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Discovering statistics using SPSS: and sex and drugs and rock'n'roll

Author: Field, Andy Publisher: Sage, 2009.Edition: 3rd ed.Language: EnglishDescription: 821 p. : Graphs/Ill./Photos ; 27 cm.ISBN: 9781847879073Type of document: BookNote: Doriot: for 2014-2015 courses Bibliography/Index: Includes bibliographical references and index and glossary
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Doriot: for 2014-2015 courses

Includes bibliographical references and index and glossary

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Discovering Statistics Using SPSS (and Sex and Drugs and Rock 'n' Roll) Contents Preface How to use this book Acknowledgements Dedication Symbols used in this book Some maths revision xix xxiv xxviii xxx xxxi xxxiii 1 Why is my evil lecturer forcing me to learn statistics? 1.1. 1.2. 1.3. 1.4. 1.5. 1 1 2 3 3 4 7 7 10 11 12 12 13 17 18 18 20 23 24 26 28 28 29 29 30 What will this chapter tell me? 1 What the hell am I doing here? I don't belong here 1 1.2.1. The research process 1 Initial observation: finding something that needs explaining 1 Generating theories and testing them 1 Data collection 1: what to measure 1 1.5.1. Variables 1 1.5.2. Measurement error 1 1.5.3. Validity and reliability 1 1.6. Data collection 2: how to measure 1 1.6.1. Correlational research methods 1 1.6.2. Experimental research methods 1 1.6.3. Randomization 1 1.7. Analysing data 1 1.7.1. Frequency distributions 1 1.7.2. The centre of a distribution 1 1.7.3. The dispersion in a distribution 1 1.7.4. Using a frequency distribution to go beyond the data 1 1.7.5. Fitting statistical models to the data 1 What have I discovered about statistics? 1 Key terms that Ive discovered Smart Alex's stats quiz Further reading Interesting real research 2 Everything you ever wanted to know about statistics (well, sort of) 2.1. 2.2. 2.3. 2.4. What will this chapter tell me? 1 Building statistical models 1 Populations and samples 1 Simple statistical models 1 2.4.1. The mean: a very simple statistical model 1 2.4.2. Assessing the fit of the mean: sums of squares, variance and standard deviations 1 2.4.3. Expressing the mean as a model 2 31 31 32 34 35 35 35 38 40 40 43 48 52 54 55 56 58 59 59 59 60 60 2.5. Coing beyond the data 1 2.5.1. The standard error 1 2.5.2. Confidence intervals 1 2.6. Using statistical models to test research questions 1 2.6.1. Test statistics 1 2.6.2. One- and two-tailed tests 1 2.6.3. Type I and Type II errors 1 2.6.4. Effect sizes 2 2.6.5. Statistical power 2 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's stats quiz Further reading Interesting real research 3 The SPSS environment 3.1. 3.2. 3.3. 3.4. What will this chapter tell me? 1 Versions of SPSS 1 Getting started 1 The data editor 1 3.4.1. Entering data into the data editor 1 3.4.2. The 'Variable View' 1 3.4.3. Missing values 1 61 61 62 62 63 69 70 77 78 81 82 83 84 85 85 85 86 86 3.5. 3.6. 3.7. 3.8. 3.9. The SPSS viewer 1 The SPSS SmartViewer 1 The syntax window 3 Saving files 1 Retrieving a file 1 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorials 4 Exploring data with graphs 4.1. 4.2. 87 87 88 88 90 What will this chapter tell me? 1 The art of presenting data 1 4.2.1. What makes a good graph? 