Understanding Statistics in Psychology with SPSS
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Understanding Statistics in Psychology with SPSS

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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

* Combines coverage of statistics with full guidance on how 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:

o Key concept boxes detailing important terms.

o Focus on sections exploring complex topics in greater depth.

o Explaining statistics sections clarify important statistical concepts.

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Dennis Howitt and Duncan Cramer are with Loughborough University.
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Product details

  • Paperback | 752 pages
  • 195 x 265mm
  • Harlow, United Kingdom
  • New edition
  • 8th New edition
  • 1292282304
  • 9781292282305

Table of contents

Chapter 1 Why statistics?



Part 1 Descriptive 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



Part 2 Significance testing

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



Part 3 Introduction to analysis of variance

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



Part 4 More advanced correlational statistics

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





Part 5 Assorted advanced techniques

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



Part 6 Advanced qualitative or nominal techniques

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
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About Dennis Howitt

Dennis Howitt and Duncan Cramer are based at Loughborough University.
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