Is common method variance a “deadly plague”? Unsolved contention, fresh insights, and practical recommendations
ZHU Haiteng1,2(),LI Chuanyun1
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
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.
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