Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction (2nd Edition)

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Download Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction (2nd Edition) written by Rand R. Wilcox in EPUB format. This book is under the category Social and bearing the isbn/isbn13 number 1498796788/9781498796781. You may reffer the table below for additional details of the book.

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Specifications

book-author

Rand R. Wilcox

publisher

Chapman and Hall/CRC; 2nd Edition

file-type

EPUB

pages

731 pages

language

English

asin

B074TW42DR

isbn10

1498796788

isbn13

9781498796781


Book Description

Requiring no previous training; Modern Statistics for the Social and Behavioral Sciences; 2nd Edition; (PDF/ePub) provides a two-semester; graduate-level introduction to basic statistical techniques that takes into account the latest advances and insights that are typically ignored in an introductory course.

Thousands of journal articles make it clear that basic techniques; routinely taught and used; can perform poorly when dealing with skewed distributions; outliers; heteroscedasticity (unequal variances); and curvature. Methods for dealing with these problems have been derived and can provide a deeper; more accurate; and more nuanced understanding of data. A conceptual basis is given for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are explained and illustrated.

Features of the Second edition:

  • Includes an R package with over 1300 functions
  • Provides numerous illustrations using the software R
  • Presents an in-depth description of both classic and modern methods
  • Includes a solution manual giving detailed answers to all of the exercises
  • Explains and illustrates why recent advances can provide more power and a deeper understanding of data

This 2nd edition describes many latest advances relevant to basic techniques. For instance; a vast array of new and improved methods is now available for dealing with regression; including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described.

NOTE: The product includes the ebook; Modern Statistics for the Social and Behavioral Sciences 2nd Edition in PDF and ePub. No access codes are included.

