Introduction to Statistics in Psychology
3rd Edition

By Dennis Howitt & Duncan Cramer
December 2005
ISBN: 0131399853
257 pages, Illustrated, 7 ½” x 9 ¾”
$67.50 Paper Original


Statistics can be tricky, but the 3rd edition of Howitt and Cramer’s popular statistics text makes it much easier. Users of statistics at all levels find this comprehensive and modern approach indispensable.

This new edition has been redesigned for maximum clarity with difficult concepts explained in simple steps using a wide variety of examples.
This book can be used on its own or in conjunction with ‘Introduction to SPSS 12 in Psychology’ and Introduction to Research Methods in Psychology’ by the same authors.

Part 1: Descriptive statistics
1. Why you need statistics: Types of data
1.1 Introduction
1.2 Variables and measurement
1.3 Major types of measurement
Key points
2. Describing variables: Tables and diagrams
2.1 Introduction
2.2 Choosing tables and diagrams
2.3 Errors to avoid
Key points
3. Describing variables numerically: Averages, variation and spread
3.1 Introduction
3.2 Typical scores: mean, median and mode
3.3 Comparison of mean, median and mode
3.4 The spread of scores: variability
Key points
4. Shapes of distributions of scores
4.1 Histograms and frequency curves
4.2 The normal curve
4.3 Distorted curves
4.4 Other frequency curves
Key points
5. Standard deviation: The standard unit of measurement in statistics
5.1 Introduction
5.2 Theoretical background
5.3 Measuring the number of standard deviations – the z-score
5.4 A use of z-scores
5.5 The standard normal distribution
5.6 An important feature of z-scores
Key points
6. Relationships between two or more variables: Diagrams and tables
6.1 Introduction
6.2 The principles of diagrammatic and tabular presentation
6.3 Type A: both variables numerical scores
6.4 Type B: both variables nominal categories
6.5 Type C: one variable nominal categories, the other numerical scores
Key points
7. Correlation coefficients: Pearson correlation and Spearman’s rho
7.1 Introduction
7.2 Principles of the correlation coefficient
7.3 Some rules to check out
7.4 Coefficient of determination
7.5 Significance testing
7.6 Spearman’s rho – another correlation coefficient
7.7 An example from the literature
Key points
8. Regression: Prediction with precision
8.1 Introduction
8.2 Theoretical background and regression equations
8.3 Standard error: how accurate are the predicted score and the regression equations?
8.4 Notes and recommendations
Key points
Part 2: Significance testing
9. Samples and populations: Generalising and inferring
9.1 Theoretical considerations
9.2 The characteristics of random samples
9.3 Confidence intervals
Key points
10. Statistical significance for the correlation coefficient: A practical introduction to statistical inference
9.4 Theoretical considerations
9.5 Back to the real world: the null hypothesis
10.3 Pearson’s correlation coefficient again
10.4 The Spearman’s rho correlation coefficient
Key points
11. Standard error: The standard deviation of the means of samples
11.1 Theoretical considerations
11.2 Estimated standard deviation and standard error
Key points
12. The t-test: Comparing two samples of correlated/related scores
12.1 Introduction
12.2 Dependent and independent variables
12.3 Some basic revision
12.4 Theoretical considerations
12.5 Cautionary note
Key points
13. The t-test: Comparing two samples of unrelated/uncorrelated scores
13.1 Introduction
13.2 Theoretical considerations
13.3 Standard deviation and standard error
13.4 Cautionary note
Key points
14. Chi-square: Differences between samples of frequency data
14.1 Introduction
14.2 Theoretical issues
14.3 Partitioning chi-square
14.4 Important warnings
14.5 Alternatives to chi-square
14.6 Chi-square and known populations
14.7 Chi-square for related samples – the McNemar Test
14.8 Example from the literature
Key points
15. Probability
15.1 Introduction
15.2 The principles of probability
15.3 Implications
Key points
16. Reporting significance levels succinctly
16.1 Introduction
16.2 Shortened forms
16.3 Examples from the published literature
Key points
17. One-tailed versus two-tailed significance testing
17.1 Introduction
17.2 Theoretical considerations
17.3 Further requirements
Key points
18. Ranking tests: Nonparametric statistics
18.1 Introduction
18.2 Theoretical considerations
18.3 Nonparametric statistical tests
18.4 Three or more groups of scores
18.5 Notes and recommendations
Part 3: Introduction to analysis of variance
19. The variance ratio test: The F-ratio to compare two variances
19.1 The research problem
19.2 Theoretical issues and an application
Key points
20. Analysis of variance (ANOVA): Introduction to the one-way unrelated or uncorrelated ANOVA
20.1 Introduction
20.2 Some revision and some new material
20.3 Theoretical considerations
20.4 Degrees of freedom
20.5 The analysis of variance summary table
20.6 Quick calculation methods for ANOVA
Key points
21. Analysis of variance for correlated scores or repeated measures
21.1 Introduction
21.2 Theoretical considerations
21.3 Examples
Key points
22. Two-way analysis of variance for unrelated/uncorrelated scores: Two experiments for the price of one?
