Descripciones mejoradas de Syndetics:

SPSS is now PASW Statistics!

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 a full 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 familiarize the reader with inputting data and choosing statistical techniques. Please visit www.psypress.com/pasw-statistics for more details.

Tabla de contenidos provista por Syndetics

• Preface(p. xv)
• Chapter 1 Introduction(p. 1)
• 1.1 Measurements and Data(p. 1)
• 1.1.1 Variables: quantitative and qualitative(p. 1)
• 1.1.2 Levels of measurement: scale, ordinal and nominal data(p. 2)
• 1.1.3 A grey area: ratings(p. 2)
• 1.1.4 Univariate, bivariate and multivariate data sets(p. 3)
• 1.2 Experimental Versus Correlational Research(p. 3)
• 1.2.1 A simple experiment(p. 3)
• 1.2.2 A more complex experiment(p. 4)
• 1.2.3 Correlational research(p. 5)
• 1.2.4 The Pearson correlation coefficient(p. 7)
• 1.2.5 Correlation and causation(p. 8)
• 1.2.6 Quasi-experiments(p. 8)
• 1.3 Choosing A Statistical Test: Some Guidelines(p. 8)
• 1.3.1 Considerations in choosing a statistical test(p. 9)
• 1.3.2 Five common research situations(p. 9)
• 1.4 Is A Difference Significant?(p. 10)
• 1.4.1 The design of the experiment: independent versus related samples(p. 10)
• 1.4.2 Flow chart for selecting a suitable test for differences between means(p. 11)
• 1.5 Are Two Variables Associated?(p. 13)
• 1.5.1 Flow chart for selecting a suitable test for association(p. 13)
• 1.5.2 Measuring association in ordinal data(p. 14)
• 1.5.3 Measuring association in nominal data: Contingency tables(p. 14)
• 1.5.4 Multi-way contingency tables(p. 15)
• 1.6 Can We Predict a Score From Scores on Other Variables?(p. 15)
• 1.6.1 Flow chart for predicting a score or category membership(p. 15)
• 1.6.2 Simple regression(p. 16)
• 1.6.3 Multiple regression(p. 16)
• 1.6.4 Predicting category membership: Discriminant analysis and logistic regression(p. 17)
• 1.7 From Sample to Population(p. 17)
• 1.7.1 Flow chart for selecting the appropriate one-sample test(p. 17)
• 1.7.2 Goodness-of-fit: scale data(p. 18)
• 1.7.3 Goodness-of-fit: nominal data(p. 18)
• 1.7.4 Inferences about the mean of a single population(p. 18)
• 1.8 The Search For Latent Variables(p. 19)
• 1.9 Multivariate Statistics(p. 19)
• 1.10 Some Statistical Terms and Concepts(p. 20)
• 1.10.1 Description or confirmation?(p. 20)
• 1.10.2 Samples and populations(p. 20)
• 1.10.3 Parameters and statistics(p. 21)
• 1.10.4 Statistical inference(p. 21)
• 1.10.5 One-sample and two-sample tests of hypotheses about means(p. 24)
• 1.10.6 Sampling distributions(p. 25)
• 1.10.7 The standard normal distribution(p. 26)
• 1.10.8 When the population variance and standard deviation are unknown: the t distribution(p. 28)
• 1.10.9 Errors in hypothesis testing(p. 32)
• 1.11 A Final Word(p. 34)
• Recommended reading(p. 35)
• Chapter 2 Getting started with PASW Statistics 17.0(p. 36)
• 2.1 Outline of a PASW Session(p. 36)
• 2.1.1 Entering the data(p. 36)
• 2.1.2 Selecting the exploratory and statistical procedures(p. 37)
• 2.1.3 Examining the output(p. 37)
