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SPSS 17 Made Simple / Edition 1
     

SPSS 17 Made Simple / Edition 1

2.3 3
by Paul R Kinnear
 

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ISBN-10: 1848720262

ISBN-13: 2901848720267

Pub. Date: 07/17/2009

Publisher: Taylor & Francis

Reflecting the latest developments in statistics software from SPSS Inc., this new edition of one of the most widely read textbooks in its field keeps the reader abreast of the latest improvements in PASW Statistics 17 (the new name for SPSS Statistics 17).

This friendly and informal textbook is a non-technical and readable introduction to one of the most

Overview

Reflecting the latest developments in statistics software from SPSS Inc., this new edition of one of the most widely read textbooks in its field keeps the reader abreast of the latest improvements in PASW Statistics 17 (the new name for SPSS Statistics 17).

This friendly and informal textbook is a non-technical and readable introduction to one of the most powerful and versatile statistical packages on the market. The new edition combines clarity of presentation with coverage of the latest improvements in the software and where necessary, the coverage has been extended to include topics in which our readers have expressed particular interest. The previous edition included more advice about the use of the control language or syntax, and coverage has been further extended in the present edition to show the reader how to use the improved PASW syntax editor. While being updated and expanded to cover new features, the book will continue to be useful to readers with previous versions (SPSS 16 and earlier).

Each statistical technique is presented in a realistic research context and is fully illustrated with screen shots of PASW dialog boxes and output. The book also provides guidance on the choice of statistical techniques and advice (based on the APA guidelines) on how to report the results of statistical analyses. The first chapter sets the scene with a survey of typical research situations, key terms and advice on the choice of statistical techniques. It also provides clear signposts to where each technique can be found in the body of the book. The next chapters introduce the reader to the use of PASW, beginning with the entry, description and exploration of data. There is also afull description of the powerful capabilities of the versatile Chart Builder. Each of the remaining chapters concentrates on one particular kind of research situation and the statistical techniques that are appropriate.

In summary, PASW Statistics 17 Made Simple:

Gets you started with PASW.

Shows you how to run an exploratory data analysis (EDA) using PASW's extensive graphics and data-handling menus.

Reviews the elements of statistical inference.

Helps you to choose appropriate statistical techniques.

Warns you of the pitfalls arising from the misuse of statistics.

Shows you how to report the results of a statistical analysis.

Shows you how to use syntax to implement some useful procedures and operations.

Has a comprehensive index, which allows you to find a topic by several different routes.

Has a comprehensive glossary.

The book is accompanied by online instructor resources, including a PowerPoint lecture course and a multiple-choice question bank. The book's dedicated website also features a comprehensive set of exercises to familiarise the reader with inputting data and choosing statistical techniques. Please visit psypress.com/pasw-statistics for more details.

Product Details

ISBN-13:
2901848720267
Publisher:
Taylor & Francis
Publication date:
07/17/2009
Edition description:
New Edition
Pages:
662

