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Introduction to SPSS in Psychology, 7th edition

Published by Pearson (May 1, 2017) © 2017

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

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Introduction to SPSS in Psychology, 7th edition is the essential step by step guide to SPSS for students taking their first course in statistics.  This well-established text provides a clear and comprehensive coverage of how to carry out statistical analyses using SPSS.  Full colour SPSS screenshots, clear explanation and a wide ranging coverage make it the perfect companion for students who want to be able to analyse data with confidence.

This edition includes a Companion Website.

For each statistical test, the text discusses:

· What the test is used for

· When you should and shouldn’t use it

· Data requirements and any problems in usage

· Step-by-step direction on how to carry out the test, using colour screenshots and labelled guidance on each part of the process

· How to interpret and report the output

· ‘At a glance’ summary of steps taken to do each test

The step-by-step guides make this text suitable for students to use alongside lectures or independently when needing to get to grips with SPSS.

● Compact, to-the-point chapters sans theory to make the content more approachable 

● Topics that are most likely to be used

  •  Part 1 Introduction to SPSS
  • 1 Brief introduction to statistics
  • 2 Basics of SPSS data entry and statistical analysis
  • Part 2 Descriptive statistics
  • 3 Describing variables: Tables
  • 4 Describing variables: Diagrams
  • 5 Describing variables numerically: Averages, variation and spread
  • 6 Shapes of distributions of scores
  • 7 Relationships between two or more variables: Tables
  • 8 Relationships between two or more variables: Diagrams
  • 9 Correlation coefficients: Pearson's correlation and Spearman's rho
  • 10 Regression: Prediction with precision
  • Part 3 Significance testing and basic inferential tests
  • 11 Related t-test: Comparing two samples of related/correlated/paired scores
  • 12 Unrelated t-test: Comparing two groups of unrelated/uncorrelated/independent scores
  • 13 Confidence intervals
  • 14 Chi-square: Differences between unrelated samples of frequency data
  • 15 McNemar's test: Differences between related samples of frequency data
  • 16 Ranking tests for two groups: Non-parametric statistics
  • 17 Ranking tests for three or more groups: Non-parametric statistics
  • Part 4 Analysis of variance
  • 18 Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
  • 19 Analysis of variance for one-way correlated scores or repeated measures
  • 20 Two-way analysis of variance for unrelated/uncorrelated scores
  • 21 Multiple comparison in ANOVA
  • 22 Analysis of variance for two-way correlated scores or repeated measures
  • 23 Two-way mixed analysis of variance (ANOVA)
  • 24 Analysis of covariance (ANCOVA)
  • 25 Multivariate analysis of variance (MANOVA)
  • Part 5 More advanced statistics
  • 26 Partial correlation
  • 27 Factor analysis
  • 28 Item reliability and inter-rater agreement
  • 29 Stepwise multiple regression
  • 30 Simultaneous or standard multiple regression
  • 31 Simple mediational analysis
  • 32 Hierarchical multiple regression
  • 33 Log-linear analysis
  • 34 Meta-analysis
  • Part 6 Data handling procedures
  • 35 Missing values
  • 36 Recoding values
  • 37 Computing a scale score with some values missing
  • 38 Computing a new group variable from existing group variables
  • 39 Selecting cases
  • 40 Reading ASCII or text files into the Data Editor

Dennis Howitt and Duncan Cramer are with Loughborough University.

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