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非概率样本推断与调查数据整合：一项科学图谱研究。

Inference with non-probability samples and survey data integration: a science mapping study.

作者信息

Salvatore Camilla

机构信息

Department of Economics, Management and Statistics (DEMS), University of Milano-Bicocca, Milan, Italy.

Faculty of Social and Behavioural Sciences, Universiteit Van Amsterdam, Amsterdam, The Netherlands.

出版信息

Metron. 2023;81(1):83-107. doi: 10.1007/s40300-023-00243-6. Epub 2023 Apr 8.

DOI:10.1007/s40300-023-00243-6
PMID:37284419
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10082441/
Abstract

In recent years, survey data integration and inference based on non-probability samples have gained considerable attention. Because large probability-based samples can be cost-prohibitive in many instances, combining a probabilistic survey with auxiliary data is appealing to enhance inferences while reducing the survey costs. Also, as new data sources emerge, such as big data, inference and statistical data integration will face new challenges. This study aims to describe and understand the evolution of this research field over the years with an original approach based on text mining and bibliometric analysis. In order to retrieve the publications of interest (books, journal articles, proceedings, etc.), the Scopus database is considered. A collection of 1023 documents is analyzed. Through the use of such methodologies, it is possible to characterize the literature and identify contemporary research trends as well as potential directions for future investigation. We propose a research agenda along with a discussion of the research gaps which need to be addressed.

摘要

近年来，基于非概率样本的调查数据整合与推断受到了广泛关注。由于在许多情况下，基于大样本的概率抽样成本过高，因此将概率调查与辅助数据相结合，在降低调查成本的同时增强推断能力，这很有吸引力。此外，随着大数据等新数据源的出现，推断和统计数据整合将面临新的挑战。本研究旨在通过基于文本挖掘和文献计量分析的原创方法，描述和理解该研究领域多年来的发展演变。为了检索感兴趣的出版物（书籍、期刊文章、会议论文等），我们使用了Scopus数据库。我们分析了1023篇文献。通过使用这些方法，可以对文献进行特征描述，识别当代研究趋势以及未来研究的潜在方向。我们提出了一个研究议程，并讨论了需要解决的研究空白。

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