• 2.1.4 A simple experiment(p. 37)
• 2.1.5 Preparing data for PASW(p. 38)
• 2.2 Opening PASW(p. 39)
• 2.3 The PASW Statistics Data Editor(p. 40)
• 2.3.1 Working in Variable View(p. 40)
• 2.3.2 Working in Data View(p. 45)
• 2.3.3 Entering the data(p. 45)
• 2.4 A Statistical Analysis(p. 49)
• 2.4.1 An example: Computing means(p. 49)
• 2.4.2 Keeping more than one application open(p. 53)
• 2.5 Closing PASW(p. 53)
• 2.6 Resuming Work on a Saved Data Set(p. 53)
• Exercise 1 Some simple operations with PASW Statistics 17.0(p. 53)
• Exercise 2 Questionnaire data(p. 53)
• Chapter 3 Editing and manipulating files(p. 54)
• 3.1 More About the PASW Statistics Data Editor(p. 54)
• 3.1.1 Working in Variable View(p. 54)
• 3.1.2 Working in Data View(p. 61)
• 3.2 More on The PASW Statistics Viewer(p. 68)
• 3.2.1 Editing the output(p. 69)
• 3.2.2 More advanced editing(p. 70)
• 3.2.3 Tutorials in PASW(p. 74)
• 3.3 Selecting From and Manipulating Data Files(p. 74)
• 3.3.1 Selecting cases(p. 74)
• 3.3.2 Aggregating data(p. 77)
• 3.3.3 Sorting data(p. 79)
• 3.3.4 Merging files(p. 80)
• 3.3.5 Transposing the rows and columns of a data set(p. 85)
• 3.4 Importing and Exporting Data(p. 87)
• 3.4.1 Importing data from other applications(p. 87)
• 3.4.2 Copying output(p. 90)
• 3.5 Printing From PASW(p. 92)
• 3.5.1 Printing output from the Viewer(p. 92)
• Exercise 3 Merging files - Adding cases & variables(p. 97)
• Chapter 4 Exploring your data(p. 98)
• 4.1 Introduction(p. 98)
• 4.1.1 The influence of outliers and asymmetry of distribution(p. 99)
• 4.2 Some Useful Menus(p. 99)
• 4.3 Describing Data(p. 101)
• 4.3.1 Describing nominal and ordinal data(p. 101)
• 4.3.2 Describing measurements(p. 108)
• 4.4 Manipulation of the Data Set(p. 122)
• 4.4.1 Reducing and transforming data(p. 122)
• 4.4.2 The Compute procedure(p. 123)
• 4.4.3 The Recode and Visual Binning procedures(p. 129)
• Exercise 4 Correcting and preparing your data(p. 136)
• Exercise 5 Preparing your data (continued)(p. 136)
• Chapter 5 Graphs and charts(p. 137)
• 5.1 Introduction(p. 137)
• 5.1.1 Graphs and charts on PASW(p. 137)
• 5.1.2 Viewing a chart(p. 140)
• 5.1.3 Editing charts and saving templates(p. 140)
• 5.2 Bar Charts(p. 141)
• 5.2.1 Simple bar charts(p. 141)
• 5.2.2 Clustered bar charts(p. 144)
• 5.2.3 Panelled bar charts(p. 146)
• 5.2.4 3-D charts(p. 147)
• 5.2.5 Editing a bar chart(p. 149)
• 5.2.6 Chart templates(p. 151)
• 5.3 Error Bar Charts(p. 154)
• 5.4 Boxplots(p. 155)
• 5.5 Pie Charts(p. 157)
• 5.6 Line Graphs(p. 159)
• 5.7 Scatterplots and Dot Plots(p. 162)
• 5.7.1 Scatterplots(p. 162)
• 5.7.2 Dot plots(p. 164)
• 5.8 Dual Y-Axis Graphs(p. 165)
• 5.9 Histograms(p. 167)
• 5.10 Receiver-Operating-Characteristic (ROC) Curve(p. 169)
• 5.10.1 The PASW ROC curve(p. 170)
• 5.10.2 The d' statistic(p. 173)
• Exercise 6 Charts and graphs(p. 174)
• Exercise 7 Recording data; selecting cases; line graph(p. 174)
• Chapter 6 Comparing averages: Two-sample and one-sample tests(p. 175)
• 6.1 Overview(p. 175)
• 6.2 Comparing Means: The Independent-Samples T Test With PASW(p. 176)
