Rating versus ranking: What is the best way to reduce response and language bias in cross-national research?

By Anne-Wil Harzing, Joyce Baldueza, Wilhelm Barner‐Rasmussen, Cordula Barzantny, Anne Canabal, A. Davila, Álvaro Espejo, Rita Ferreira, Axèle Giroud, Kathrin Koester and 14 more. 2009

173 citations recorded by the source corpus.

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Can Large Language Models Transform Computational Social Science? (2023, background): “The ranking-style evaluation is more reliable and less variable than scoring for generation tasks (Harzing et al., 2009; Belz and Kow, 2010).”
A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents (2016, background): “Methods in this family include “rating and ranking” [57, 93], repertory grid [26], sorting [184, 57], event co-occurence [49], and correlation/covariance [49].”
Corporate environmental responsibility, employer reputation and employee commitment: an empirical study in developed and emerging economies (2014, background): “Moreover, seven-point Likert scales are recommended for cross-country research as they are less biased by different response styles (Harzing et al. 2009).”
A Cross-Cultural Examination of Subordinates' Perceptions of and Reactions to Abusive Supervision (2015, background): “(2014) DOI: 10.1002/job further ensured that we reduced response and language bias across respondents of different cultures (Harzing et al., 2009).”