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• W4211175006 abstract "Free Access References Andrew S. Zieffler, Andrew S. Zieffler University of MinnesotaSearch for more papers by this authorJeffrey R. Harring, Jeffrey R. Harring University of MarylandSearch for more papers by this authorJeffrey D. Long, Jeffrey D. Long University of IowaSearch for more papers by this author Book Author(s):Andrew S. Zieffler, Andrew S. Zieffler University of MinnesotaSearch for more papers by this authorJeffrey R. Harring, Jeffrey R. Harring University of MarylandSearch for more papers by this authorJeffrey D. Long, Jeffrey D. Long University of IowaSearch for more papers by this author First published: 31 May 2011 https://doi.org/10.1002/9781118063682.refs AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat References Agresti, A. (2002). Categorical data analysis ( 2nd ed.). New York: Wiley. 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 (3), 317– 328. American Educational Research Association. (2006). Standards for reporting on empirical social science research in AERA publications. Educational Researcher, 35 (6), 33– 40. American Psychological Association. (2009). Publication manual of the American Psychological Association ( 6th ed.). Washington, DC: Author. American Psychological Association. (2010). Displaying your findings: A practical guide for creating figures, posters, and presentations ( 6th ed.). Washington, DC: Author. Andrews, D. F., Bickel, P. J., Hample, F. R., Huber, P. J., Rogers, W. H., & Tukey, J. W. (1972). Robust estimates of location: Survey and advances. Princeton, NJ: Princeton University Press. Badley, G. (2002). A really useful link between teaching and research. Teaching in Higher Education, 7, 443– 455. Balanda, K. P., & MacGillivray, H. L. (1988). Kurtosis: A critical review. The American Statistician, 42 (2), 111– 119. Becker, R. A. (1994). A brief history of S. In P. Dirschedl, & R. Ostermann (Eds.), Computational statistics (pp. 81– 110). Heidleberg: Physica Verlag. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57 (1), 289– 300. Benjamini, Y, & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165– 1188. Beran, R. (1987). Prepivoting to reduce level error of confidence sets. Biometrika, 74, 457– 468. Berger, J. O. (2003). Could Fisher, Jeffreys and Neyman have agreed on testing? Statistical Science, 18, 1– 32. Blackett, P. M. S. (1968). Address of the president professor P. M. S. Blackett, O.M., C.H., at the anniversary meeting, 30 November 1967. Proceedings of the Royal Society of London. Series B, Biological Sciences, 169 (1016), v– xviii. Boos, D. D. (2003). Introduction to the bootstrap world. Statistical Science, 18 (2), 168– 174. Bowman, A., & Azzalini, A. (1997). Applied smoothing techniques for data analysis: The kernel approach with S-Plus illustrations. London: Oxford University Press. Box, G. E. P. (1979). Robustness in the strategy of scientific model building. In R. Launer, & G. Wilkinson (Eds.), Robustness in statistics (pp. 201– 236). New York: Academic Press. Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces. New York: Wiley. Bretz, F., Hothorn, T., & Westfall, P. (2011). Multiple comparisons using R. Boca Raton, FL: CRC Press. Brewer, C. A. (1999). Color use guidelines for data representation. In Proceedings of the Section on Statistical Graphics, American Statistical Association (pp. 55– 60). Alexandria, VA: American Statistical Association. Brewer, C. A., Hatchard, G., & Harrower, M. A. (2003). Colorbrewer in print. Cartography and Geographic Information Science, 30 (1), 5– 32. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand-McNally. Canty, A. J. (2002). Resampling methods in R: The boot package. R News, 2/3, 2– 7. Carpenter, J., & Bithell, J. (2000). Bootstrap confidence intervals: When, which, what? a practical guide for medical statisticians. Statistics in Medicine, 19 (9), 1141– 1164. Chatterjee, S. K. (2003). Statistical thought: A perspective and history. Oxford: Oxford University Press. Cleveland, W. S. (1984). Graphical methods for data presentation: Full scale breaks, dot charts, and multibased logging. The American Statistician, 38 (4), 270– 280. Cleveland, W. S. (1993). Visualizing data. Summitt, NJ: Hobart Press. Cleveland, W. S., & McGill, R. (1983). A color-caused optical illusion on a statistical graph. The American Statistician, 37 (2), 101– 105. Cochran, W. G. (1965). The planning of observational studies in human populations. Journal of the Royal Statistical Society (Series A), 128, 134– 155. Cochran, W. G., & Rubin, D. B. (1973). Controlling bias in observational studies: A review. Sankhya, 35, 417– 446. Cohen, J. (1962). The statistical power of abnormal-social psychological research. Journal of Abnormal and Social Psychology, 65 (3), 145– 153. Cohen, J. (1965). Statistical power analysis for the behavioral sciences. New York: Academic Press. Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304– 1312. Cohen, J. (1994). The Earth is round (p < .05). American Psychologist, 49, 997– 1003. Cook, R. D., & Weisberg, S. (1999). Applied regression including computing and graphics. New York: Wiley. Cooper, M. M. (2005). An introduction to small-group learning. In N. J. Pienta, M. M. Cooper, & T. J. Greenbowe (Eds.), Chemists' guide to effective teaching (pp. 117– 128). Upper Saddle River, NJ: Pearson Prentice Hall. Cournot, A. A. (1984). Cournot's oeuvres complètes (Vol. 1; B. Bru, Ed.). Paris: Vrin. (Reprinted from Exposition de la théorie des chances et des probabilités, 1843, Paris: Hachette.) Crawley, M. J. (2007). The R book. Hoboken, NJ: Wiley. Cummins, K. (2009). Tips on writing results for a scientific paper. AmStat News, September, 39– 41. D'Agostino, R., Jr. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 77 (19), 2265– 2281. Darlington, R. B. (1970). Is kurtosis really ‘peakedness?’. The American Statistician, 24 (2), 19– 22. Dasu, T., & Johnson, T. (2003). Exploratory data mining and data cleaning. Hoboken, NJ: Wiley. David, H. A. (1995). First(?) occurrence of common terms in mathematical statistics. The American Statistician, 49 (2), 121– 133. Davison, A., & Hinkley, D. V. (1997). Bootstrap methods and their application. New York: Cambridge University Press. Dayton, C. M. (1998). Information criteria for the paired-comparisons problem. The American Statistician, 52, 144– 151. Dayton, C. M. (2003). Information criteria for pairwise comparisons. Psychological Methods, 3, 61– 71. Dayton, C. M., & Pan, X. (2005). Best subsets using information criteria. Journal of Modern Applied Statistical Methods, 4, 621– 626. Deheuvels, P. (1977). Estimation nonparamètrique de la densitè par histogrammes gènèralisès. Rivista di Statistica Applicata, 25, 5– 42. DiCicco, T. J. (1984). On parameter transformations and interval estimation. Biometrika, 71, 477– 485. DiCicco, T. J., & Efron, B. (1992). More accurate confidence intervals in exponential families. Biometrika, 79, 231– 245. DiCicco, T. J., & Efron, B. (1996). Bootstrap confidence intervals. Statistical Science, 11 (3), 189– 212. DiCicco, T. J., & Tibshirani, R. (1987). Bootstrap confidence intervals and bootstrap approximations. Journal of the American Statistical Association, 82 (397), 163– 170. Dixon, W. J., & Yuen, K. K. (1999). Trimming and winsorization: A review. The American Statistician, 53 (3), 267– 269. Duncan, D. (1955). Multiple range and multiple F tests. Biometrics, 11, 1– 42. Dunn, O. J. (1961). Multiple comparisons among means. Journal of the American Statistical Association, 56, 54– 64. Dwass, M. (1957). Modified randomization tests for nonparametric hypotheses. The Annals of Mathematical Statistics, 28, 181– 187. Dyson, F. J., & Cantab, B. A. (1943). A note on kurtosis. Journal of the Royal Statistical Society, 106 (4), 360– 361. Eckhardt, R. (1987). Stan Ulam, John von Neuman, and the Monte Carlo method. Los Alamos Science, Special Issue, 131– 143. Edgington, E. S. (1966). Statistical inference and nonrandom samples. Psychological Bulletin, 66 (6), 485– 487. Edgington, E. S., & Onghena, R. (2007). Randomization tests ( 4th ed.). Boca Raton, FL: Chapman, & Hall/CRC. Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1– 26. Efron, B. (1981a). Censored data and the bootstrap. Journal of the American Statistical Association, 76, 312– 319. Efron, B. (1981b). Nonparametric standard errors and confidence intervals. Canadian Journal of Statistics, 9, 139– 172. Efron, B. (1982). The jackknife, the bootstrap, and other resampling plans. Philadelphia, PA: Society for Industrial and Applied Mathematics. Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171– 200. Efron, B. (1994). Missing data, imputation, and the bootstrap. Journal of the American Statistical Association, 89 (426), 463– 75. Efron, B., & Gous, A. (2001). Scales of evidence for model selection: Fisher versus Jeffreys. In P. Lahiri (Ed.), Model selection (pp. 208– 246). Beachwood, OH: Institute of Mathematical Statistics. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman, & Hall. Epanechnikov, V. A. (1969). Nonparametric estimation of a multivariate probability density. Theory of Probability and Its Applications, 14, 153– 158. Ernst, M. (2004). Permutation methods: A basis for exact inference. Statistical Science, 19 (4), 676– 685. Finucan, H. M. (1964). A note on kurtosis. Journal of the Royal Statistical Society. Series B (Methodological), 26 (1), 111– 112. Fiori, A. ML, & Zenga, M. (2009). Karl Pearson and the origin of kurtosis. International Statistical Review, 77 (1), 40– 50. Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh: Oliver and Boyd. Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture, 33, 503– 513. Fisher, R. A. (1935). The design of experiments. Edinburgh: Oliver and Boyd. Fisher, R. A. (1966). The design of experiments ( 8th ed.). Edinburgh: Oliver and Boyd. Fox, J. (2005). The R commander: A basic-statistics graphical user interface to R. Journal of Statistical Software, 14 (9), 1– 42. Freedman, D., & Diaconis, R. (1981). On the histogram as a density estimator: L2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete, 57, 453– 476. Games, P. A. (1971). Multiple comparisons of means. American Educational Research Journal, 8 (53), 531– 565. Gardner, C. D., Kiazand, A., Alhassan, S., Kim, S., Stafford, R. S., Balise, R. R., et al. (2007). Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women, the A TO Z weight loss study: A randomized trial. Journal of the American Medical Association, 297 (9), 969– 977. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis ( 2nd ed.). London: CRC Press. Gelman, A., Hill, L, & Yajima, M. (in press). Why we (usually) don't have to worry about multiple comparisons (with discussion). Journal of Research on Educational Effectiveness. General Statistical Office. (2001). Statistical yearbook. Hanoi, Vietnam: Statistical Publishing House. Gentleman, R. (2004). Some perspectives on statistical computing. The Canadian Journal of Statistics, 32 (3), 209– 226. Glass, G. V. (1977). Integrating findings: The meta-analysis of research. Review of Research in Education, 5, 351– 379. Good, P. I. (2005a). Introduction to statistics through resampling methods and R/S-Plus illustrations. Hoboken, NJ: Wiley. Good, P. I. (2005b). Permutation, parametric, and bootstrap tests of hypotheses ( 3rd ed.). New York: Springer. Gottfredson, D. C., Cross, A., Wilson, D., Rorie, M., & Connell, N. (2010). An experimental evaluation of the All Stars prevention curriculum in a community after school setting. Prevention Science, 11 (2), 351– 379. Hall, P. (1986). On the bootstrap and confidence intervals. The Annals of Statistics, 14, 1431– 1452. Hall, P. (1988). Theoretical comparison of bootstrap confidence intervals (with discussion). The Annals of Statistics, 16, 927– 985. Hall, P. (2003). A short prehistory of the bootstrap. Statistical Science, 18 (2), 156– 167. Hall, P., & Martin, M. A. (1988). On bootstrap resampling and iteration. Biometrika, 75, 661– 671. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). The approach based on influence functions. New York: Wiley. Hancock, G. R., & Klockars, A. J. (1996). The quest for alpha: Developments in multiple comparison procedures in the quarter century since games (1971). Review of Educational Research, 66 (3), 269– 306. Harrower, M. A., & Brewer, C. A. (2003). Colorbrewer.org: An online tool for selecting color schemes for maps. The Cartographic Journal, 40, 27– 37. Haughton, D., Haughton, J., & Phong, N. (2001). Living standards during an economic boom: The case of Vietnam. Hanoi, Vietnam: Statistical Publishing House and United Nations Development Programme. Henry, G. T., & MacMillan, J. H. (1993). Performance data: Three comparison methods. Evaluation Review, 17, 643– 652. Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics: For the behavioral sciences ( 5th ed.). New York: Houghton Mifflin. Hinkley, D., & Wei, B. C. (1984). Improvement of jackknife confidence limit methods. Biometrika, 71, 331– 339. Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75, 800– 803. Hochberg, Y, & Tamhane, A. C. (1987). Multiple comparison procedures. New York: Wiley. