Modern statistics for the social and behavioral sciences : a practical introduction /: a practical introduction. (2012)

• Record Type: Book
• Further Information: Note: Rand Wilcox.
• Other Names: Wilcox, Rand R
• Contents: 1. Introduction -- Samples versus populations -- Software -- R Basics -- Entering data -- R functions and packages -- Data sets -- Arithmetic operations -- 2. Numerical and graphical summaries of data -- Basic summation notation -- Measures of location -- The sample mean -- R function mean -- The sample median -- R function for the median -- A criticism of the median : it might trim too many values -- R function for the trimmed mean -- A Winsorized mean -- R function winmean -- What is a measure of location? -- Measures or variation of scale -- Sample variance and standard deviation -- R functions for the variance and standard deviation -- The interquartile range -- R function idealf -- Winsorized variance -- R function winvar -- Median absolute deviation -- R function mad -- Average absolute distance from the median -- Other robust measures of variation -- R functions bivar, pbvar, tauvar, and tbs -- Detecting outliers -- A method based on the mean and variance -- A better outlier detection rule : the MAD-median rule -- R function out -- The boxplot -- R function boxplot -- Modifications of the boxplot rule for detecting outliers -- R function outbox -- Other measures of location -- R functions mom and onestep -- Histograms -- R functions hist and splot -- Kernel density estimators -- R function kdplot and akerd -- Stem-and-leaf displays -- R function stem -- Skewness -- Transforming data -- Choosing a measure of location -- Covariance and Pearson's correlation -- Exercises -- 3. Probability and related concepts -- Basic probability -- Expected values -- Conditional probability and independence -- Population variance -- The binomial probability function -- Continuous variables and the normal curve -- Computing probabilities associated with normal distributions -- R function pnorm -- Understanding the effects of non-normality -- Skewness -- Pearson's correlation and the population covariance -- Computing the population covariance and Pearson's correlation -- Some rules about expected values -- Chi-squared distributions -- Exercises. 4. Sampling distributions and confidence intervals -- Random sampling -- Sampling distributions -- Sampling distribution of the sample mean -- Computing probabilities associated with the sample mean -- A confidence interval for the population mean -- Known variance -- Confidence intervals when ơ is not known -- R function pt and qt -- Confidence interval for the population mean using student's T -- R function t. test -- Judging location estimators based on their sampling distribution -- Trimming and accuracy : another perspective -- An approach to non-normality : the central limit theorem -- Student's T and non-normality -- Confidence intervals for the trimmed mean -- Estimating the standard error of a trimmed mean -- R function trimse -- A confidence interval for the population trimmed mean -- R function trimci -- Transforming data -- Confidence interval for the population median -- R function sint -- Estimating the standard error of the sample median -- R function msmedse -- More about MOM and M-estimators -- Confidence intervals for the probability of success -- R functions binomci and acbinomci -- Exercises -- 5. Hypothesis testing -- The basics of hypothesis testing -- P-value or significance level -- R function t. test -- Criticisms of two-sided hypothesis testing and p-values -- Summary and generalization -- Power and type II errors -- Understanding how n, [symbol] and ơ are related to power -- Testing hypotheses about the mean when ơ is not known -- Controlling power and determining n -- Choosing n prior to collecting data -- R function power.t.test -- Stein's method : judging the sample size when data are available -- R functions stein1 and stein2 -- Practical problems with student's T test -- Hypothesis testing based on a trimmed mean -- R function trimci -- R functions stein1.tr and stein2.tr -- Testing hypotheses about the population median -- R function sintv2 -- Making decisions about which measure of location to use -- Exercises -- 6. Regression and correlation -- The least squares principle -- Confidence intervals and hypothesis testing -- Classic inferential techniques -- Multiple regression -- R functions ols, lm, and olsplot -- Standardized regression -- Practical concerns about least squares regression and how they might be addressed -- The effect of outliers on least squares regression -- Beware of bad leverage