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2016 | OriginalPaper | Chapter

1. Introduction

Authors : Kenneth J. Berry, Paul W. Mielke Jr., Janis E. Johnston

Published in: Permutation Statistical Methods

Publisher: Springer International Publishing

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Abstract

Chapter 1 of Permutation Statistical Methods provides an introduction to the next 10 chapters, presenting and comparing the two models of statistical inference—the population model and the permutation model—and the three main approaches to permutation statistical methods—exact, moment approximation, and resampling approximation. Advantages of permutation statistical methods are elucidated and recursion techniques are described and illustrated.

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next chapter Completely Randomized Data
Footnotes
1
The actual quote, from the Father Brown mystery “The Head of Caesar” by G. K Chesterton is “What we all dread most is a maze with no centre” [68, p. 229].
 
2
The terms “permutation test” and “randomization test” are often used interchangeably.
 
3
As was customary in scientific societies at the time, these special papers were printed in advance and circulated to the membership of the Society. Then, only a brief summary was made by the author at the meeting and the remaining time was devoted to a discussion of the paper. By tradition, the “proposer of the vote of thanks” advanced what he thought was commendable about the paper, and the seconder put forward what he thought was not so worthy. Subsequently, there was a general discussion by the Fellows of the Society and often a number of prominent statisticians offered comments, suggestions, or criticisms, all of which were subsequently printed along with the published paper in the journal of the Society [50, p. 41].
 
4
The experiment was obviously inspired by an actual tea-tasting experiment at the Rothamsted Experimental Station some dozen years prior, where Fisher was employed as a statistician from 1919 to 1933. The woman tasting the tea was Dr. B. Muriel Bristol, an algologist at the Station. For descriptions of the tea-tasting experiment at the Rothamsted Experimental Station, see discussions by Agresti, [2, pp. 91–97], Berry, Johnston, and Mielke [41, pp. 58–61, 429–432], Box [48], Box [49, pp. 134–135], Fisher [119, pp. 11–29], Fisher [121, Chap. 6], Gridgeman [155], Hall [165], Lehmann [236, pp. 63–64], Okamoto [324], Salsburg [361, pp. 1–2], Senn [369‐371], and Springate [384].
 
5
For a concise summary of the Zea mays experiment, see an informative discussion by Erich Lehmann in his posthumously published 2011 book on Fisher, Neyman, and the Creation of Classical Statistics [236, pp. 65–66].
 
6
Olaf Tedin (1898–1966) was a Swedish geneticist who spent most of his professional career as a plant breeder with the Swedish Seed Association, Svalöf, where he was in charge of breeding barley and fodder roots in the Weibullsholm Plant Breeding Station, Landskrona.
 
7
For a brief history of R.A. Fisher and the origins of α = 0. 05, see a 2011 book by Erich Lehmann on Fisher, Neyman, and the Creation of Classical Statistics [236].
 
8
It was the Pearson type III distribution that Student (W.S. Gosset) used to fit the distribution of sample variances in his classic 1908 article on “The probable error of a mean” [390, p. 4].
 
9
The Pearson type III distribution was one of four distributions introduced by Karl Pearson in 1895 [333], although the type III distribution had previously been presented without discussion by Pearson in 1893 [332, p. 331]. The type V distribution introduced by Pearson in 1895 was simply the normal distribution and the Pearson type I distribution was a generalized beta distribution.
 
10
Mielke, Berry, and Brier were not the first to adopt the Pearson type III distribution to approximate a discrete permutation distribution. For example, B.L. Welch utilized the Pearson type III distribution in a 1936 paper on the specification of rules for rejecting too variable a product [417] and used it again in a 1938 paper on testing the significance of differences between the means of two independent samples when the population variances were unequal [419].
 
11
For a one-way analysis of variance utilizing a moment-approximation approach, see a 1983 article by Berry and Mielke [23].
 
12
It is generally accepted that the term “Monte Carlo” method was coined by Stanislaw Ulam, John von Neumann, and Nicholas Metropolis in 1946 while they were working on nuclear weapon projects at the Los Alamos National Laboratory [278, 415]. However, in a 2012 book on Turing’s Cathedral, George Dyson attributes the coining of the term “Monte Carlo” solely to Nicholas Metropolis [102, p. 192].
 
13
It should be noted that the 1957 Dwass article on modified randomization tests for non-parametric hypotheses relied heavily on the theoretical contributions of an article titled “On the theory of some non-parametric hypotheses” by Erich Lehmann and Charles Stein published in The Annals of Mathematical Statistics in 1949 [237].
 
14
The Mehta–Patel network algorithm was subsequently applied to many more statistical analyses than the highly limited analysis of r×c contingency tables.
 
15
For a detailed description of the Mehta–Patel network enumeration algorithm, see Berry, Johnston, and Mielke [41, pp. 288–293].
 
16
For the importance of data-dependent analysis, see a 1988 article by Biondini, Mielke, and Berry on “Data-dependent permutation techniques for the analysis of ecological data” [44] and a 2002 article by Mielke and Berry on “Data-dependent analyses in psychological research” [296].
 
17
Emphasis in the original.
 
18
See also a short but comprehensive 2010 article on this topic by Tom Siegfried in Science News [377].
 
19
A recursive process is one in which items are defined in terms of items of similar kind. Using a recursive relation, a class of items can be constructed from one or a few initial values (a base) and a small number of relationships (rules). For example, given the base, F 0 = 0 and \(F_{1} = F_{2} = 1\), the Fibonacci series {0, 1, 1, 2, 3, 5, 8, 13, 21, …} can be constructed by the recursive rule \(F_{n} = F_{n-1} + F_{n-2}\) for n > 2.
 
20
Exact probability values in this example are given to six places to demonstrate the accuracy of recursion processes with an arbitrary initial value.
 
21
The letter F for the analysis of variance (variance-ratio) test statistic was introduced in 1934 by George Snedecor at Iowa State University, much to the displeasure of R.A. Fisher [378, p. 15]. Prior to 1934 the test statistic was indicated by z, the letter originally assigned to it by Fisher.
 
