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Parametric and Nonparametric Tests
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A potential source of confusion in working out what statistics to use in analyzing data is whether your data allows for parametric or non-parametric statistics. It is safe to say that most people who use statistics are more familiar with parametric analyses than their nonparametric counterpart. In this video, I will be introducing to you the definitions of parametric and nonparametric tests; their advantages and disadvantages; and when to use these in testing hypothesis for your quantitative research.
Andrews, D. F., Bickel, P. J., Hampel, F. R., Rogers, W. H., & Tukey, J. W. (1972). Robust estimation of location: Survey and advances. Princeton, NJ: Princeton University Press
Blair, R. C., & Higgins, J. J. (1985). Comparison of the power of the paired samples t test to that of Wilcoxon’s signed-ranks test under various population shapes. Psychological Bulletin, 97, 119–128. doi:10.1037/0033-2909.97.1.119
Blair, R. C., & Higgins, J. J. (1980). A comparison of the power of Wilcoxon’s rank-sum statistic to that of Student’s t statistic under various non-normal distributions. Journal of Educational Statistics, 5, 309–335. doi:10.2307/1164905
Boneau, C. A. (1960). The effects of violation of assumptions underlying the t test. Psychological Review, 69, 49–64.
H´ajek, J., ˇSid´ak, Z., & Sen, P. K. (1999). Theory of rank tests. New York: Academic Press.
Hodges, J. L., & Lehmann, E. L. (1956). The efficiency of some nonparametric competitors of the t-test. Annals of Mathematical Statistics, 27, 324–335. doi:10.1214/aoms/1177728261
Lehmann, E. L. (2006). Nonparametrics: Statistical methods based on ranks (rev. 1st ed.). New York: Springer
Pearson, E. S. (1931). The analysis of variance in cases of non-normal variation. Biometrika, 23, 114–133.
Randles, R. H. (1980). Nonparametric statistical tests of hypotheses. In R. V. Hogg (Eds.), Modern statistics: Methods and applications (pp. 31–40). Providence, RI: American Mathematical Society.
Randles, R. H., & Wolfe, D. A. (1979). Introduction to the theory of nonparametric statistics. New York: Wiley.
Zimmerman, Donald. (2011). A simple and effective decision rule for choosing a significance test to protect against non-normality. The British journal of mathematical and statistical psychology. 64. 388-409. 10.1348/000711010X524739.
Andrews, D. F., Bickel, P. J., Hampel, F. R., Rogers, W. H., & Tukey, J. W. (1972). Robust estimation of location: Survey and advances. Princeton, NJ: Princeton University Press
Blair, R. C., & Higgins, J. J. (1985). Comparison of the power of the paired samples t test to that of Wilcoxon’s signed-ranks test under various population shapes. Psychological Bulletin, 97, 119–128. doi:10.1037/0033-2909.97.1.119
Blair, R. C., & Higgins, J. J. (1980). A comparison of the power of Wilcoxon’s rank-sum statistic to that of Student’s t statistic under various non-normal distributions. Journal of Educational Statistics, 5, 309–335. doi:10.2307/1164905
Boneau, C. A. (1960). The effects of violation of assumptions underlying the t test. Psychological Review, 69, 49–64.
H´ajek, J., ˇSid´ak, Z., & Sen, P. K. (1999). Theory of rank tests. New York: Academic Press.
Hodges, J. L., & Lehmann, E. L. (1956). The efficiency of some nonparametric competitors of the t-test. Annals of Mathematical Statistics, 27, 324–335. doi:10.1214/aoms/1177728261
Lehmann, E. L. (2006). Nonparametrics: Statistical methods based on ranks (rev. 1st ed.). New York: Springer
Pearson, E. S. (1931). The analysis of variance in cases of non-normal variation. Biometrika, 23, 114–133.
Randles, R. H. (1980). Nonparametric statistical tests of hypotheses. In R. V. Hogg (Eds.), Modern statistics: Methods and applications (pp. 31–40). Providence, RI: American Mathematical Society.
Randles, R. H., & Wolfe, D. A. (1979). Introduction to the theory of nonparametric statistics. New York: Wiley.
Zimmerman, Donald. (2011). A simple and effective decision rule for choosing a significance test to protect against non-normality. The British journal of mathematical and statistical psychology. 64. 388-409. 10.1348/000711010X524739.
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