Statistics and quantitative methods are brought to life for social science students in this tutorial course. This revised edition provides an overview of entry- and intermediate-level statistics, and the material on the accompanying website provides extensive practice. Both the text and the website are structured to make learning self-directed, thus numerous worked examples, exercises, activities and tests are included. The emphasis, throughout, is on practice. Students are expected to engage with the material and experience multiple aspects of data and statistical analysis. Most of the tutorials include detailed examples of how to conduct analyses in Microsoft Excel, SPSS, or R.
Author(s): Colin Tredoux, Kevin Durrheim
Publisher: Juta And Company
Year: 2019
Language: English
Pages: 681
Tags: Social sciences: Data processing, Social sciences: Statistical Methods
Front cover......Page 1
Title page......Page 2
Imprint page......Page 3
Table of contents......Page 4
Preface......Page 11
Contributors......Page 13
Glossary of symbols......Page 14
Section 1: Statistics......Page 16
Tutorial 1: Numbers, variables, and measurement......Page 17
The advantages of quantitative methods......Page 18
Functions of quantification......Page 22
Some basic concepts......Page 24
Summary......Page 29
Exercises......Page 30
Tutorial 2: Displaying data......Page 31
Bar graphs......Page 32
Histograms......Page 39
Cumulative frequency diagrams......Page 44
Line graphs......Page 47
Basic rules for creating graphs......Page 55
Worked example......Page 56
Summary......Page 58
Exercises......Page 59
Further reading......Page 61
Tutorial 3: Central tendency and variation......Page 62
Measures of central tendency......Page 63
Measures of variability......Page 70
Estimating population parameters from sample data......Page 79
Worked example......Page 84
Summary......Page 88
Exercises......Page 89
Probability as frequency......Page 91
Probability and games of chance......Page 92
The multiplication and addition rules ofprobability......Page 94
Probabilities of multiple outcomes......Page 97
Worked example......Page 105
Exercises......Page 108
Tutorial 5: The standard normal distribution......Page 110
The standard normal distribution......Page 111
Two worlds: the statistical world and the real world......Page 116
Worked example......Page 121
Summary......Page 123
Exercises......Page 124
Tutorial 6: The sampling distribution of the mean......Page 126
Sampling means......Page 128
The Central Limit Theorem......Page 130
The sampling distribution and the standard normal distribution......Page 132
The standard error......Page 134
Worked example......Page 138
Summary......Page 139
Exercises......Page 140
Tutorial 7: Hypothesis testing: the z-test......Page 142
Hypothesis testing......Page 143
The z-test......Page 146
Example 1......Page 147
Worked Example 1......Page 151
Worked Example 2......Page 153
Summary......Page 155
Exercises......Page 156
Tutorial 8: Hypothesis testing: the t-test......Page 158
Using confidence intervals of the mean......Page 160
The logic of two-sample t-tests......Page 162
Independent samples t-test......Page 166
Worked Example 1......Page 168
Effect size......Page 170
The t-test for repeated measures......Page 171
Worked Example 2......Page 172
One-sample t-test......Page 174
Worked Example 3......Page 175
Exercises......Page 178
Paired or bivariate data......Page 180
Graphing paired data......Page 184
Linear and other types of relationship......Page 187
Positive and negative relationships......Page 190
Linear models and scatter......Page 194
A dataset to illustrate the calculation of Pearson’s product–moment correlation coefficient......Page 195
The meaning of r......Page 197
Calculating Pearson’s r......Page 198
Autocorrelation......Page 199
Rank correlation......Page 201
Exercises......Page 207
Tutorial 10 Simple regression......Page 212
Estimating the linear function......Page 214
Various forms of the regression equation......Page 217
Predictions versus observed values......Page 218
Calculating the regression coefficients......Page 219
Correlation is symmetric, but regression is not......Page 223
Interpreting regression coefficients......Page 225
Measuring scatter around the regression line......Page 226
Variance and regression......Page 230
Worked example......Page 235
Exercises......Page 242
Measuring a construct......Page 245
Evaluating a scale or test......Page 256
Worked example......Page 272
Summary......Page 278
Exercises......Page 279
Tutorial 12: Statistical power......Page 281
Error and statistical tests......Page 282
What determines the power of an investigation?......Page 283
Effect size......Page 286
The ‘trial’ strategy and power......Page 288
Power calculations......Page 289
Factors that influence choice of sample size......Page 295
Worked example......Page 296
Summary......Page 302
Exercises......Page 303
Tutorial 13: Analysis of variance(ANOVA)......Page 304
The rationale for using ANOVA......Page 306
The logic of ANOVA......Page 308
Comparing variance within and between groups......Page 310
Calculating one-way ANOVA......Page 312
Worked Example 1......Page 316
Multiple comparisons and effect size......Page 317
Using SPSS to do one-way ANOVA......Page 320
Worked Example 2......Page 321