• 6.2.1 Preparing the data file(p. 176)
• 6.2.2 Exploring the data(p. 177)
• 6.2.3 Running the t test(p. 179)
• 6.2.4 Interpreting the output(p. 181)
• 6.2.5 Two-tailed and one-tailed p-values(p. 182)
• 6.2.6 The effects of extreme scores and outliers in a small data set(p. 183)
• 6.2.7 Measuring effect size(p. 183)
• 6.2.8 Reporting the results of a statistical test(p. 185)
• 6.3 The Related-Samples (or Paired-Samples) T Test With PASW(p. 186)
• 6.3.1 Preparing the data file(p. 187)
• 6.3.2 Exploring the data(p. 187)
• 6.3.3 Running the t test(p. 188)
• 6.3.4 Interpreting the output(p. 189)
• 6.3.5 Measuring effect size(p. 190)
• 6.3.6 Reporting the results of the test(p. 190)
• 6.3.7 A one-sample test(p. 191)
• 6.4 The Mann-Whitney U Test(p. 191)
• 6.4.1 Nonparametric test in PASW(p. 191)
• 6.4.2 Independent samples: The Mann-Whitney U test(p. 192)
• 6.4.3 Output for the Mann-Whitney U test(p. 194)
• 6.4.4 Effect size(p. 194)
• 6.4.5 The report(p. 195)
• 6.5 The Wilcoxon Matched-Pairs Test(p. 196)
• 6.5.1 The Wilcoxon matched-pairs tests in PASW(p. 196)
• 6.5.2 The output(p. 197)
• 6.5.3 Effect size(p. 198)
• 6.5.4 The report(p. 198)
• 6.6 The Sign And Binomial Tests(p. 198)
• 6.6.1 The sign test in PASW(p. 199)
• 6.6.2 Bernoulli trials: the binomial test(p. 201)
• 6.7 Effect Size, Power and the Number of Participants(p. 204)
• 6.7.1 How many participants shall I need in my experiment?(p. 204)
• 6.8 A Final Word(p. 206)
• Exercise 8 Comparing the averages of two independent samples of data(p. 206)
• Exercise 9 Comparing the averages of two related samples of data(p. 206)
• Exercise 10 One-sample tests(p. 206)
• Chapter 7 The one-way ANOVA(p. 207)
• 7.1 Introduction(p. 207)
• 7.1.1 A more complex drug experiment(p. 207)
• 7.1.2 ANOVA models(p. 208)
• 7.1.3 The one-way ANOVA(p. 208)
• 7.2 The One-Way ANOVA (Compare Means Menu)(p. 215)
• 7.2.1 Entering the data(p. 215)
• 7.2.2 Running the one-way ANOVA(p. 218)
• 7.2.3 The output(p. 218)
• 7.2.4 Effect size(p. 219)
• 7.2.5 Report of the primary analysis(p. 222)
• 7.2.6 The two-group case: equivalence of F and t(p. 222)
• 7.3 The One-Way ANOVA (GLM Menu)(p. 223)
• 7.3.1 Factors with fixed and random effects(p. 223)
• 7.3.2 The analysis of covariance (ANCOVA)(p. 224)
• 7.3.3 Univariate versus multivariate statistical tests(p. 224)
• 7.3.4 The one-way ANOVA with GLM(p. 224)
• 7.3.5 The GLM output(p. 226)
• 7.3.6 Requesting additional items(p. 227)
• 7.3.7 Additional output from GLM(p. 229)
• 7.4 Making Comparisons Among the Treatment Means(p. 232)
• 7.4.1 Planned and unplanned comparisons(p. 232)
• 7.4.2 Linear contrasts(p. 236)
• 7.5 Trend Analysis(p. 247)
• 7.5.1 Polynomials(p. 248)
• 7.6 Power and Effect Size in the One-Way ANOVA(p. 249)
• 7.6.1 How many participants shall I need? Using GPower 3(p. 250)
• 7.7 Alternatives to the One-Way ANOVA(p. 252)
• 7.7.1 The Kruskal-Wallis k-sample test(p. 252)
• 7.7.2 Dichotomous nominal data: the chi-square test(p. 259)
• 7.8 A Final Word(p. 259)
• Recommended reading(p. 260)
• Exercise 11 One-factor between subjects ANOVA(p. 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(p. 260)