Table of Contents

Preface xv

Chapter 1 Introduction 1

1.1 Measurements and Data 1

1.1.1 Variables: quantitative and qualitative 1

1.1.2 Levels of measurement: scale, ordinal and nominal data 2

1.1.3 A grey area: ratings 2

1.1.4 Univariate, bivariate and multivariate data sets 3

1.2 Experimental Versus Correlational Research 3

1.2.1 A simple experiment 3

1.2.2 A more complex experiment 4

1.2.3 Correlational research 5

1.2.4 The Pearson correlation coefficient 7

1.2.5 Correlation and causation 8

1.2.6 Quasi-experiments 8

1.3 Choosing A Statistical Test: Some Guidelines 8

1.3.1 Considerations in choosing a statistical test 9

1.3.2 Five common research situations 9

1.4 Is A Difference Significant? 10

1.4.1 The design of the experiment: independent versus related samples 10

1.4.2 Flow chart for selecting a suitable test for differences between means 11

1.5 Are Two Variables Associated? 13

1.5.1 Flow chart for selecting a suitable test for association 13

1.5.2 Measuring association in ordinal data 14

1.5.3 Measuring association in nominal data: Contingency tables 14

1.5.4 Multi-way contingency tables 15

1.6 Can We Predict a Score From Scores on Other Variables? 15

1.6.1 Flow chart for predicting a score or category membership 15

1.6.2 Simple regression 16

1.6.3 Multiple regression 16

1.6.4 Predicting category membership: Discriminant analysis and logistic regression 17

1.7 From Sample to Population 17

1.7.1 Flow chart for selecting the appropriate one-sample test 17

1.7.2 Goodness-of-fit: scale data 18

1.7.3 Goodness-of-fit: nominal data 18

1.7.4 Inferences about the mean of a single population 18

1.8 TheSearch For Latent Variables 19

1.9 Multivariate Statistics 19

1.10 Some Statistical Terms and Concepts 20

1.10.1 Description or confirmation? 20

1.10.2 Samples and populations 20

1.10.3 Parameters and statistics 21

1.10.4 Statistical inference 21

1.10.5 One-sample and two-sample tests of hypotheses about means 24

1.10.6 Sampling distributions 25

1.10.7 The standard normal distribution 26

1.10.8 When the population variance and standard deviation are unknown: the t distribution 28

1.10.9 Errors in hypothesis testing 32

1.11 A Final Word 34

Recommended reading 35

Chapter 2 Getting started with PASW Statistics 17.0 36

2.1 Outline of a PASW Session 36

2.1.1 Entering the data 36

2.1.2 Selecting the exploratory and statistical procedures 37

2.1.3 Examining the output 37

2.1.4 A simple experiment 37

2.1.5 Preparing data for PASW 38

2.2 Opening PASW 39

2.3 The PASW Statistics Data Editor 40

2.3.1 Working in Variable View 40

2.3.2 Working in Data View 45

2.3.3 Entering the data 45

2.4 A Statistical Analysis 49

2.4.1 An example: Computing means 49

2.4.2 Keeping more than one application open 53

2.5 Closing PASW 53

2.6 Resuming Work on a Saved Data Set 53

Exercise 1 Some simple operations with PASW Statistics 17.0 53

Exercise 2 Questionnaire data 53

Chapter 3 Editing and manipulating files 54

3.1 More About the PASW Statistics Data Editor 54

3.1.1 Working in Variable View 54

3.1.2 Working in Data View 61

3.2 More on The PASW Statistics Viewer 68

3.2.1 Editing the output 69

3.2.2 More advanced editing 70

3.2.3 Tutorials in PASW 74

3.3 Selecting From and Manipulating Data Files 74

3.3.1 Selecting cases 74

3.3.2 Aggregating data 77

3.3.3 Sorting data 79

3.3.4 Merging files 80

3.3.5 Transposing the rows and columns of a data set 85

3.4 Importing and Exporting Data 87

3.4.1 Importing data from other applications 87

3.4.2 Copying output 90

3.5 Printing From PASW 92

3.5.1 Printing output from the Viewer 92

Exercise 3 Merging files - Adding cases & variables 97

Chapter 4 Exploring your data 98

4.1 Introduction 98

4.1.1 The influence of outliers and asymmetry of distribution 99

4.2 Some Useful Menus 99

4.3 Describing Data 101

4.3.1 Describing nominal and ordinal data 101

4.3.2 Describing measurements 108

4.4 Manipulation of the Data Set 122

