Likert scales, levels of measurement and the “laws” of statistics
- 36k Downloads
Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for “getting the wrong answer”.
KeywordsLikert Statistics Robustness ANOVA
- Berk, R. A. (1979). Generalizability of behavioral observations: a clarification of interobserver agreement and interobserver reliability. American Journal of Mental Deficiency., 83, 460–472.Google Scholar
- Fletcher, K. E., French, C. T., Corapi, K. M., Irwin, R. S. & Norman, G. R. (2010). Prospective measures provide more accurate assessments than retrospective measures of the minimal important difference in quality of life. Journal of Clinical Epidemiology (in press).Google Scholar
- Havlicek, L. L., & Peterson, N. L. (1976). Robustness of the Pearson correlation against violation of assumption. Perceptual and Motor Skills, 43, 1319–1334.Google Scholar
- Pearson, E. S. (1931). The analysis of variance in the case of non-normal variation. Biometrika, 23, 114–133.Google Scholar