• Appendix 7.5.1 An Illustration of trend analysis(p. 261)
• Chapter 8 Between subjects factorial experiments(p. 265)
• 8.1 Introduction(p. 265)
• 8.1.1 An experiment with two treatment factors(p. 265)
• 8.1.2 Main effects and interactions(p. 267)
• 8.1.3 Profile plots(p. 267)
• 8.2 How the Two-Way ANOVA Works(p. 269)
• 8.2.1 The two-way ANOVA(p. 269)
• 8.2.2 Degrees of freedom(p. 270)
• 8.2.3 The two-way ANOVA summary table(p. 271)
• 8.3 The Two-Way ANOVA A With PASW(p. 272)
• 8.3.1 Entering the data for the factorial ANOVA(p. 273)
• 8.3.2 Exploring the data: boxplots(p. 274)
• 8.3.3 Choosing a factorial ANOVA(p. 274)
• 8.3.4 Output for a factorial ANOVA(p. 276)
• 8.3.5 Measuring effect size in the two-way ANOVA(p. 278)
• 8.3.6 Reporting the results of the two-way ANOVA(p. 281)
• 8.4 Further Analysis(p. 282)
• 8.4.1 The danger with multiple comparisons(p. 282)
• 8.4.2 Unpacking significant main effects: post hoc tests(p. 282)
• 8.4.3 The analysis of interactions(p. 283)
• 8.5 Testing For Simple Main Effects With Syntax(p. 285)
• 8.5.1 The syntax editor(p. 285)
• 8.5.2 Building syntax files automatically(p. 286)
• 8.5.3 Using the MANOVA command to run the univariate ANOVA(p. 286)
• 8.6 How Many Participants Shall I Need For My Two-Factor Experiment?(p. 294)
• 8.7 More Complex Experiments(p. 294)
• 8.7.1 Three-way interactions(p. 295)
• 8.7.2 The three-way ANOVA(p. 296)
• 8.7.3 How the three-way ANOVA works(p. 297)
• 8.7.4 Measures of effect size in the three-way ANOVA(p. 299)
• 8.7.5 How many participants shall I need?(p. 299)
• 8.7.6 The three-way ANOVA with PASW(p. 299)
• 8.7.7 Follow-up analysis following a significant three-way interaction(p. 302)
• 8.7.8 Using PASW syntax to test for simple interactions and simple, simple main effects(p. 303)
• 8.7.9 Unplanned multiple comparisons following a significant three-way interaction(p. 306)
• 8.8 A Final Word(p. 309)
• Recommended reading(p. 309)
• Exercise 12 Between subjects factorial ANOVA (two-way ANOVA)(p. 309)
• Chapter 9 Within subjects experiments(p. 310)
• 9.1 Introduction(p. 310)
• 9.1.1 Rationale of a within subjects experiment(p. 310)
• 9.1.2 How the within subjects ANOVA works(p. 311)
• 9.1.3 A within subjects experiment on the effect of target shape on shooting accuracy(p. 314)
• 9.1.4 Order effects: counterbalancing(p. 315)
• 9.1.5 Assumptions underlying the within subjects ANOVA: homogeneity of covariance(p. 315)
• 9.2 A One-Factor Within Subjects ANOVA with PASW(p. 317)
• 9.2.1 Entering the data(p. 317)
• 9.2.2 Exploring the data: Boxplots for within subjects factors(p. 317)
• 9.2.3 Running the within subjects ANOVA(p. 319)
• 9.2.4 Output for a one-factor within subjects ANOVA(p. 323)
• 9.2.5 Effect size in the within subjects ANOVA(p. 327)
• 9.3 Power and Effect Size: How Many Participants Shall I Need?(p. 329)
• 9.4 Nonparametric Equivalents of the Within Subjects ANOVA(p. 330)
• 9.4.1 The Friedman test for ordinal data(p. 330)
• 9.4.2 Cochran's Q test for nominal data(p. 333)
