Understanding Statistics in Psychology with SPSS
8th Edition
Published on 19. March 2020
Book
Paperback/Softback
752 pages
978-1-292-28230-5 (ISBN)
Description
Develop confidence conducting statistical analysis with this trusted text Understanding Statistics in Psychology with SPSS, eighth edition, by Dennis Howitt and Duncan Cramer is the comprehensive guide that helps you conduct statistical analyses using SPSS with confidence.
Combining coverage of statistics with full guidance on how to use SPSS to analyse data, the book's straightforward content is neatly organised into short, accessible chapters that can be used in class or for independent study. Clear diagrams and full colour screenshots from SPSS make the text suitable for beginners, while the broad coverage of topics helps you progress to more advanced techniques.
This edition provides an engaging learning aid packed with examples from real psychological studies that illustrate how statistical techniques are used in practice. Learning features including key concept boxes, 'focus' sections and 'explaining statistics' sections ensure solid understanding of underpinning principles.
This trusted book is the ideal companion for undergraduate students in psychology.
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:
Key conceptboxes detailing important terms.
Focus onsections exploring complex topics in greater depth.
Explaining statisticssections clarify important statistical concepts.
Dennis Howitt and Duncan Cramer are with Loughborough University.
Combining coverage of statistics with full guidance on how to use SPSS to analyse data, the book's straightforward content is neatly organised into short, accessible chapters that can be used in class or for independent study. Clear diagrams and full colour screenshots from SPSS make the text suitable for beginners, while the broad coverage of topics helps you progress to more advanced techniques.
This edition provides an engaging learning aid packed with examples from real psychological studies that illustrate how statistical techniques are used in practice. Learning features including key concept boxes, 'focus' sections and 'explaining statistics' sections ensure solid understanding of underpinning principles.
This trusted book is the ideal companion for undergraduate students in psychology.
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:
Key conceptboxes detailing important terms.
Focus onsections exploring complex topics in greater depth.
Explaining statisticssections clarify important statistical concepts.
Dennis Howitt and Duncan Cramer are with Loughborough University.
More details
Edition
8th edition
Language
English
Place of publication
Harlow
United Kingdom
Target group
College/higher education
Dimensions
Height: 264 mm
Width: 193 mm
Thickness: 28 mm
Weight
1380 gr
ISBN-13
978-1-292-28230-5 (9781292282305)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Dennis Howitt | Duncan Cramer
Understanding Statistics in Psychology
Book
11/2024
9th Edition
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Understanding Statistics in Psychology with SPSS
E-Book
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8th Edition
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Dennis Howitt | Duncan Cramer
Understanding Statistics in Psychology with SPSS
Book
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7th Edition
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Persons
Dennis Howitt and Duncan Cramer are based at Loughborough University.
Content
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
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