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65– 70. Hommel, G. (1988). A stagewise rejective multiple test procedure based on a modified bonferroni test. Biometrika, 75, 385– 386. Hornik, K. (2010). The R FAQ. http://CRAN.R-project.org/doc/FAQ/R-FAQ.html. (ISBN 3-900051-08-9.) Hothorn, T., Hornik, K., van de Wiel, M. A., & Zeileis, A. (2008). Implementing a class of permutation tests: The coin package. Journal of Statistical Software, 28 (8), 1– 23. Hsu, J. (1996). Multiple comparisons: Theory and methods. New York: Chapman, & Hall. Hubbard, R., & Bayarri, M. J. (2003). Confusion over measures of evidence (p's) versus errors (α'S) in classical statistical testing (with discussion). The American Statistician, 57, 171– 182. Huber, P. J. (1981). Robust statistics. New York: Wiley. Huberty, C. J. (2002). A history of effect size indices. Educational and Psychological Measurement, 62, 227– 240. Ihaka, R. (2003). Colour for presentation graphics. In K. Hornik, F. Leisch, & A. Zeileis (Eds.), Proceedings of the 3rd international workshop on distributed statistical computing (DSC 2003). Vienna, Austria, http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/. Institute of Education Sciences. (2008). What works clearinghouse: Procedures and standards handbook (version 2.0). Washington, DC: United States Department of Education. Joanes, D. N., & Gill, C. A. (1998). Comparing measures of sample skewness and kurtosis. The Statistician, 47 (Part 1), 183– 189. Jones, M. C., Marron, J. S., & Sheather, S. J. (1996). A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association, 97 (433), 401– 407. Kahn, H. (1954). Use of different Monte Carlo sampling techniques. In H. A. Meyer (Ed.), Symposium on Monte Carlo methods (pp. 146– 155). New York: Wiley. Kaiser, P. K., & Boynton, R. M. (1996). Human color vision ( 2nd ed.). Washington, DC: Optical Society of America. Kempthorne, O. (1955). The randomization theory of experimental inference. Journal of the American Statistical Association, 50 (271), 946– 967. Kempthorne, O. (1977). Why randomize? Journal of Statistical Planning and Inference, 1 (1), 1– 25. Kempthorne, O. (1979). Sampling inference, experimental inference and observation inference. Sankhya: The Indian Journal of Statistics, Series B, 40 (3/4), 115– 145. Kempthorne, O. (1986). Randomization—II. In S. Kotz, & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (Vol. 7, pp. 519– 524). New York: Wiley. Kempthorne, O., & Doerfler, T. E. (1969). The behavior of some significance tests under experimental randomization. Biometrika, 56 (2), 231– 248. Keppel, G., & Wickens, T. D. (2007). Design and analysis: A researcher's handbook ( 5th ed.). Englewood Cliffs, NJ: Prentice Hall. Kirk, R. E. (1995). Experimental design: Procedures for the behavioral sciences ( 3rd ed.). Pacific Grove, CA: Brooks/Cole. Kirk, R. E. (1996). Practical significance: A concept whose time has come. Educational and Psychological Measurement, 56, 746– 759. Kirk, R. E. (2001). Promoting good statistical practices: Some suggestions. Educational and Psychological Measurement, 61 (2), 213– 218. Koyama, T. (n.d.). Beware of dynamite, http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TatsukiKoyama/Poster3.pdf. Kruskal, W. (1988). Miracles and statistics: The casual assumption of independence. Journal of the American Statistical Association, 83 (404), 929– 940. Kupfersmid, J. (1988). Improving what is published: A model in search of an editor. American Psychologist, 43, 635– 642. Leaf, M. (2002). A tale of two villages: Globalization and peri-urban change in china and Vietnam. Cities, 19 (1), 23– 31. Lee, S. M. S., & Young, G. A. (1995). Asymptotic iterated bootstrap confidence intervals. The Annals of Statistics, 23, 1301– 1330. V. S. Lee (Ed.). (2004). Teaching and learning through inquiry: A guidebook for institutions and instructors. Sterling, VA: Stylus. Lehmann, E. L. (1993). The Fisher, Neyman-Pearson theories of testing hypotheses: One theory or two? Journal of the American Statistical Association, 55, 62– 71. Leinhardt, G., & Leinhardt, S. (1980). Chapter 3: Exploratory data analysis: New tools for the analysis of empirical data. Review of Research in Education, 18 (185), 85– 157. Liao, T. F. (2002). Statistical group comparison. New York: Wiley. Light, R. J., Singer, J. D., & Willett, J. B. (1990). By design: Planning research on higher education. Cambridge, MA: Harvard University Press. Lindley, D. V. (2000). The philosophy of statistics. The Statistician, 49 (Part 3), 293– 337. Little, R. J. A. (1989). Testing the equality of two independent binomial proportions. The American Statistician, 43, 283– 288. Lochman, J. E., Lampron, L. B., Gemmer, T. C., Harris, S. E., & Wyckoff, G. M. (1989). Teacher consultation and cognitive-behavioral interventions with aggressive boys. Psychology in the Schools, 26 (2), 179– 188. Loh, W.