• 9.5 The Two-Factor Within Subjects ANOVA(p. 334)
• 9.5.1 Preparing the data set(p. 336)
• 9.5.2 Running the two-factor within subjects ANOVA(p. 336)
• 9.5.3 Output for a two-factor within subjects ANOVA(p. 339)
• 9.5.4 Unpacking a significant interaction with multiple comparisons(p. 343)
• 9.6 A Final Word(p. 345)
• Recommended reading(p. 346)
• Exercise 13 One-factor within subjects (repeated measures) ANOVA(p. 346)
• Exercise 14 Two-factor within subjects ANOVA(p. 346)
• Chapter 10 Mixed factorial experiments(p. 347)
• 10.1 Introduction(p. 347)
• 10.1.1 A mixed factorial experiment(p. 347)
• 10.1.2 Classifying mixed factorial designs(p. 348)
• 10.1.3 Rationale of the mixed ANOVA(p. 349)
• 10.2 The Two-Factor Mixed Factorial ANOVA with PASW(p. 351)
• 10.2.1 Preparing the PASW data set(p. 351)
• 10.2.2 Exploring the results: Boxplots(p. 352)
• 10.2.3 Running the ANOVA(p. 353)
• 10.2.4 Output for the two-factor mixed ANOVA(p. 355)
• 10.2.5 Simple effects analysis with syntax(p. 360)
• 10.3 The Three-Factor Mixed ANOVA(p. 365)
• 10.3.1 The two three-factor designs(p. 365)
• 10.3.2 Two within subjects factors(p. 366)
• 10.3.3 Using syntax to test for simple effects(p. 371)
• 10.3.4 One within subjects factor and two between subjects factors: the AxBx(C) mixed factorial design(p. 375)
• 10.4 The Multivariate Analysis of Variance (MANOVA)(p. 382)
• 10.4.1 What the MANOVA does(p. 382)
• 10.4.2 How the MANOVA works(p. 384)
• 10.4.3 Assumptions of MANOVA(p. 387)
• 10.4.4 Application of MANOVA to the shape recognition experiment(p. 387)
• 10.5 A Final Word(p. 391)
• Recommended reading(p. 392)
• Exercise 15 Mixed ANOVA: two-factor experiment(p. 392)
• Exercise 16 Mixed ANOVA: three-factor experiment(p. 392)
• Chapter 11 Measuring statistical association(p. 393)
• 11.1 Introduction(p. 393)
• 11.1.1 A correlational study(p. 394)
• 11.1.2 Linear relationships(p. 395)
• 11.1.3 Error in measurement(p. 395)
• 11.2 The Pearson Correlation(p. 396)
• 11.2.1 Formula for the Pearson correlation(p. 396)
• 11.2.2 The range of values of the Pearson correlation(p. 397)
• 11.2.3 The sign of a correlation(p. 397)
• 11.2.4 Testing an obtained value of r for significance(p. 398)
• 11.2.5 A word of warning about the correlation coefficient(p. 399)
• 11.2.6 Effect size(p. 399)
• 11.3 Correlation with PASW(p. 401)
• 11.3.1 Preparing the PASW data set(p. 402)
• 11.3.2 Obtaining the scatterplot(p. 402)
• 11.3.3 Obtaining the Pearson correlation(p. 403)
• 11.3.4 Output for the Pearson correlation(p. 404)
• 11.4 Other Measures of Association(p. 405)
• 11.4.1 Spearman's rank correlation(p. 405)
• 11.4.2 Kendall's tau statistics(p. 406)
• 11.4.3 Rank correlations with PASW(p. 406)
• 11.5 Nominal Data(p. 408)
• 11.5.1 The approximate chi-square goodness-of-fit test with three or more categories(p. 408)
• 11.5.2 Running a chi-square goodness-of-fit test on PASW(p. 409)
• 11.5.3 Measuring effect size following a chi-square test of goodness-of-fit(p. 412)
• 11.5.4 Testing for association between two qualitative variables in a contingency table(p. 414)