points -- Beware of discarding outliers among the Y values -- Do not assume homoscedasticity or that the regression line is straight -- Violating assumptions when testing hypotheses -- Dealing with heteroscedasticity : the HC4 method -- R functions olshc4 and hc4test -- Pearson's correlation and the coefficient of determination -- A closer look at interpreting r -- Testing H₀: p = 0 -- R functions cor.test and pwr.t.test -- R function pwr.r.test -- Testing H₀: p = 0 when there is heteroscedasticity -- R function pcorhc4 -- When is it safe to conclude that two variables are independent? -- A regression method for estimating the median of Y and other quantiles -- R function rqfit. Detecting Heteroscedasticity -- R function khomreg -- Concluding remarks -- Exercises -- 7. Bootstrap methods -- Bootstrap-t method -- Symmetric confidence intervals -- Exact nonparametric confidence intervals for means are impossible -- The percentile bootstrap method -- Inferences about robust measures of location -- Using the percentile method -- R functions onesampb, momci, and trimpb -- The bootstrap-t method based on trimmed means -- R function trimcibt -- Estimating power when testing hypotheses about a trimmed mean -- R function powt1est and powt1an -- A bootstrap estimate of standard errors -- R function bootse -- Inferences about Pearson's correlation : dealing with heteroscedasticity -- R function pcorb -- Bootstrap methods for least squares regression -- R functions hc4wtest, olswbtest, lsfitci -- Detecting associations even when there is curvature -- R functions indt and medind -- Quantile regression -- R functions qregci and rqtest -- A test for homoscedasticity using a quantile regression approach -- R function qhomt -- Regression : which predictors are best? -- R function regpre -- Least angle regression -- R function larsR -- Comparing correlations -- R functions TWOpov and TWOpNOV -- Empirical likelihood -- Exercises -- 8. Comparing two independent groups -- Student's T test -- Choosing the sample sizes -- R function power.t.test -- Relative merits of student's T -- Welch's heteroscedastic method for means -- R function t. test -- Tukey's three-decision rule -- Non-normality and Welch's method -- Three modern insights regarding methods for comparing means -- Methods for comparing medians and trimmed means -- Yuen's method for trimmed means -- R functions yuen and fac2list -- Comparing medians -- R function msmed -- Percentile bootstrap methods for comparing measures of location -- Using other measures of location -- Comparing medians -- R function medpb2 -- Some guidelines on when to use the percentile bootstrap method -- R function trimpb2 and pb2gen -- Bootstrap-t methods for comparing measures of location -- Comparing means -- Bootstrap-t method when comparing trimmed means -- R functions yuenbt and yhbt -- Estimating power and judging the sample sizes -- R function powest and pow2an -- Permutation tests -- R function permg -- Rank-based and nonparametric methods -- Wilcoxon-Mann-Whitney test -- R functions wmw and wilcox.test -- Handling tied values and heteroscedasticity -- Cliff's method -- R functions cid and cidv2 -- The Brunner-Munzel method -- R function bmp -- The Kolmogorov-Smirnov test -- R function ks -- Comparing all quantiles simultaneously : an extension of the Kolmogorov-Smirnov test -- R function sband. Graphical methods for comparing groups -- Error bars -- R function ebarplot -- Plotting the shift function -- Plotting the distributions -- R function sumplot2g -- Other approaches -- Comparing measures of scale -- Methods for comparing measures of variation -- R function comvar2 -- Brown-Forsythe method -- Comparing robust measures of variation -- Measuring effect size -- R functions yuenv2 and akp.effect -- Comparing correlations and regression slopes -- R functions twopcor, twolsreg, and tworegwb -- Comparing two binomials -- Storer-Kim method -- Beal's method -- R functions twobinom, twobici, and power.prop.test -- Making decisions about which method to use -- Exercises -- 9. Comparing two dependent groups -- The paired T test -- When does the paired T test perform well? -- R function t. test -- Comparing robust measures of location -- R functions yuend, ydbt, and dmedpb -- Comparing marginal M-estimators -- R function rmmest -- Handling missing values -- R functions rm2miss and rmmismcp -- A different perspective when using robust measures of location -- R function loc2dif and l2drmci -- The sign test -- R function signt -- Wilcoxon signed rank test -- R function wilcox.test -- Comparing variances -- Comparing robust measures of scale -- R function rmrvar -- Comparing