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Serious Stats

Serious Stats

Thomas Baguley

(2012)

Additional Information

Book Details

Abstract

Ideal for experienced students and researchers in the social sciences who wish to refresh or extend their understanding of statistics, and to apply advanced statistical procedures using SPSS or R. Key theory is reviewed and illustrated with examples of how to apply these concepts using real data.
Bridges the gap between the undergraduate and postgraduate levels, providing readers with a refresher of the skills they have learnt andthen progressing to more advanced statistical methods
Thomas Baguley is Professor of Experimental Psychology at Nottingham Trent University, UK. He is an experimental psychologist working particularly on the statistical or mathematical modelling of long-term memory and spatial cognition. He has over 20 years of teaching and research experience and is Editor of the British Journal of Mathematical and Statistical Psychology.
"This is an excellent text on advanced univariate statistics covering the range from probability distributions to multilevel models. The book is perfect for advanced undergraduates, honours students, and beginning graduate students." - Toon Cillessen, Professor of Psychology at Radboud University, Nijmegen, The Netherlands

"Serious Stats is a tour de force and a highly accessible exception amongst statistics textbooks. Blow the dust and presumptions off your undergraduate statistics and learn about the things you really need to know about modelling data and how to achieve them, especially if you're R-curious. Baguley will become the must-have resource for researchers who are serious about getting the most from their experiments." - Philip J. Benson, Senior Lecturer, School of Psychology, University of Aberdeen, UK

"A good resource for those wishing to build a fundamental understanding of probability and statistics and their applications to complex scientific data." - Adam Moore, The Center for Advanced Brain Imaging, Georgia Institute of Technology, USA

"A nice and almost encyclopaedic companion for behavioural scientists, which covers not only the usual topics of ANOVA and regression, but also such important themes like effect size and power, resampling methods, Bayesian inference, discrete outcomes, and multilevel models, with many useful references for further reading." - Gerard van Breukelen, Associate Professor of Statistics, Maastricht University, The Netherlands

"This book gives one of the most comprehensive, detailed and, importantly, readable accounts of statistical methods used in behavioural research. It provides superb coverage of the principles and assumptions of standard methods of analysis, as well as of the more complex and powerful techniques available in the statistical armoury, such as multilevel modelling. It is a book which every Experimental Psychologist will find helpful to have on their shelf." - Michael Pilling, Lecturer in Psychology, Oxford Brookes University, UK