4.4.1 Reducing and transforming data 122

4.4.2 The Compute procedure 123

4.4.3 The Recode and Visual Binning procedures 129

Exercise 4 Correcting and preparing your data 136

Exercise 5 Preparing your data (continued) 136

Chapter 5 Graphs and charts 137

5.1 Introduction 137

5.1.1 Graphs and charts on PASW 137

5.1.2 Viewing a chart 140

5.1.3 Editing charts and saving templates 140

5.2 Bar Charts 141

5.2.1 Simple bar charts 141

5.2.2 Clustered bar charts 144

5.2.3 Panelled bar charts 146

5.2.4 3-D charts 147

5.2.5 Editing a bar chart 149

5.2.6 Chart templates 151

5.3 Error Bar Charts 154

5.4 Boxplots 155

5.5 Pie Charts 157

5.6 Line Graphs 159

5.7 Scatterplots and Dot Plots 162

5.7.1 Scatterplots 162

5.7.2 Dot plots 164

5.8 Dual Y-Axis Graphs 165

5.9 Histograms 167

5.10 Receiver-Operating-Characteristic (ROC) Curve 169

5.10.1 The PASW ROC curve 170

5.10.2 The d' statistic 173

Exercise 6 Charts and graphs 174

Exercise 7 Recording data; selecting cases; line graph 174

Chapter 6 Comparing averages: Two-sample and one-sample tests 175

6.1 Overview 175

6.2 Comparing Means: The Independent-Samples T Test With PASW 176

6.2.1 Preparing the data file 176

6.2.2 Exploring the data 177

6.2.3 Running the t test 179

6.2.4 Interpreting the output 181

6.2.5 Two-tailed and one-tailed p-values 182

6.2.6 The effects of extreme scores and outliers in a small data set 183

6.2.7 Measuring effect size 183

6.2.8 Reporting the results of a statistical test 185

6.3 The Related-Samples (or Paired-Samples) T Test With PASW 186

6.3.1 Preparing the data file 187

6.3.2 Exploring the data 187

6.3.3 Running the t test 188

6.3.4 Interpreting the output 189

6.3.5 Measuring effect size 190

6.3.6 Reporting the results of the test 190

6.3.7 A one-sample test 191

6.4 The Mann-Whitney U Test 191

6.4.1 Nonparametric test in PASW 191

6.4.2 Independent samples: The Mann-Whitney U test 192

6.4.3 Output for the Mann-Whitney U test 194

6.4.4 Effect size 194

6.4.5 The report 195

6.5 The Wilcoxon Matched-Pairs Test 196

6.5.1 The Wilcoxon matched-pairs tests in PASW 196

6.5.2 The output 197

6.5.3 Effect size 198

6.5.4 The report 198

6.6 The Sign And Binomial Tests 198

6.6.1 The sign test in PASW 199

6.6.2 Bernoulli trials: the binomial test 201

6.7 Effect Size, Power and the Number of Participants 204

6.7.1 How many participants shall I need in my experiment? 204

6.8 A Final Word 206

Exercise 8 Comparing the averages of two independent samples of data 206

Exercise 9 Comparing the averages of two related samples of data 206

Exercise 10 One-sample tests 206

Chapter 7 The one-way ANOVA 207

7.1 Introduction 207

7.1.1 A more complex drug experiment 207

7.1.2 ANOVA models 208

7.1.3 The one-way ANOVA 208

7.2 The One-Way ANOVA (Compare Means Menu) 215

7.2.1 Entering the data 215

7.2.2 Running the one-way ANOVA 218

7.2.3 The output 218

7.2.4 Effect size 219

7.2.5 Report of the primary analysis 222

7.2.6 The two-group case: equivalence of F and t 222

7.3 The One-Way ANOVA (GLM Menu) 223

7.3.1 Factors with fixed and random effects 223

7.3.2 The analysis of covariance (ANCOVA) 224

7.3.3 Univariate versus multivariate statistical tests 224

7.3.4 The one-way ANOVA with GLM 224

7.3.5 The GLM output 226

7.3.6 Requesting additional items 227

7.3.7 Additional output from GLM 229

7.4 Making Comparisons Among the Treatment Means 232

7.4.1 Planned and unplanned comparisons 232

7.4.2 Linear contrasts 236

7.5 Trend Analysis 247

7.5.1 Polynomials 248

7.6 Power and Effect Size in the One-Way ANOVA 249

7.6.1 How many participants shall I need? Using GPower 3 250

7.7 Alternatives to the One-Way ANOVA 252

7.7.1 The Kruskal-Wallis k-sample test 252

7.7.2 Dichotomous nominal data: the chi-square test 259

7.8 A Final Word 259

Recommended reading 260

Exercise 11 One-factor between subjects ANOVA 260

Appendix 7.4.2.4 Partition of the between groups sum of squares into the sums of squares of the contrasts in an orthogonal set 260

