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Understanding Statistics in Psychology with SPSS

Understanding Statistics in Psychology with SPSS

Publisher Pearson Education
Year 01/03/2020
Edition 8
Pages 752
Version paperback
Readership level College/higher education
Language English
ISBN 9781292282305
Categories Psychological testing & measurement, Mathematical & statistical software
Replaces 9781292134215
$66.96 (with VAT)
247.38 PLN / €54.80 / £47.24
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Book description

Understanding Statistics in Psychology with SPSS, eighth edition, offers students a trusted, straightforward, and engaging way of learning to do statistical analyses confidently using SPSS. Comprehensive and practical, the text is organised into short accessible chapters, making it the ideal text for undergraduate psychology students needing to get to grips with statistics in class or independently. Clear diagrams and full colour screenshots from SPSS make the text suitable for beginners while the broad coverage of topics ensures that students can continue to use it as they progress to more advanced techniques. 



Key features

Â?    Combines coverage of statistics with full guidance on how to use SPSS to analyse data.

Â?    Suitable for use with all versions of SPSS.

Â?    Examples from a wide range of real psychological studies illustrate how statistical techniques are used in practice.

Â?    Includes clear and detailed guidance on choosing tests, interpreting findings and reporting and writing up research.

Â?    Student-focused pedagogical approach including:

  Key concept boxes detailing important terms.

o Focus on sections exploring complex topics in greater depth.

o Explaining statistics sections clarify important statistical concepts.





Dennis Howitt and Duncan Cramer are with Loughborough University.

Understanding Statistics in Psychology with SPSS

Table of contents

  • Chapter 1 Why statistics?
  • Chapter 2 Some basics: Variability and measurement
  • Chapter 3 Describing variables: Tables and diagrams
  • Chapter 4 Describing variables numerically: Averages, variation and spread
  • Chapter 5 Shapes of distributions of scores
  • Chapter 6 Standard deviation and z-scores: Standard unit of measurement in statistics
  • Chapter 7 Relationships between two or more variables: Diagrams and tables
  • Chapter 8 Correlation coefficients: Pearsonâ??s correlation and Spearmanâ??s rho
  • Chapter 9 Regression: Prediction with precision
  • Chapter 10 Samples from populations
  • Chapter 11 Statistical significance for the correlation coefficient: A practical introduction to statistical inference
  • Chapter 12 Standard error: Standard deviation of the means of samples
  • Chapter 13 Related t-test: Comparing two samples of related/correlated/paired scores
  • Chapter 14 Unrelated t-test: Comparing two samples of unrelated/uncorrelated/ independent scores
  • Chapter 15 What you need to write about your statistical analysis
  • Chapter 16 Confidence intervals
  • Chapter 17 Effect size in statistical analysis: Do my findings matter?
  • Chapter 18 Chi-square: Differences between samples of frequency data
  • Chapter 19 Probability
  • Chapter 20 One-tailed versus two-tailed significance testing
  • Chapter 21 Ranking tests: Nonparametric statistics
  • Chapter 22 Variance ratio test: F-ratio to compare two variances
  • Chapter 23 Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
  • Chapter 24 ANOVA for correlated scores or repeated measures
  • Chapter 25 Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?
  • Chapter 26 Multiple comparisons with in ANOVA: A priori and post hoc tests
  • Chapter 27 Mixed-design ANOVA: Related and unrelated variables together
  • Chapter 28 Analysis of covariance (ANCOVA): Controlling for additional variables
  • Chapter 29 Multivariate analysis of variance (MANOVA)
  • Chapter 30 Discriminant (function) analysis â?? especially in MANOVA
  • Chapter 31 Statistics and analysis of experiments
  • Chapter 32 Partial correlation: Spurious correlation, third or confounding variables, suppressor variables
  • Chapter 33 Factor analysis: Simplifying complex data

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