收集和分析纵向数据是个体和多层次人口学研究的标准程序。使用这种方法清单进行了许多研究,极大地丰富了我们对个人决策和行为的了解。与横断面数据相比,纵向数据显著改善了识别潜在因果机制“真实”影响的条件。虽然回顾性信息已经很有用,但前瞻性小组设计能够更适当、更全面地收集相关信息,并对个人行为、其倾向和动机驱动因素、其情境条件及其随时间变化的结果之间的相互依存关系进行更精确的统计建模。面板数据也适用于另一类突出的方法,即事件历史分析技术(Blossfeld/Rohwer 2002;Kreyenfeld 2021)。在本期《比较人口研究》特刊中,我们回顾了小组研究中的方法创新在多大程度上有助于正确识别人口行为研究中的因果机制,并最终确定人口变化。在第一篇文章中,讨论了面板数据分析的方法论问题,并通过估计母亲对生活满意度的影响的例子进行了说明。接下来的四篇文章涉及人口研究主要领域的特刊核心问题:《比较人口研究》第46卷(2021):487-502(发布日期:2021年11月24日)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Editorial on the Special Issue “The identification of causal mechanisms in demographic research”
Explaining demographic behaviour and population change means identifying the causal mechanisms which “drive” them over time. Based on theoretical modelling and guided by empirical fi ndings in prior studies, demographic and social research pursues the improvement of knowledge about those mechanisms and the relationships between the involved factors. In demography, as in the social sciences in general, theoretical and methodological advancements over the past 50 years have greatly contributed to accomplishing this goal. Methods of longitudinal data collection as well as individualand multilevel longitudinal data analysis have gained relevance. This trend was paralleled by the development of the life course perspective in the social sciences and conceptual refi nements in cohort analysis in demographic research. Meanwhile, collecting and analysing longitudinal data is a standard procedure in individualand multi-level demographic research. Many studies using this methodological inventory have been conducted, enriching our knowledge on individual decision-making and behaviour considerably. Compared to crosssectional data, longitudinal data signifi cantly improve the conditions for identifying the “true” effects of underlying causal mechanisms. While retrospective information is already of great use, prospective panel designs enable a more appropriate and manifold collection of relevant information, as well as more refi ned statistical modelling of the interdependence between individual behaviour, its dispositional and motivational drivers, its situational conditions, and its outcomes over time. Panel data are also useful for another prominent class of methods, i. e. techniques of event history analysis (Blossfeld/Rohwer 2002; Kreyenfeld 2021). In this Special Issue of Comparative Population Studies, we review the degree to which methodological innovations in panel studies have been useful in properly identifying causal mechanisms in the study of demographic behaviour, and ultimately population change. In the fi rst contribution, methodological issues of panel data analysis are discussed and illustrated by the example of estimating the effect of motherhood on life satisfaction. The next four articles address the core question of the Special Issue with regard to major fi elds of demographic research: Comparative Population Studies Vol. 46 (2021): 487-502 (Date of release: 24.11.2021)
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.80
自引率
0.00%
发文量
15
审稿时长
26 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信