大数据时代下中国社会调查的科学新观

doi:10.11959/j.issn.2096-0271.2016016

摘要/Abstract

摘要：

大数据已经成为这个时代的显著特征，大数据的发展为入户调查数据带来了极大的冲击和挑战。在这种情况下，社会调查需要有新的基于中国古老智慧的管理理论，并且把大数据和云计算等都纳入社会调查系统，使其成为社会调查运作系统的有机构成部分。利用大数据分析技术，对社会调查过程中的行为数据进行分析和利用，可以大大提高社会调查的精准度，有效实施社会关系的精准管理。最后，对于大数据和调查数据的未来发展提出了几点看法。

关键词: 大数据, 社会调查, 大智慧, 行为数据

Abstract:

Big data has become a significant feature of this age. The development of big data brings great impact and challenge to social surveys. To face the challenge, a new management theory based on China’s traditional wisdom of social surveys is needed. Big data and cloud computing should become the constituent parts of total survey management system. The big data analytics can give insights of paradata, which can improve the accuracy of social surveys significantly and implementation of precise management of social relations. Finally, some views on the future development of big data and survey data were proposed.

Key words: big data, social survey, big wisdom, paradata

中图分类号:

C915

顾佳峰. 大数据时代下中国社会调查的科学新观[J]. 大数据, 2016, 2(2): 29-37.

Jiafeng GU. Social science research in China social surveys under the big data revolution[J]. Big Data Research, 2016, 2(2): 29-37.

图/表 4

参考文献 16

[1]	MEYER B D , MOK C , SULLIVAN J X . . Household surveys in crisis[J]. Journal of Economic Perspectives, 2015,29(4): 199-226.
[2]	顾佳峰 . 调查机构管理: 理论与实践[M]. 北京: 人民出版社, 2013.
	GU J F . Survey Organization Management:Theory and Practice[M]. Beijing: People’s Publishing House, 2013.
[3]	XIE Y , LU P . The sampling design of the China family panel studies (CFPS)[J]. Chinese Journal of Sociology, 2015,1(4): 471-484.
[4]	STIEGER S , REIPS U . What are participants doing while filling in an online questionnaire: a paradata collection tool and an empirical study[J]. Computers in Human Behavior, 2010,26(6): 1488-1495.
[5]	KREUTER F . Improving Surveys with Paradata: Analytic Uses of Process Information[M]. New York: John Wiley &Sons, 2013.
[6]	MAEHR W . eMotion: estimation of user's emotional state by mouse motions[J]. Elsevier, 2008,15(3): 15-17.
[7]	GALESIC M , TOURANGEAU R , COUPER M P , et al . Eye-tracking data:new insights on response order effects and other cognitive shortcuts in survey responding[J]. Public Opinion Quarterly, 2008,72(5): 892-913.
[8]	TACH L , CORNWELL B . Social networks and social capital: new directions for a household panel survey[J]. Journal of Economic and Social Measurement, 2015,40(1-4): 249-281.
[9]	PAKIZE S , GANDOMI A . Comparative study of classification algorithms based on MapReduce model[J]. International Journal of Innovative Research in Advanced Engineering, 2014,1(7): 251-254.
[10]	LAZER D , KENNEDY R , KING G , et al . The parable of Google flu: traps in big data analysis[J]. Journal Science, 2014,343(6176): 1203-1205.
[11]	顾佳峰 . 调查机构公共关系经营与管理[M]. 北京: 经济日报出版社, 2014.
	GU J F . Public Relations Management for Survey Institute[M]. Beijing: The Publishing House of the Economic Daily, 2014.
[12]	WILLIAMS T , GONG J . Predicting construction cost overruns using text mining, numerical data and ensemble classifiers[J]. Journal of Automation in Construction, 2014,43(7): 23-29.
[13]	HEARST M . TextTiling: segmenting text into multi-paragraph subtopic passages[J]. Computational Linguistics, 1997,23(1): 33-64.
[14]	CHAWLA N V , DAVIS D A . Bringing big data to personalized healthcare: a patient-centered framework[J]. Journal of General Internal Medicine, 2013,28(3): 660-665.
[15]	COLLINS F S , VARMUS H . A new initiative on precision medicine[J]. The New England Journal of Medicine, 2015,372(9): 1-3.
[16]	DANAH B , KATE C . Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon[J]. Information, Communication & Society, 2012,15(5): 662-679.