ISSN 1671-3710
CN 11-4766/R

心理科学进展 ›› 2019, Vol. 27 ›› Issue (4): 587-599.doi: 10.3724/SP.J.1042.2019.00587

• 研究方法 • 上一篇    下一篇


朱海腾1,2(), 李川云1   

  1. 1国防大学政治学院, 上海 200433
    2陆军炮兵防空兵学院军政基础系, 合肥 230031
  • 收稿日期:2018-06-04 出版日期:2019-04-15 发布日期:2019-02-22
  • 通讯作者: 朱海腾
  • 基金资助:
    * 国防大学政治学院2017年度院级科研项目(17ZY03-12);原陆军军官学院2016年度第二批自主立项资助(2016-02-ZZLX-40)

Is common method variance a “deadly plague”? Unsolved contention, fresh insights, and practical recommendations

ZHU Haiteng1,2(), LI Chuanyun1   

  1. 1 College of Politics, National Defence University, PLA, Shanghai 200433, China
    2 Department of Military and Political Basic Education, Army Academy of Artillery and Air Defense, PLA, Hefei 230031, China
  • Received:2018-06-04 Online:2019-04-15 Published:2019-02-22
  • Contact: ZHU Haiteng


共同方法变异是由构念间相似的测量方法特征引起的系统变异, 可歪曲构念间的关系, 造成共同方法偏差。60年来, 这一问题在社会科学研究中被反复提及, 但它是否严重威胁研究效度尚无定论。虽然实证证据表明, 共同方法变异普遍存在, 数据来源、测量时间、问卷设计等因素可导致共同方法偏差, 使自我报告的横断式调查研究饱受质疑, 但部分学者从测量误差和非共同方法变异的制约作用等角度做出了回应和辩护, 认为无需过度担忧。以测量为中心的新视角强调共同方法变异是测量方法和被测构念交互作用的产物, 应从方法和构念两个维度评估共同方法变异风险。建议研究者树立均衡无偏的态度, 接纳共同方法变异的存在, 纠正对自我报告的偏见, 着重通过改进研究设计做好预先应对。

关键词: 共同方法变异, 共同方法偏差, 自我报告, 研究设计, 效度


Common method variance (CMV) is a form of systematic variance attributed to similarities of measurement method facets between constructs. It has potential to distort observational correlations and thus elicits common method bias (CMB). Although it has been noted repeatedly in social science research for almost 60 years, its threat to research validity hasn’t been overwhelmingly acknowledged and remains to be scrutinized. Extant empirical evidence has demonstrated the ubiquity of CMV and identified distinct factors triggering CMB, including data source, time interval, and questionnaire design. As a result, cross-sectional self-reporting surveys are particularly subjected to extensive criticism. Nonetheless, some researchers contend that measurement error and uncommon method variance can offset or alleviate the underlying detriment so that pervasive anxiety regarding CMV is exaggerated and unjustified. The measure-centric approach underlines that CMV originates from the interplay between methods and constructs, and the two-dimensional CMV risk evaluation procedure should be conducted with simultaneous consideration of method and construct. From our view, it is preferable to cultivate a balanced and impartial attitude towards CMV, embrace its existence, discard the prejudice against self-reporting, and, above all, take proactive countermeasures based on the optimization of research design.

Key words: common method variance, common method bias, self-reporting, research design, validity