• 11.5.5 Analysis of contingency tables with PASW(p. 419)
• 11.5.6 Getting help with the output(p. 425)
• 11.5.7 Some cautions and caveats(p. 426)
• 11.5.8 Other problems with traditional chi-square analyses(p. 431)
• 11.6 Do Doctors Agree? Cohen's Kappa(p. 432)
• 11.7 Partial Correlation(p. 434)
• 11.7.1 Correlation does not imply causation(p. 434)
• 11.7.2 Meaning of partial correlation(p. 435)
• 11.8 Correlation in Mental Testing: Reliability(p. 437)
• 11.8.1 Reliability and number of items: coefficient alpha(p. 438)
• 11.8.2 Measuring agreement among judges: the intraclass correlation(p. 440)
• 11.8.3 Reliability analysis with PASW(p. 441)
• 11.9 A Final Word(p. 443)
• Recommended reading(p. 443)
• Exercise 17 The Pearson correlation(p. 443)
• Exercise 18 Other measures of association(p. 443)
• Exercise 19 The analysis of nominal data(p. 443)
• Chapter 12 Regression(p. 444)
• 12.1 Introduction(p. 444)
• 12.1.1 Simple, two-variable regression(p. 444)
• 12.1.2 Residuals(p. 446)
• 12.1.3 The least squares criterion(p. 447)
• 12.1.4 Partition of the sum of squares in regression(p. 447)
• 12.1.5 Effect size in regression(p. 449)
• 12.1.6 Shrinkage(p. 450)
• 12.1.7 Regression models(p. 450)
• 12.1.8 Beta-weights(p. 451)
• 12.1.9 Significance testing in simple regression(p. 452)
• 12.2 Simple Regression with PASW(p. 453)
• 12.2.1 Drawing scatterplots with regression lines(p. 453)
• 12.2.2 A problem in simple regression(p. 455)
• 12.2.3 Procedure for simple regression(p. 456)
• 12.2.4 Output for simple regression(p. 459)
• 12.3 Multiple Regression(p. 464)
• 12.3.1 The multiple correlation coefficient R(p. 465)
• 12.3.2 Significance testing in multiple regression(p. 466)
• 12.3.3 Partial and semipartial correlation(p. 467)
• 12.4 Multiple Regression with PASW(p. 472)
• 12.4.1 Simultaneous multiple regression(p. 474)
• 12.4.2 Stepwise multiple regression(p. 477)
• 12.5 Regression and Analysis of Variance(p. 480)
• 12.5.1 The point-biserial correlation(p. 480)
• 12.5.2 Regression and the one-way ANOVA for two groups(p. 481)
• 12.5.3 Regression and dummy coding: the two-group case(p. 483)
• 12.5.4 Regression and the one-way ANOVA(p. 485)
• 12.6 Multilevel Regression Models(p. 489)
• 12.7 A Final Word(p. 489)
• Recommended reading(p. 489)
• Exercise 20 Simple, two-variable regression(p. 490)
• Exercise 21 Multiple regression(p. 490)
• Chapter 13 Analyses of multiway frequency tables & multiple response sets(p. 491)
• 13.1 Introduction(p. 491)
• 13.1.1 Multiple response sets(p. 492)
• 13.2 Some Basics of Loglinear Modelling(p. 492)
• 13.2.1 Loglinear models and ANOVA models(p. 493)
• 13.2.2 Model-building and the hierarchical principle(p. 494)
• 13.2.3 The main-effects-only loglinear model and the traditional chi-square test for association(p. 497)
• 13.2.4 Analysis of the residuals(p. 497)
• 13.3 Modelling A Two-Way Contingency Table(p. 498)
• 13.3.1 PASW procedures for loglinear analysis(p. 498)
• 13.3.2 Fitting an unsaturated model(p. 504)
• 13.3.3 Summary(p. 508)
• 13.4 Modelling a Three-Way Frequency Table(p. 508)
• 13.4.1 Exploring the data(p. 509)