22.1 Introduction
22.2 Theoretical considerations
22.3 Steps in the an.
22.4 More on interactions
22.5 Calculation of two-way ANOVA using quick method
22.6 Three or more independent variables
Key points
23. Multiple comparisons in ANOVA: Just where do the differences lie?
23.1 Introduction
23.2 Methods
23.3 Planned versus a posteriori (post hoc) comparisons
23.4 The Scheffé Test for one-way ANOVA
23.5 Multiple comparisons for multifactorial ANOVA
Key points
24. More analysis of variance designs: Mixed-design ANOVA and analysis of covariance (ANCOVA)
Overvie.2 Mixed designs and repeated measures
24.3 Analysis of covariance
Key points
25. Statistics and the analysis of experiments
25.1 Introduction
25.2 The Patent Stats Pack
25.3 Checklist
25.4 Special cases
Key points
Part 4: More advanced correlational statistics
26. Partial correlation: Spurious correlation, third or confounding variables, suppressor variables
26.1 Introduction
26.2 Theoretical considerations
26.3 The calculation
26.4 Interpretation
26.5 Multiple control variables
26.6 Suppressor variables
26.7 An example from the research literature
26.8 An example from a student’s work
Key points
27. Factor analysis: Simplifying complex data
27.1 Introduction
27.2 A bit of history
27.3 Concepts in factor analysis
27.4 Decisions, decisions, decisions
27.5 Exploratory and confirmatory factor analysis
27.6 An example of factor analysis from the literature
27.7 Reporting the results
Key points
28. Multiple regression and multiple correlation
28.1 Introduction
28.2 Theoretical considerations
28.3 Stepwise multiple regression example
28.4 Reporting the results
28.5 An example from the published literature
Key points
29. Path analysis
29.1 Introduction
29.2 Theoretical considerations
29.3 An example from published research
29.4 Reporting the results
Key points
30. The analysis of a questionnaire/survey project
30.1 Introduction
30.2 The research project
30.3 The research hypothesis
30.4 Initial variable classification
30.5 Further coding of data
30.6 Data cleaning
30.7 Data analysis
Key points
Part 5:Assorted advanced techniques
31. Statistical power analysis: Do my findings matter?
31.1 Statistical significance
31.2 Method and statistical power
31.3 Size of the effect in studies
31.4 An approximation for nonparametric tests
31.5 Analysis of variance (ANOVA)
Key points
32. Meta-analysis: Combining and exploring statistical findings from previous research
32.1 Introduction
32.2 The Pearson correlation coefficient as the effect size
32.3 Other measures of effect size
32.4 Effects of different characteristics of studies
32.5 First steps in meta-analysis
32.6 Illustrative example
32.7 Comparing a study with a previous study
32.8 Reporting the results
Key points
33. Reliability in scales and measurement: Consistency and agreement
33.1 Internal consistency of scales and measurements
33.2 Item-analysis using item–total correlation
33.3 Split-half reliability
33.5 Agreement between raters
Key points
34. Confidence intervals
34.1 Introduction
34.2 The relationship between significance and confidence intervals
34.3 Regression
34.4 Other confidence intervals
Key points
Part 6: Advanced qualitative or nominal techniques
35. The analysis of complex contingency tables: Log-linear methods
35.1 Introduction
35.2 A two-variable example
35.3 A three-variable example
35.4 Reporting the results
Key points
36. Multinomial logistic regression: Distinguishing between several different categories or groups
36.1 Introduction
36.2 Dummy variables
36.3 What can multinomial logistic regression do?
36.4 Worked example
36.5 Accuracy of the prediction
36.6 How good are the predictors?
36.7 The prediction
36.8 What have we found?
36.9 Reporting the findings
Key points
37. Binomial Logistic Regression
37.1 Introduction
37.2 Typical example
37.3 Applying the logistic regression procedure
37.4 The regression formula
37.5 Reporting the findings
Key points
Appendix A: Testing for excessively skewed distributions
Appendix B: Large sample formulae for the nonparametric tests
Appendix B2: Nonparametric tests for three or more groups
Appendix C: Extended table of significance for the Pearson correlation coefficient
Appendix D: Table of significance for the Spearman correlation coefficient
Appendix E: Extended table of significance for the t-test
Appendix F: Table of significance for Chi-square
Appendix G: Extended table of significance for the sign test
Appendix H: Table of significance for the Wilcoxon Matched Pairs Test
Appendix I: Table of significance for the Mann-Whitney U-test
Appendix J: Table of significance values for the F-distribution
Appendix K: Table of significant values of t when making multiple t-tests

• An accessible introduction to all the introductory and advanced statistics students need as undergraduates
• Comprehensive coverage including significance testing, confidence intervals, reliability, meta-analysis, regression (including logistic regression), item analysis and other essential techniques in modern research
• Teaches how to choose appropriate statistical tests and how to analyse data of all sorts


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