Assumptions underlying ANOVA......Page 324
Worked Example 3......Page 327
Summary......Page 329
Exercises......Page 331
Tutorial 14: Factorial analysis of variance......Page 333
Why use factorial designs?......Page 335
The logic of factorial ANOVA......Page 336
Analysing factorial ANOVA designs......Page 343
Assumptions of factorial ANOVA......Page 338
Types of interactions......Page 351
Conclusion......Page 352
Summary......Page 354
Exercises......Page 355
Decomposition of variance......Page 357
Repeated measures designs and reduction/decomposition of variance......Page 360
Worked example......Page 386
Summary......Page 389
Exercises......Page 390
Tutorial 16 Multiple regression......Page 392
Worked Example 1......Page 395
Regression coefficients......Page 399
Partial correlation and multicollinearity......Page 400
Standardised regression coefficients......Page 402
The multiple correlation coefficient (R) and the standard error of estimate......Page 403
Testing statistical significance in multiple regression......Page 404
Which variables? Methods of model building......Page 405
Inspection of descriptive data and zero order correlations......Page 406
The sequential F-test......Page 409
Stepwise multiple regression......Page 410
Hierarchical multiple regression......Page 414
Mediation......Page 418
Interaction/moderation......Page 422
Categorical predictors/dummy variables......Page 425
Cross-validation......Page 430
Assumptions and limitations......Page 433
Worked Example 2......Page 435
How to write up the results of a multiple regression analysis......Page 440
Summary......Page 442
Exercises......Page 445
Tutorial17: Factor analysis......Page 449
The two classes of factor analysis......Page 450
Manifest variables and latent structure......Page 451
How do we find latent structure?......Page 453
Two families of EFA......Page 455
Principal component analysis (PCA)......Page 456
Interpreting and naming components or factors......Page 471
Factor or component scores......Page 473
PFA versus PCA......Page 475
Worked example......Page 477
Practical considerations when doing a factor analysis......Page 481
Reporting a factor analysis......Page 483
Exercises......Page 485
Classifications......Page 487
Contingency tables......Page 488
The χ2 significance test......Page 489
Measures of association in tables based on the χ2 statistic......Page 493
Isolating sources of association in r × c tables......Page 497
Assumptions of the χ2 test......Page 499
Worked example......Page 504
Summary......Page 505
Exercises......Page 506
Tutorial 19: Distribution-free......Page 508
The advantages and disadvantages of distribution-free tests......Page 509
A cornucopia of tests......Page 510
Related samples: the sign test......Page 511
Related samples: The Wilcoxon matched pairs test......Page 512
Unrelated samples: The Mann–Whitney U-test......Page 513
Three or more groups of scores: Kruskal–Wallis test for unrelated samples......Page 516
Three or more groups of scores: Friedman’srank test for related samples......Page 517
Worked example......Page 519
Exercises......Page 524
Tutorial 20: Bootstrapping and randomisation methods......Page 527
Data tables in Microsoft Excel......Page 529
Estimating a population mean using resampling in Microsoft Excel......Page 531
The spreadsheet layout......Page 533
Bootstrapping correlations......Page 538
Bootstrapping contingency tables......Page 541
Assumptions of bootstrapping......Page 550
Justifications for using bootstrapping......Page 551
Criticisms of bootstrapping......Page 552
Summary......Page 555
Exercises......Page 556
Tutorial 21: Statistical reasoning......Page 558
Rules for making statistical decisions......Page 560
Multiple means......Page 563
Variability in outcome and procedure......Page 564
Defensible reasoned argument......Page 567
Best practices......Page 571
Worked example......Page 574
Summary......Page 576
Exercises......Page 577
Section 2: Mathematics and software support......Page 578
Do you need this chapter?......Page 579
Elementary operations......Page 580
Negative numbers......Page 583
Fractions......Page 585
Decimal numbers......Page 588
Frequencies, proportions, percentages and ratios......Page 590
Power, exponents, roots......Page 591
Answers to exercises......Page 594
Do you need this tutorial?......Page 596
Some basic terms......Page 597
Equations......Page 598
Summary......Page 600
Solutions to exercises......Page 604
About graphs......Page 606
Example 1: Study and leisure hours......Page 607
Example 2: The relation between word length and recognition latency......Page 610
Example 3: A preference curve......Page 611
The direction of the line......Page 612
Points to remember......Page 613
Exercises......Page 616
Appendices......Page 618
Appendix 1: Statistical Tables......Page 619
Appendix 2: Starting with SPSS......Page 632
The SPSS environment......Page 633
Using the SPSS Data Editor......Page 636
Compute......Page 640
Recode......Page 641
Conducting statistical analyses with SPSS......Page 642
Generating graphical displays with SPSS......Page 649
Working with SPSS output......Page 652
Summary......Page 655
Exercises......Page 656
Appendix 3: Installing and learning R......Page 658
Learning how to use R and RStudio......Page 659
References......Page 662
Index......Page 672
