Description

Book Synopsis

Dennis Howitt and Duncan Cramer are based at Loughborough University.



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

Understanding Statistics in Psychology with SPSS

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A Paperback by Dennis Howitt, Duncan Cramer

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    View other formats and editions of Understanding Statistics in Psychology with SPSS by Dennis Howitt

    Publisher: Pearson Education
    Publication Date: 3/19/2020 12:00:00 AM
    ISBN13: 9781292282305, 978-1292282305
    ISBN10: 1292282304
    Also in:
    Mathematical and statistical software Psychological testing and measurement

    Description

    Book Synopsis

    Dennis Howitt and Duncan Cramer are based at Loughborough University.



    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

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