Learning to use statistical tests in psychology /
This widely acclaimed text is an accessible and comprehensible introduction to the use of statistical tests in psychology experiments. Its key objective is to enable students to select appropriate statistical tests to evaluate the significance of data obtained from psychological experiments. An impo...
Clasificación: | Libro Electrónico |
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Autor principal: | |
Otros Autores: | |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Maidenhead ; New York :
Open University Press,
2005.
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Edición: | 3rd ed. |
Temas: | |
Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Research in Psychology: Psychological research and statistics
- Variability in human behaviour
- Relationships between variables
- Research hypothesis
- Null hypothesis
- Rationale for using statistical tests
- Participants in psychological research
- Experiments in psychology: The experimental method
- Independent and dependent variables
- Experimental and control conditions
- Same participants (related designs)
- Different participants (unrelated designs)
- Selecting statistical tests: basic principles
- Number of experimental conditions
- Related and unrelated designs
- Non-parametric or parametric tests
- Using the Decision Chart
- Using statistical tests: Variability in data
- Probabilities in statistical tables
- Selecting a level of significance
- One-tailed and two-tailed hypotheses
- Statistical tests for experiments: Introduction to non-parametric tests
- Wilcoxon test (related)
- Mann-Whitney test (unrelated)
- Introduction to parametric t tests
- t test (related)
- t test (unrelated)
- Friedman test (related)
- Kruskal-Wallis test (unrelated)
- Analysis of variance: Introduction to ANOVA
- One-way ANOVA (unrelated)
- One-way ANOVA (related)
- Comparisons between ANOVA conditions
- Introduction to two-way ANOVA
- Two-way ANOVA (unrelated)
- Two-way ANOVA (related)
- Relationships between variables: Chi-square
- Pearson product moment correlation
- Introduction to sijmple linear regression
- Multiple regression
- General linear model
- Recommended reading.