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Discovering Statistics Using IBM SPSS Statistics: North American Edition 5th Revised edition [Pehme köide]

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  • Formaat: Paperback / softback, 816 pages, kaal: 1810 g
  • Ilmumisaeg: 05-Dec-2017
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1526436566
  • ISBN-13: 9781526436566
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Discovering Statistics Using IBM SPSS Statistics: North American Edition 5th Revised editionSuurem pilt
  • Formaat: Paperback / softback, 816 pages, kaal: 1810 g
  • Ilmumisaeg: 05-Dec-2017
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1526436566
  • ISBN-13: 9781526436566
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781526436566.html
Märksõnad:
Computers and statistical programs make it possible for people who went into behavioral and social sciences in order to avoid mathematics become adept and sometimes even expert at statistics, says Field, and also make it easy for them to make complete idiots of themselves because they do not know what they are doing. He is here to help, using one of the more popular proprietary software packages. Among his topics are the phoenix of statistics, exploring data with graphs, the linear model (regression), general linear model: comparing means adjusted for other predictors (analysis of covariance), and categorical outcomes: chi-square and loglinear analysis. Annotation ©2017 Ringgold, Inc., Portland, OR (protoview.com)

With an exciting new look, math diagnostic tool, and a research roadmap to navigate projects, this new edition of Andy Field’s award-winning text offers a unique combination of humor and step-by-step instruction to make learning statistics compelling and accessible to even the most anxious of students. The Fifth Edition takes students from initial theory to regression, factor analysis, and multilevel modeling, fully incorporating IBM SPSS Statistics© version 25 and fascinating examples throughout.

Arvustused

Ive been using Discovering... with SPSS... for a couple of years & have just updated to the 5th edition. I LOVE chapter 3, and have actually been covering a lot of that material in my class, so its nice to have it explicit in the text as well.





Jennifer Gutbezahl, Harvard University -- Jennifer Gutbezahl "By the way, has anyone had a chance to look at the text? I love it! Its the first text Ive come across that has been written in such a captivating way. Theres humor, tons of information, and awesome resources both within and on the companion website. Kudos to Prof. Field!"





Anonymous Student, Harvard University





"I never thought I would find a statistics textbook amusing but somehow our text pulls it off.  I also appreciated the online supplementary tools provided by the publisher.  If you havent seen them yet, you should check them out.  They provide a good synthesis of each of the chapters and some easy options to review."





Anonymous Student, Harvard University





"I get started on the text and cant agree more with you on how the book is. I also appreciate how the author made the text interesting to read, but the content is rich enough to provide readers good knowledge on how to draw insights from stats and data. Also, it provides a lot of practical guides for reporting results and findings for research paper. Cant wait to take a deeper dive into the text!"





Anonymous Student, Harvard University -- Students of Jennifer Gutbezahl * Harvard University *