Additional information

book-author

Rand R. Wilcox

publisher

Chapman and Hall/CRC; 2nd Edition

file-type

EPUB

pages

731 pages

language

English

asin

B074TW42DR

isbn10

1498796788

isbn13

9781498796781

Table of contents


Table of contents :
Note continued: 13.6.4. R Function cmanova —
13.7. Multivariate Regression —
13.7.1. Multivariate Regression Using R —
13.7.2. Robust Multivariate Regression —
13.7.3. R Function mlrreg and mopreg —
13.8. Principal Components —
13.8.1. R Functions prcomp and regpca —
13.8.2. Robust Principal Components —
13.8.3. R Functions outpca, robpca, robpcaS, Ppca, and Ppca.summary —
13.9. Exercises —
ch. 14 Robust Regression and Measures of Association —
14.1. Robust Regression Estimators —
14.1.1. The Theil —
Sen Estimator —
14.1.2. R Functions tsreg, tshdreg, and regplot —
14.1.3. Least Median of Squares —
14.1.4. Least Trimmed Squares and Least Trimmed Absolute Value Estimators —
14.1.5. R Functions lmsreg, ltsreg, and ltareg —
14.1.6. M-estimators —
14.1.7. R Function chreg —
14.1.8. Deepest Regression Line —
14.1.9. R Function mdepreg —
14.1.10. Skipped Estimators —
14.1.11. R Functions opreg and opregMC —
14.1.12. S-estimators and an E-type Estimator —
14.1.13. R Function tstsreg —
14.2. Comments on Choosing a Regression Estimator —
14.3. Inferences Based on Robust Regression Estimators —
14.3.1. Testing Hypotheses About the Slopes —
14.3.2. Inferences About the Typical Value of Y Given X —
14.3.3. R Functions regtest, regtestMC, regci, regciMC, regYci, and regYband —
14.3.4. Comparing Measures of Location via Dummy Coding —
14.4. Dealing with Curvature: Smoothers —
14.4.1. Cleveland’s Smoother —
14.4.2. R Functions lowess, lplot, lplot.pred, and lplotCI —
14.4.3. Smoothers Based on Robust Measures of Location —
14.4.4. R Functions rplot, rplotCIS, rplotCI, rplotCIv2, rplotCIM, rplot.pred, qhdsm, and qhdsm.pred —
14.4.5. Prediction When X Is Discrete: The R Function rundis —
14.4.6. Seeing Curvature with More Than Two Predictors —
14.4.7. R Function prplot —
14.4.8. Some Alternative Methods —
14.4.9. Detecting Heteroscedasticity Using a Smoother —
14.4.10. R Function rhom —
14.5. Some Robust Correlations and Tests of Independence —
14.5.1. Kendall’s tau —
14.5.2. Spearman’s rho —
14.5.3. Winsorized Correlation —
14.5.4. R Function wincor —
14.5.5. OP or Skipped Correlation —
14.5.6. R Function scor —
14.5.7. Inferences about Robust Correlations: Dealing with Heteroscedasticity —
14.5.8. R Functions corb and scorci —
14.6. Measuring the Strength of an Association Based on a Robust Fit —
14.7. Comparing the Slopes of Two Independent Groups —
14.7.1. R Function reg2ci —
14.8. Tests for Linearity —
14.8.1. R Functions lintest, lintestMC, and linchk —
14.9. Identifying the Best Predictors —
14.9.1. Inferences Based on Independent Variables Taken in Isolation —
14.9.2. R Functions regpord, ts2str, and sm2strv7 —
14.9.3. Inferences When Independent Variables Are Taken Together —
14.9.4. R Function reglVcom —
14.10. Interactions and Moderator Analyses —
14.10.1. R Functions olshc4.inter, ols.plot.inter, regci.inter, reg.plot.inter and adtest —
14.10.2. Graphical Methods for Assessing Interactions —
14.10.3. R Functions kercon, runsm2g, regi —
14.11. ANCOVA —
14.11.1. Classic ANCOVA —
14.11.2. Robust ANCOVA Methods Based on a Parametric Regression Model —
14.11.3. R Functions ancJN, ancJNmp, anclin, reg2plot, and reg2g.p2plot —
14.11.4. ANCOVA Based on the Running-interval Smoother —
14.11.5. R Functions ancsm, Qancsm, ancova, ancovaWMW, ancpb, ancov-aUB, ancboot, ancdet, runmean2g, qhdsm2g, and 12plot —
14.11.6. R Functions Dancts, Dancols, Dancova, Dancovapb, DancovaUB, and Dancdet —
14.12. Exercises —
ch. 15 Basic Methods for Analyzing Categorical Data —
15.1. Goodness of Fit —
15.1.1. R Functions chisq.test and pwr.chisq.test —
15.2. A Test of Independence —
15.2.1. R Function chi.test.ind —
15.3. Detecting Differences in the Marginal Probabilities —
15.3.1. R Functions contab and mcnemar.test —
15.4. Measures of Association —
15.4.1. The Proportion of Agreement —
15.4.2. Kappa —
15.4.3. Weighted Kappa —
15.4.4. R Function Ckappa —
15.5. Logistic Regression —
15.5.1. R Functions glm and logreg —
15.5.2. A Confidence Interval for the Odds Ratio —
15.5.3. R Function ODDSR. CI —
15.5.4. Smoothers for Logistic Regression —
15.5.5. R Functions logrsm, rplot.bin, and logSM —
15.6. Exercises —
Appendix A Answers to Selected Exercises —
Appendix B TABLES —
Appendix C BASIC MATRIX ALGEBRA. Note continued: 7.8.9. R Function ks —