Appendix 7.5.1 An Illustration of trend analysis 261

Chapter 8 Between subjects factorial experiments 265

8.1 Introduction 265

8.1.1 An experiment with two treatment factors 265

8.1.2 Main effects and interactions 267

8.1.3 Profile plots 267

8.2 How the Two-Way ANOVA Works 269

8.2.1 The two-way ANOVA 269

8.2.2 Degrees of freedom 270

8.2.3 The two-way ANOVA summary table 271

8.3 The Two-Way ANOVA A With PASW 272

8.3.1 Entering the data for the factorial ANOVA 273

8.3.2 Exploring the data: boxplots 274

8.3.3 Choosing a factorial ANOVA 274

8.3.4 Output for a factorial ANOVA 276

8.3.5 Measuring effect size in the two-way ANOVA 278

8.3.6 Reporting the results of the two-way ANOVA 281

8.4 Further Analysis 282

8.4.1 The danger with multiple comparisons 282

8.4.2 Unpacking significant main effects: post hoc tests 282

8.4.3 The analysis of interactions 283

8.5 Testing For Simple Main Effects With Syntax 285

8.5.1 The syntax editor 285

8.5.2 Building syntax files automatically 286

8.5.3 Using the MANOVA command to run the univariate ANOVA 286

8.6 How Many Participants Shall I Need For My Two-Factor Experiment? 294

8.7 More Complex Experiments 294

8.7.1 Three-way interactions 295

8.7.2 The three-way ANOVA 296

8.7.3 How the three-way ANOVA works 297

8.7.4 Measures of effect size in the three-way ANOVA 299

8.7.5 How many participants shall I need? 299

8.7.6 The three-way ANOVA with PASW 299

8.7.7 Follow-up analysis following a significant three-way interaction 302

8.7.8 Using PASW syntax to test for simple interactions and simple, simple main effects 303

8.7.9 Unplanned multiple comparisons following a significant three-way interaction 306

8.8 A Final Word 309

Recommended reading 309

Exercise 12 Between subjects factorial ANOVA (two-way ANOVA) 309

Chapter 9 Within subjects experiments 310

9.1 Introduction 310

9.1.1 Rationale of a within subjects experiment 310

9.1.2 How the within subjects ANOVA works 311

9.1.3 A within subjects experiment on the effect of target shape on shooting accuracy 314

9.1.4 Order effects: counterbalancing 315

9.1.5 Assumptions underlying the within subjects ANOVA: homogeneity of covariance 315

9.2 A One-Factor Within Subjects ANOVA with PASW 317

9.2.1 Entering the data 317

9.2.2 Exploring the data: Boxplots for within subjects factors 317

9.2.3 Running the within subjects ANOVA 319

9.2.4 Output for a one-factor within subjects ANOVA 323

9.2.5 Effect size in the within subjects ANOVA 327

9.3 Power and Effect Size: How Many Participants Shall I Need? 329

9.4 Nonparametric Equivalents of the Within Subjects ANOVA 330

9.4.1 The Friedman test for ordinal data 330

9.4.2 Cochran's Q test for nominal data 333

9.5 The Two-Factor Within Subjects ANOVA 334

9.5.1 Preparing the data set 336

9.5.2 Running the two-factor within subjects ANOVA 336

9.5.3 Output for a two-factor within subjects ANOVA 339

9.5.4 Unpacking a significant interaction with multiple comparisons 343

9.6 A Final Word 345

Recommended reading 346

Exercise 13 One-factor within subjects (repeated measures) ANOVA 346

Exercise 14 Two-factor within subjects ANOVA 346

Chapter 10 Mixed factorial experiments 347

10.1 Introduction 347

10.1.1 A mixed factorial experiment 347

10.1.2 Classifying mixed factorial designs 348

10.1.3 Rationale of the mixed ANOVA 349

10.2 The Two-Factor Mixed Factorial ANOVA with PASW 351

10.2.1 Preparing the PASW data set 351

10.2.2 Exploring the results: Boxplots 352

10.2.3 Running the ANOVA 353

10.2.4 Output for the two-factor mixed ANOVA 355

10.2.5 Simple effects analysis with syntax 360

