Understanding and Applying Basic Statistical Methods Using R

REFERENCES

1. Agresti, A. (1990). Categorical Data Analysis. New York: John Wiley & Sons, Inc.
2. Agresti, A. (1996). An Introduction to Categorical Data Analysis. New York: John Wiley & Sons, Inc.
3. Agresti, A. & Coull, B. A. (1998). Approximate is better than “exact” for interval estimation of binomial proportions. American Statistician, 52, 119–126.
4. Alexander, R. A. & McGovern, D. (1994). A new and simpler approximation for ANOVA under variance heterogeneity. Journal of Educational Statistics, 19, 91–101.
5. Algina, J., Keselman, H. J. & Penfield, R. D. (2005). An alternative to Cohen's standardized mean difference effect size: a robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10, 317–328.
6. Algina, J., Oshima, T. C. & Lin, W.-Y. (1994). Type I error rates for Welch's test and Jamse's second-order test under nonnormality and inequality of variance when there are two groups. Journal of Educational and Behavioral Statistics, 19, 275–291.
7. Andersen, E. B. (1997). Introduction to the Statistical Analysis of Categorical Data. New York: Springer-Verlag.
8. Asiribo, O. & Gurland, J. (1989). Some simple approximate solutions to the Behrens–Fisher problem. Communications in Statistics—Theory and Methods, 18, 1201–1216.
9. Atkinson, R. L., Atkinson, R. C., Smith, E. E. & Hilgard, E. R. (1985). Introduction to Psychology, 9th Edition. San Diego, CA: Harcourt, Brace, Jovanovich.
10. Bakker, M. & Wicherts, J. M. (2014). Outlier removal, sum scores, and the ...