-Y. (1987). Calibrating confidence coefficients. Journal of the American Statistical Association, 82, 155– 162. Lohr, S. (2010). Sampling: Design and analysis ( 2nd ed.). Boston, MA: Brooks/Cole. Lomax, R. G. (2007). Statistical concepts: A second course ( 3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. Long, J. D., Feng, D., & Cliff, N. (2003). Ordinal analysis of behavioral data. In J. Schinka, & W. F. Velicer (Eds.), Research methods in psychology. Volume 2 of handbook of psychology (pp. 635– 662). New York: Wiley. Lord, F. M. (1953). On the statistical treatment of football numbers. American Psychologist, 8, 750– 751. Lumley, T. (2006). Color coding and color blindness in statistical graphics. ASA Statistical Computing, & Graphics Newsletter, 17 (2), 4– 7. Marriot, M. (2007, February 8). If Leonardo had made toys. New York Times. Moore, D. S. (1990). Uncertainty. In L. A. Steen (Ed.), On the shoulders of giants: New approaches to numeracy (pp. 95– 137). Washington, DC: National Academy Press. Moors, J. J. A. (1986). The meaning of kurtosis: Darlington reexamined. The American Statistician, 40 (4), 283– 284. Mosteller, F., & Tukey, J. W. (1977). Data analysis and regression. Reading, MA: Addison-Wesley. Neyman, J. (1950). First course in probability and statistics. New York: Holt. Neyman, J., & Pearson, E. (1928a). On the use and interpretation of certain test criteria for purposes of statistical inference, part i. Biometrika, 20A, 175– 240. Neyman, J., & Pearson, E. (1928b). On the use and interpretation of certain test criteria for purposes of statistical inference. Part II. Biometrika, 20A, 263– 294. Neyman, J., & Pearson, E. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Statistical Society of London, Ser. A, 231, 289– 337. Oakes, J. M., & Johnson, P. J. (2006). Propensity score matching for social epidemiology. In J. M. Oakes, & J. S. Kaufman (Eds.), Methods in social epidemiology (pp. 364– 386). New York: Jossey-Bass. O'Brien, P. C. (1983). The appropriateness of analysis of variance and multiple comparison procedures. Biometrics, 39, 784– 794. Oehlert, G. (2000). A first course in design and analysis of experiments. New York: W. H. Freeman. Pearson, K. (1894). Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society A, 185, 71– 114. Pearson, K. (1895). Contributions to the mathematical theory of evolution. II. Skew variation in homogeneous material. Philosophical Transactions of the Royal Society A, 186, 343– 414. Pearson, K. (1901). On the correlation of characters not quantitatively measurable. Philosophical Transactions of the Royal Society of London, 195, 1– 47. Pedhazur, E. J. (1997). Multiple regression in behavioral research ( 3rd ed.). Fort Worth, TX: Harcourt Brace. Pitman, E. J. G. (1937). Significance tests which may be applied to samples from any populations. Supplement to the Journal of the Royal Statistical Society, 4 (1), 119– 130. Portnoy, S., & Koenker, R. (1989). Adaptive L estimation of linear models. Annals of Statistics, 17, 362– 381. Prince, M. J., & Felder, R. M. (2006). Inductive teaching and learning methods: Definitions, comparisons, and research bases. Journal of Engineering Education, 95 (2), 123– 138. Quenouille, M. H. (1949). Approximate tests of correlation in time series. Journal of the Royal Statistical Society, Series B, 11, 18– 84. Quenouille, M. H. (1956). Notes on bias in estimation. Biometrika, 43, 353– 360. R Development Core Team. (2009). R data import/export (version 2.10.1). Vienna, Austria: R Foundation for Statistical Computing. Raspe, R. E. (1948). Singular travels, campaigns and adventures of Baron Munchausen ( J. Carswell, Ed.). London: Cresset Press. (Original work published 1786.) Rice, W. R. (1988). A new probability model for determining exact p values for 2 × 2 contingency tables (with discussion). Biometrics, 44, 1– 22. Rissanen, J., Speed, T., & Yu, B. (1992). Density estimation by stochastic complexity. IEEE Trans, on Information Theory, 38, 315– 323. Rosenbaum, P. R. (1995). Observational studies. New York: Springer. