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Discovering Statistics Using IBM SPSS Statistics

Discovering Statistics Using IBM SPSS Statistics

Fifth Edition
Experience with SAGE edge

February 2018 | 1 104 pages | SAGE Publications Ltd

With an exciting new look, new characters to meet, and its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities.

What’s brand new:

  • A radical new design with original illustrations and even more colour
  • A maths diagnostic tool to help students establish what areas they need to revise and improve on. 
  • A revamped digital resource that uses video, case studies, datasets and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills
  • New sections on replication, open science and Bayesian thinking
  • Now fully up to date with latest versions of IBM SPSS Statistics©.

Please note that ISBN: 9781526445780 comprises the paperback edition of the Fifth Edition and the student version of IBM SPSS Statistics. More information on this version of the software's features can be found here.

Chapter 1: Why is my evil lecturer forcing me to learn statistics?
What the hell am I doing here? I don’t belong here  
The research process  
Initial observation: finding something that needs explaining  
Generating and testing theories and hypotheses  
Collecting data: measurement  
Collecting data: research design  
Reporting Data  
Chapter 2: The SPINE of statistics
What is the SPINE of statistics?  
Statistical models  
Populations and Samples  
P is for parameters  
E is for Estimating parameters  
S is for standard error  
I is for (confidence) Interval  
N is for Null hypothesis significance testing, NHST  
Reporting significance tests  
Chapter 3: The phoenix of statistics
Problems with NHST  
NHST as part of wider problems with science  
A phoenix from the EMBERS  
Sense, and how to use it  
Preregistering research and open science  
Effect sizes  
Bayesian approaches  
Reporting effect sizes and Bayes factors  
Chapter 4: The IBM SPSS Statistics environment
Versions of IBM SPSS Statistics  
Windows, MacOS and Linux  
Getting started  
The Data Editor  
Entering data into IBM SPSS Statistics  
Importing Data  
The SPSS Viewer  
Exporting SPSS Output  
The Syntax Editor  
Saving files  
Opening files  
Extending IBM SPSS Statistics  
Chapter 5: Exploring data with graphs
The art of presenting data  
The SPSS Chart Builder  
Boxplots (box-whisker diagrams)  
Graphing means: bar charts and error bars  
Line charts  
Graphing relationships: the scatterplot  
Editing graphs  
Chapter 6: The beast of bias
What is bias?  
Overview of assumptions  
Additivity and Linearity  
Normally distributed something or other  
Homoscedasticity/Homogeneity of Variance  
Spotting outliers  
Spotting normality  
Spotting linearity and heteroscedasticity/heterogeneity of variance  
Reducing Bias  
Chapter 7: Non-parametric models
When to use non-parametric tests  
General procedure of non-parametric tests in SPSS  
Comparing two independent conditions: the Wilcoxon rank-sum test and Mann– Whitney test  
Comparing two related conditions: the Wilcoxon signed-rank test  
Differences between several independent groups: the Kruskal–Wallis test  
Differences between several related groups: Friedman’s ANOVA  
Chapter 8: Correlation
Modelling relationships  
Data entry for correlation analysis  
Bivariate correlation  
Partial and semi-partial correlation  
Comparing correlations  
Calculating the effect size  
How to report correlation coefficents  
Chapter 9: The Linear Model (Regression)
An Introduction to the linear model (regression)  
Bias in linear models?  
Generalizing the model  
Sample size in regression  
Fitting linear models: the general procedure  
Using SPSS Statistics to fit a linear model with one predictor  
Interpreting a linear model with one predictor  
The linear model with two of more predictors (multiple regression)  
Using SPSS Statistics to fit a linear model with several predictors  
Interpreting a linear model with several predictors  
Robust regression  
Bayesian regression  
Reporting linear models  
Chapter 10: Comparing two means
Looking at differences  
An example: are invisible people mischievous?  
Categorical predictors in the linear model  
The t-test  
Assumptions of the t-test  
Comparing two means: general procedure  
Comparing two independent means using SPSS Statistics  
Comparing two related means using SPSS Statistics  
Reporting comparisons between two means  
Between groups or repeated measures?  
Chapter 11: Moderation, mediation and multicategory predictors
The PROCESS tool  
Moderation: Interactions in the linear model  
Categorical predictors in regression  
Chapter 12: GLM 1: Comparing several independent means
Using a linear model to compare several means  
Assumptions when comparing means  
Planned contrasts (contrast coding)  
Post hoc procedures  
Comparing several means using SPSS Statistics  
Output from one-way independent ANOVA  
Robust comparisons of several means  
Bayesian comparison of several means  
Calculating the effect size  
Reporting results from one-way independent ANOVA  
Chapter 13: GLM 2: Comparing means adjusted for other predictors (analysis of covariance)
What is ANCOVA?  
ANCOVA and the general linear model  
Assumptions and issues in ANCOVA  
Conducting ANCOVA using SPSS Statistics  
Interpreting ANCOVA  
Testing the assumption of homogeneity of regression slopes  
Robust ANCOVA  
Bayesian analysis with covariates  
Calculating the effect size  
Reporting results  
Chapter 14: GLM 3: Factorial designs
Factorial designs  
Independent factorial designs and the linear model  
Model assumptions in factorial designs  
Factorial designs using SPSS Statistics  
Output from factorial designs  
Interpreting interaction graphs  
Robust models of factorial designs  
Bayesian models of factorial designs  
Calculating effect sizes  
Reporting the results of two-way ANOVA  
Chapter 15: GLM 4: Repeated-measures designs
Introduction to repeated-measures designs  
A grubby example  
Repeated-measures and the linear model  
The ANOVA approach to repeated-measures designs  
The F-statistic for repeated-measures designs  
Assumptions in repeated-measures designs  
One-way repeated-measures designs using SPSS  
Output for one-way repeated-measures designs  
Robust tests of one-way repeated-measures designs  
Effect sizes for one-way repeated-measures designs  
Reporting one-way repeated-measures designs  
A boozy example: a factorial repeated-measures design  
Factorial repeated-measures designs using SPSS Statistics  
Interpreting factorial repeated-measures designs  
Effect Sizes for factorial repeated-measures designs  
Reporting the results from factorial repeated-measures designs  
Chapter 16: GLM 5: Mixed designs
Mixed designs  
Assumptions in mixed designs  
A speed dating example  
Mixed designs using SPSS Statistics  
Output for mixed factorial designs  
Calculating effect sizes  
Reporting the results of mixed designs  
Chapter 17: Multivariate analysis of variance (MANOVA)
Introducing MANOVA  
Introducing matrices  
The theory behind MANOVA  
MANOVA using SPSS Statistics  
Interpreting MANOVA  
Reporting results from MANOVA  
Following up MANOVA with discriminant analysis  
Interpreting discriminant analysis  
Reporting results from discriminant analysis  
The final interpretation  
Chapter 18: Exploratory factor analysis
When to use factor analysis  
Factors and Components  
Discovering factors  
An anxious example  
Factor analysis using SPSS statistics  
Interpreting factor analysis  
How to report factor analysis  
Reliability analysis  
Reliability analysis using SPSS Statistics  
Interpreting Reliability analysis  
How to report reliability analysis  
Chapter 19: Categorical outcomes: chi-square and loglinear analysis
Analysing categorical data  
Associations between two categorical variables  
Associations between several categorical variables: loglinear analysis  
Assumptions when analysing categorical data  
General procedure for analysing categorical outcomes  
Doing chi-square using SPSS Statistics  
Interpreting the chi-square test  
Loglinear analysis using SPSS Statistics  
Interpreting loglinear analysis  
Reporting the results of loglinear analysis  
Chapter 20: Categorical outcomes: logistic regression
What is logistic regression?  
Theory of logistic regression  
Sources of bias and common problems  
Binary logistic regression  
Interpreting logistic regression  
Reporting logistic regression  
Testing assumptions: another example  
Predicting several categories: multinomial logistic regression  
Chapter 21: Multilevel linear models
Hierarchical data  
Theory of multilevel linear models  
The multilevel model  
Some practical issues  
Multilevel modelling using SPSS Statistics  
Growth models  
How to report a multilevel model  
A message from the octopus of inescapable despair  
Chapter 22: Epilogue