Literature
1.
Agresti, A.: Measures of nominal-ordinal association. J. Am. Stat. Assoc. 76, 524–529 (1981)MATHCrossRef
2.
3.
Agresti, A., Finley, B.: Statistical Methods for the Social Sciences. Prentice-Hall, Upper Saddle River (1997)
4.
Agresti, A., Liu, I.: Modeling a categorical variable allowing arbitrarily many category choices. Biometrics 55, 936–943 (1999)MATHCrossRef
5.
Agresti, A., Liu, I.: Strategies for modeling a categorical variable allowing multiple category choies. Sociol. Method Res. 29, 403–434 (2001)MathSciNetCrossRef
6.
Altman, D.G., Bland, J.M.: Measurement in medicine: the analysis of method comparison studies. Statistician 32, 307–317 (1983)CrossRef
7.
Anderson, T.W.: An Introduction to Multivariate Statistical Analysis, 2nd edn. Wiley, New York (1984)MATH
8.
Anderson, T.W.: Two of Harold Hotelling’s contributions to multivariate analysis. Tech. Rep. 40, Stanford University, Stanford (1990)
9.
Anderson, D.R., Sweeney, D.J., Williams, T.A.: Introduction to Statistics: Concepts and Applications. West, New York (1994)
10.
11.
12.
Arabie, P.: Was Euclid an unnecessarily sophisticated psychologist? Psychometrika 56, 567–587 (1991)MATHCrossRef
13.
Arbuckle, J., Aiken, L.S.: A program for Pitman’s permutation test for differences in location. Behav. Res. Methods Instrum. 7, 381 (1975)CrossRef
14.
Author: Resampling Stats User’s Guide. Resampling Stats, Arlington (1999)
15.
Author: StatXact for Windows. Cytel Software, Cambridge (2000)
16.
Bailer, A.J.: Testing variance equality with randomization tests. J. Stat. Comput. Simul. 31, 1–8 (1989)MATHCrossRef
17.
Bakan, D.: The test of significance in psychological research. Psychol. Bull. 66, 423–437 (1966)CrossRef
18.
Bakeman, R., Robinson, B.F., Quera, V.: Testing sequential association: estimating exact p values using sampled permutations. Psychol. Methods 1, 4–15 (1996)CrossRef
19.
Bartko, J.J.: On various intraclass correlation reliability coefficients. Psychol. Bull. 83, 762–765 (1976)CrossRef
20.
Bartko, J.J.: Measurement and reliability: statistical thinking considerations. Schizophr. Bull. 17, 483–489 (1991)CrossRef
21.
Bartlett, M.S.: A note on tests of significance in multivariate analysis. Proc. Camb. Philos. Soc. 34, 33–40 (1939)MATHCrossRef
22.
Bernardin, H.J., Beatty, R.W.: Performance Appraisal: Assessing Human Behavior at Work. Kent, Boston (1984)
23.
Berry, K.J., Mielke, P.W.: Moment approximations as an alternative to the F test in analysis of variance. Br. J. Math. Stat. Psychol. 36, 202–206 (1983)MATHCrossRef
24.
Berry, K.J., Mielke, P.W.: An APL function for Radlow and Alf’s exact chi-square test. Behav. Res. Methods Instrum. Comput. 17, 131–132 (1985)CrossRef
25.
Berry, K.J., Mielke, P.W.: Goodman and Kruskal’s tau-b statistic: a nonasymptotic test of significance. Sociol. Methods Res. 13, 543–550 (1985)CrossRef
26.
Berry, K.J., Mielke, P.W.: Subroutines for computing exact chi-square and Fisher’s exact probability tests. Educ. Psychol. Meas. 45, 153–159 (1985)CrossRef
27.
Berry, K.J., Mielke, P.W.: A generalization of Cohen’s kappa agreement measure to interval measurement and multiple raters. Educ. Psychol. Meas. 48, 921–933 (1988)CrossRef
28.
Berry, K.J., Mielke, P.W.: A family of multivariate measures of association for nominal independent variables. Educ. Psychol. Meas. 52, 41–55 (1992)CrossRef
29.
Berry, K.J., Mielke, P.W.: Spearman’s footrule as a measure of agreement. Psychol. Rep. 80, 839–846 (1997)CrossRef
30.
Berry, K.J., Mielke, P.W.: Extension of Spearman’s footrule to multiple rankings. Psychol. Rep. 82, 376–378 (1998)CrossRef
31.
Berry, K.J., Mielke, P.W.: Least absolute regression residuals: analyses of block designs. Psychol. Rep. 83, 923–929 (1998)CrossRef
32.
Berry, K.J., Mielke, P.W.: Least sum of absolute deviations regression: distance, leverage, and influence. Percept. Mot. Skills 86, 1063–1070 (1998)CrossRef
33.
Berry, K.J., Mielke, P.W.: Least sum of Euclidean regression residuals: estimation of effect size. Psychol. Rep. 91, 955–962 (2002)CrossRef
34.
Berry, K.J., Mielke, P.W.: Longitudinal analysis of data with multiple binary category choices. Psychol. Rep. 93, 127–131 (2003)CrossRef
35.
Berry, K.J., Martin, T.W., Olson, K.F.: Testing theoretical hypotheses: a PRE statistic. Soc. Forces 53, 190–196 (1974)CrossRef
36.
Berry, K.J., Martin, T.W., Olson, K.F.: A note on fourfold point correlation. Educ. Psychol. Meas. 34, 53–56 (1974)CrossRef
37.
Berry, K.J., Mielke, P.W., Iyer, H.K.: Factorial designs and dummy coding. Percept. Mot. Skills 87, 919–927 (1998)CrossRef
38.
Berry, K.J., Mielke, P.W., Mielke, H.W.: The Fisher–Pitman permutation test: an attractive alternative to the F test. Psychol. Rep. 90, 495–502 (2002)CrossRef
39.
Berry, K.J., Johnston, J.E., Mielke, P.W.: Exact and resampling probability values for measures associated with ordered R by C contingency tables. Psychol. Rep. 99, 231–238 (2006)
40.
Berry, K.J., Johnston, J.E., Mielke, P.W.: An alternative measure of effect size for Cochran’s Q test for related proportions. Percept. Mot. Skills 104, 1236–1242 (2007)
41.
Berry, K.J., Johnston, J.E., Mielke, P.W.: A Chronicle of Permutation Statistical Methods: 1920–2000 and Beyond. Springer, Cham (2014)MATHCrossRef
42.
Bilder, C.R., Loughin, T.M.: On the first-order Rao–Scott correction of the Umesh–Loughin–Scherer statistic. Biometrics 57, 1253–1255 (2001)MathSciNetMATHCrossRef
43.
Bilder, C.R., Loughin, T.M., Nettleton, D.: Multiple marginal independence-testing for pick any/c variables. Commun. Stat. Simul. Comput. 29, 1285–1316 (2000)MATHCrossRef
44.
Biondini, M.E., Mielke, P.W., Berry, K.J.: Data-dependent permutation techniques for the analysis of ecological data. Vegetatio 75, 161–168 (1988). [The name of the journal was changed to Plant Ecology in 1997]
45.
Blalock, H.M.: A double standard in measuring degree of association. Am. Sociol. Rev. 28, 988–989 (1963)
46.
Blattberg, R., Sargent, T.: Regression with non-Gaussian stable disturbances. Econometrica 39, 501–510 (1971)CrossRef
47.
Borgatta, E.F.: My student, the purist: a lament. Soc. Q. 9, 29–34 (1968)CrossRef
48.
49.
Box, J.F.: R. A. Fisher: The Life of a Scientist. Wiley, New York (1978)
50.
Box, G.E.P.: An Accidental Statistician: The Life and Memories of George E. P. Box. Wiley, New York (2013). [Inscribed “With a little help from my friend, Judith L. Allen”]
51.
52.
Bradley, J.V.: Distribution-free Statistical Tests. Prentice-Hall, Englewood Cliffs (1968)MATH
53.
Bradley, J.V.: A common situation conducive to bizarre distribution shapes. Am. Stat. 31, 147–150 (1977)
54.
Brandeau, M.L., Chiu, S.S.: Parametric facility location on a tree network with an L p norm cost function. Transp. Sci. 22, 59–69 (1988)MathSciNetMATHCrossRef
55.
Brennan, P.F., Hays, B.J.: The kappa statistic for establishing interrater reliability in the secondary analysis of qualitative clinical data. Res. Nurs. Heal. 15, 153–158 (1992)CrossRef
56.
Brennan, R.L., Prediger, D.J.: Coefficient kappa: some uses, misuses, and alternatives. Educ. Psychol. Meas. 41, 687–699 (1981)CrossRef
57.
Brillinger, D.R., Jones, L.V., Tukey, J.W.: The role of statistics in weather resources management. Tech. Rep. II, Weather Modification Advisory Board, United States Department of Commerce, Washington, DC (1978)
58.
Bross, I.D.J.: Is there an increased risk? Fed. Proc. 13, 815–819 (1954)
59.
Brown, G.W., Mood, A.M.: On median tests for linear hypotheses. In: Neyman, J. (ed.) Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, vol. II, pp. 159–166. University of California Press, Berkeley (1951)
60.
61.
Burry-Stock, J.A., Laurie, D.G., Chissom, B.S.: Rater agreement indexes for performance assessment. Educ. Psychol. Meas. 56, 251–262 (1996)CrossRef
62.
