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The Impact of Extreme Response Style on the Mean Comparison of Two Independent Samples

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  • Yingbin Zhang
  • Zhaoxi Yang
  • Yehui Wang

Abstract

Extreme response style (ERS) is prevalent in survey research using rating scales. It may cause biased results in group comparisons. This research conducted two sets of simulation studies to explore the magnitude of the ERS impact on mean comparisons between two independent samples. Data were generated from a multidimensional nominal response model. Study 1 examined the influence of ERS on the estimate of group differences in the variable of interest. The results indicated that ERS led to biased estimates, especially when these groups differed significantly in ERS. The correlation between ERS and the variable of interest also moderated the ERS impact. The results were illustrated with an empirical example. Study 2 investigated the impact of ERS on the type I error and type II error in the independent t -test based on scale scores. When the variable of interest had no true difference between groups, ERS inflated the type I error rate . When the difference existed, ERS inflated the type II error rate . Two groups’ true difference in ERS and the variable of interest, unequal ERS variances, the correlation between ERS and the variable of interest, and the number of items moderated the impact of ERS on type I and II error rates. The implications for practices and further research are discussed.

Suggested Citation

  • Yingbin Zhang & Zhaoxi Yang & Yehui Wang, 2022. "The Impact of Extreme Response Style on the Mean Comparison of Two Independent Samples," SAGE Open, , vol. 12(2), pages 21582440221, June.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221108168
    DOI: 10.1177/21582440221108168
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    1. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    2. Ke-Hai Yuan & Peter Bentler, 2006. "Mean Comparison: Manifest Variable Versus Latent Variable," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 139-159, March.
    3. R. Darrell Bock, 1972. "Estimating item parameters and latent ability when responses are scored in two or more nominal categories," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 29-51, March.
    4. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    5. Mingnan Liu & Frederick G. Conrad & Sunghee Lee, 2017. "Comparing acquiescent and extreme response styles in face-to-face and web surveys," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 941-958, March.
    6. David Thissen & Lynne Steinberg, 1986. "A taxonomy of item response models," Psychometrika, Springer;The Psychometric Society, vol. 51(4), pages 567-577, December.
    7. Hoffmann, Stefan & Mai, Robert & Cristescu, Anamaria, 2013. "Do culture-dependent response styles distort substantial relationships?," International Business Review, Elsevier, vol. 22(5), pages 814-827.
    8. Natalia Kieruj & Guy Moors, 2013. "Response style behavior: question format dependent or personal style?," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 193-211, January.
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