Preface xiii
How to Use This Book xvii
Thank You xxi
Dedication xxiii
Symbols Used In This Book xxiv
A Maths Review xxvi
1 Why Is My Evil Lecturer Forcing Me To Learn Statistics?
1(36)
1.1 What will this chapter tell me?
2(1)
1.2 What the hell am I doing here? I don't belong here
3(1)
1.3 The research process
3(1)
1.4 Initial observation: finding something that needs explaining
3(1)
1.5 Generating and testing theories and hypotheses
4(4)
1.6 Collecting data: measurement
8(5)
1.7 Collecting data: research design
13(5)
1.8 Analyzing data
18(13)
1.9 Reporting data
31(3)
1.10 Brian's attempt to woo Jane
34(1)
1.11 What next?
34(1)
1.12 Key terms that I've discovered
34(3)
Smart Alex's tasks
35(2)
2 The Spine of Statistics
37(36)
2.1 What will this chapter tell me?
38(1)
2.2 What is the SPINE of statistics?
39(1)
2.3 Statistical models
39(3)
2.4 Populations and samples
42(1)
2.5 P is for parameters
43(3)
2.6 E is for estimating parameters
46(2)
2.7 S is for standard error
48(2)
2.8 I is for (confidence) interval
50(5)
2.9 N is for null hypothesis significance testing
55(14)
2.10 Reporting significance tests
69(1)
2.11 Brian's attempt to woo Jane
69(1)
2.12 What next?
69(1)
2.13 Key terms that I've discovered
70(3)
Smart Alex's tasks
71(2)
3 The Phoenix of Statistics
73(30)
3.1 What will this chapter tell me?
74(1)
3.2 Problems with NHST
75(5)
3.3 NHST as part of wider problems with science
80(4)
3.4 A phoenix from the EMBERS
84(2)
3.5 Sense, and how to use it
86(1)
3.6 Pre-registering research and open science
86(1)
3.7 Effect sizes
87(5)
3.8 Bayesian approaches
92(8)
3.9 Reporting effect sizes and Bayes factors
100(1)
3.10 Brian's attempt to woo Jane
100(1)
3.11 What next?
101(1)
3.12 Key terms that I've discovered
101(2)
Smart Alex's tasks
101(2)
4 The Ibm Spss Statistics Environment
103(32)
4.1 What will this chapter tell me?
104(1)
4.2 Versions of IBM SPSS Statistics
105(1)
4.3 Windows, Mac OS, and Linux
105(1)
4.4 Getting started
106(1)
4.5 The data editor
106(4)
4.6 Entering data into IBM SPSS Statistics
110(10)
4.7 Importing data
120(1)
4.8 The SPSS viewer
121(3)
4.9 Exporting SPSS output
124(1)
4.10 The syntax editor
124(2)
4.11 Saving files
126(1)
4.12 Opening files
127(1)
4.13 Extending IBM SPSS Statistics
127(3)
4.14 Brian's attempt to woo Jane
130(1)
4.15 What next?
131(1)
4.16 Key terms that I've discovered
131(4)
Smart Alex's tasks
132(3)
5 Exploring Data With Graphs
135(34)
5.1 What will this chapter tell me?
136(1)
5.2 The art of presenting data
137(2)
5.3 The SPSS Chart Builder
139(1)
5.4 Histograms
140(5)
5.5 Boxplots (box-whisker diagrams)
145(2)
5.6 Graphing means: bar charts and error bars
147(9)
5.7 Line charts
156(1)
5.8 Graphing relationships: the scatterplot
156(7)
5.9 Editing graphs
163(3)
5.10 Brian's attempt to woo Jane
166(1)
5.11 What next?
166(1)
5.12 Key terms that I've discovered
167(2)
Smart Alex's tasks
167(2)
6 The Beast of Bias
169(42)
6.1 What will this chapter tell me?
170(1)
6.2 What is bias?
171(1)
6.3 Outliers
171(1)
6.4 Overview of assumptions
172(1)
6.5 Additivity and linearity
173(1)
6.6 Normally distributed something or other
173(6)
6.7 Homoscedasticity/homogeneity of variance
179(1)
6.8 Independence
180(1)
6.9 Spotting outliers
180(3)
6.10 Spotting normality
183(10)
6.11 Spotting linearity and heteroscedasticity/heterogeneity of variance
193(3)
6.12 Reducing bias
196(12)
6.13 Brian's attempt to woo Jane
208(1)
6.14 What next?
208(1)
6.15 Key terms that I've discovered
209(2)
Smart Alex's tasks
210(1)
7 Non-Parametric Models
211(38)
7.1 What will this chapter tell me?
212(1)
7.2 When to use non-parametric tests
213(1)
7.3 General procedure of non-parametric tests in using SPSS Statistics
214(2)
7.4 Comparing two independent conditions: the Wilcoxon rank-sum test and Mann-Whitney test
216(7)
7.5 Comparing two related conditions: the Wilcoxon signed-rank test
223(7)
7.6 Differences between several independent groups: the Kruskal--Wallis test
