A Practical Guide to Data Analysis

Using R and IBM SPSS Statistics

Häftad, Engelska, 2025

AvChristiansen,Andrew Jones

659 kr

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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.

Produktinformation

Utgivningsdatum2025-12-26
Mått195 x 265 x 27 mm
Vikt1 010 g
FormatHäftad
SpråkEngelska
Antal sidor512
Upplaga1
FörlagSAGE Publications
ISBN9781529792225

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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.Dr Andrew Jones is a Senior Lecturer in the Department of Psychology at the University of Liverpool. He is a psychologist and statistics expert with 10+ years experience in substance use and obesity research.

Chapter 1: The SPSS and R studio working environmentsChapter 2: Central tendency and dispersionChapter 3: General statistical tools (Distributions and Outliers)Chapter 4: Chi square (?2)Chapter 5: Correlating variablesChapter 6: Linear Regression part oneChapter 7: Linear Regression part two, hierarchical and assumption checkingChapter 8: Logistic regressionChapter 9. Comparing a sample distribution against a reference value (one-sample tests)Chapter 10. Comparing two dependent samplesChapter 11: Comparing two independent samplesChapter 12: Comparing three or more dependent samplesChapter 13: Comparing three or more independent samplesChapter 14: Complex ANOVAsChapter 15: Analysis of Covariance (ANCOVA)Chapter 16: Multivariate Analysis of Variance (MANOVA)Chapter 17: Reliability analysisChapter 18: Dimension reduction and Exploratory factor analysis