all quantiles -- R function lband -- Plots for dependent groups -- R function g2plotdifxy -- Exercises -- 10. One-way ANOVA -- Analysis of variance for independent groups -- A conceptual overview -- ANOVA via least squares regression and dummy coding -- R functions anova, anova1, aov, and fac2list -- Controlling power and choosing the sample sizes -- R functions power.anova.test and anova.power -- Dealing with unequal variances -- Welch's test -- Judging sample sizes and controlling power when data are available -- R functions bdanova1 and bdanova2 -- Trimmed means -- R functions t1way, t1wayv2, and t1wayF -- Comparing groups based on medians -- R functions med1way -- Bootstrap methods -- A bootstrap-t method -- R function t1waybt -- Two percentile bootstrap methods -- R functions b1way and pbadepth -- Choosing a method -- Random effects model -- A measure of effect size -- A heteroscedastic method -- A method based on trimmed means -- R function rananova -- Rank-based methods -- The Kruskal-Wallis test -- R function kruskal.test -- Method BDM -- R function bdm -- Exercises -- 11. Two-way and three-way designs -- Basics of a two-way ANOVA design -- Interactions -- R functions interaction.plot and interplot -- Interactions when there are more than two levels -- Testing hypotheses about main effects and interactions. R function anova -- Inferences about disordinal interactions -- The two-way ANOVA model -- Heteroscedastic methods for trimmed means, including means -- R function t2way -- Bootstrap methods -- R function pbad2way and t2waybt -- Testing hypotheses based on medians -- R function m2way -- A rank-based method for a two-way design -- R function bdm2way -- The Patel-Hoel -- Approach to interactions -- Three-way ANOVA -- R functions anova and t3way -- Exercises -- 12. Comparing more than two dependent groups -- Comparing means in a one-way design -- R function aov -- Comparing trimmed means when dealing with a one-way design -- R functions rmanova and rmdat2mat -- A bootstrap-t method for trimmed means -- R function rmanovab -- Percentile bootstrap methods from a one-way design -- Method based on marginal measures of location -- R function bd1way -- Inferences based on difference scores -- R function rmdzero -- Rank-based methods for a one-way design -- Friedman's test -- R function friedman.test -- Method BPRM -- R function bprm -- Comments on which method to use -- Between-by-within designs -- Method for trimmed means -- R function bwtrim and bw2list -- A bootstrap-t method -- R function tsplitbt -- Inferences based on M-estimators and other robust measures of location -- R functions sppba, sppbb, and sppbi -- A rank-based test -- R function bwrank -- Within-by-within design -- R function wwtrim -- Three-way designs -- R functions bbwtrim, bwwtrim, and wwwtrim -- Data management : R functions bw2list and bbw2list -- Exercises -- 13. Multiple comparisons -- One-way ANOVA, independent groups -- Fisher's least significant difference method -- The Tukey-Kramer method -- R function TukeyHSD -- Tukey-Kramer and the ANOVA F test -- A step-down method -- Dunnett's T3 -- Games-Howell method -- Comparing trimmed means -- R function lincon -- Alternative methods for controlling FWE -- Percentile bootstrap methods for comparing trimmed means, medians, and M-estimators -- R functions medpb, tmcppb, pbmcp, and mcppb20 -- A bootstrap-t method -- R function linconb -- Rank-based methods -- R functions cidmul, cidmulv2, and bmpmul -- Two-way, between-by-between design -- Scheffé's homoscedastic method -- Heteroscedastic methods -- Extension of Welch-S̆idák and Kaiser-Bowden methods to trimmed means -- R function kbcon -- R function con2way -- Linear contrasts based on medians -- R functions msmed and mcp2med -- Bootstrap methods -- R functions linconb, mcp2a, and bbmcppb -- The Patel-Hoel rank-based interaction method -- R function rimul -- Judging sample sizes -- Tamhane's procedure -- R function tamhane -- Hochberg's procedure. R function hochberg -- Methods for dependent groups -- Linear contrasts based on trimmed means -- R function rmmcp -- Comparing M-estimators -- R functions rmmcppb, dmedpb, and dtrimpb -- Bootstrap-t method -- R function bptd -- Between-by-within designs -- R functions bwmcp, bwamcp, bwbmcp, bwimcp, spmcpa, spmcpb, and bwmcppb -- Within-by-within designs -- Three-way designs -- R functions con3way, mcp3atm, and rm3mcp -- Bootstrap methods for three-way designs -- R functions bbwmcp, bwwmcp, bbbmcppb, bbwmcppb, bwwmcppb, and wwwmcppb -- Exercises -- 14. Some multivariate methods -- Location, scatter, and detecting outliers -- Detecting outliers via robust