1 4.2.2. Lies, damned lies, and ... erm graphs 1 4.3. The SPSS Chart Builder 1 4.4. Histograms: a good way to spot obvious problems 1 4.5. Boxplots (box--whisker diagrams) 4.6. Graphing means: bar charts and error bars1 4.6.1. Simple bar charts for independent means 1 4.6.2. Clustered bar charts for independent means 1 4.6.3. Simple bar charts for related means 1 4.6.4. Clustered 91 93 99 103 105 107 109 111 113 115 116 117 119 121 123 125 126 126 129 130 130 130 130 130 bar charts for related means 1 4.6.5. Clustered bar charts for `mixed' designs 1 4.7. Line charts 1 4.8. Graphing relationships: the scatterplot 1 4.8.1. Simple scatterplot 1 4.8.2. Grouped scatterplot 1 4.8.3. Simple and grouped 3-D scatterplots 1 4.8.4. Matrix scatterplot 1 4.8.5. Simple dot plot or density plot 1 4.8.6. Drop-line graph 1 4.9. Editing graphs 1 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorial Interesting real research 5 Exploring assumptions 5.1. What will this chapter tell me? 1 5.2. What are assumptions? 1 5.3. Assumptions of parametric data 1 5.4. The assumption of normality 1 5.4.1. Oh no, it's that pesky frequency distribution again: checking normality visually 1 5.4.2. Quantifying 131 131 132 132 133 normality with numbers 1 5.4.3. Exploring groups of data 1 5.5. Testing whether a distribution is normal 1 5.5.1. Doing the Kolmogorov-Smirnov test on SPSS 1 5.5.2. Output from the explore procedure 1 5.5.3. Reporting the K-S test 1 134 136 140 144 145 146 148 5.6. Testing for homogeneity of variance 1 5.6.1. Levene's test 1 5.6.2. Reporting Levene's test 1 149 150 152 5.7. Correcting problems in the data 2 5.7.1. Dealing with outliers 2 5.7.2. Dealing with non-normality and unequal variances 2 5.7.3. Transforming the data using SPSS 2 5.7.4. When it all goes horribly wrong 3 153 153 153 156 162 164 164 165 165 165 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's tasks Online tutorial Further reading 6 Correlation 6.1. What will this chapter tell me? 1 6.2. Looking at relationships 1 6.3. How do we measure relationships? 1 6.3.1. A detour into the murky world of covariance 1 6.3.2. Standardization and the correlation coefficient 1 6.3.3. The significance of the correlation coefficient 3 6.3.4. Confidence intervals for r 3 6.3.5. A word of warning about interpretation: causality 1 6.4. Data entry for correlation analysis using SPSS 1 6.5. Bivariate correlation 1 6.5.1. General procedure for running correlations on SPSS 1 6.5.2. Pearson's correlation coefficient 1 6.5.3. Spearman's correlation coefficient 1 6.5.4. Kendall's tau (non-parametric) 1 6.5.5. Biserial and point­biserial correlations 3 166 166 167 167 167 169 171 172 173 174 175 175 177 179 181 182 6.6. Partial correlation 2 6.6.1. The theory behind part and partial correlation 2 6.6.2. Partial correlation using SPSS 2 6.6.3. Semi-partial (or part) correlations 2 6.7. Comparing correlations 3 6.7.1. Comparing independent rs 3 6.7.2. Comparing dependent rs 3 186 186 188 190 191 191 191 6.8. Calculating the effect size 1 6.9. How to report correlation coefficents 1 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorial Interesting real research 192 193 195 195 195 196 196 196 7 Regression 7.1. What will this chapter tell me? 1 7.2. An introduction to regression 1 7.2.1. Some important information about straight lines 1 7.2.2. The method of least squares 1 7.2.3. Assessing the goodness of fit: sums of squares, R and R2 1 7.2.4. Assessing individual predictors 1 7.3. Doing simple regression on SPSS 1 7.4. Interpreting a simple regression 1 7.4.1. Overall fit of the model 1 7.4.2. Model parameters 1 7.4.3. Using the model 1 197 197 198 199 200 201 204 205 206 206 207 7.5. Multiple regression: the basics 2 7.5.1. An example of a multiple regression model 2 7.5.2. Sums of squares, R and R2 2 7.5.3. Methods of regression 2 7.6. How accurate is my regression model? 