Detecting Common Method Bias: Performance of the Harman's Single-Factor Test
By Miguel I. Aguirre-Urreta, Jiang Hu. DATB, 2019
574 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/f2fe3e2cca1845cc0c08bae15b9a65c1218cb12d
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In Economics and Econometrics
In Management Science and Operations Research
Citations in the corpus
- Impact of employee empowerment on organizational commitment through job satisfaction in four and five stars hotel industry (2021, methodology): “After data collection Harman’s single factor test is conducted to identify common method variance the result of extraction sums of squared loading is 35.98% of variance which is less than 50%, which means there is no common method biased issue in data (Aguirre-Urreta & Hu, 2019).”