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A Practical Guide to Data Analysis
Using R and IBM SPSS Statistics
- Paul Christiansen - University of Liverpool, UK
- Andrew Jones - University of Liverpool, Liverpool, UK
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Other Titles in:
Quantitative/Statistical Research (General) | Research Methods (General) | Social Research Methods
Quantitative/Statistical Research (General) | Research Methods (General) | Social Research Methods
November 2025 | 528 pages | SAGE Publications Ltd
Using statistics to analyse research data can be tricky when you are getting started. This book shows you how to effectively conduct statistical analysis in both R and SPSS without getting overwhelmed by complex theories and formulas.
It is a practical manual that uses worked examples to help you get to grips with running statistical tests using commonly used software. Straightforward and clear, it assumes no prior knowledge and calmly takes you from reading the first page to completing your own analysis.
It also:
- Covers all the statistics you need to know to pass your exam and do well at coursework
- Presents varied, adaptable solutions to common problems.
- Embeds road-tested best practice into every stage of your analysis.
- Provides you with programming skills that boost your employability.
- Gives any essential theory in a simple, easy to follow, manner
- Helps to bridge the gap between using SPSS and R (or vice versa)
If you want to strengthen your grasp of statistics, overcome statistics anxiety or just pass your course - this is the guide for you.
Chapter 1: The SPSS and R studio working environments
Chapter 2: Central tendency and dispersion
Chapter 3: General statistical tools (Distributions and Outliers)
Chapter 4: Chi square (?2)
Chapter 5: Correlating variables
Chapter 6: Linear Regression part one
Chapter 7: Linear Regression part two, hierarchical and assumption checking
Chapter 8: Logistic regression
Chapter 9. Comparing a sample distribution against a reference value (one-sample tests)
Chapter 10. Comparing two dependent samples
Chapter 11: Comparing two independent samples
Chapter 12: Comparing three or more dependent samples
Chapter 13: Comparing three or more independent samples
Chapter 14: Complex ANOVAs
Chapter 15: Analysis of Covariance (ANCOVA)
Chapter 16: Multivariate Analysis of Variance (MANOVA)
Chapter 17: Reliability analysis
Chapter 18: Dimension reduction and Exploratory factor analysis