2 208 209 210 211 212 214 7.6.1. Assessing the regression model I: diagnostics 2 7.6.2. Assessing the regression model II: generalization 2 214 220 225 225 225 227 229 230 231 233 233 234 237 241 241 244 247 251 252 253 253 256 261 261 262 263 263 263 7.7. How to do multiple regression using SPSS 2 7.7.1. Some things to think about before the analysis 2 7.7.2. Main options 2 7.7.3. Statistics 2 7.7.4. Regression plots 2 7.7.5. Saving regression diagnostics 2 7.7.6. Further options 2 7.8. Interpreting multiple regression 2 7.8.1. Descriptives 2 7.8.2. Summary of model 2 7.8.3. Model parameters 2 7.8.4. Excluded variables 2 7.8.5. Assessing the assumption of no multicollinearity 2 7.8.6. Casewise diagnostics 2 7.8.7. Checking assumptions 7.9. What if I violate an assumption? 2 7.10. How to report multiple regression 2 7.11. Categorical predictors and multiple regression 3 7.11.1. Dummy coding 3 7.11.2. SPSS output for dummy variables 3 What have I discovered about statistics? 3 Key terms that I've discovered 1 Smart Alex's tasks Further reading Online tutorial Interesting real research 8 Logistic regression 8.1. 8.2. 8.3. 264 264 265 265 267 268 269 270 271 273 273 273 274 276 277 278 279 279 280 281 282 What will this chapter tell me? 1 Background to logistic regression 1 What are the principles behind logistic regression? 3 8.3.1. Assessing the model: the log-likelihood statistic 3 8.3.2. Assessing the model: R and R 2 3 8.3.3. Assessing the contribution of predictors: the Wald statistic 2 8.3.4. The odds ratio: Exp(B) 3 8.3.5. Methods of logistic regression 2 8.4. Assumptions and things that can go wrong 4 8.4.1. Assumptions 2 8.4.2. Incomplete information from the predictors 4 8.4.3. Complete separation 4 8.4.4. Overdispersion 4 8.5. Binary logistic regression: an example that will make you feel eel 2 8.5.1. The main analysis 2 8.5.2. Method of regression 2 8.5.3. Categorical predictors 2 8.5.4. Obtaining residuals 2 8.5.5. Further options 2 8.6. Interpreting logistic regression 2 8.6.1. The initial model 2 8.6.2. Step 1: intervention 3 8.6.3. Listing predicted probabilities 2 8.6.4. Interpreting residuals 2 8.6.5. Calculating the effect size 2 8.7. 8.8. 282 284 291 292 294 294 294 296 297 300 301 304 305 306 312 313 313 313 315 315 315 How to report logistic regression 2 Testing assumptions: another example 2 8.8.1. Testing for linearity of the logit 3 8.8.2. Testing for multicollinearity 3 8.9. Predicting several categories: multinomial logistic regression 3 8.9.1. Running multinomial logistic regression in SPSS 3 8.9.2. Statistics 3 8.9.3. Other options 3 8.9.4. Interpreting the multinomial logistic regression output 3 8.9.5. Reporting the results What have I discovered about statistics? 1 Key terms thatI've discovered Smart Alex's tasks Further reading Online tutorial Interesting real research 9 Comparing two means 9.1. What will this chapter tell me? 1 9.2. Looking at differences 1 9.2.1. A problem with error bar graphs of repeated-measures designs 1 9.2.2. Step 1: calculate the mean for each participant 2 9.2.3. Step 2: calculate the grand mean 2 9.2.4. Step 3: calculate the adjustment factor 2 9.2.5. Step 4: create adjusted values for each variable 2 316 316 317 317 320 320 322 323 324 325 326 326 327 327 329 329 330 332 333 334 334 337 339 341 341 342 342 344 9.3. The t-test 1 9.3.1. Rationale for the t-test 1 9.3.2. Assumptions of the t-test 1 9.4. The dependent t-test 1 9.4.1. Sampling distributions and the standard error 1 9.4.2. The dependent t-test equation explained 1 9.4.3. The dependent t-test and the assumption of normality 1 9.4.4. Dependent t-tests using SPSS 1 9.4.5. Output from the dependent t-test 1 9.4.6. Calculating the effect size 2 9.4.7. Reporting the dependent t-test 1 9.5. The independent t-test 1 9.5.1. The independent t-test equation explained 1 9.5.2. The independent t-test using SPSS 1 9.5.3. Output from the independent t-test 1 9.5.4. Calculating the effect size 2 9.5.5. Reporting the independent t-test 1 9.6. Between groups or repeated measures? 