 This book turned my hatred of stats and SPSS into love. 

Sharmina August
MSc in Applied Quantitative Methods

This is a great book for learners with minimal previous experience in statistics. The examples and case studies provided throughout the book are very effective and learners will find them memorable. Easy to use, practical and a must have for anyone serious about learning to analyse their data using SPSS.

Dr Canford Chiroro
Centre for International Development, Wolverhampton University
February 5, 2018

Currently one of the best on the market. A must for all statistics courses.
One of the few textbooks a student can buy at the start of their undergraduate studies that will support them all the way through to graduation and beyond.
Worth the money and the weight on the bookshelf!

Miss Helen Coleman
Library Science, Glyndwr University
September 26, 2017

These books keep getting better and better with every edition. The main improvement in the fifth edition is the links with R. You need to tolerate the particular style of humour and the slightly gothicy "decorations" but it is worth it for the comprehensive instructions for both stats and SPSS. These books are useful for our doctoral students who are doing quantitative projects as they cover quite a wide range of stats techniques in one book (eg mediation) and give a foundation for moving to more advanced texts where necessary.

Ms Linda Morison
School of Psychology, Surrey University
September 26, 2017

Professor Field just nailed it once again. The fifth edition of the classic Discovering Statistics Using IBM SPSS Statistics is one of the best textbooks out on the market.

Dr Bruno Schivinski
Department of Management, Birkbeck, University of London
September 26, 2017

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