Practical statistics in pain research.
10.3344/kjp.2017.30.4.243
- Author:
Tae Kyun KIM
1
Author Information
1. Department of Anesthesia and Pain Medicine, Pusan National University School of Medicine, Yangsan, Korea. anesktk@pusan.ac.kr
- Publication Type:Review
- Keywords:
Analysis of variance;
Biostatistics;
Nonparametric;
Normal distribution;
Pain measurement;
Visual analog scale
- MeSH:
Analysis of Variance;
Biostatistics;
Normal Distribution;
Pain Measurement;
Visual Analog Scale;
Weights and Measures
- From:The Korean Journal of Pain
2017;30(4):243-249
- CountryRepublic of Korea
- Language:English
-
Abstract:
Pain is subjective, while statistics related to pain research are objective. This review was written to help researchers involved in pain research make statistical decisions. The main issues are related with the level of scales that are often used in pain research, the choice of statistical methods between parametric or nonparametric statistics, and problems which arise from repeated measurements. In the field of pain research, parametric statistics used to be applied in an erroneous way. This is closely related with the scales of data and repeated measurements. The level of scales includes nominal, ordinal, interval, and ratio scales. The level of scales affects the choice of statistics between parametric or non-parametric methods. In the field of pain research, the most frequently used pain assessment scale is the ordinal scale, which would include the visual analogue scale (VAS). There used to be another view, however, which considered the VAS to be an interval or ratio scale, so that the usage of parametric statistics would be accepted practically in some cases. Repeated measurements of the same subjects always complicates statistics. It means that measurements inevitably have correlations between each other, and would preclude the application of one-way ANOVA in which independence between the measurements is necessary. Repeated measures of ANOVA (RMANOVA), however, would permit the comparison between the correlated measurements as long as the condition of sphericity assumption is satisfied. Conclusively, parametric statistical methods should be used only when the assumptions of parametric statistics, such as normality and sphericity, are established.