Campbell, M.J., Gardner, M.J.: Calculating confidence intervals for some non-parametric analyses. Br. Med. J. 296, 1454–1456 (1988)CrossRef
63.
Capraro, R.M., Capraro, M.M.: Treatments of effect sizes and statistical significance tests in textbooks. Educ. Psychol. Meas. 62, 771–782 (2002)MathSciNetCrossRef
64.
Capraro, R.M., Capraro, M.M.: Exploring the APA fifth edition Publication Manual’s impact of the analytic preferences of journal editorial board members. Educ. Psychol. Meas. 63, 554–565 (2003)MathSciNetCrossRef
65.
Carroll, R.M., Nordholm, L.A.: Sampling characteristics of Kelley’s ε 2 and Hays’ \(\hat{\omega }^{2}\). Educ. Psychol. Meas. 35, 541–554 (1975)CrossRef
66.
Carver, R.P.: The case against statistical significance testing. Harv. Educ. Rev. 48, 378–399 (1978)CrossRef
67.
Carver, R.P.: The case against statistical significance testing, revisited. J. Exp. Educ. 61, 287–292 (1993)CrossRef
68.
Chesterton, G.K.: The Complete Father Brown Stories: “The Head of Caesar”. Star Books, Vancouver (2003)
69.
70.
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960)CrossRef
71.
Cohen, J.: Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol. Bull. 70, 213–220 (1968)CrossRef
72.
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Academic Press, New York (1969)MATH
73.
Cohen, J.: Things I have learned (so far). Am. Psychol. 45, 1304–1312 (1990)CrossRef
74.
Cohen, J.: The earth is round (p < . 05). Am. Psychol. 49, 997–1003 (1994)
75.
Cohen, J., Cohen, P.: Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Erlbaum, Hillsdale (1975)
76.
Colwell, D.J., Gillett, J.R.: Spearman versus Kendall. Math. Gaz. 66, 307–309 (1982)CrossRef
77.
Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. Wiley, New York (1999)
78.
Conti, L.H., Musty, R.E.: The effects of delta-9-tetrahydrocannabinol injections to the nucleus accumbens on the locomotor activity of rats. In: Arurell, S., Dewey, W.L., Willette, R.E. (eds.) The Cannabinoids: Chemical, Pharmacologic, and Therapeutic Aspects, pp. 649–655. Academic Press, New York (1984)CrossRef
79.
Coombs, C.H.: A Theory of Data. Wiley, New York (1964)
80.
Costner, H.L.: Criteria for measures of association. Am. Sociol. Rev. 30, 341–353 (1965)CrossRef
81.
Cramér, H.: Mathematical Methods of Statistics. Princeton University Press, Princeton (1946)MATH
82.
Crittenden, K.S., Montgomery, A.C.: A system of paired asymmetric measures of association for use with ordinal dependent variables. Soc. Forces 58, 1178–1194 (1980)CrossRef
83.
84.
Cureton, E.E.: Rank-biserial correlation when ties are present. Educ. Psychol. Meas. 28, 77–79 (1968)CrossRef
85.
Curran-Everett, D.: Explorations in statistics: standard deviations and standard errors. Adv. Physiol. Educ. 32, 203–208 (2008)CrossRef
86.
Daniel, W.W.: Statistical significance versus practical significance. Sci. Educ. 61, 423–427 (1977)CrossRef
87.
Daniels, H.E.: Rank correlation and population models (with discussion). J. R. Stat. Soc. Ser. B Methodol. 12, 171–191 (1950)MathSciNetMATH
88.
Daniels, H.E.: Note on Durbin and Stuart’s formula for E(r s ). J. R. Stat. Soc. Ser. B Methodol. 13, 310 (1951)
89.
Darwin, C.R.: The Effects of Cross and Self Fertilization in the Vegetable Kingdom. John Murray, London (1876)CrossRef
90.
David, F.N.: Review of “Rank Correlation Methods” by M. G. Kendall. Biometrika 37, 190 (1950)CrossRef
91.
de Mast, J., Akkerhuis, T., Erdmann, T.: The statistical evaluation of categorical measurements: simple scales, but treacherous complexity underneath (2014). [Originally a paper presented at the First Stu Hunter Research Conference in Heemskerk, Netherlands, March, 2013]
92.
Decady, Y.R., Thomas, D.R.: A simple test of association for contingency tables with multiple column responses. Biometrics 56, 893–896 (2000)MATHCrossRef
93.
Diekhoff, G.: Statistics for the Social and Behavioral Sciences: Univariate, Bivariate, Multivariate. Brown, Dubuque (1992)
94.
Dielman, T.E.: A comparison of forecasts from least absolute and least squares regression. J. Forecast. 5, 189–195 (1986)CrossRef
95.
Dielman, T.E.: Corrections to a comparison of forecasts from least absolute and least squares regression. J. Forecast. 8, 419–420 (1989)CrossRef
96.
Dielman, T.E., Pfaffenberger, R.: Least absolute value regression: necessary sample sizes to use normal theory inference procedures. Decis. Sci. 19, 734–743 (1988)CrossRef
97.
Dielman, T.E., Rose, E.L.: Forecasting in least absolute value regression with autocorrelated errors: a small-sample study. Int. J. Forecast. 10, 539–547 (1994)CrossRef
98.
Dodd, D.H., Schultz, R.F.: Computational procedures for estimating magnitude of effects for some analysis of variance designs. Psychol. Bull. 79, 391–395 (1973)CrossRef
99.
Durbin, J., Stuart, A.: Inversions and rank correlation coefficients. J. R. Stat. Soc. Ser. B Methodol. 13, 303–309 (1951)MathSciNetMATH
100.
101.
Dwyer, J.H.: Analysis of variance and the magnitude of effect: a general approach. Psychol. Bull. 81, 731–737 (1974)CrossRef
102.
Dyson, G.: Turing’s Cathedral: The Origins of the Digital Universe. Pantheon/Vintage, New York (2012)MATH
103.
Eden, T., Yates, F.: On the validity of Fisher’s z test when applied to an actual example of non-normal data. J. Agric. Sci. 23, 6–17 (1933)CrossRef
104.
105.
Edgington, E.S.: Statistical inference and nonrandom samples. Psychol. Bull. 66, 485–487 (1966)CrossRef
106.
Edgington, E.S.: Approximate randomization tests. J. Psychol. 72, 143–149 (1969)CrossRef
107.
Edgington, E.S.: Statistical Inference: The Distribution-Free Approach. McGraw-Hill, New York (1969)
108.
Edgington, E.S.: Randomization Tests. Marcel Dekker, New York (1980)MATH
109.
Edgington, E.S., Onghena, P.: Randomization Tests, 4th edn. Chapman & Hall/CRC, Boca Raton (2007)MATH
110.
111.
Everitt, B.S.: Moments of the statistics kappa and weighted kappa. Br. J. Math. Stat. Psychol. 21, 97–103 (1968)CrossRef
112.
Ezekiel, M.J.B.: Methods of Correlation Analysis. Wiley, New York (1930)MATH
113.
Feinstein, A.R.: Clinical biostatistics XXIII: the role of randomization in sampling, testing, allocation, and credulous idolatry (Part 2). Clin. Pharmacol. Ther. 14, 898–915 (1973)CrossRef
114.
Feinstein, A.R.: Clinical Biostatistics. C.V. Mosby, St. Louis (1977)
115.
Ferguson, G.A.: Statistical Analysis in Psychology and Education, 5th edn. McGraw-Hill, New York (1981)
116.
Festinger, L.: The significance of differences between means without reference to the frequency distribution function. Psychometrika 11, 97–105 (1946)MathSciNetMATHCrossRef
117.
Fidler, F., Thompson, B.: Computing correct confidence intervals for ANOVA fixed- and random-effects effect sizes. Educ. Psychol. Meas. 61, 575–604 (2001)MathSciNet
118.
Fisher, R.A.: Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh (1925)MATH
119.
Fisher, R.A.: The Design of Experiments. Oliver and Boyd, Edinburgh (1935)
120.
Fisher, R.A.: The logic of inductive inference (with discussion). J. R. Stat. Soc. 98, 39–82 (1935)MATHCrossRef
121.
Fisher, R.A.: Mathematics of a lady tasting tea. In: Newman, J.R. (ed.) The World of Mathematics, vol. III, section VIII, pp. 1512–1521. Simon & Schuster, New York (1956)
122.
Fisher, R.A.: The Design of Experiments, 7th edn. Hafner, New York (1960)
123.
Fleiss, J.L.: Estimating the magnitude of experimental effects. Psychol. Bull. 72, 273–276 (1969)CrossRef
124.
Fleiss, J.L., Cohen, J., Everitt, B.S.: Large sample standard errors of kappa and weighted kappa. Psychol. Bull. 72, 323–327 (1969)CrossRef
125.
Franklin, L.A.: Exact tables of Spearman’s footrule for n = 11(1)18 with estimate of convergence and errors for the normal approximation. Stat. Probab. Lett. 6, 399–406 (1988)MathSciNetMATHCrossRef
126.
Freeman, L.C.: Elementary Applied Statistics. Wiley, New York (1965)
127.
