Revisiting an “urban legend”: an experimental assessment of common method variance’s impact on relationships in self-reported data
manqian cui et al.
Abstract
Despite the ubiquity of self-reported data in social science and public administration research, widespread concerns persist regarding common method variance (CMV) and its potential to distort observed correlations. In this article, we estimate CMV’s biasing effects through five preregistered studies (including eight survey experiments) with UK and Chinese civil servants (N = 3,159), focusing on the relationship between public service motivation (PSM) and job performance–a proposition of PSM theory often subject to CMV concerns. Our findings indicate that procedures widely advocated by methodological scholars to mitigate CMV did not substantially attenuate the PSM-performance relationship. A single-paper meta-analysis integrating these survey experiments reinforced this result, revealing a negligible overall moderating effect (mean effect size = -0.018, 95% CI [-0.08, 0.04]). Our results offer insights into the quality of self-reported measures, call into question the notion that CMV uniformly biases self-reported correlations, and strengthen the PSM theory by providing evidence for the validity of its core theoretical relationships against the CMV’s biasing effect.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.