Detecting Common Method Bias

Abstract

Lack of careful consideration of common method effects in empirical research can lead to several negative consequences for the interpretation of research outcomes, such as biased estimates of the validity and reliability of the measures employed as well as bias in the estimates of the relationships between constructs of interest, which in turn can affect hypothesis testing. Taken together, these make it very difficult to make any interpretations of the results when those are affected by substantive common method effects. In the literature, there are several preventive, detective, and corrective techniques that can be employed to assuage concerns about the possibility of common method effects underlying observed results. Among these, the most popular has been Harman's Single-Factor Test. Though researchers have argued against its effectiveness in the past, the technique has continued to be very popular in the discipline. Moreover, there is a dearth of empirical evidence on the actual effectiveness of the technique, which we sought to remedy with this research. Our results, based on extensive Monte Carlo simulations, indicate that the approach shows limited effectiveness in detecting the presence of common method effects and may thus be providing a false sense of security to researchers. We therefore argue against the use of the technique moving forward and provide evidence to support our position.