Frick, R.W.: Interpreting statistical testing: process and propensity, not population and random sampling. Behav. Res. Methods Instrum. Comput. 30, 527–535 (1998)CrossRef
128.
Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675–701 (1937)MATHCrossRef
129.
130.
Friedman, H.: Magnitude of experimental effect and a table for its rapid estimation. Psychol. Bull. 70, 245–251 (1968)CrossRef
131.
Gaebelein, J.W., Soderquist, J.A., Powers, W.A.: A note on the variance explained in the mixed analysis of variance model. Psychol. Bull. 83, 1110–1112 (1976)CrossRef
132.
Gail, M., Mantel, N.: Counting the number of r × c contingency tables with fixed margins. J. Am. Stat. Assoc. 72, 859–862 (1977)MathSciNetMATH
133.
Gardner, M.J., Altman, D.G.: Statistics with Confidence: Confidence Intervals and Statistical Guidelines. British Medical Journal, London (1989)
134.
Geary, R.C.: Some properties of correlation and regression in a limited universe. Metron 7, 83–119 (1927)MATH
135.
136.
Gebhard, J., Schmitz, N.: Permutation tests: a revival? I. Optimum properties. Stat. Pap. 39, 75–85 (1998)MathSciNetMATH
137.
Glass, G.V.: Note on rank-buserial correlation. Educ. Psychol. Meas. 26, 623–631 (1966)CrossRef
138.
Glass, G.V.: Primary, secondary, and meta-analysis of research. Educ. Res. 5, 3–8 (1976)CrossRef
139.
Glass, G.V.: Statistical Methods in Education and Psychology, 2nd edn. Prentice-Hall, Englewood Cliffs (1984)
140.
Glass, G.V., Hakstian, A.R.: Measures of association in comparative experiments: their development and interpretation. Am. Educ. Res. J. 6, 403–414 (1969)CrossRef
141.
Glass, G.V., Peckham, P.D., Sanders, J.R.: Consequences of failure to meet assumptions underlying the fixed effects analysis of variance and covariance. Rev. Educ. Res. 42, 237–288 (1972)CrossRef
142.
Glass, G.V., McGraw, B., Smith, M.L.: Meta-Analysis in Social Research: Individual and Neighbourhood Reactions. Sage, Beverly Hills (1981)
143.
Golding, S.L.: Flies in the ointment: methodological problems in the analysis of the percentage of variance due to persons and situations. Psychol. Bull. 82, 278–289 (1975)CrossRef
144.
Good, I.J.: Further comments concerning the lady tasting tea or beer: P-values and restricted randomization. J. Stat. Comput. Simul. 40, 263–267 (1992)CrossRef
145.
Good, P.I.: Permutation, Parametric and Bootstrap Tests of Hypotheses. Springer, New York (1994)MATHCrossRef
146.
Good, P.I.: Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer, New York (1994)MATHCrossRef
147.
Good, P.I.: Resampling Methods: A Practical Guide to Data Analysis. Birkhäuser, Boston (1999)MATHCrossRef
148.
Good, P.I.: Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, 2nd edn. Springer, New York (2000)MATHCrossRef
149.
Good, P.I.: Resampling Methods: A Practical Guide to Data Analysis, 2nd edn. Birkhäuser, Boston (2001)MATHCrossRef
150.
Good, P.I.: Extensions of the concept of exchangeability and their applications. J. Mod. Appl. Stat. Methods 1, 243–247 (2002)
151.
Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications. J. Am. Stat. Assoc. 49, 732–764 (1954)MATH
152.
Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications, III: approximate sampling theory. J. Am. Stat. Assoc. 58, 310–364 (1963)MathSciNet
153.
Gravetter, F.J., Wallnau, L.B.: Essentials of Statistics for the Behavioral Sciences, 8th edn. Wadsworth, Belmont (2014)
154.
155.
Gridgeman, N.T.: The lady tasting tea, and allied topics. J. Am. Stat. Assoc. 54, 776–783 (1959)MATH
156.
Grier, D.A.: Statistical laboratories and the origins of computing. Chance 12, 14–20 (1999)
157.
Grissom, R.J., Kim, J.J.: Effect Sizes for Research: A Broad Practical Approach. Lawrence Erlbaum, Mahwah (2005)
158.
Grissom, R.J., Kim, J.J.: Effect Sizes for Research: Univariate and Multivariate Applications. Routledge, New York (2012)
159.
Guggenmoos-Holzmann, I.: How reliable are chance-corrected measures of agreement? Stat. Med 12, 2191–2205 (1993)CrossRef
160.
Guggenmoos-Holzmann, I.: Comment on “Modeling covariate effects in observer agreement studies: the case of nominal scale agreement” by P. Graham. Stat. Med. 14, 2285–2286 (1995)CrossRef
161.
Guilford, J.P.: Fundamental Statistics in Psychology and Education. McGraw-Hill, New York (1950)MATH
162.
Hald, A.: History of Probability and Statistics and Their Applications Before 1750. Wiley, New York (1990)MATHCrossRef
163.
Hald, A.: A History of Mathematical Statistics from 1750 to 1930. Wiley, New York (1998)MATH
164.
Haldane, J.B.S., Smith, C.A.B.: A simple exact test for birth-order effect. Ann. Eugen. 14, 117–124 (1948)CrossRef
165.
Hall, N.S.: R. A. Fisher and his advocacy of randomization. J. Hist. Biol. 40, 295–325 (2007)
166.
Hanley, J.A.: Standard error of the kappa statistic. Psychol. Bull. 102, 315–321 (1987)CrossRef
167.
Harding, E.F.: An efficient, minimal-storage procedure for calculating the Mann–Whitney U, generalized U and similar distributions. J. R. Stat. Soc.: Ser. C: Appl. Stat. 33, 1–6 (1984)
168.
Hayes, A.F.: Permutation test is not distribution-free: testing H 0: ρ = 0. Psychol. Methods 1, 184–198 (1996)CrossRef
169.
Hays, W.L.: Statistics. Holt, Rinehart and Winston, New York (1963)MATH
170.
Hedges, L.V.: Estimation of effect size from a series of independent experiments. Psychol. Bull. 92, 490–499 (1982)CrossRef
171.
172.
Hellman, M.: A study of some etiological factors of malocclusion. Dent. Cosmos 56, 1017–1032 (1914)
173.
174.
Henson, R.K., Smith, A.D.: State of the art in statistical significance and effect size reporting: a review of the APA task force report and current trends. J. Res. Dev. Educ. 33, 285–296 (2000)
175.
Hess, B., Olejnik, S., Huberty, C.J.: The efficacy of two improvement-over-chance effect sizes for two-group univariate comparisons. Educ. Psychol. Meas. 61, 909–936 (2001)MathSciNetCrossRef
176.
Higgins, J.J.: Introduction to Modern Nonparametric Tests. Brooks/Cole, Pacific Grove (2004)
177.
Hitchcock, D.B.: Yates and contingency tables: 75 years later. Electron. J. Hist. Probab. Stat. 5, 1–14 (2009)MathSciNetMATH
178.
Hodges, J.L., Lehmann, E.L.: Rank methods for combination of independent experiments in analysis of variance. Ann. Math. Stat. 33, 482–497 (1962)MathSciNetMATHCrossRef
179.
180.
Hope, A.C.A.: A simplified Monte Carlo significance test procedure. J. R. Stat. Soc. Ser. B Methodol. 30, 582–598 (1968)MATH
181.
Hotelling, H.: The generalization of student’s ratio. Ann. Math. Stat. 2, 360–378 (1931)MATHCrossRef
182.
Hotelling, H.: A generalized T test and measure of multivariate dispersion. In: Neyman, J. (ed.) Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, vol. II, pp. 23–41. University of California Press, Berkeley (1951)
183.
Hotelling, H., Pabst, M.R.: Rank correlation and tests of significance involving no assumption of normality. Ann. Math. Stat. 7, 29–43 (1936)MATHCrossRef
184.
Howell, D.C.: Statistical Methods for Psychology, 6th edn. Wadsworth, Belmont (2007)
185.
Howell, D.C.: Statistical Methods for Psychology, 8th edn. Wadsworth, Belmont (2013)
186.
Hubbard, R.: Alphabet soup: Blurring the distinctions between p’s and α’s in psychological research. Theor. Psychol. 14, 295–327 (2004)CrossRef
187.
Hubert, L.J.: A note on Freeman’s measure of association for relating an ordered to an unordered factor. Psychometrika 39, 517–520 (1974)MathSciNetMATHCrossRef
188.
Hunter, A.A.: On the validity of measures of association: the nominal-nominal two-by-two case. Am. J. Sociol. 79, 99–109 (1973)CrossRef
189.
Hutchinson, T.P.: Kappa muddles together two sources of disagreement: Tetrachoric correlation is preferable. Res. Nurs. Health 16, 313–315 (1993)CrossRef
190.
Huynh, H., Feldt, L.S.: Conditions under which mean square ratios in repeated measurements designs have exact F distributions. J. Am. Stat. Assoc. 65, 1582–1589 (1970)MATHCrossRef
191.