1 9.7. The t-test as a general linear model 2 9.8. What if my data are not normally distributed? 2 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's task Further reading Online tutorial Interesting real research 345 345 346 346 346 346 10 Comparing several means: ANOVA (GLM 1) 10.1. What will this chapter tell me? 1 10.2, The theory behind ANOVA 2 10.2.1. Inflated error rates 2 10.2.2. Interpreting F 2 10.2.3. ANOVA as regression 2 10.2.4. Logic of the F-ratio 2 10.2.5. Total sum of squares (SST) 2 10.2.6. Model sum of squares (SSM) 2 10.2.7. Residual sum of squares (SSR) 2 10.2.8. Mean squares 2 10.2.9. The F-ratio 2 10.2.10. Assumptions of ANOVA 3 10.2.11. Planned contrasts 2 10.2.12. Post hoc procedures 2 347 347 348 348 349 349 354 356 356 357 358 358 359 360 372 375 376 378 379 381 381 384 385 389 390 391 392 392 393 394 394 394 10.1 Running one-way ANOVA on SPSS 2 10.3.1. Planned comparisons using SPSS 2 10.3.2. Post hoc tests in SPSS 2 10.3.3. Options 2 10.4. Output from one-way ANOVA 2 10.4.1. Output for the main analysis 2 10.4.2. Output for planned comparisons 2 10.4.3. Output for post hoc tests 2 10.5. Calculating the effect size 2 10.6. Reporting results from one-way independent ANOVA 2 10.7. Violations of assumptions in one-way independent ANOVA 2 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorials Interesting real research 11 Analysis of covariance, ANCOVA (GLM 2) 11.1. What will this chapter tell me? 2 11.2. What is ANCOVA? 2 11.3. Assumptions and issues in ANCOVA 3 11.3.1. Independence of the covariate and treatment effect 3 11.3.2. Homogeneity of regression slopes 3 395 395 396 397 397 399 399 399 400 11.4. Conducting ANCOVA on SPSS 2 11.4.1. Inputting data 1 11.4.2. Initial considerations: testing the independence of the independent variable and covariate 2 11.4.3. The main analysis 2 11.4.4. Contrasts and other options 2 401 401 404 404 405 407 408 408 413 415 417 418 418 419 419 420 420 420 11.5. Interpreting the output from ANCOVA 2 11.5.1. What happens when the covariate is excluded? 2 11.5.2. The main analysis 2 11.5.3. 11.5.4. Contrasts 2 Interpreting the covariate 2 11.6. ANCOVA run as a multiple regression 2 11.7. Testing the assumption of homogeneity of regression slopes 3 11.8. Calculating the effect size 2 11.9. Reporting results 2 11.10, What to do when assumptions are violated in ANCOVA 3 What have I discovered about statistics? 2 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorials Interesting real research 12 Factorial ANOVA (GLM 3) 12.1. What will this chapter tell me? 2 12.2. Theory of factorial ANOVA (between-groups) 2 12.2.1.Factorial designs 2 12.2.2. An example with two independent variables 2 12.2.3. Total sums of squares (SST)2 12.2.4. The model sum of squares (SSM) 2 12.2.5. The residual sum of squares (SSR) 2 12.2.6. The F-ratios 2 421 421 422 422 423 424 426 428 429 430 430 432 432 434 434 435 435 436 436 439 440 441 443 446 448 450 454 454 455 455 12.3. Factorial ANOVA using SPSS 2 12.3.1. Entering the data and accessing the main dialog box 2 12.3.2. Graphing interactions 2 12.3.3. Contrasts 2 12.3.4. Post hoc tests 2 12.3.5. Options 2 12.4. Output from factorial ANOVA 2 12.4.1. Output for the preliminary analysis 2 12.4.2. Levene's test 2 12.4.3. The main ANOVA table 2 12.4.4. Contrasts 2 12.4.5. Simple effects analysis3 12.4.6. Post hoc analysis 2 12.5. Interpreting interaction graphs 2 12.6. Calculating effect sizes 3 12.7. Reporting the results of two-way ANOVA 2 12.8. Factorial ANOVA as regression 3 12.9. What to do when assumptions are violated in factorial ANOVA 3 What have I discovered about statistics?2 Key terms thatI I've discovered Smart Alex's tasks Further reading Online tutorials Interesting real research 456 456 456 13 Repeated-measures designs (GLM 4) 13.1. What will this chapter tell me? 2 13.2. Introduction to repeated-measures designs 2 13.2.1. The assumption of sphericity 2 13.2.2. How is sphericity measured? 