• 13.4.2 Loglinear analysis of the data on gender and helpfulness(p. 510)
• 13.4.3 The main-effects-only model and the traditional chi-square test(p. 514)
• 13.4.4 Collapsing a multi-way table: the requirement of conditional independence(p. 516)
• 13.4.5 An alternative data set for the gender and helpfulness experiment(p. 518)
• 13.4.6 Reporting the results of a loglinear analysis(p. 521)
• 13.5 Multiple Response Sets(p. 521)
• 13.5.1 How PASW produces multiple response profiles(p. 522)
• 13.6 A Final Word(p. 530)
• Recommended reading(p. 530)
• Exercise 22 Loglinear analysis(p. 531)
• Chapter 14 Discriminant analysis and logistic regression(p. 532)
• 14.1 Introduction(p. 532)
• 14.1.1 Discriminant analysis(p. 533)
• 14.1.2 Types of discriminant analysis(p. 534)
• 14.1.3 Stepwise Discriminant analysis(p. 534)
• 14.1.4 Restrictive assumptions of discriminant analysis(p. 535)
• 14.2 Discriminant Analysis With PASW(p. 535)
• 14.2.1 Preparing the data set(p. 536)
• 14.2.2 Exploring the data(p. 536)
• 14.2.3 Running discriminant analysis(p. 537)
• 14.2.4 Output for discriminant analysis(p. 539)
• 14.2.5 Predicting group membership(p. 547)
• 14.3 Binary Logistic Regression(p. 549)
• 14.3.1 Logistic regression(p. 549)
• 14.3.2 How logistic regression works(p. 551)
• 14.3.3 An example of a binary logistic regression with quantitative independent variables(p. 553)
• 14.3.4 Binary logistic regression with categorical independent variables(p. 562)
• 14.4 Multinomial Logistic Regression(p. 565)
• 14.4.1 Running multinomial logistic regression(p. 566)
• 14.5 A Final Word(p. 569)
• Recommended reading(p. 570)
• Exercise 23 Predicting category membership: Discriminant analysis and binary logistic regression(p. 570)
• Chapter 15 Latent variables: exploratory factor analysis & canonical correlation(p. 571)
• 15.1 Introduction(p. 571)
• 15.1.1 Stages in an exploratory factor analysis(p. 573)
• 15.1.2 The extraction of factors(p. 574)
• 15.1.3 The rationale of rotation(p. 574)
• 15.1.4 Some issues in factor analysis(p. 574)
• 15.1.5 Some key technical terms(p. 575)
• 15.2 A Factor Analysis of Data on Six Variables(p. 576)
• 15.2.1 Entering the data for a factor analysis(p. 576)
• 15.2.2 Running a factor analysis on PASW(p. 576)
• 15.2.3 Output for factor analysis(p. 579)
• 15.3 Using PASW SYNTAX to Run a Factor Analysis(p. 590)
• 15.3.1 Running a factor analysis with PASW syntax(p. 590)
• 15.3.2 Using a correlation matrix as input for factor analysis(p. 590)
• 15.3.3 Progressing with PASW syntax(p. 593)
• 15.4 Canonical Correlation(p. 593)
• 15.4.1 Running canomical correlation on PASW(p. 594)
• 15.4.2 Output for canonical correlation(p. 595)
• 15.5 A Final Word(p. 600)
• Recommended reading(p. 601)
• Exercise 24 Factor analysis(p. 601)
• Appendix(p. 602)
• Glossary(p. 605)
• References(p. 624)
• Index(p. 626)

Notas de autor provistas por Syndetics

Paul Kinnear, formerly Head of the School of Psychology, University of Aberdeen

Colin Gray, Senior Lecturer, School of Psychology, College of Life Sciences and Medicine, University of Aberdeen