DISCOVERING STATISTICS USING IBM SPSS STATISTICS 5E - ANDY FIELD 9781526419521

TITLE : DISCOVERING STATISTICS USING IBM SPSS STATISTICS - ANDY FIELD

ISBN13 : 9781526419521

PUBLISHER : SAGE PUBLICATION (2018)

EDITION : 5E PAPERBACK

PAGES : 1104 FULL COLOR PAGES

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 online resource that uses video, case studies, datasets, testbanks 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©.

Table of Content
Chapter 1: Why is my evil lecturer forcing me to learn statistics?
Chapter 2: The SPINE of statistics
Chapter 3: The phoenix of statistics
Chapter 4: The IBM SPSS Statistics environment
Chapter 5: Exploring data with graphs
Chapter 6: The beast of bias
Chapter 7: Non-parametric models
Chapter 8: Correlation
Chapter 9: The Linear Model (Regression)
Chapter 10: Comparing two means
Chapter 11: Moderation, mediation and multicategory predictors
Chapter 12: GLM 1: Comparing several independent means
Chapter 13: GLM 2: Comparing means adjusted for other predictors (analysis of covariance)
Chapter 14: GLM 3: Factorial designs
Chapter 15: GLM 4: Repeated-measures designs
Chapter 16: GLM 5: Mixed designs
Chapter 17: Multivariate analysis of variance (MANOVA)
Chapter 18: Exploratory factor analysis
Chapter 19: Categorical outcomes: chi-square and loglinear analysis
Chapter 20: Categorical outcomes: logistic regression
Chapter 21: Multilevel linear models
Chapter 22: Epilogue