7.8.10. Comparing All Quantiles Simultaneously: An Extension of the Kolmogorov-Smirnov Test —
7.8.11. R Function sband —
7.9. Graphical Methods for Comparing Groups —
7.9.1. Error Bars —
7.9.2. R Functions ebarplot and ebarplot.med —
7.9.3. Plotting the Shift Function —
7.9.4. Plotting the Distributions —
7.9.5. R Function sumplot2g —
7.9.6. Other Approaches —
7.10. Comparing Measures of Variation —
7.10.1. R Function comvar2 —
7.10.2. Brown-Forsythe Method —
7.10.3. Comparing Robust Measures of Variation —
7.11. Measuring Effect Size —
7.11.1. R Functions yuenv2 and akp.effect —
7.12. Comparing Correlations and Regression Slopes —
7.12.1. R Functions twopcor, twolsreg, and tworegwb —
7.13. Comparing Two Binomials —
7.13.1. Storer-Kim Method —
7.13.2. Beal’s Method —
7.13.3. R Functions twobinom, twobici, bi2KMSv2, and power.prop.test —
7.13.4. Comparing Two Discrete Distributions —
7.13.5. R Function disc2com —
7.14. Making Decisions About which Method to Use —
7.15. Exercises —
ch. 8 Comparing Two Dependent Groups —
8.1. The Paired T Test —
8.1.1. When Does the Paired T Test Perform Well? —
8.1.2. R Function t. test —
8.2. Comparing Robust Measures of Location —
8.2.1. R Functions yuend, ydbt, and dmedpb —
8.2.2. Comparing Marginal M-Estimators —
8.2.3. R Function rmmest —
8.2.4. Measuring Effect Size —
8.2.5. R Function D.akp.effect —
8.3. Handling Missing Values —
8.3.1. R Functions rm2miss and rmmismcp —
8.4. A Different Perspective when Using Robust Measures of Location —
8.4.1. R Functions loc2dif and 12drmci —
8.5. The Sign Test —
8.5.1. R Function signt —
8.6. Wilcoxon Signed Rank Test —
8.6.1. R Function wilcox.test —
8.7. Comparing Variances —
8.7.1. R Function comdvar —
8.8. Comparing Robust Measures of Scale —
8.8.1. R Function rmrvar —
8.9. Comparing All Quantiles —
8.9.1. R Functions lband —
8.10. Plots for Dependent Groups —
8.10.1. R Function g2plotdifxy —
8.11. Exercises —
ch. 9 One-Way Anova —
9.1. Analysis of Variance for Independent Groups —
9.1.1. A Conceptual Overview —
9.1.2. ANOVA via Least Squares Regression and Dummy Coding —
9.1.3. R Functions anova, anoval, aov, and fac2list —
9.1.4. Controlling Power and Choosing the Sample Sizes —
9.1.5. R Functions power.anova.test and anova.power —
9.2. Dealing with Unequal Variances —
9.2.1. Welch’s Test —
9.3. Judging Sample Sizes and Controlling Power when Data are Available —
9.3.1. R Functions bdanoval and bdanova2 —
9.4. Trimmed Means —
9.4.1. R Functions t1way, tlwayv2, t1wayF, and g5plot —
9.4.2. Comparing Groups Based on Medians —
9.4.3. R Function med1way —
9.5. Bootstrap Methods —
9.5.1. A Bootstrap-t Method —
9.5.2. R Functions t1waybt and BFBANOVA —
9.5.3. Two Percentile Bootstrap Methods —
9.5.4. R Functions b1way, pbadepth, and Qanova —
9.5.5. Choosing a Method —
9.6. Random Effects Model —
9.6.1. A Measure of Effect Size —
9.6.2. A Heteroscedastic Method —
9.6.3. A Method Based on Trimmed Means —
9.6.4. R Function rananova —
9.7. Rank-Based Methods —
9.7.1. The Kruskall —
Wallis Test —
9.7.2. R Function kruskal.test —
9.7.3. Method BDM —
9.7.4. R Functions bdm and bdmP —
9.8. Exercises —
ch. 10 Two-Way and Three-Way Designs —
10.1. Basics of a Two-Way Anova Design —
10.1.1. Interactions —
10.1.2. R Functions interaction.plot and interplot —
10.1.3. Interactions When There Are More Than Two Levels —
10.2. Testing Hypotheses About Main Effects and Interactions —
10.2.1. R function anova —
10.2.2. Inferences About Disordinal Interactions —
10.2.3. The Two-Way ANOVA Model —
10.3. Heteroscedastic Methods for Trimmed Means, Includingmeans —
10.3.1. R Function t2way —
10.4. Bootstrap Methods —
10.4.1. R Functions pbad2way and t2waybt —
10.5. Testing Hypotheses Based on Medians —
10.5.1. R Function m2way —
10.6. A Rank-Based Method for a Two-Way Design —
10.6.1. R Function bdm2way —
10.6.2. The Patel —
Hoel Approach to Interactions —
10.7. Three-Way Anova —
10.7.1. R Functions anova and t3way —
10.8. Exercises —
ch. 11 Comparing More than Two Dependent Groups —
11.1. Comparing Means in a One-Way Design —
11.1.1. R Function aov —
11.2. Comparing Trimmed Means When Dealing With a One-Way Design —
11.2.1. R Functions rmanova and rmdat2mat —
11.2.2. A Bootstrap-t Method for Trimmed Means —
11.2.3. R Function rmanovab —
11.3. Percentile Bootstrap Methods for a One-Way Design —
11.3.1. Method Based on Marginal Measures of Location —
11.3.2. R Function bdlway —
11.3.3. Inferences Based on Difference Scores —
11.3.4. R Function rmdzero —
11.4. Rank-Based Methods for a One-Way Design —
11.4.1. Friedman’s Test —