Irwin, J.O.: Tests of significance for differences between percentages based on small numbers. Metron 12, 83–94 (1935)MATH
192.
Isaacson, W.: The Innovators. Simon & Schuster, New York (2014)
193.
Jockel, K.H.: Finite sample properties and asymptotic efficiency of Monte Carlo tests. J. Stat. Comput. Simul. 14, 336–347 (1986)MathSciNetMATH
194.
Johnston, J.E., Berry, K.J., Mielke, P.W.: A measure of effect size for experimental designs with heterogeneous variances. Percept. Mot. Skills 98, 3–18 (2004)CrossRef
195.
Johnston, J.E., Berry, K.J., Mielke, P.W.: Permutation tests: precision in estimating probability values. Percept. Mot. Skills 105, 915–920 (2007)
196.
197.
Kahaner, D., Moler, C., Nash, S.: Numerical Methods and Software. Prentice-Hall, Englewood Cliffs (1988)MATH
198.
Kaufman, E.H., Taylor, G.D., Mielke, P.W., Berry, K.J.: An algorithm and FORTRAN program for multivariate LAD (ℓ 1 of ℓ 2) regression. Computing 68, 275–287 (2002)MathSciNetMATHCrossRef
199.
Keller-McNulty, S., Higgins, J.J.: Effect of tail weight and outliers and power and type-I error of robust permutation tests for location. Commun. Stat. Simul. Comput. 16, 17–35 (1987)MathSciNetCrossRef
200.
Kelley, T.L.: An unbiased correlation ratio measure. Proc. Natl. Acad. Sci. 21, 554–559 (1935)MATHCrossRef
201.
Kempthorne, O.: The Design and Analysis of Experiments. Wiley, New York (1952)MATH
202.
Kempthorne, O.: The randomization theory of experimental inference. J. Am. Stat. Assoc. 50, 946–967 (1955)MathSciNet
203.
204.
205.
206.
207.
Kendall, M.G.: Rank Correlation Methods. Griffin, London (1948)MATH
208.
Kendall, M.G.: Rank Correlation Methods, 3rd edn. Griffin, London (1962)MATH
209.
210.
211.
Kendall, M.G., Kendall, S.F.H., Babington Smith, B.: The distribution of Spearman’s coefficient of rank correlation in a universe in which all rankings occur an equal number of times. Biometrika 30, 251–273 (1939)MATH
212.
Kennedy, P.E.: Randomization tests in econometrics. J. Bus. Econ. Stat. 13, 85–94 (1995)MathSciNet
213.
Kenny, D.A.: Statistics for the Social and Behavioral Sciences. Little Brown, Boston (1987)
214.
Keppel, G.: Design and Analysis: A Researcher’s Handbook, 2nd edn. Prentice-Hall, Englewood Cliffs (1982)
215.
Keppel, G., Zedeck, S.: Data Analysis for Research Designs: Analysis of Variance and Multiple Regression/Correlation Approaches. Freeman, New York (1989)
216.
Kim, M.J., Nelson, C.R., Startz, R.: Mean revision in stock prices? a reappraisal of the empirical evidence. Rev. Econ. Stud. 58, 515–528 (1991)CrossRef
217.
Kingman, J.F.C.: Uses of exchangeability. Ann. Probab. 6, 183–197 (1978). [Abraham Wald memorial lecture delivered in Aug 1977 in Seattle, Washington]
218.
Kirk, R.E.: Experimental Design: Procedures for the Behavioral Sciences. Brooks/Cole, Belmont (1968)MATH
219.
Kirk, R.E.: Practical significance: a concept whose time has come. Educ. Psychol. Meas. 56, 746–759 (1996)CrossRef
220.
Kirk, R.E.: Effect magnitude: a different focus. J. Stat. Plan. Inference 137, 1634–1646 (2006). [Keynote address delivered at the 2003 International Conference on Statistics, Combinatorics, and Related Areas, held at the University of Southern Maine]
221.
Kraft, C.A., van Eeden, C.: A Nonparametric Introduction to Statistics. Macmillan, New York (1968)
222.
Krause, E.F.: Taxicab Geometry. Addison-Wesley, Menlo Park (1975)
223.
Krippendorff, K.: Bivariate agreement coefficients for reliability of data. In: Borgatta, E.G. (ed.) Sociological Methodology, pp. 139–150. Jossey-Bass, San Francisco (1970)
224.
Kruskal, W.H.: Historical notes on the Wilcoxon unpaired two-sample test. J. Am. Stat. Assoc. 52, 356–360 (1957)MATHCrossRef
225.
Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952). [Erratum: J. Am. Stat. Assoc. 48, 907–911 (1953)]
226.
Lachin, J.M.: Statistical properties of randomization in clinical trials. Control. Clin. Trials 9, 289–311 (1988)CrossRef
227.
LaFleur, B.J., Greevy, R.A.: Introduction to permutation and resampling-based hypothesis tests. J. Clin. Child Adolesc. 38, 286–294 (2009)CrossRef
228.
Lance, C.E.: More statistical and methodological myths and urban legends. Organ. Res. Methods 14, 279–286 (2011)CrossRef
229.
Lange, J.: Crime as Destiny: A Study of Criminal Twins. Allen & Unwin, London (1931). [Translated by C. Haldane]
230.
Larson, S.C.: The shrinkage of the coefficient of multiple correlation. J. Educ. Psychol. 22, 45–55 (1931)CrossRef
231.
Larson, R.C., Sadiq, G.: Facility locations with the Manhattan metric in the presence of barriers to travel. Oper. Res. 31, 652–669 (1983)MathSciNetMATHCrossRef
232.
233.
Lawley, D.N.: Corrections to “A generalization of Fisher’s z test”. Biometrika 30, 467–469 (1939)MATH
234.
Leach, C.: Introduction to Statistics: A Nonparametric Approach for the Social Sciences. Wiley, New York (1979)
235.
Lehmann, E.L.: Parametrics vs. nonparametrics: two alternative methodologies. J. Nonparametr. Stat. 21, 397–405 (2009)
236.
Lehmann, E.L.: Fisher, Neyman, and the Creation of Classical Statistics. Springer, New York (2011)MATHCrossRef
237.
238.
Levine, J.H.: Joint-space analysis of “pick-any” data: analysis of choices from an unconstrained set of alternatives. Psychometrika 44, 85–92 (1979)CrossRef
239.
Levine, T.R., Hullett, C.R.: Eta squared, partial eta squared, and misreporting of effect size in communication research. Hum. Commun. Res. 28, 612–625 (2002)CrossRef
240.
Levine, T.R., Weber, R., Hullett, C.R., Park, H.S., Massi Lindsey, L.L.: A critical assessment of null hypothesis significance testing in quantitative communication research. Hum. Commun. Res. 34, 171–187 (2008)CrossRef
241.
Levine, T.R., Weber, R., Park, H.S., Hullett, C.R.: A communication researchers’ guide to null hypothesis significance testing and alternatives. Hum. Commun. Res. 34, 188–209 (2008)CrossRef
242.
Light, R.J.: Measures of response agreement for qualitative data: some generalizations and alternatives. Psychol. Bull. 76, 365–377 (1971)CrossRef
243.
244.
Linn, R.L., Baker, E.L., Dunbar, S.B.: Complex performance-based assessment: expectations and validation criterion. Educ. Res. 20, 15–21 (1991)CrossRef
245.
Loether, H.J., McTavish, D.G.: Descriptive and Inferential Statistics: An Introduction, 4th edn. Allyn and Bacon, Boston (1993)MATH
246.
Loughin, T.M., Scherer, P.N.: Testing for association in contingency tables with multiple column responses. Biometrics 54, 630–637 (1998)MATHCrossRef
247.
Ludbrook, J.: Advantages of permutation (randomization) tests in clinical and experimental pharmacology and physiology. Clin. Exp. Pharmacol. Physiol. 21, 673–686 (1994)CrossRef
248.
Ludbrook, J.: Issues in biomedical statistics: comparing means by computer-intensive tests. Aust. N. Z. J. Surg. 65, 812–819 (1995)CrossRef
249.
Ludbrook, J.: The Wilcoxon–Mann–Whitney test condemned. Br. J. Surg. 83, 136–137 (1996)CrossRef
250.
Ludbrook, J.: Statistical techniques for comparing measures and methods of measurement: a critical review. Clin. Exp. Pharmacol. Physiol. 29, 527–536 (2002)CrossRef
251.
Ludbrook, J.: Outlying observations and missing values: how should they be handled? Clin. Exp. Pharmacol. Physiol. 35, 670–678 (2008)CrossRef
252.
Ludbrook, J., Dudley, H.A.F.: Issues in biomedical statistics: analyzing 2 × 2 tables of frequencies. Aust. N. Z. J. Surg. 64, 780–787 (1994)CrossRef
253.
Ludbrook, J., Dudley, H.A.F.: Issues in biomedical statistics: statistical inference. Aust. N. Z. J. Surg. 64, 630–636 (1994)CrossRef
254.
Ludbrook, J., Dudley, H.A.F.: Why permutation tests are superior to t and F tests in biomedical research. Am. Stat. 52, 127–132 (1998)
255.
Ludbrook, J., Dudley, H.A.F.: Discussion of “Why permutation tests are superior to t and F tests in biomedical research” by J. Ludbrook and H.A.F. Dudley. Am. Stat. 54, 87 (2000)
256.
