Understanding Statistics in Psychology with SPSS, 8th edition

Published by Pearson (March 12, 2020) © 2020

  • Dennis Howitt University of Loughborough
  • Duncan Cramer University of Loughborough

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Help your students gain confidence conducting statistical analysis

Understanding Statistics in Psychology with SPSS, eighth edition,

combines coverage of statistics with guidance on using SPSS to analyse data. The book is organised into accessible chapters suitable for classroom or independent study. Clear diagrams and screenshots from SPSS make the text beginner friendly, helping students progress through the book.

This edition provides examples from real psychological studies to help students connect theory to practice, while key concept boxes and focus sections ensure solid understanding of core concepts.

With a range of features, this book is ideal for undergraduate students in psychology.

This edition includes a Companion Website.

  • Comprehensive and practical coverage of statistics with step by step guidance of how to use SPSS to use SPSS to analyse data
  • Suitable for use with all versions of SPSS
  • Examples from a wide range of real psychological studies illustrate how statistical techniques are used in practice
  • Includes clear and detailed guidance on choosing tests, interpreting findings and reporting and writing up research
  • Student focused pedagogical approach including
  • Key concept boxes detailing important terms
  • Focus on sections exploring complex topics in greater depth
  • Explaining statistics sections clarify important statistical concepts.

·    New chapter on Data Mining and Big Data

·    Updated SPSS screenshots throughout the book

·    Updated examples from a wide range of real psychological studies to illustrate how statistical techniques are used in practice

  • 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: Pearson’s correlation and Spearman’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 – 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
  • Chapter 34 Multiple regression and multiple correlation
  • Chapter 35 Path analysis
  • Chapter 36 Meta-analysis: Combining and exploring statistical findings from previous research
  • Chapter 37 Reliability in scales and measurement: Consistency and agreement
  • Chapter 38 Influence of moderator variables on relationships between two variables
  • Chapter 39 Statistical power analysis: Getting the sample size right
  • Chapter 40 Log-linear methods: Analysis of complex contingency tables
  • Chapter 41 Multinomial logistic regression: Distinguishing between several different categories or groups
  • Chapter 42 Binomial logistic regression
  • Chapter 43 Data mining and big data

Dennis Howitt and Duncan Cramer are based at Loughborough University.

Need help? Get in touch

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