230(11)
7.7 Differences between several related groups: Friedman's ANOVA
241(5)
7.8 Brian's attempt to woo Jane
246(1)
7.9 What next?
247(1)
7.10 Key terms that I've discovered
247(2)
Smart Alex's tasks
248(1)
8 Correlation
249(26)
8.1 What will this chapter tell me?
250(1)
8.2 Modeling relationships
251(6)
8.3 Data entry for correlation analysis
257(1)
8.4 Bivariate correlation
257(9)
8.5 Partial and semi-partial correlation
266(4)
8.6 Comparing correlations
270(1)
8.7 Calculating the effect size
271(1)
8.8 How to report correlation coefficents
271(2)
8.9 Brian's attempt to woo Jane
273(1)
8.10 What next?
273(1)
8.11 Key terms that I've discovered
274(1)
Smart Alex's tasks
274(1)
9 The Linear Model (Regression]
275(50)
9.1 What will this chapter tell me?
276(1)
9.2 An introduction to the linear model (regression)
277(6)
9.3 Bias in linear models?
283(5)
9.4 Generalizing the model
288(1)
9.5 Sample size and the linear model
289(1)
9.6 Fitting linear models: the general procedure
290(1)
9.7 Using SPSS Statistics to fit a linear model with one predictor
291(1)
9.8 Interpreting a linear model with one predictor
292(3)
9.9 The linear model with two or more predictors (multiple regression)
295(3)
9.10 Using SPSS Statistics to fit a linear model with several predictors
298(5)
9.11 Interpreting a linear model with several predictors
303(12)
9.12 Robust regression
315(3)
9.13 Bayesian regression
318(2)
9.14 Reporting linear models
320(1)
9.15 Brian's attempt to woo Jane
321(1)
9.16 What next?
321(1)
9.17 Key terms that I've discovered
322(3)
Smart Alex's tasks
322(3)
10 Comparing Two Means
325(32)
10.1 What will this chapter tell me?
326(1)
10.2 Looking at differences
327(1)
10.3 A mischievous example
327(2)
10.4 Categorical predictors in the linear model
329(2)
10.5 Thef-test
331(5)
10.6 Assumptions of the t-test
336(1)
10.7 Comparing two means: general procedure
336(1)
10.8 Comparing two independent means using SPSS Statistics
336(7)
10.9 Comparing two related means using SPSS Statistics
343(10)
10.10 Reporting comparisons between two means
353(1)
10.11 Between groups or repeated measures?
354(1)
10.12 Brian's attempt to woo Jane
354(1)
10.13 What next?
355(1)
10.14 Key terms that I've discovered
355(2)
Smart Alex's tasks
356(1)
11 Moderation, Mediation and Multicategory Predictors
357(28)
11.1 What will this chapter tell me?
358(1)
11.2 The PROCESS tool
359(1)
11.3 Moderation: interactions in the linear model
359(10)
11.4 Mediation
369(8)
11.5 Categorical predictors in regression
377(5)
11.6 Brian's attempt to woo Jane
382(1)
11.7 What next?
382(1)
11.8 Key terms that I've discovered
383(2)
Smart Alex's tasks
384(1)
12 Glm 1: Comparing Several Independent Means
385(38)
12.1 What will this chapter tell me?
386(1)
12.2 Using a linear model to compare several means
387(8)
12.3 Assumptions when comparing means
395(3)
12.4 Planned contrasts (contrast coding)
398(8)
12.5 Post hoc procedures
406(2)
12.6 Comparing several means using SPSS Statistics
408(4)
12.7 Output from one-way independent ANOVA
412(5)
12.8 Robust comparisons of several means
417(1)
12.9 Bayesian comparison of several means
418(1)
12.10 Calculating the effect size
419(1)
12.11 Reporting results from one-way independent ANOVA
419(1)
12.12 Brian's attempt to woo Jane
420(1)
12.13 What next?
421(1)
12.14 Key terms that I've discovered
421(2)
Smart Alex's tasks
421(2)
13 Glm 2: Comparing Means Adjusted for Other Predictors (Analysis of Covariance]
423(24)
13.1 What will this chapter tell me?
424(1)
13.2 What is ANCOVA?
425(1)
13.3 ANCOVA and the general linear model
425(3)
13.4 Assumptions and issues in ANCOVA
428(3)
13.5 Conducting ANCOVA using SPSS Statistics
431(5)
13.6 Interpreting ANCOVA
436(3)
13.7 Testing the assumption of homogeneity of regression slopes
439(2)
13.8 Robust ANCOVA
441(2)
13.9 Bayesian analysis with covariates
443(1)