Lunneborg, C.E.: Data Analysis by Resampling: Concepts and Applications. Duxbury, Pacific Grove (2000)
257.
Maclure, M., Willett, W.C.: Misinterpretation and misuse of the kappa statistic. Am. J. Epidemiol. 126, 161–169 (1987)CrossRef
258.
Manly, B.F.J.: Randomization and Monte Carlo Methods in Biology. Chapman & Hall, London (1991)MATHCrossRef
259.
Manly, B.F.J.: Randomization and Monte Carlo Methods in Biology, 2nd edn. Chapman & Hall, London (1997)MATH
260.
Manly, B.F.J.: Randomization, Bootstrap and Monte Carlo Methods in Biology, 3rd edn. Chapman & Hall/CRC, Boca Raton (2007)MATH
261.
Manly, B.F.J., Francis, R.I.C.: Analysis of variance by randomization when variances are unequal. Aust. N. Z. J. Stat. 41, 411–429 (1999)MATHCrossRef
262.
Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947)MathSciNetMATHCrossRef
263.
Margolin, B.H., Light, R.J.: An analysis of variance for categorical data, II: small sample comparisons with chi square and other competitors. J. Am. Stat. Assoc. 69, 755–764 (1974)MathSciNetMATH
264.
Mathew, T., Nordström, K.: Least squares and least absolute deviation procedures in approximately linear models. Stat. Probab. Lett. 16, 153–158 (1993)MathSciNetMATHCrossRef
265.
Maxim, P.S.: Quantitative Research Methods in the Social Sciences. Oxford, New York (1999)
266.
Maxwell, S.E., Camp, C.J., Arvey, R.D.: Measures of strength of association: a comparative examination. J. Appl. Psychol. 66, 525–534 (1981)CrossRef
267.
May, R.B., Hunter, M.A.: Some advantages of permutation tests. Can. Psychol. 34, 401–407 (1993)CrossRef
268.
May, S.M.: Modelling observer agreement: an alternative to kappa. J. Clin. Epidemiol. 47, 1315–1324 (1994)CrossRef
269.
270.
McGrath, R.E., Meyer, G.J.: When effect sizes disagree: the case of r and d. Psychol. Methods 11, 386–401 (2006)
271.
McHugh, R.B., Mielke, P.W.: Negative variance estimates and statistical dependence in nested sampling. J. Am. Stat. Assoc. 63, 1000–1003 (1968)
272.
McLean, J.E., Ernest, J.M.: The role of statistical significance testing in educational research. J. Health Soc. Behav. 5, 15–22 (1998)
273.
McNemar, Q.: Note on the sampling error of the differences between correlated proportions and percentages. Psychometrika 12, 153–157 (1947)CrossRef
274.
McQueen, G.: Long-horizon mean-reverting stock priced revisited. J. Financ. Quant. Anal. 27, 1–17 (1992)CrossRef
275.
Mehta, C.R., Patel, N.R.: Algorithm 643: FEXACT. A FORTRAN subroutine for Fisher’s exact test on unordered r × c contingency tables. ACM Trans. Math. Softw. 12, 154–161 (1986)
276.
Mehta, C.R., Patel, N.R.: A hybrid algorithm for Fisher’s exact test in unordered r × c contingency tables. Commun. Stat. Theory Methods 15, 387–403 (1986)MathSciNetMATHCrossRef
277.
Mehta, C.R., Patel, N.R., Gray, R.: On computing an exact confidence interval for the common odds ratio in several 2 × 2 contingency tables. J. Am. Stat. Assoc. 80, 969–973 (1985)MathSciNetMATH
278.
279.
Meyer, G.J.: Assessing reliability: critical corrections for a critical examination of the Rorschach comprehensive system. Psychol. Assess. 9, 480–489 (1997)CrossRef
280.
Micceri, T.: The unicorn, the normal curve, and other improbable creatures. Psychol. Bull. 105, 156–166 (1989)CrossRef
281.
Mielke, P.W.: Asymptotic behavior of two-sample tests based on powers of ranks for detecting scale and location alternatives. J. Am. Stat. Assoc. 67, 850–854 (1972)MathSciNetMATHCrossRef
282.
Mielke, P.W.: Squared rank test appropriate to weather modification cross-over design. Technometrics 16, 13–16 (1974)MathSciNetMATH
283.
Mielke, P.W.: Convenient beta distribution likelihood techniques for describing and comparing meteorological data. J. Appl. Meterol. 14, 985–990 (1975)CrossRef
284.
Mielke, P.W.: Meteorological applications of permutation techniques based on distance functions. In: Krishnaiah, P.R., Sen, P.K. (eds.) Handbook of Statistics, vol. IV, pp. 813–830. North-Holland, Amsterdam (1984)
285.
Mielke, P.W.: Geometric concerns pertaining to applications of statistical tests in the atmospheric sciences. J. Atmos. Sci. 42, 1209–1212 (1985)CrossRef
286.
287.
Mielke, P.W.: The application of multivariate permutation methods based on distance functions in the earth sciences. Earth Sci. Rev. 31, 55–71 (1991)CrossRef
288.
Mielke, P.W., Berry, K.J.: An extended class of permutation techniques for matched pairs. Commun. Stat. Theory Methods 11, 1197–1207 (1982)MathSciNetCrossRef
289.
Mielke, P.W., Berry, K.J.: Asymptotic clarifications, generalizations, and concerns regarding an extended class of matched pairs tests based on powers of ranks. Psychometrika 48, 483–485 (1983)CrossRef
290.
Mielke, P.W., Berry, K.J.: Cumulant methods for analyzing independence of r-way contingency tables and goodness-of-fit frequency data. Biometrika 75, 790–793 (1988)MathSciNetMATHCrossRef
291.
Mielke, P.W., Berry, K.J.: Permutation tests for common locations among samples with unequal variances. J. Educ. Behav. Stat. 19, 217–236 (1994)CrossRef
292.
Mielke, P.W., Berry, K.J.: Nonasymptotic inferences based on Cochran’s Q test. Percept. Mot. Skill 81, 319–322 (1995)CrossRef
293.
Mielke, P.W., Berry, K.J.: Permutation-based multivariate regression analysis: the case for least sum of absolute deviations regression. Ann. Oper. Res. 74, 259–268 (1997)MATHCrossRef
294.
Mielke, P.W., Berry, K.J.: Permutation covariate analyses of residuals based on Euclidean distance. Psychol. Rep. 81, 795–802 (1997)CrossRef
295.
Mielke, P.W., Berry, K.J.: Euclidean distance based permutation methods in atmospheric science. Data Min. Knowl. Disc. 4, 7–27 (2000)MATHCrossRef
296.
Mielke, P.W., Berry, K.J.: Data-dependent analyses in psychological research. Psychol. Rep. 91, 1225–1234 (2002)CrossRef
297.
Mielke, P.W., Berry, K.J.: Permutation Methods: A Distance Function Approach, 2nd edn. Springer, New York (2007)MATH
298.
Mielke, P.W., Berry, K.J.: A note on Cohen’s weighted kappa coefficient of agreement with linear weights. Stat. Methodol. 6, 439–446 (2009)MathSciNetCrossRef
299.
Mielke, P.W., Iyer, H.K.: Permutation techniques for analyzing multi-response data from randomized block experiments. Commun. Stat. Theory Methods 11, 1427–1437 (1982)MATHCrossRef
300.
Mielke, P.W., Berry, K.J., Johnson, E.S.: Multi-response permutation procedures for a priori classifications. Commun. Stat. Theory Methods 5, 1409–1424 (1976)MATHCrossRef
301.
Mielke, P.W., Berry, K.J., Brier, G.W.: Application of multi-response permutation procedures for examining seasonal changes in monthly mean sea-level pressure patterns. Mon. Weather Rev. 109, 120–126 (1981)CrossRef
302.
Mielke, H.W., Anderson, J.C., Berry, K.J., Mielke, P.W., Chaney, R.L., Leech, M.: Lead concentrations in inner-city soils as a factor in the child lead problem. Am. J. Public Health 73, 1366–1369 (1983)CrossRef
303.
Mielke, P.W., Berry, K.J., Landsea, C.W., Gray, W.M.: Artificial skill and validation in meteorological forecasting. Weather Forecast. 11, 153–169 (1996)CrossRef
304.
Mielke, P.W., Berry, K.J., Neidt, C.O.: A permutation test for multivariate matched-pairs analyses: comparisons with Hotelling’s multivariate matched-pairs T 2 test. Psychol. Rep. 78, 1003–1008 (1996)CrossRef
305.
Mielke, P.W., Berry, K.J., Johnston, J.E.: A FORTRAN program for computing the exact variance of weighted kappa. Percept. Mot. Skill 101, 468–472 (2005)
306.
Mielke, P.W., Berry, K.J., Johnston, J.E.: The exact variance of weighted kappa with multiple raters. Psychol. Rep. 101, 655–660 (2007)
307.
Mielke, P.W., Berry, K.J., Johnston, J.E.: Resampling programs for multiway contingency tables with fixed marginal frequency totals. Psychol. Rep. 101, 18–24 (2007)
308.
Mielke, P.W., Berry, K.J., Johnston, J.E.: Resampling probability values for weighted kappa with multiple raters. Psychol. Rep. 102, 606–613 (2008)CrossRef
