In applied statistics, (e.g., applied to the social sciences and psychometrics), common-method variance (CMV) is the spurious "variance that is attributable to the measurement method rather than to the constructs the measures are assumed to represent"[1] or equivalently as "systematic error variance shared among variables measured with and introduced as a function of the same method and/or source".
If measures are affected by CMV or common-method bias, the intercorrelations among them can be inflated or deflated depending upon several factors.
[3] Although it is sometimes assumed that CMV affects all variables, evidence suggests that whether or not the correlation between two variables is affected by CMV is a function of both the method and the particular constructs being measured.
[4] Several ex ante remedies exist that help to avoid or minimize possible common method variance.
[7] Kock (2015) discusses a full collinearity test that is successful in the identification of common method bias with a model that nevertheless passes standard convergent and discriminant validity assessment criteria based on a CFA.