Understanding Statistics in Psychology with SPSS eBook, 8th Edition

Dennis Howitt all

Understanding Statistics in Psychology with SPSS eBook, 8th Edition

By Dennis Howitt, Duncan Cramer
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Dennis Howitt all
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A clear and comprehensive introduction to Statistics with step by step guidance on using SPSS to carry out statistical analysis. Understanding Statistics in Psychology with SPSS  is geared towards helping students to properly understand statistical techniques so gaining the confidence to apply them with the help of SPSS.

  • 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.
  • 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: 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