ISSN 1671-3710
CN 11-4766/R

›› 2012, Vol. 20 ›› Issue (5): 757-769.

• 研究方法 • Previous Articles     Next Articles

Common Method Variance Effects and the Models of Statistical Approaches for Controlling It

XIONG Hong-Xing;ZHANG Jing;YE Bao-Juan;ZHENG Xue;SUN Pei-Zhen   

  1. (1 Research Center of Psychological Application, South China Normal University, Guangzhou 510631, China)
    (2 Center of Psychological Counseling, Jiangxi Normal University, Nanchang 330022, China)
    (3 School of Psychology, Jiangxi Normal University, Nanchang 330022, China)
    (4 Department of Psychology, Xuzhou Normal University, Xuzhou 221116, China)
  • Received:2011-11-14 Revised:1900-01-01 Online:2012-05-15 Published:2012-05-15
  • Contact: ZHENG Xue

Abstract: Common Method Variance (CMV) refers to the overlap in variance between two variables because of the type of measurement instrument used rather than representing a true relationship between the underlying constructs. Researchers should give careful consideration to CMV although it may not surely bias the conclusions about the relationships between measures. CMV effect is often created by using the same method — especially a survey — to measure each variable. Procedural design and statistical control solutions are provided to minimize its likelihood in studies. A statistical control technique is a good solution if it can separate construct varience, method varience and error, and distinguish method bias at the item level from method bias at the construct level, and takes account of Method×Trait interactions. Thus, method-factor approaches are better than partial correlation approaches. It’s very important to understand the model of every method-factor approache for selecting statistical remedies correctly for different types of research settings. Etimating evaluate the effect of CMV within specific research domains and the effect of CMV on empirical findings within a theoretical domain should be concerned for further research.

Key words: common method variance (CMV), common method bias (CMB), statistical techniques for addressing common method variance, confirmatory factor analysis) (CFA)