2 13.2.3. Assessing the severity of departures from sphericity 2 13.2.4. What is the effect of violating the assumption of sphericity? 2 13.2.5. What do you do if you violate sphericity? 2 457 457 458 459 459 460 460 461 462 464 465 466 467 467 467 468 468 468 471 471 474 474 474 475 477 478 479 481 482 484 488 488 490 491 492 492 493 495 496 498 501 502 503 503 504 13.3. Theory of one-way repeated-measures ANOVA 2 13.3.1. The total sum of squares (SST) 2 13.3.2. The within-participant (SSM) 2 13.3.3. The model sum of squares (SSM) 2 13.3.4. The residual sum of squares (SSR) 2 13.3.5. The mean squares 2 13.3.6. The F-ratio 2 13.3.7. The between-participant sum of squares 2 13.4. One-way repeated-measures ANOVA using SPSS 2 13.4.1. The main analysis 2 13.4.2. Defining contrasts for repeated-measures 2 13.4.3. Post hoc tests and additional options 3 13.5. Output for one-way repeated-measures ANOVA 2 13.5.1. Descriptives and other diagnostics 1 13.5.2. Assessing and correcting for sphericity: Mauchly's test 2 13.5.3. The main ANOVA 2 13.5.4. Contrasts 2 13.5.5. Post hoc tests 2 13.6. Effect sizes for repeated-measures ANOVA 3 13.7. Reporting one-way repeated-measures ANOVA 2 13.8. Repeated-measures with several independent variables 2 13.8.1. The main analysis 2 13.8.2. Contrasts 2 13.8.3. Simple effects analysis 3 13.8.4. Graphing interactions2 13.8.5. Other options 2 13.9. Output for factorial repeated-measures ANOVA 2 13.9.1. Descriptives and main analysis 2 13.9.2. The effect of drink 2 13.9.3. The effect of imagery 2 13.9.4. The interaction effect (drink x imagery) 2 13.9.5. Contrasts for repeated-measures variables 2 13.10. Effect sizes for factorial repeated-measures ANOVA 3 13.11. Reporting the results from factorial repeated-measures ANOVA 2 13.12. What to do when assumptions are violated in repeated-measures ANOVA 3 What have I discovered about statistics? 2 Key terms that Ive discovered Smart Alex's tasks Further reading Online tutorials Interesting real research 504 505 505 505 14 Mixed design ANOVA (GLM 5) 14,1. What will this chapter tell me? 1 14,2, Mixed designs 2 14.3. What do men and women look for in a partner? 2 14.4. Mixed ANOVA on SPSS 2 14.4.1. The main analysis 2 14.4.2. Other options 2 506 506 507 508 508 508 513 514 517 518 520 521 523 524 527 14.5. Output for mixed factorial ANOVA: main analysis 3 14.5.1. The main effect of gender 2 14.5.2. The main effect of looks 2 14.5.3. The main effect of charisma 2 14.5.4. The interaction between gender and looks 2 14.5.5. The interaction between gender and charisma 2 14.5.6. The interaction between attractiveness and charisma 2 14.5.7. The interaction between looks, charisma and gender 3 14.5.8. Conclusions 3 14.6 Calculating effect sizes 3 14.7. Reporting the results of mixed ANOVA 2 14.8. What to do when assumptions are violated in mixed ANOVA 3 What have I discovered about statistics? 2 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorials Interesting real research 530 531 533 536 536 537 537 538 538 538 15 Non-parametric tests 15.1. What will this chapter tell me? 1 15.2. When to use non-parametric tests 1 15.3. Comparing two independent conditions: the Wilcoxon rank-sum test and Mann­Whitney test 1 15.3.1. Theory 2 15.3.2. Inputting data and provisional analysis 1 15.3.3. Running the analysis 1 15.3.4. Output from the Mann­Whitney test 1 15.3.5. Calculating an effect size 2 15.3.6. Writing the results 1 539 539 540 540 542 545 546 