Application of Data Screening Procedures in Stress Research

  • Daniel Cruz


In the analysis of salivary cortisol data, researchers often perform statistical analysis for hypothesis testing in the absence of data mining procedures. In this article, I demonstrate the utility of screening data from a study investigating the effects of acute stress on salivary cortisol reactivity through the application of procedures recommended by Tabachnick and Fidell (2001). Specifically, an examination for the presence of both univariate and multivariate outliers (Study 1) and methods for correcting skewed distributions (Study 2) were used in order to demonstrate the efficacy of screening data prior to hypothesis testing. The results suggest that there were no outliers present in the data set. Application of algorithms from a family of transformations showed that they were effective in reducing skewness, kurtosis and variability. 


Alleva, E., & Santucci, D. (2001). Psychosocial vs. “physical” stress situations in rodents and humans: Role of neurotrophins. Physiology & Behavior, 73, 313-320.

Baker, G. A. (1934). Transformations of non-normal frequency distribution into normal distributions. The Annals of Mathematical Statistics, 5, 113-123.

Bartlett, M. S. (1947). The use of transformation. Biometrics, 3, 39-52.

Becerril, C. M., Wilcox, C. J., Wiggans, G. R., & Sigmon, K. N. (1994). Transformation of measurements of white coat color for Holsteins and estimation of heritability.Journal of Dairy Science, 77, 2651-2657.

Bocchino, C. C., Hartman, B. W., & Foley, P. F. (2003). The relationship between person-organizational congruence, perceived violations of the psychological contract, and occupational stress symptoms. Consulting Psychology Journal: Practice and Research, 55, 203-214.

Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, 26, 211-252.

Guerrero, V. M., & Johnson, R. A. (1982). Use of Box-Cox transformations with binary response models. Biometrika, 69, 309-314.

Hamilton, L. C. (1992). Regression with graphics: A second course in applied statistics. Belmont, CA: Duxbury.

Muthen, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.

Nolan, S. A., & Heinzen, T. E. (2008). Statistics for the behavioral sciences. New York, NY: Worth Publishers.

Ott, R. L., & Longnecker, M. (2001). An introduction to statistical methods and data analysis (5th ed.). Pacific Grove, CA: Duxbury.

Patil, P. G., Apelbaum, J. L., Zacny, J. P. (1995). Effects of a cold-water stressor on psychomotor and cognitive functioning in humans. Physiology &. Behavior, 58, 1281-1286.

Peltier, M. R., Wilcox, C. J., & Sharp, D. C. (1998). Technical note: Application of the Box-Cox data transformation to animal science experiments. Journal of Animal Science, 76, 847-849.

Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.

Witte, R. S., & Witte, J. S. (2008). Statistics (8th ed.). New York: Harcourt Brace College Publishers.

Yuan, K., Fung, W. K., & Reise, S. P. (2004). Three Ma halanobis distances and their role in assessing unidemensionality. British Journal of Mathematical and Statistical Psychology, 57, 151-165.