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Nonparametric statistics for the behavioral sciences Sidney Siegel
Publisher: McGraw-Hill

Müller K, Anderson CW, Birch GE: Linear and nonlinear methods for brain-computer interfaces. Dominance statistics: Ordinal analyses to answer ordinal questions. Knowledge of introductory statistics is assumed. €� http://biomet.oxfordjournals.org/cgi/reprint/44/3-4/538-a.pdf * pdf. Nonparametric Statistics for the Behavioral Sciences. Wong (1992) called the common language effect size statistic, is insensitive to base rates and more robust to several other factors (e.g., extreme scores, nonlinear transformations). Psychological Bulletin 74 (1970):138-140. Determining appropriate sample sizes is emphasized Intended for courses in intermediate statistics and/or statistics II taught in education and/or the behavioral sciences, predominantly at the master's or doctoral level. Statistical power analysis for the behavioral sciences (2nd ed.). London: McGraw-Hill Publishing Co. Practical Nonparametric Statistics. As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these methods are more frequently encountered when working with real data. Nonparametric Methods for the Behavioral Sciences. Siegel S, Castellan JN Jr: Nonparametric Statistics for the Behavioral Sciences. Journal of the American Statistical Association, 76, 162-168. The probability-based measure A, the nonparametric generalization of what K.