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70 (1), 41– 55. Rosenberger, J. L., & Gasko, M. (1983). Comparing location estimators: Trimmed means, medians, and trimeans. In D. C. Hoaglin, F. Mosteller, & J. W. Tukey (Eds.), Understanding robust and exploratory data analysis (pp. 297– 328). New York: Wiley. Rosenthal, R. (1991). Meta-analytic procedures for social research. Thousand Oaks, CA: Sage. Rosenthal, R., & Rosnow, R. L. (1985). Contrast analysis: Focused comparisons in the analysis of variance. Cambridge: Cambridge University Press. Rosenthal, R., Rosnow, R. L., & Rubin, D. B. (2000). Contrasts and effect sizes in behavioral research: A correlational approach. Cambridge: Cambridge University Press. Rosenthal, R., & Rubin, D. B. (1983). Ensemble-adjusted p values. Psychological Bulletin, 94 (3), 540– 541. Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. Amercian Psychologist, 44, 1276– 1284. Rosnow, R. L., & Rosenthal, R. (1996). Computing contrasts, effect sizes, and counternulls on other people's published data: General procedures for research consumers. Psychological Methods, 1, 331– 340. Rothman, K. (1990). No adjustments are needed for multiple comparisons. Epidemiology, 1, 43– 46. Rubin, D. (1981). The Bayesian bootstrap. The Annals of Statistics, 9, 130– 134. Sain, S. R. (1994). Adaptive kernel density estimation. Unpublished doctoral dissertation, Rice University. Sarkar, D. (2008). Lattice: Multivariate data visualization with R. New York: Springer. Saville, D. J. (1990). Multiple comparison procedures: The practical solution. The American Statistician, 44, 174– 180. Scheffé, H. (1953). A method for judging all contrasts in the analysis of variance. Biometrika, 40, 87– 104. Scott, D.W. (1979). On optimal and data-based histograms. Biometrika, 66, 605– 610. Senn, S. (2008). A century of t-tests. Significance, 5 (1), 37– 39. Serlin, R. A., & Lapsley, D. K. (1993). Rational appraisal of psychological research and the good-enough principle. In G. Keren, & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 199– 228). Hillsdale, NJ: Erlbaum. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin Company. Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561– 584. Shaver, J. (1985). Chance and nonsense: A conversation about interpreting tests of statistical significance, part 2. Phi Delta Kappan, 67 (2), 138– 141. Sheather, S. J. (2004). Density estimation. Statistical Science, 19 (4), 588– 597. Sheather, S. J. (2009). A modern approach to regression with R. New York: Springer. Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall. Simon, J. L. (1997). Resampling: The new statistics ( 2nd ed.). Boston: Wadsworth. Simonoff, J. S. (1996). Smoothing methods in statistics. New York: Springer. Sohn, D. (2000). Significance testing and the science. American Psychologist, 55, 964– 965. Spector, P. (2008). Data manipulation with R. New York: Springer. Stamps, K., & Bohon, S. A. (2006). Educational attainment in new and established Latino metropolitan destinations. Social Science Quarterly, 87 (5), 1225– 1240. Staudte, R. G., & Sheather, S. J. (1990). Robust estimation and testing. New York: Wiley-Interscience. Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103 (2684), 677– 680. Stevens, S. S. (1951). Mathematics, measurement, and psychophysics. In S. S. Stevens (Ed.), Handbook of experimental psychology (pp. 1– 49). New York: Wiley. Student. (1908). On the probable error of a mean. Biometrika, 6, 1– 24. Student. (1927). Errors of routine analysis. Biometrika, 19 (1/2), 151– 164. Sturges, H. (1926). The choice of a class-interval. Journal of the American Statistical Association, 21, 65– 66. Tankard, J. W., Jr. (1984). W. S. Gosset and the t-test. In The statistical pioneers (pp. 87– 109). Cambridge, MA: Schenkman Books. Terrell, G. R. (1990). The maximal smoothing principle in density estimation. Journal of the American Statistical Association, 85 (410), 470– 477. Terrell, G. R., & Scott, D. W. (1992). Variable kernel density estimation. The Annals of Statistics, 20, 1236– 1265. Thisted, R. A., & Velleman, P. F. (1992). Computers