13.10 Calculating the effect size
443(1)
13.11 Reporting results
444(1)
13.12 Brian's attempt to woo Jane
445(1)
13.13 What next?
445(1)
13.14 Key terms that I've discovered
445(2)
Smart Alex's tasks
446(1)
14 GLM 3: Factorial Designs
447(30)
14.1 What will this chapter tell me?
448(1)
14.2 Factorial designs
449(1)
14.3 Independent factorial designs and the linear model
449(7)
14.4 Model assumptions in factorial designs
456(1)
14.5 Factorial designs using SPSS Statistics
457(4)
14.6 Output from factorial designs
461(6)
14.7 Interpreting interaction graphs
467(2)
14.8 Robust models of factorial designs
469(2)
14.9 Bayesian models of factorial designs
471(2)
14.10 Calculating effect sizes
473(1)
14.11 Reporting the results of factorial designs
474(1)
14.12 Brian's attempt to woo Jane
475(1)
14.13 What next?
475(1)
14.14 Key terms that I've discovered
475(2)
Smart Alex's tasks
476(1)
15 GLM 4: Repeated-Measures Designs
477(38)
15.1 What will this chapter tell me?
478(1)
15.2 Introduction to repeated-measures designs
479(1)
15.3 A grubby example
479(1)
15.4 Repeated-measures and the linear model
480(1)
15.5 The ANOVA approach to repeated-measures designs
481(4)
15.6 The F-statistic for repeated-measures designs
485(2)
15.7 Assumptions in repeated-measures designs
487(1)
15.8 One-way repeated-measures designs using SPSS
488(3)
15.9 Output for one-way repeated-measures designs
491(6)
15.10 Robust tests of one-way repeated-measures designs
497(1)
15.11 Effect sizes for one-way repeated-measures designs
498(1)
15.12 Reporting one-way repeated-measures designs
499(1)
15.13 A boozy example: a factorial repeated-measures design
499(1)
15.14 Factorial repeated-measures designs using SPSS Statistics
500(4)
15.15 Interpreting factorial repeated-measures designs
504(7)
15.16 Effect sizes for factorial repeated-measures designs
511(1)
15.17 Reporting the results from factorial repeated-measures designs
512(1)
15.18 Brian's attempt to woo Jane
513(1)
15.19 What next?
513(1)
15.20 Key terms that I've discovered
514(1)
Smart Alex's tasks
514(1)
16 GLM 5: Mixed Designs
515(24)
16.1 What will this chapter tell me?
516(1)
16.2 Mixed designs
517(1)
16.3 Assumptions in mixed designs
517(1)
16.4 A speed-dating example
517(2)
16.5 Mixed designs using SPSS Statistics
519(2)
16.6 Output for mixed factorial designs
521(12)
16.7 Calculating effect sizes
533(1)
16.8 Reporting the results of mixed designs
533(3)
16.9 Brian's attempt to woo Jane
536(1)
16.10 What next?
536(1)
16.11 Key terms that I've discovered
537(2)
Smart Alex's tasks
537(2)
17 Multivariate Analysis of Variance (Manova)
539(30)
17.1 What will this chapter tell me?
540(1)
17.2 Introducing MANOVA
541(1)
17.3 Introducing matrices
542(2)
17.4 The theory behind MANOVA
544(7)
17.5 Practical issues when conducting MANOVA
551(2)
17.6 MANOVA using SPSS Statistics
553(1)
17.7 Interpreting MANOVA
554(4)
17.8 Reporting results from MANOVA
558(1)
17.9 Following up MANOVA with discriminant analysis
559(2)
17.10 Interpreting discriminant analysis
561(2)
17.11 Reporting results from discriminant analysis
563(1)
17.12 The final interpretation
564(1)
17.13 Brian's attempt to woo Jane
565(1)
17.14 What next?
566(1)
17.15 Key terms that I've discovered
567(2)
Smart Alex's tasks
567(2)
18 Exploratory Factor Analysis
569(42)
18.1 What will this chapter tell me?
570(1)
18.2 When to use factor analysis
571(1)
18.3 Factors and components
571(5)
18.4 Discovering factors
576(6)
18.5 An anxious example
582(3)
18.6 Factor analysis using SPSS Statistics
585(5)
18.7 Interpreting factor analysis
590(10)
18.8 How to report factor analysis
600(1)
18.9 Reliability analysis
601(2)
18.10 Reliability analysis using SPSS Statistics
603(1)
18.11 Interpreting reliability analysis
604(2)
18.12 How to report reliability analysis
606(1)
18.13 Brian's attempt to woo Jane
607(1)
18.14 What next?
608(1)
18.15 Key terms that I've discovered
608(3)
Smart Alex's tasks