548 15.4. Comparing two related conditions: the Wilcoxon signed-rank test 1 15.4.1. Theory of the Wilcoxon signed-rank test 2 15.4.2. Running the analysis 1 15.4.3. Output for the ecstasy group 1 15.4.4. Output for the alcohol group 1 15.4.5. Calculating an effect size 2 15.4.6. Writing the results 1 550 550 552 552 554 556 557 558 558 15.5. Differences between several independent groups: the Kruskal--Wallis test 1 15.5.1. Theory of the Kruskal-Wallis test 2 15.5.2. Inputting data and provisional analysis 1 15.5.3. Doing the Kruskal-Wallis test on SPSS 1 15.5.4. Output from the Kruskal-Wallis test 1 15.5.5. Post hoc tests for the Kruskal-Wallis test 2 15.5.6. Testing for trends: the Jonckheere-Terpstra test 2 15.5.7. Calculating an effect size 2 15.5.8. Writing and interpreting the results 1 559 560 562 562 564 565 568 570 571 573 573 575 575 576 577 579 580 581 582 582 583 583 583 15.6. Differences between several related groups: Friedman's ANOVA 1 15.6.1. Theory of Friedman's ANOVA 2 15.6.2. Inputting data and provisional analysis 1 15.6.3. Doing Friedman's ANOVA on SPSS 1 15.6.4. Output from Friedman's ANOVA 1 15.6.5. Post hoc tests for Friedman's ANOVA 2 15.6.6. Calculating an effect size 2 15.6.7. Writing and interpreting the results 1 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorial Interesting real research 16 Multivariate analysis of variance (MANOVA) 16.1. What will this chapter tell me? 2 16.2. When to use MANOVA 2 16.3. Introduction: similarities and differences to ANOVA 2 16.3.1. Words of warning 2 16.3.2. The example for this chapter 2 584 584 585 585 587 587 588 588 590 591 598 603 603 604 605 605 606 607 607 608 608 608 609 611 613 16.4. Theory of MANOVA 3 16.4.1. Introduction to matrices 3 16.4.2. Some important matrices and their functions 3 16.4.3. Calculating MANOVA by hand: a worked example 3 16.4.4. Principle of the MANOVA test statistic 4 16.5. Practical issues when conducting MANOVA 3 16.5.1. Assumptions and how to check them 3 16.5.2. Choosing a test statistic 3 16.5.3. Follow-up analysis 3 16.6. MANOVA on SPSS 2 16.6.1. The main analysis 2 16.6.2. Multiple comparisons in MANOVA 2 16.6.3. Additional options 3 16.7. Output from MANOVA 3 16.7.1. Preliminary analysis and testing assumptions 3 16.7.2. MANOVA test statistics 3 16.7.3. Univariate test statistics 2 16.7.4. SSCP Matrices 3 16.7.5. Contrasts 3 16.8. Reporting results from MANOVA 2 16.9. Following up MANOVA with discriminant analysis 3 16.10. Output from the discriminant analysis 4 16.11. Reporting results from discriminant analysis 2 16.12. Some final remarks 4 16.12.1. The final interpretation 4 16.12.2. Univariate ANOVA or discriminant analysis? 614 615 618 621 622 622 624 624 624 625 625 626 626 626 16.13. What to do when assumptions are violated in MANOVA 3 What have I discovered about statistics? 2 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorials Interesting real research 17 Exploratory factor analysis 17.1. What will this chapter tell me? 1 17.2. When to use factor analysis 2 17.3. Factors 2 17.3.1. Graphical representation of factors 2 17.3.2. Mathematical representation of factors 2 17.3.3. Factor scores 2 627 627 628 628 630 631 633 636 636 637 638 638 639 642 645 645 650 651 653 654 654 655 656 660 664 669 671 671 673 673 675 676 678 681 17.4. Discovering factors 2 17.4.1. Choosing a method 2 17.4.2. Communality 2 17.4.3. Factor analysis vs. principal component analysis 2 17.4.4. Theory behind principal component analysis 3 17.4.5. Factor extraction: eigenvalues and the scree plot 2 17.4.6. Improving interpretation: factor rotation 3 17.5. Research example 2 17.5.1. Before you Begin 2 17.6. Running the analysis 2 17.6.1. Factor extraction on SPSS 2 17.6.2. Rotation 2 17.6.3. Scores 2 17.6.4. Options 2 17.7. Interpreting output from SPSS 2 17.7.1. Preliminary analysis 2 17.7.2. Factor extraction 2 17.7.3. Factor rotation 2 17.7.4. Factor scores 2 17.7.5. Summary 2 17.8. How to report factor analysis 1 17.9. Reliability analysis 2 17.9.1. Measures of reliability 3 17.9.2. Interpreting Cronbach's a (some cautionary tales ...) 