309.
Mielke, P.W., Berry, K.J., Johnston, J.E.: Robustness without rank order statistics. J. Appl. Stat. 38, 207–214 (2011)MathSciNetCrossRef
310.
Minkowski, H.: Über die positiven quadratishen formen und über kettenbruchähnliche algorithmen. Crelle’s J (J. Reine Angew. Math.) 107, 278–297 (1891). [Also available in H. Minkowski, Gesammelte Abhandlungen, vol. 1, AMS Chelsea, New York, 1967]
311.
Mitchell, C., Hartmann, D.P.: A cautionary note on the use of omega squared to evaluate the effectiveness of behavioral treatments. Behav. Assess. 3, 93–100 (1981)CrossRef
312.
313.
Moses, L.E.: Statistical theory and research design. Ann. Rev. Psychol. 7, 233–258 (1956)CrossRef
314.
Murphy, K.R., Cleveland, J.: Understanding Performance Appraisal: Social, Organizational, and Goal-Based Perspectives. Sage, Thousand Oaks (1995)
315.
Myers, J.L., Well, A.D.: Research Design and Statistical Analysis. HarperCollins, New York (1991)
316.
317.
Neave, H.R., Worthington, P.L.: Distribution-Free Tests. Unwin Hyman, London (1988)
318.
Newson, R.: Parameters behind “nonparametric” statistics: Kendall’s tau, Somers’ D and median differences. Stata J. 2, 45–64 (2002)
319.
Neyman, J., Pearson, E.S.: On the use and interpretation of certain test criteria for purposes of statistical inference: part I. Biometrika 20A, 175–240 (1928)MATH
320.
Neyman, J., Pearson, E.S.: On the use and interpretation of certain test criteria for purposes of statistical inference: part II. Biometrika 20A, 263–294 (1928)MATH
321.
Nix, T.W., Barnette, J.J.: The data analysis dilemma: Ban or abandon. A review of null hypothesis significance testing. Res. Schools 5, 3–14 (1998)
322.
Nix, T.W., Barnette, J.J.: A review of hypothesis testing revisited: Rejoinder to Thompson, Knapp, and Levin. Res. Schools 5, 55–57 (1998)
323.
O’Boyle, Jr., E., Aguinis, H.: The best and the rest: revisiting the norm of normality of individual performance. Percept. Psychophys. 65, 79–119 (2012)
324.
Okamoto, D.: Letter to the editor: does it work for coffee? Significance 10, 45–46 (2013)CrossRef
325.
Olds, E.G.: Distribution of sums of squares of rank differences for small numbers of individuals. Ann. Math. Stat. 9, 133–148 (1938)MATHCrossRef
326.
Olejnik, S., Algina, J.: Measures of effect size for comparative studies: applications, interpretations, and limitations. Contemp. Educ. Psychol. 25, 241–286 (2000)CrossRef
327.
Olson, C.L.: On choosing a test statistic in multivariate analysis of variance. Psychol. Bull. 83, 579–586 (1976)CrossRef
328.
Olson, C.L.: Practical considerations in choosing a MANOVA test statistic: a rejoinder to Stevens. Psychol. Bull. 86, 1350–1352 (1979)CrossRef
329.
Osgood, C.E., Suci, G., Tannenbaum, P.: The Measurement of Meaning. University of Illinois Press, Urbana (1957)
330.
Overall, J.E., Spiegel, D.K.: Concerning least squares analysis of experimental data. Psychol. Bull. 72, 311–322 (1969)CrossRef
331.
Pagano, R.R.: Understanding Statistics in the Behavioral Sciences, 6th edn. Wadsworth, Pacific Grove (2001)
332.
Pearson, K.: Contributions to the mathematical theory of evolution. Proc. R. Soc. Lond. 54, 329–333 (1893)CrossRef
333.
Pearson, K.: Contributions to the mathematical theory of evolution, II. Skew variation in homogeneous material. Philos. Trans. R. Soc. Lond. A 186, 343–414 (1895)CrossRef
334.
Pearson, K.: Mathematical contributions to the theory of evolution, XIII. On the theory of contingency and its relation to association and normal correlation. In: Drapers’ Company Research Memoirs, Biometric Series I, pp. 1–35. Cambridge University Press, Cambridge (1904)
335.
Pearson, E.S.: Untitled. Nature 123, 866–867 (1929). [Review by E.S. Pearson of the second edition of R.A. Fisher’s Statistical Methods for Research Workers]
336.
Pearson, K., Heron, D.: On theories of association. Biometrika 9, 159–315 (1913)CrossRef
337.
Pfaffenberger, R., Dinkel, J.: Absolute deviations curve-fitting: an alternative to least squares. In: David, H.A. (ed.) Contributions to Survey Sampling and Applied Statistics, pp. 279–294. Academic Press, New York (1978)
338.
Picard, R.: Randomization and design: II. In: Feinberg, S.E., Hinkley, D.V. (eds.) R. A. Fisher: An Appreciation, pp. 46–58. Springer, Heidelberg (1980)CrossRef
339.
340.
Pitman, E.J.G.: Significance tests which may be applied to samples from any populations. Suppl. J. R. Stat. Soc. 4, 119–130 (1937)MATHCrossRef
341.
Pitman, E.J.G.: Significance tests which may be applied to samples from any populations: II. The correlation coefficient test. Suppl. J. R. Stat. Soc. 4, 225–232 (1937)MATHCrossRef
342.
Pitman, E.J.G.: Significance tests which may be applied to samples from any populations: III. The analysis of variance test. Biometrika 29, 322–335 (1938)MATH
343.
Randles, R.H., Wolfe, D.A.: Introduction to the Theory of Nonparametric Statistics. Wiley, New York (1979)MATH
344.
345.
Reinhart, A.: Statistics Done Wrong: The Woefully Complete Guide. No Starch Press, San Francisco (2015)
346.
347.
Ricketts, C., Berry, J.S.: Teaching statistics through resampling. Teach. Stat. 16, 41–44 (1994)CrossRef
348.
Roberts, J.K., Henson, R.K.: Correcting for bias in estimating effect sizes. Educ. Psychol. Meas. 62, 241–253 (2002)MathSciNetCrossRef
349.
Robinson, W.S.: Ecological correlations and the behavior of individuals. Am. Soc. Rev. 15, 351–357 (1950). [Reprinted in Int. J. Epidemiol. 38, 337–341 (2009)]
350.
Robinson, W.S.: The statistical measurement of agreement. Am. Sociol. Rev. 22, 17–25 (1957)CrossRef
351.
Robinson, W.S.: The geometric interpretation of agreement. Am. Sociol. Rev. 24, 338–345 (1959)CrossRef
352.
Rosenberg, B., Carlson, D.: A simple approximation of the sampling distribution of least absolute residuals regression estimates. Commun. Stat. Simul. Comput. 6, 421–438 (1977)MATHCrossRef
353.
Rosenthal, R., Rosnow, R.L., Rubin, D.B.: Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press, Cambridge (2000)
354.
Rouanet, H., Lépine, D.: Comparison between treatments in a repeated measures design: ANOVA and multivariate methods. Br. J. Math. Stat. Psychol. 23, 147–164 (1970)MATHCrossRef
355.
356.
357.
358.
Roy, S.N.: Some Aspects of Multivariate Analysis. Wiley, New York (1957)
359.
Saal, F.E., Downey, R.G., Lahey, M.A.: Rating the ratings: assessing the quality of rating data. Psychol. Bull. 88, 413–428 (1980)CrossRef
360.
361.
Salsburg, D.: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. Holt, New York (2001)MATH
362.
Särndal, C.E.: A comparative study of association measures. Psychometrika 39, 165–187 (1974)MATHCrossRef
363.
Satterthwaite, F.E.: An approximate distribution of estimates of variance components. Biom. Bull. 2, 110–114 (1946)CrossRef
364.
365.
Scheffé, H.: The Analysis of Variance. Wiley, New York (1959)MATH
366.
Schmidt, F.L., Johnson, R.H.: Effect of race on peer ratings in an industrial situation. J. Appl. Psychol. 57, 237–241 (1973)CrossRef
367.
Schuster, C.: A note on the interpretation of weighted kappa and its relations to other rater agreement statistics for metric scales. Educ. Psychol. Meas. 64, 243–253 (2004)MathSciNetCrossRef
368.
Scott, W.A.: Reliability of content analysis: the case of nominal scale coding. Public Opin. Q. 19, 321–325 (1955)CrossRef
369.
Senn, S.: Fisher’s game with the devil. Stat. Med. 13, 217–230 (1994). [Publication of a paper presented at the Statisticians in the Pharmaceutical Industry (PSI) annual conference held in Sept 1991 in Bristol, England]
370.
Senn, S.: Tea for three: of infusions and inferences and milk in first. Significance 9, 30–33 (2012)CrossRef
371.
Senn, S.: Response to “Tea break” by S. Springate. Significance 10, 46 (2013)
372.
Sheynin, O.B.: R. J. Boscovich’s work on probability. Arch. Hist. Exact Sci. 9, 306–324 (1973)