11.4.2. R Function friedman.test —
11.4.3. Method BPRM —
11.4.4. R Function bprm —
11.5. Comments on Which Method to Use —
11.6. Between-By-Within Designs —
11.6.1. Method for Trimmed Means —
11.6.2. R Function bwtrim and bw2list —
11.6.3. A Bootstrap-t Method —
11.6.4. R Function tsplitbt —
11.6.5. Inferences Based on M-estimators and Other Robust Measures of Location —
11.6.6. R Functions sppba, sppbb, and sppbi —
11.6.7. A Rank-Based Test —
11.6.8. R Function bwrank —
11.7. Within-By-Within Design —
11.7.1. R Function wwtrim —
11.8. Three-Way Designs —
11.8.1. R Functions bbwtrim, bwwtrim, and wwwtrim —
11.8.2. Data Management: R Functions bw2list and bbw2list —
11.9. Exercises —
ch. 12 Multiple Comparisons —
12.1. One-Way Anova and Related Situations, Independent Groups —
12.1.1. Fisher’s Least Significant Difference Method —
12.1.2. The Tukey —
Kramer Method —
12.1.3. R Function TukeyHSD —
12.1.4. Tukey —
Kramer and the ANOVA F Test —
12.1.5. Step-Down Methods —
12.1.6. Dunnett’s T3 —
12.1.7. Games —
Howell Method —
12.1.8. Comparing Trimmed Means —
12.1.9. R Functions lincon, stepmcp and twoKlin —
12.1.10. Alternative Methods for Controlling FWE —
12.1.11. Percentile Bootstrap Methods for Comparing Trimmed Means, Medians, and M-estimators —
12.1.12. R Functions medpb, linconpb, pbmcp, and p.adjust —
12.1.13. A Bootstrap-t Method —
12.1.14. R Function linconbt —
12.1.15. Rank-Based Methods —
12.1.16. R Functions cidmul, cidmulv2, and bmpmul —
12.1.17. Comparing the Individual Probabilities of Two Discrete Distributions —
12.1.18. R Functions binband, splotg2, cumrelf, and cumrelfT —
12.1.19. Comparing the Quantifies of Two Independent Groups —
12.1.20. R Functions qcomhd and qcomhdMC —
12.1.21. Multiple Comparisons for Binomial and Categorical Data —
12.1.22. R Functions skmcp and discmcp —
12.2. Two-Way, Between-By-Between Design —
12.2.1. Scheffe’s Homoscedastic Method —
12.2.2. Heteroscedastic Methods —
12.2.3. Extension of Welch —
Sidak and Kaiser —
Bowden Methods to Trimmed Means —
12.2.4. R Function kbcon —
12.2.5. R Functions con2way and conCON —
12.2.6. Linear Contrasts Based on Medians —
12.2.7. R Functions msmed and mcp2med —
12.2.8. Bootstrap Methods —
12.2.9. R Functions mcp2a, bbmcppb, bbmcp —
12.2.10. The Patel —
Hoel Rank-Based Interaction Method —
12.2.11. R Function rimul —
12.3. Judging Sample Sizes —
12.3.1. Tamhane’s Procedure —
12.3.2. R Function tamhane —
12.3.3. Hochberg’s Procedure —
12.3.4. R Function hochberg —
12.4. Methods for Dependent Groups —
12.4.1. Linear Contrasts Based on Trimmed Means —
12.4.2. R Function rmmcp —
12.4.3. Comparing M-estimators —
12.4.4. R Functions rmmcppb, dmedpb, dtrimpb, and boxdif —
12.4.5. Bootstrap-t Method —
12.4.6. R Function bptd —
12.4.7. Comparing the Quantiles of the Marginal Distributions —
12.4.8. R Function Dqcomhd —
12.5. Between-By-Within Designs —
12.5.1. R Functions bwmcp, bwamcp, bwbmcp, bwimcp, spmcpa, spmcpb, spmcpi, and bwmcppb —
12.6. Within-By-Within Designs —
12.6.1. Three-Way Designs —
12.6.2. R Functions con3way, mcp3atm, and rm3mcp —
12.6.3. Bootstrap Methods for Three-Way Designs —
12.6.4. R Functions bbwmcp, bwwmcp, bwwmcppb, bbbmcppb, bbwmcppb, bwwmcppb, and wwwmcppb —
12.7. Exercises —
ch. 13 Some Multivariate Methods —
13.1. Location, Scatter, and Detecting Outliers —
13.1.1. Detecting Outliers Via Robust Measures of Location and Scatter —
13.1.2. R Functions cov.mve and cov.mcd —
13.1.3. More Measures of Location and Covariance —
13.1.4. R Functions rmba, tbs, and ogk —
13.1.5. R Function out —
13.1.6. A Projection-Type Outlier Detection Method —
13.1.7. R Functions outpro, outproMC, outproad, outproadMC, and out3d —
13.1.8. Skipped Estimators of Location —
13.1.9. R Function smean —
13.2. One-Sample Hypothesis Testing —
13.2.1. Comparing Dependent Groups —
13.2.2. R Functions smeancrv2, hotel, and rmdzeroOP —
13.3. Two-Sample Case —
13.3.1. R Functions smcan2, mat2grp, matsplit, and mat2list —
13.3.2. R functions matsplit, mat2grp, and mat2list —
13.4. Manova —
13.4.1. R Function manova —
13.4.2. Robust MANOVA Based on Trimmed Means —
13.4.3. R Functions MULtr.anova and MULAOVp —
13.5. A Multivariate Extension of the Wilcoxon-Mann-Whitney Test —
13.5.1. Explanatory Measure of Effect Size: A Projection-Type Generalization —
13.5.2. R Function mulwmwv2 —
13.6. Rank-Based Multivariate Methods —
13.6.1. The Munzel —
Brunner Method —
13.6.2. R Function mulrank —
13.6.3. The Choi —
Marden Multivariate Rank Test

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