Upon successful completion of the course, students are expected to:
1. Be able to choose, apply and explain statistical methods that are appropriate for data coming from relatively complex experimental research. This is accomplished by dealing with numerical examples and exercises.
2. Acquire basic knowledge to be able to assess the main psychometric characteristics of a psychological test on the basis of particular statistical methods.
3. Acquire the appropriate knowledge and skills to apply the statistical package SPSS in order to enter, transform and analyze data of an empirical research and explain the results applying the statistical methodology that has been taught in the courses Statistics I and II.
Course Content (Syllabus)
Introduction to ANOVA. One-way analysis of variance, one-way analysis of variance with repeated measures, effect size, introduction to multiple comparisons, Scheffe’s test. Introduction to two-way ANOVA and the analysis of higher order experimental designs. Non-parametric statistical methods: Mann-Whitney U test, Sign test, Wilcoxon signed-rank test, Spearman’s rank-order correlation. Non-parametric analysis of variance: Kruskal-Wallis test, Friedman test, Cochran’s Q test, non-parametric tests for multiple comparisons. Reliability of measurements, methods of estimating reliability, Cronbach and Kuder-Richardson coefficients of internal consistency. Introduction to principal component analysis. Statistical data analysis using SPSS (in the computer lab).
ANOVA, non parametric methods, Non-parametric ANOVA, reliability, principal component analysis, SPSS