10.3 The Three-Factor Mixed ANOVA 365

10.3.1 The two three-factor designs 365

10.3.2 Two within subjects factors 366

10.3.3 Using syntax to test for simple effects 371

10.3.4 One within subjects factor and two between subjects factors: the AxBx(C) mixed factorial design 375

10.4 The Multivariate Analysis of Variance (MANOVA) 382

10.4.1 What the MANOVA does 382

10.4.2 How the MANOVA works 384

10.4.3 Assumptions of MANOVA 387

10.4.4 Application of MANOVA to the shape recognition experiment 387

10.5 A Final Word 391

Recommended reading 392

Exercise 15 Mixed ANOVA: two-factor experiment 392

Exercise 16 Mixed ANOVA: three-factor experiment 392

Chapter 11 Measuring statistical association 393

11.1 Introduction 393

11.1.1 A correlational study 394

11.1.2 Linear relationships 395

11.1.3 Error in measurement 395

11.2 The Pearson Correlation 396

11.2.1 Formula for the Pearson correlation 396

11.2.2 The range of values of the Pearson correlation 397

11.2.3 The sign of a correlation 397

11.2.4 Testing an obtained value of r for significance 398

11.2.5 A word of warning about the correlation coefficient 399

11.2.6 Effect size 399

11.3 Correlation with PASW 401

11.3.1 Preparing the PASW data set 402

11.3.2 Obtaining the scatterplot 402

11.3.3 Obtaining the Pearson correlation 403

11.3.4 Output for the Pearson correlation 404

11.4 Other Measures of Association 405

11.4.1 Spearman's rank correlation 405

11.4.2 Kendall's tau statistics 406

11.4.3 Rank correlations with PASW 406

11.5 Nominal Data 408

11.5.1 The approximate chi-square goodness-of-fit test with three or more categories 408

11.5.2 Running a chi-square goodness-of-fit test on PASW 409

11.5.3 Measuring effect size following a chi-square test of goodness-of-fit 412

11.5.4 Testing for association between two qualitative variables in a contingency table 414

11.5.5 Analysis of contingency tables with PASW 419

11.5.6 Getting help with the output 425

11.5.7 Some cautions and caveats 426

11.5.8 Other problems with traditional chi-square analyses 431

11.6 Do Doctors Agree? Cohen's Kappa 432

11.7 Partial Correlation 434

11.7.1 Correlation does not imply causation 434

11.7.2 Meaning of partial correlation 435

11.8 Correlation in Mental Testing: Reliability 437

11.8.1 Reliability and number of items: coefficient alpha 438

11.8.2 Measuring agreement among judges: the intraclass correlation 440

11.8.3 Reliability analysis with PASW 441

11.9 A Final Word 443

Recommended reading 443

Exercise 17 The Pearson correlation 443

Exercise 18 Other measures of association 443

Exercise 19 The analysis of nominal data 443

Chapter 12 Regression 444

12.1 Introduction 444

12.1.1 Simple, two-variable regression 444

12.1.2 Residuals 446

12.1.3 The least squares criterion 447

12.1.4 Partition of the sum of squares in regression 447

12.1.5 Effect size in regression 449

12.1.6 Shrinkage 450

12.1.7 Regression models 450

12.1.8 Beta-weights 451

12.1.9 Significance testing in simple regression 452

12.2 Simple Regression with PASW 453

12.2.1 Drawing scatterplots with regression lines 453

12.2.2 A problem in simple regression 455

12.2.3 Procedure for simple regression 456

12.2.4 Output for simple regression 459

12.3 Multiple Regression 464

12.3.1 The multiple correlation coefficient R 465

12.3.2 Significance testing in multiple regression 466

12.3.3 Partial and semipartial correlation 467

12.4 Multiple Regression with PASW 472

12.4.1 Simultaneous multiple regression 474

12.4.2 Stepwise multiple regression 477

12.5 Regression and Analysis of Variance 480

12.5.1 The point-biserial correlation 480