373.
Shrout, P.E., Fleiss, J.L.: Intraclass correlations: uses in assessing rater relaibility. Psychol. Bull. 86, 420–428 (1979)CrossRef
374.
Shrout, P.E., Spitzer, R.L., Fleiss, J.L.: Quantification of agreement in psychiatric diagnosis revisited. Arch. Gen. Psychiatry 44, 172–177 (1987)CrossRef
375.
Siegel, S., Castellan, N.J.: Nonparametric Statistics for the Behavioral Sciences, 2nd edn. McGraw-Hill, New York (1988)
376.
Siegel, S., Tukey, J.W.: A nonparametric sum of ranks procedure for relative spread in unpaired samples. J. Am. Stat. Assoc. 55, 429–445 (1960). [Corrigendum: J. Am. Stat. Assoc. 56, 1005 (1961)]
377.
378.
Snedecor, G.W.: Calculation and Interpretation of Analysis of Variance and Covariance. Collegiate Press, Ames (1934)CrossRef
379.
Snyder, P., Lawson, S.: Evaluating results using corrected and uncorrected effect size estimates. J. Exp. Educ. 61, 334–349 (1993)CrossRef
380.
Somers, R.H.: A new asymmetric measure of association for ordinal variables. Am. Sociol. Rev. 27, 799–811 (1962)CrossRef
381.
Spearman, C.E.: The proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904)CrossRef
382.
Spearman, C.E.: ‘Footrule’ for measuring correlation. Br. J. Psychol. 2, 89–108 (1906)
383.
Spitznagel, E.L., Helzer, J.E.: A proposed solution to the base rate problem in the kappa statistic. Arch. Gen. Psychiatry 42, 725–728 (1985)CrossRef
384.
385.
Stark, R., Roberts, I.: Contemporary Social Research Methods. Micro-Case, Bellevue (1996)
386.
Stevens, J.P.: Applied Multivariate Statistics for the Social Sciences. Erlbaum, Hillsdale (1986)MATH
387.
Stevens, J.P.: Intermediate Statistics: A Modern Approach. Erlbaum, Hillsdale (1990)MATH
388.
Still, A.W., White, A.P.: The approximate randomization test as an alternative to the F test in analysis of variance. Br. J. Math. Stat. Psychol. 34, 243–252 (1981)CrossRef
389.
390.
“Student”: The probable error of a mean. Biometrika 6, 1–25 (1908). [“Student” is a nom de plume for William Sealy Gosset]
391.
Susskind, E.C., Howland, E.W.: Measuring effect magnitude in repeated measures ANOVA designs: implications for gerontological research. J. Gerontol. 35, 867–876 (1980)CrossRef
392.
Tabachnick, B.G., Fidell, L.S.: Using Multivariate Statistics, 5th edn. Pearson, Boston (2007)
393.
Taha, M.A.H.: Rank test for scale parameter for asymmetrical one-sided distributions. Publ. Inst. Stat. Univ. Paris 13, 169–180 (1964)MathSciNetMATH
394.
Taylor, L.D.: Estimation by minimizing the sum of absolute errors. In: Zarembka, P. (ed.) Frontiers in Econometrics, pp. 169–190. Academic Press, New York (1974)
395.
Tedin, O.: The influence of systematic plot arrangements upon the estimate of error in field experiments. J. Agric. Sci. 21, 191–208 (1931)CrossRef
396.
Thompson, D.W.: On Growth and Form: The Complete Revised Edition. Dover, New York (1992)CrossRef
397.
398.
399.
Thompson, W.D., Walter, S.D.: A reappraisal of the kappa coefficient. J. Clin. Epidemiol. 41, 949–958 (1988)CrossRef
400.
401.
Trafimow, D., Marks, M.: Editorial. Basic Appl. Soc. Psychol. 37, 1–2 (2015)CrossRef
402.
Tschuprov, A.A.: Principles of the Mathematical Theory of Correlation. Hodge, London (1939). [Translated by M. Kantorowitsch]
403.
Tukey, J.W.: Data analysis and behavioral science (1962). [Unpublished manuscript]
404.
405.
Tukey, J.W.: Randomization and re-randomization: the wave of the past in the future. In: Statistics in the Pharmaceutical Industry: Past, Present and Future. Philadelphia Chapter of the American Statistical Association (1988). [Presented at a Symposium in Honor of Joseph L. Ciminera held in June 1988 at Philadelphia, Pennsylvania]
406.
Umesh, U.N.: Predicting nominal variable relationships with multiple response. J. Forecast. 14, 585–596 (1995)CrossRef
407.
Umesh, U.N., Peterson, R.A., Sauber, M.H.: Interjudge agreement and the maximum value of kappa. Educ. Psychol. Meas. 49, 835–850 (1989)CrossRef
408.
Ury, H.K., Kleinecke, D.C.: Tables of the distribution of Spearman’s footrule. J. R. Stat. Soc.: Ser. C: Appl. Stat. 28, 271–275 (1979)
409.
van der Reyden, D.: A simple statistical significance test. Rhod. Agric. J. 49, 96–104 (1952)
410.
Vanbelle, S., Albert, A.: A note on the linearly weighted kappa coefficient for ordinal scales. Stat. Methodol. 6, 157–163 (2008)MathSciNetMATHCrossRef
411.
Vaughan, G.M., Corballis, M.C.: Beyond tests of significance: estimating strength of effects in selected ANOVA designs. Psychol. Bull. 79, 391–395 (1969)
412.
von Eye, A., von Eye, M.: On the marginal dependency of Cohen’s κ. Eur. Pychol. 13, 305–315 (2008)
413.
Wald, A., Wolfowitz, J.: An exact test for randomness in the non-parametric case based on serial correlation. Ann. Math. Stat. 14, 378–388 (1943)MathSciNetMATHCrossRef
414.
Wallis, W.A.: The correlation ratio for ranked data. J. Am. Stat. Assoc. 34, 533–538 (1939)MATHCrossRef
415.
416.
Watterson, I.G.: Nondimensional measures of climate model performance. Int. J. Climatol. 16, 379–391 (1996)CrossRef
417.
Welch, B.L.: The specification of rules for rejecting too variable a product, with particular reference to an electric lamp problem. Suppl. J. R. Stat. Soc. 3, 29–48 (1936)MATHCrossRef
418.
Welch, B.L.: On the z-test in randomized blocks and Latin squares. Biometrika 29, 21–52 (1937)MATHCrossRef
419.
Welch, B.L.: The significance of the difference between two means when the population variances are unequal. Biometrika 29, 350–362 (1938)MATHCrossRef
420.
421.
Welkowitz, J., Ewen, R.B., Cohen, J.: Introductory Statistics for the Behavioral Sciences, 5th edn. Harcourt Brace, Orlando (2000)
422.
Wherry, R.J.: A new formula for predicting the shrinkage of the coefficient of multiple correlation. Ann. Math. Stat. 2, 440–457 (1931)MATHCrossRef
423.
Whitehurst, G.J.: Interrater agreement for journal manuscript reviews. Am. Psychol. 39, 22–28 (1984)CrossRef
424.
Whitfield, J.W.: Rank correlation between two variables, one of which is ranked, the other dichotomous. Biometrika 34, 292–296 (1947)MathSciNetMATHCrossRef
425.
Wickens, T.D.: Multiway Contingency Tables Analysis for the Social Sciences. Erlbaum, Hillsdale (1989)MATH
426.
Wilcox, R.R.: Statistics for the Social Sciences. Academic Press, San Diego (1996)
427.
Wilcox, R.R.: Applying Contemporary Statistical Techniques. Academic Press, San Diego (2003)MATH
428.
Wilcox, R.R., Muska, J.: Measuring effect size: a non-parametric analgue of \(\hat{\omega }^{2}\). Br. J. Math. Stat. Psychol. 52, 93–110 (1999)CrossRef
429.
Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1, 80–83 (1945)CrossRef
430.
Wilkinson, L.: Statistical methods in psychology journals: guidelines and explanations. Am. Psychol. 54, 594–604 (1999)CrossRef
431.
Wilks, S.S.: Certain generalizations in the analysis of variance. Biometrika 24, 471–494 (1932)MATHCrossRef
432.
Wilson, H.G.: Least squares versus minimum absolute deviations estimation in linear models. Decis. Sci. 9, 322–325 (1978)CrossRef
433.
Yates, F.: Contingency tables involving small numbers and the χ 2 test. Suppl. J. R. Stat. Soc. 1, 217–235 (1934)MATHCrossRef
434.
Yule, G.U.: On the association of attributes in statistics: with illustrations from the material childhood society. Philos. Trans. R. Soc. Lond. 194, 257–319 (1900)MATHCrossRef
435.
Yule, G.U.: On the methods of measuring association between two attributes. J. R. Stat. Soc. 75, 579–652 (1912). [Originally a paper read before the Royal Statistical Society on 23 April 1912]
436.
Metadata
Title
Introduction
Authors
Kenneth J. Berry
Paul W. Mielke Jr.
Janis E. Johnston
Copyright Year
2016
Book
Permutation Statistical Methods

Print ISBN: 978-3-319-28768-3

Electronic ISBN: 978-3-319-28770-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-28770-6_1

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