Table of Contents

Section Title Page Action Price
Cover Cover
Contents vii
List of tables xii
List of figures xiv
List of boxes xviii
List of key concepts xix
Preface xx
1 Data, samples and statistics 1
1.1 Chapter overview 2
1.2 What are data? 2
1.3 Samples and populations 3
1.4 Central tendency 6
1.5 Dispersion within a sample 17
1.6 Description, inference and bias 25
1.7 R code for Chapter 1 27
1.8 Notes on SPSS syntax for Chapter 1 34
1.9 Bibliography and further reading 36
2 Probability distributions 37
2.1 Chapter overview 38
2.2 Why are probability distributions important in statistics? 38
2.3 Discrete distributions 42
2.4 Continuous distributions 48
2.5 R code for Chapter 2 65
2.6 Notes on SPSS syntax for Chapter 2 72
2.7 Bibliography and further reading 73
3 Confidence intervals 74
3.1 Chapter overview 75
3.2 From point estimates to interval estimates 75
3.3 Confidence intervals 76
3.4 Confidence intervals for a difference 86
3.5 Using Monte Carlo methods to estimate confidence intervals 93
3.6 Graphing confidence intervals 100
3.7 R code for Chapter 3 103
3.8 Notes on SPSS syntax for Chapter 3 115
3.9 Bibliography and further reading 117
4 Significance tests 118
4.1 Chapter overview 119
4.2 From confidence intervals to significance tests 119
4.3 Null hypothesis significance tests 120
4.4 t tests 125
4.5 Tests for discrete data 130
4.6 Inference about other parameters 142
4.7 Good practice in the application of significance testing 143
4.8 R code for Chapter 4 144
4.9 Notes on SPSS syntax for Chapter 4 154
4.10 Bibliography and further reading 157
5 Regression 158
5.1 Chapter overview 159
5.2 Regression models, prediction and explanation 159
5.3 Mathematics of the linear function 160
5.4 Simple linear regression 162
5.5 Statistical inference in regression 173
5.6 Fitting and interpreting regression models 182
5.7 Fitting curvilinear relationships with simple linear regression 190
5.8 R code for Chapter 5 192
5.9 Notes on SPSS syntax for Chapter 5 200
5.10 Bibliography and further reading 204
6 Correlation and covariance 205
6.1 Chapter overview 206
6.2 Correlation, regression and association 206
6.3 Statistical inference with the product-moment correlation coefficient 211
6.4 Correlation, error and reliability 214
6.5 Alternative correlation coefficients 218
6.6 Inferences about differences in slopes 224
6.7 R code for Chapter 6 226
6.8 Notes on SPSS syntax for Chapter 6 232
6.9 Bibliography and further reading 233
7 Effect size 234
7.1 Chapter overview 235
7.2 The role of effect size in research 235
7.3 Selecting an effect size metric 238
7.4 Effect size metrics for continuous outcomes 242
7.5 Effect size metrics for discrete variables 259
7.6 R code for Chapter 7 270
7.7 Notes on SPSS syntax for Chapter 7 275
7.8 Bibliography and further reading 276
7.9 Online supplement 1: Meta-analysis 276
8 Statistical power 277
8.1 Chapter overview 278
8.2 Significance tests, effect size and statistical power 278
8.3 Statistical power and sample size 280
8.4 Statistical power analysis 289
8.5 Accuracy in parameter estimation (AIPE) 294
8.6 Estimating ? 297
8.7 R code for Chapter 8 299
8.8 Notes on SPSS syntax for Chapter 8 303
8.9 Bibliography and further reading 303
9 Exploring messy data 304
9.1 Chapter overview 305
9.2 Statistical assumptions 305
9.3 Tools for detecting and assessing problems 311
9.4 Model checking in regression 325
9.5 R code for Chapter 9 331
9.6 Notes on SPSS syntax for Chapter 9 336
9.7 Bibliography and further reading 338
10 Dealing with messy data 339
10.1 Chapter overview 340
10.2 Dealing with violations of statistical assumptions 340
10.3 Robust methods 344
10.4 Transformations 349
10.5 R code for Chapter 10 358
10.6 Notes on SPSS syntax for Chapter 10 361
10.7 Bibliography and further reading 362
10.8 Online supplement 2: Dealing with missing data 362
11 Alternatives to classical statistical inference 363
11.1 Chapter overview 364
11.2 The null hypothesis significance testing controversy 364
11.3 Frequentist responses to the NHST controversy 369
11.4 Likelihood 375
11.5 Bayesian inference 387
11.6 Information criteria 401
11.7 R code for Chapter 11 408
11.8 Notes on SPSS syntax for Chapter 11 420
11.9 Bibliography and further reading 422
11.10 Online supplement 3: Replication probabilities and prep 422
12 Multiple regression and the general linear model 423
12.1 Chapter overview 424
12.2 The multiple linear regression model 424
12.3 The impact of individual predictors on the model 441
12.4 Building a statistical model 456
12.5 R code for Chapter 12 460
12.6 Notes on SPSS syntax for Chapter 12 470
12.7 Bibliography and further reading 471
13 ANOVA and ANCOVA with independent measures 472
13.1 Chapter overview 473
13.2 ANOVA and ANCOVA as special cases of regression 473
13.3 One-way analysis of variance with independent measures 478
13.4 Exploring differences between level means 490
13.5 Analysis of covariance 503
13.6 ANOVA, ANCOVA and multiple regression 511
13.7 R code for Chapter 13 511
13.8 Notes on SPSS syntax for Chapter 13 522
13.9 Bibliography and further reading 526
14 Interactions 527
14.1 Chapter overview 528
14.2 Modeling interaction effects 528
14.3 Interactions in regression: moderated multiple regression 529
14.4 Polynomial regression 540
14.5 Factorial ANOVA 542
14.6 ANCOVA and homogeneity of covariance 562
14.7 Effect size in factorial ANOVA, ANCOVA and multiple regression 564
14.8 Statistical power to detect interactions 568
14.9 Problems with interactions in ANOVA and regression 569
14.10 R code for Chapter 14 571
14.11 Notes on SPSS syntax for Chapter 14 586
14.12 Bibliography and further reading 589
15 Contrasts 590
15.1 Chapter overview 591
15.2 Contrasts and the design matrix 591
15.3 Interaction contrasts 605
15.4 Post hoc contrasts and correction for multiple testing 609
15.5 Contrasts of adjusted means in ANCOVA 611
15.6 The role of contrasts in other statistical models 612
15.7 R code for Chapter 15 613
15.8 Notes on SPSS syntax for Chapter 15 620
15.9 Bibliography and further reading 621
16 Repeated measures ANOVA 622
16.1 Chapter overview 623
16.2 Modeling correlated or repeated measures 623
16.3 ANOVA with repeated measures 623
16.4 Combining independent and repeated measures: mixed ANOVA designs 638
16.5 Comparisons, contrasts and simple effects with repeated measures 642
16.6 MANOVA 647
16.7 ANCOVA with repeated measures 650
16.8 R code for Chapter 16 656
16.9 Notes on SPSS syntax for Chapter 16 664
16.10 Bibliography and further reading 666
17 Modeling discrete outcomes 667
17.1 Chapter overview 668
17.2 Modeling discrete outcomes in the general linear model 668
17.3 Generalized linear models 669
17.4 Logistic regression 672
17.5 Modeling count data 694
17.6 Modeling discrete outcomes with correlated measures 706
17.7 R code for Chapter 17 708
17.8 Notes on SPSS syntax for Chapter 17 720
17.9 Bibliography and further reading 722
17.10 Online supplement 4: Pseudo-R2 and related measures 723
17.11 Online supplement 5: Loglinear models 723
18 Multilevel models 724
18.1 Chapter overview 725
18.2 From repeated measures ANOVA to multilevel models 725
18.3 Multilevel regression models 731
18.4 Building up a multilevel model 741
18.5 Crossed versus nested random factors 762
18.6 Multilevel generalized linear models 766
18.7 R code for Chapter 18 769
18.8 Notes on SPSS syntax for Chapter 18 782
18.9 Bibliography and further reading 784
Notes 785
References 798
Author index 817
Subject index 821