Christiansen, Andrew Jones

A Practical Guide to Data Analysis (eBook, ePUB)

Using R and IBM SPSS Statistics

• Format: ePub

Produktbeschreibung

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 a range of statistics taught at undergraduate level.
• 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.
• Supports understanding and communicating findings effectively.
• Helps to bridge the gap between using SPSS and R.

If you want to strengthen your grasp of statistics, overcome statistics anxiety or just pass your course - this is the guide for you.

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Produktdetails

• Verlag: SAGE Publications
• Seitenzahl: 512
• Erscheinungstermin: 12. Dezember 2025
• Englisch
• ISBN-13: 9781529615838
• Artikelnr.: 74322838

Autorenporträt

Dr Paul Christiansen is a Senior Lecturer in Statistics in the Department of Psychology at the University of Liverpool. He is interested in research integrity with a particular focus on accurate measurement (psychometrics). He works across a range of fields in Psychology as well as medical research.

Inhaltsangabe

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