Understanding Statistics in Psychology
Description
Become confident with the most common statistical techniques so that you can grasp the fundamentals and transition from a student to a professional researcher
Now in its ninth edition, Understanding Statistics in Psychology , by Dennis Howitt and Duncan Cramer continues to provide an accessible introduction to the intimidating subject of statistics in psychology for students of all years and abilities.
Clear explanations and diagrams break down the statistical techniques that are used in modern psychological research and updated examples of real-life studies bring the topic to life by showing you how statistics are used in practice.
The new software-agnostic approach of this edition means that you will gain a solid understanding of statistics which can be applied to whichever statistical package you are using to analyse your data. The modular structure of this text and its small accessible chapters also mean that it is easy to dip in and out of, concentrating on the techniques that are the most relevant for you and your own research projects.
This text does not just focus on how to analyse data but also contains clear and detailed guidance of the whole research process, from how to choose the appropriate tests, to interpreting your findings and successfully writing up your research.
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Persons
Dennis Howitt is a reader in Psychology at Loughborough University, a chartered forensic psychologist and fellow of the British Psychological Society, with a specific interest in the study of mass communications and the application of psychology to social issues.
Duncan Cramer is an emeritus professor at Loughborough University with a specific interest in topics such as mental health, personality, personal relationships, organizational commitment, psychotherapy and counselling.
Content
Preface
- Why statistics?
- Some basics: Variability and measurement
- Describing variables: Tables and diagrams
- Describing variables numerically: Averages, variation and spread
- Shapes of distributions of scores
- Standard deviation and z-scores: Standard unit of measurement in statistics
- Relationships between two or more variables: Diagrams and tables
- Correlation coefficients: Pearsons correlation and Spearman's rho
- Regression: Prediction with precision
- Samples from populations
- Statistical significance for the correlation coefficient: Practical introduction to statistical inference
- Standard error: Standard deviation of the means of samples
- Related or paired-samples t-test: Comparing two samples of related/correlated/paired scores
- Unrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scores
- What you need to write about your statistical analysis
- Confidence intervals
- Effect size in statistical analysis: Do my findings matter?
- Chi-square: Differences between samples of frequency data
- Probability
- One- versus two-tailed or -sided significance testing
- Ranking tests: Nonparametric statistics
- Variance ratio test: F-ratio to compare two variances
- Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
- ANOVA for correlated scores or repeated measures
- Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?
- Multiple comparisons in ANOVA: A priori and post hoc tests
- Mixed-design ANOVA: Related and unrelated variables together
- Analysis of covariance (ANCOVA): Controlling for additional variables
- Multivariate analysis of variance (MANOVA)
- Discriminant (function) analysis especially in MANOVA
- Statistics and analysis of experiments
- Partial correlation: Spurious correlation, third or confounding variables, suppressor variables
- Factor analysis: Simplifying complex data
- Multiple regression and multiple correlation
- Path analysis
- Analysis of a questionnaire/survey project
- Meta-analysis: Combining and exploring statistical findings from previous research
- Reliability in scales and measurement: Consistency and agreement
- Influence of moderator variables on relationships between two variables
- Statistical power analysis: Getting the sample size right
- Log-linear methods: Analysis of complex contingency tables
- Multinomial logistic regression: Distinguishing between several different categories or groups
- Binomial logistic regression
- Data mining and Big Data
- Towards a masterplan
Appendices
Glossary
References
Index