12.5.2 Regression and the one-way ANOVA for two groups 481

12.5.3 Regression and dummy coding: the two-group case 483

12.5.4 Regression and the one-way ANOVA 485

12.6 Multilevel Regression Models 489

12.7 A Final Word 489

Recommended reading 489

Exercise 20 Simple, two-variable regression 490

Exercise 21 Multiple regression 490

Chapter 13 Analyses of multiway frequency tables & multiple response sets 491

13.1 Introduction 491

13.1.1 Multiple response sets 492

13.2 Some Basics of Loglinear Modelling 492

13.2.1 Loglinear models and ANOVA models 493

13.2.2 Model-building and the hierarchical principle 494

13.2.3 The main-effects-only loglinear model and the traditional chi-square test for association 497

13.2.4 Analysis of the residuals 497

13.3 Modelling A Two-Way Contingency Table 498

13.3.1 PASW procedures for loglinear analysis 498

13.3.2 Fitting an unsaturated model 504

13.3.3 Summary 508

13.4 Modelling a Three-Way Frequency Table 508

13.4.1 Exploring the data 509

13.4.2 Loglinear analysis of the data on gender and helpfulness 510

13.4.3 The main-effects-only model and the traditional chi-square test 514

13.4.4 Collapsing a multi-way table: the requirement of conditional independence 516

13.4.5 An alternative data set for the gender and helpfulness experiment 518

13.4.6 Reporting the results of a loglinear analysis 521

13.5 Multiple Response Sets 521

13.5.1 How PASW produces multiple response profiles 522

13.6 A Final Word 530

Recommended reading 530

Exercise 22 Loglinear analysis 531

Chapter 14 Discriminant analysis and logistic regression 532

14.1 Introduction 532

14.1.1 Discriminant analysis 533

14.1.2 Types of discriminant analysis 534

14.1.3 Stepwise Discriminant analysis 534

14.1.4 Restrictive assumptions of discriminant analysis 535

14.2 Discriminant Analysis With PASW 535

14.2.1 Preparing the data set 536

14.2.2 Exploring the data 536

14.2.3 Running discriminant analysis 537

14.2.4 Output for discriminant analysis 539

14.2.5 Predicting group membership 547

14.3 Binary Logistic Regression 549

14.3.1 Logistic regression 549

14.3.2 How logistic regression works 551

14.3.3 An example of a binary logistic regression with quantitative independent variables 553

14.3.4 Binary logistic regression with categorical independent variables 562

14.4 Multinomial Logistic Regression 565

14.4.1 Running multinomial logistic regression 566

14.5 A Final Word 569

Recommended reading 570

Exercise 23 Predicting category membership: Discriminant analysis and binary logistic regression 570

Chapter 15 Latent variables: exploratory factor analysis & canonical correlation 571

15.1 Introduction 571

15.1.1 Stages in an exploratory factor analysis 573

15.1.2 The extraction of factors 574

15.1.3 The rationale of rotation 574

15.1.4 Some issues in factor analysis 574

15.1.5 Some key technical terms 575

15.2 A Factor Analysis of Data on Six Variables 576

15.2.1 Entering the data for a factor analysis 576

15.2.2 Running a factor analysis on PASW 576

15.2.3 Output for factor analysis 579

15.3 Using PASW SYNTAX to Run a Factor Analysis 590

15.3.1 Running a factor analysis with PASW syntax 590

15.3.2 Using a correlation matrix as input for factor analysis 590

15.3.3 Progressing with PASW syntax 593

15.4 Canonical Correlation 593

15.4.1 Running canomical correlation on PASW 594

15.4.2 Output for canonical correlation 595

15.5 A Final Word 600

Recommended reading 601

Exercise 24 Factor analysis 601

Appendix 602

Glossary 605

References 624

Index 626

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