Extrinsic drivers of scientific impact in second language assessment: A bibliometric path analysis

This study examined how multiple factors, independent of research quality, influence the scientific impact of language assessment articles. A total of 447 papers published between 2018 and 2022 were investigated using a path analysis approach to identify both direct and indirect ways in which extrinsic factors affect citation counts. A conceptual path model was constructed based on existing literature and assessed using a comprehensive bibliometric dataset. The final model showed an excellent fit to the data (χ 2 = 65.6, df = 52, p = .097; standardized root mean square residual [SRMR] = .034, root mean square error of approximation [RMSEA] = .024, comparative fit index [CFI] = .980, Tucker–Lewis index [TLI] = .970) and accounted for 57.75% of the variance in citation counts ( R² = .5775, p < .001). Among nine examined factors, seven demonstrated significant direct or indirect effects on citation counts: number of self-citations, article age, journal CiteScore, number of countries, number of authors, open access, and research topic. Based on these findings, the study deepens our understanding of citation practices and offers broader implications for citation-based research evaluation in language assessment.