and modern statistics. In D. C. Hoaglin, & D. S. Moore (Eds.), Perspectives on contemporary statistics, MAA notes no. 21 (pp. 41– 53). Washington, DC: Mathematical Association of America. Thompson, B. (1994). The concept of statistical significance testing (Report No. EDO-TM-94-1). Washington, DC: Office of Educational Research and Improvement. Thompson, B. (1996). AERA editorial polices regarding statistical significance testing: Three suggested reforms. Educational Researcher, 25 (2), 26– 30. Thompson, B. (1997). Editorial policies regarding statistical significance tests: Further comments. Educational Researcher, 26 (5), 29– 32. Thompson, B. (2007). Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychology in the Schools, 44 (5), 423– 432. Tufte, E. R. (1990). Envisioning information. Cheshire, CT: Graphics Press. Tukey, J. W. (1953). The problem of multiple comparisons. Unpublished manuscript. Princeton University. Tukey, J. W. (1958). Bias and confidence in not-quite large samples. The Annals of Mathematical Statistics, 29, 614. Tukey, J. W. (1962). The future of data analysis. The Annals of Mathematical Statistics, 33, 1– 67. Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley. Turlach, B. (1991). Bandwidth selection in kernel density estimation: A review. http://ideas.repec.org/p/wop/humbse/9307.html. Upton, G. J. G. (1982). A comparison of alternative tests for the 2 × 2 comparative trial. Journal of the Royal Statistical Society, Ser. A, 148, 86– 105. Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with s ( 4th ed.). New York: Springer. Venables, W. N., Smith, D. M., & the R Development Core Team. (2009). An introduction to R. notes on R: A programming environment for data analysis and graphics (version 2.10.1). Vienna, Austria: R Foundation for Statistical Computing. Wainer, H. (1984). How to display data badly. The American Statistician, 38 (2), 137– 147. Wand, M. P. (1997). Data-based choice of histogram bin width. The American Statistician, 51, 59– 64. Wand, M. P., & Jones, M. C. (1995). Kernel smoothing. London: Chapman and Hall. Westfall, P. H. (1997). Multiple testing of general contrasts using logical constraints and correlations. Journal of the American Statistical Association, 92 (431), 299– 306. Westfall, P. H., & Young, S. S. (1993). Resampling-based multiple testing. New York: Wiley. Wilcox, R. R. (2001). Fundamentals of modern statistical methods: Substantially improving power and accuracy. New York: Springer. Wilcox, R. R. (2004). Kernel density estimators: An approach to understanding how groups differ. Understanding Statistics, 3 (4), 338– 348. Wilcox, R. R. (2005). Introduction to robust estimation and hypothesis testing ( 2nd ed.). San Diego, CA: Academic Press. Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22, 392– 399. Wilkinson, L. (2006). The grammar of graphics. New York: Springer. Williams, V. S. L., Jones, L. V., & Tukey, J. W. (1999). Controlling error in multiple comparisons, with examples from state-to-state differences in educational achievement. Journal of Educational and Behavioral Statistics, 24 (1), 42– 69. Winch, R. E., & Campbell, D. T. (1969). Proof? no evidence? yes. the significance of tests of significance. The American Sociologist, 4 (May), 140– 144. WorldBank. (1999). World development indicators 1999. CD-ROM. Washington, DC: WorldBank. Yates, F. (1984). Tests of significance for 2 × 2 contingency tables (with discussion). Journal of the Royal Statistical Society, Ser. A, 147, 426– 463. Zahn, I. (2006). Learning to Sweave in APA style. The PracTeX Journal, 1. Zeileis, A., & Hornik, K. (2006, October). Choosing color palettes for statistical graphics (Report No. 41). Department of Statistics and Mathematics, Wirtschaftsuniversit at Wien, Research Report Series. Zeileis, A., Hornik, K., & Murrell, P. (2007, November). Escaping RGBland: Selecting colors for statistical graphics (Report No. 61). Department of Statistics and Mathematics, Wirtschaftsuniversit at Wien, Research Report Series. Zhang, H., & Montag, E. D. (2006). How well can people use different color attributes. Color Research, & Application, 31 (6), 445– 457. Comparing Groups: Randomization and Bootstrap Methods Using R ReferencesRelatedInformation" @default.
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• W4211175006 title "References" @default.