measures of location and scatter -- R functions cov.mve and com.mcd -- More measures of location and covariance -- R functions rmba, tbs, and ogk -- R function out -- A projection-type outlier detection method -- R functions outpro, outproMC, outproad, outproadMC, and out3d -- Skipped estimators of location -- R functions smean -- One-sample hypothesis testing -- Comparing dependent groups -- R functions smeancrv2, hotel1, and rmdzeroOP -- Two-sample case -- R functions smean2, mat2grp, and matsplit -- MANOVA -- R function manova -- Robust MANOVA based on trimmed means -- R functions MULtr.anova and MULAOVp -- A multivariate extension of the Wilcoxon-Mann-Whitney test -- Explanatory measure of effect size : a projection-type generalization -- R function mulwmwv2 -- Rank-based multivariate methods -- The Munzel-Brunner method -- R function mulrank -- The Choi-Marden multivariate rank test -- R function cmanova -- Multivariate regression -- Multivariate regression using R -- Robust multivariate regression -- R function mlrreg and mopreg -- Principal components -- R functions prcomp and regpca -- Robust principal components -- R function outpca, robpca, robpcaS, Ppca, and Ppca.summary -- Exercises -- 15. Robust regression and measures of association -- Robust regression estimators -- The Theil-Sen estimator -- R functions tsreg and regplot -- Least median of squares -- Least trimmed squares and least trimmed absolute value estimators -- R functions lmsreg, ltsreg, and ltareg -- M-estimators -- R function chreg -- Deepest regression line -- R function mdepreg -- Skipped estimators -- R functions opreg and opregMC -- S-estimators and an E-type estimator -- R function tsts -- Comments on choosing a regression estimator -- Testing hypotheses when using robust regression estimators -- R functions regtest, regtestMC, regci, and regciMC -- Comparing measures of location via dummy coding -- Dealing with curvature : smoothers -- Cleveland's smoother -- R functions lowess and lplot -- Smoothers based on robust measures of location -- R functions rplot and rplotsm -- More smoothers. R functions kerreg, runpd, and qsmcobs -- Prediction when X is discrete : the R function rundis -- Seeing curvature with more than two predictors -- R function prplot -- Some alternative methods -- Some robust correlations and tests of independence -- Kendall's tau -- Spearman's rho -- Winsorized correlation -- R function wincor -- OP correlation -- R function scor -- Inferences about robust correlations : dealing with heteroscedasticity -- R function corb -- Measuring the strength of an association based on a robust fit -- Comparing the slopes of two independent groups -- R functions reg2ci, runmean2g, and l2plot -- Tests for linearity -- R functions lintest, lintestMC, and linchk -- Identifying the best predictors -- R functions regpord, ts2str, and sm2strv7 -- Detecting interactions and moderator analysis -- R functions adtest -- Graphical methods for assessing interactions -- R functions kercon, runsm2g, regi, ols.polt.inter, and reg.plot.inter -- ANCOVA -- Classic ANCOVA -- Some modern ANCOVA methods -- R functions ancsm, Qancsm, ancova, ancpb, ancbbpb, and ancboot -- Exercises -- 16. Basic methods for analyzing categorical data -- Goodness of fit -- R functions chisq.test and pwr.chisq.test -- A test of independence -- R function chi.test.ind -- Detecting differences in the marginal probabilities -- R functions contab and mcnemar.test -- Measures of association -- The proportion of agreement -- Kappa -- Weighted Kappa -- R function Ckappa -- Logistic regression -- R functions glm and logreg -- A confidence interval for the odds ratio -- R function ODDSR. CI -- Smoothers for logistic regression -- R functions logrsm, rplot.bin, and logSM -- Exercises -- Appendix A: Answers to selected exercises -- Appendix B: Tables -- Appendix C: Basic matrix algebra -- Appendix D: References.
• Publisher Details: Boca Raton : Taylor & Francis
• Publication Date: 2012
• Extent: 1 online resource (xx, 840 pages), illustrations
• Subjects: 519.5; Social sciences -- Statistical methods; Psychology -- Statistical methods; Psychology -- Statistical methods; Social sciences -- Statistical methods; Psychology -- Statistical methods; Social sciences -- Statistical methods; Statistik; Datenanalyse; Sozialwissenschaften; Datenanalyse; Sozialwissenschaften; Statistik; Electronic books
• Languages: English
• ISBNs: 9781466503236; 1466503238
• Related ISBNs: 9781439834565; 1439834563
• Notes: Note: Includes bibliographical references and index (pages 831-840).; Note: Print version record.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.