609(2)
19 Categorical Outcomes: Chi-Square and Loglinear Analysis
611(30)
19.1 What will this chapter tell me?
612(1)
19.2 Analyzing categorical data
613(1)
19.3 Associations between two categorical variables
613(5)
19.4 Associations between several categorical variables: loglinear analysis
618(2)
19.5 Assumptions when analyzing categorical data
620(1)
19.6 General procedure for analyzing categorical outcomes
621(1)
19.7 Doing chi-square using SPSS Statistics
621(3)
19.8 Interpreting the chi-square test
624(6)
19.9 Loglinear analysis using SPSS Statistics
630(3)
19.10 Interpreting loglinear analysis
633(4)
19.11 Reporting the results of loglinear analysis
637(1)
19.12 Brian's attempt to woo Jane
637(1)
19.13 What next?
638(1)
19.14 Key terms that I've discovered
639(2)
Smart Alex's tasks
639(2)
20 Categorical Outcomes: Logistic Regression
641(42)
20.1 What will this chapter tell me?
642(1)
20.2 What is logistic regression?
643(1)
20.3 Theory of logistic regression
643(4)
20.4 Sources of bias and common problems
647(3)
20.5 Binary logistic regression
650(7)
20.6 Interpreting logistic regression
657(7)
20.7 Reporting logistic regression
664(1)
20.8 Testing assumptions: another example
665(4)
20.9 Predicting several categories: multinomial logistic regression
669(9)
20.10 Reporting multinomial logistic regression
678(1)
20.11 Brian's attempt to woo Jane
679(1)
20.12 What next?
679(1)
20.13 Key terms that I've discovered
680(3)
Smart Alex's tasks
680(3)
21 Multilevel Linear Models
683(40)
21.1 What will this chapter tell me?
684(1)
21.2 Hierarchical data
685(2)
21.3 Theory of multilevel linear models
687(3)
21.4 The multilevel model
690(2)
21.5 Some practical issues
692(3)
21.6 Multilevel modeling using SPSS Statistics
695(12)
21.7 Growth models
707(10)
21.8 How to report a multilevel model
717(1)
21.9 A message from the octopus of inescapable despair
718(1)
21.10 Brian's attempt to woo Jane
718(1)
21.11 What next?
719(1)
21.12 Key terms that I've discovered
720(3)
Smart Alex's tasks
721(2)
Epilogue 723(2)
Appendix 725(10)
Glossary 735(22)
References 757(10)
Index 767
Andy Field is Professor of Quantitative Methods at the University of Sussex. He has published widely (100+ research papers, 29 book chapters, and 17 books in various editions) in the areas of child anxiety and psychological methods and statistics. His current research interests focus on barriers to learning mathematics and statistics.

He is internationally known as a statistics educator. He has written several widely used statistics textbooks including Discovering Statistics Using IBM SPSS Statistics (winner of the 2007 British Psychological Society book award), Discovering Statistics Using R, and An Adventure in Statistics (shortlisted for the British Psychological Society book award, 2017; British Book Design and Production Awards, primary, secondary and tertiary education category, 2016; and the Association of Learned & Professional Society Publishers Award for innovation in publishing, 2016), which teaches statistics through a fictional narrative and uses graphic novel elements. He has also written the adventr and discovr packages for the statistics software R that teach statistics and R through interactive tutorials.

His uncontrollable enthusiasm for teaching statistics to psychologists has led to teaching awards from the University of Sussex (2001, 2015, 2016, 2018, 2019), the British Psychological Society (2006) and a prestigious UK National Teaching fellowship (2010).

Hes done the usual academic things: had grants, been on editorial boards, done lots of admin/service but he finds it tedious trying to remember this stuff. None of them matter anyway because in the unlikely event that youve ever heard of him itll be as the Stats book guy. In his spare time, he plays the drums very noisily in a heavy metal band, and walks his cocker spaniel, both of which he finds therapeutic.