• W4211175006 cites W1513618424 @default.
• W4211175006 cites W1537791028 @default.
• W4211175006 cites W1551385575 @default.
• W4211175006 cites W1596515083 @default.
• W4211175006 cites W176201993 @default.
• W4211175006 cites W1909851795 @default.
• W4211175006 cites W1964404845 @default.
• W4211175006 cites W1964405087 @default.
• W4211175006 cites W1966455501 @default.
• W4211175006 cites W1969473151 @default.
• W4211175006 cites W1973886547 @default.
• W4211175006 cites W1979593580 @default.
• W4211175006 cites W1980718559 @default.
• W4211175006 cites W1981638405 @default.
• W4211175006 cites W1982366717 @default.
• W4211175006 cites W1983716954 @default.
• W4211175006 cites W1984486655 @default.
• W4211175006 cites W1985143556 @default.
• W4211175006 cites W1987715244 @default.
• W4211175006 cites W1990417870 @default.
• W4211175006 cites W1993568306 @default.
• W4211175006 cites W1995062267 @default.
• W4211175006 cites W1997280152 @default.
• W4211175006 cites W2001994823 @default.
• W4211175006 cites W2005568157 @default.
• W4211175006 cites W2017342120 @default.
• W4211175006 cites W2020850287 @default.
• W4211175006 cites W2021746604 @default.
• W4211175006 cites W2022852240 @default.
• W4211175006 cites W2026593185 @default.
• W4211175006 cites W2026632944 @default.
• W4211175006 cites W2030360178 @default.
• W4211175006 cites W2031083541 @default.
• W4211175006 cites W2032161278 @default.
• W4211175006 cites W2033593365 @default.
• W4211175006 cites W2036714085 @default.
• W4211175006 cites W2038290376 @default.
• W4211175006 cites W2040792642 @default.
• W4211175006 cites W2043415670 @default.
• W4211175006 cites W2045602503 @default.
• W4211175006 cites W2048092465 @default.
• W4211175006 cites W2048538718 @default.
• W4211175006 cites W2049280444 @default.
• W4211175006 cites W2050947749 @default.
• W4211175006 cites W2057720927 @default.
• W4211175006 cites W2061082809 @default.
• W4211175006 cites W2061554433 @default.
• W4211175006 cites W2061647004 @default.
• W4211175006 cites W2063641214 @default.
• W4211175006 cites W2063869058 @default.
• W4211175006 cites W2065122769 @default.
• W4211175006 cites W2070754351 @default.
• W4211175006 cites W2076237237 @default.
• W4211175006 cites W2076983043 @default.
• W4211175006 cites W2077514434 @default.
• W4211175006 cites W2081574221 @default.
• W4211175006 cites W2089189550 @default.
• W4211175006 cites W2090822039 @default.
• W4211175006 cites W2091982937 @default.
• W4211175006 cites W2092713296 @default.
• W4211175006 cites W2093823431 @default.
• W4211175006 cites W2096102246 @default.
• W4211175006 cites W2097581696 @default.
• W4211175006 cites W2101804891 @default.
• W4211175006 cites W2102634733 @default.
• W4211175006 cites W2103862350 @default.
• W4211175006 cites W2104162921 @default.
• W4211175006 cites W2110065044 @default.
• W4211175006 cites W2112223757 @default.
• W4211175006 cites W2114566476 @default.
• W4211175006 cites W2115012618 @default.
• W4211175006 cites W2115669451 @default.
• W4211175006 cites W2117897510 @default.
• W4211175006 cites W2124181495 @default.
• W4211175006 cites W2126728600 @default.
• W4211175006 cites W2127937344 @default.
• W4211175006 cites W2133777675 @default.
• W4211175006 cites W2135445066 @default.
• W4211175006 cites W2135832933 @default.
• W4211175006 cites W2136105491 @default.
• W4211175006 cites W2137295153 @default.
• W4211175006 cites W2138790649 @default.
• W4211175006 cites W2143292997 @default.
• W4211175006 cites W2150104128 @default.
• W4211175006 cites W2150291618 @default.
• W4211175006 cites W2155239356 @default.
• W4211175006 cites W2155920093 @default.
• W4211175006 cites W2155982020 @default.
• W4211175006 cites W2164297461 @default.
• W4211175006 cites W2166880790 @default.
• W4211175006 cites W2183510664 @default.
• W4211175006 cites W2320170108 @default.
• W4211175006 cites W2327188017 @default.

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