2 17.9.3. Reliability analysis on SPSS 2 17.9.4. Interpreting the output 2 17.10. How to report reliability analysis 2 What have I discovered about statistics? 2 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorial Interesting real research 682 682 683 685 685 685 18 Categorical data 18.1. What will this chapter tell me? 1 18.2 Analysing categorical data 1 18.3. Theory of analysing categorical data 1 18.3.1. Pearson's chi-square test 1 18.3.2. Fisher's exact test 1 18.3.3. The likelihood ratio 2 18.3.4. Yates' correction 2 686 686 687 687 688 690 690 691 691 692 692 692 694 696 698 699 700 702 702 708 710 711 711 712 714 719 720 721 722 722 722 724 724 724 18.4. Assumptions of the chi-square test 1 18.5. Doing chi-square on SPSS 1 18.5.1. Entering data: raw scores 1 18.5.2. Entering data: weight cases 1 18.5.3. Running the analysis 1 18.5.4. Output for the chi-square test 1 18.5.5. Breaking clown a significant chi-square test with standardized residuals2 18.5.6. Calculating an effect size 2 18.5.7. Reporting the results of chi-square 1 18.6. Several categorical variables: loglinear analysis 3 18.6.1. Chi-square as regression 4 18.6.2. Loglinear analysis 3 18.7. Assumptions in loglinear analysis 2 18.8. Loglinear analysis using SPSS 2 18.8.1. Initial considerations 2 18.8.2. The loglinear analysis 2 18.9. Output from loglinear analysis 3 18.10. Following up loglinear analysis 2 18.11. Effect sizes in loglinear analysis 2 18.12. Reporting the results of loglinear analysis 2 What have I discovered about statistics? 1 Key terms that I've discovered Smart Alex's tasks Further reading Online tutorial Interesting real research 19 Multilevel Linear models 19.1. What will this chapter tell me? 1 19.2. Hierarchical data 2 19.2.1. The intraclass correlation 2 19.2.2. Benefits of multilevel models 2 725 725 726 728 729 730 19,3. Theory of multilevel linear models 3 19.3.1. An example 2 19.3.2. Fixed and random coefficients 3 730 732 734 737 737 739 739 740 740 741 742 742 746 749 752 756 761 761 761 763 767 774 775 776 777 777 778 778 778 779 781 797 19.4. The multilevel model 4 19.4.1. Assessing the fit and comparing multilevel models 4 19.4.2. Types of covariance structures 4 19.5. Some practical issues 3 19.5.1. Assumptions 3 19.5.2. Sample size and power 3 19.5.3. Centring variables 4 19.6. Multilevel modelling on SPSS 4 19.6.1. Entering the data 2 19.6.2. Ignoring the data structure: ANOVA 2 19.6.3. Ignoring the data structure: ANCOVA 2 19.6.4. Factoring in the data structure: random intercepts 2 19.6.5. Factoring in the data structure: random intercepts and slopes 4 19.6.6. Adding an interaction to the model 4 19.7. Growth models 4 19.7.1. Growth curves (polynomials) 4 19.7.2. An example: the honeymoon period 2 19.7.3. Restructuring the data 3 19.7.4. Running a growth model on SPSS 4 19.7.5. Further analysis 4 19.8. How to report a multilevel model 3 What have I discovered about statistics? 2 Key terms that Ive discovered Smart Alex's tasks Further reading Online tutorial Interesting real research Epilogue Glossary Appendix A.1. A.2. A.3. A.4. References Index Table of the standard normal distribution Critical values of the t-distribution Critical values of the F-distribution Critical values of the chi-square distribution 797 803 804 808 809 816

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