(in Polish) Statystyka w psychologii 2 2404-P-2-SP-sj

1. Student's t-test for repeated measures.
Nomenclature. Assumptions of the test, what data can be used.
The test statistic and its form. How the value of the test statistic is obtained. What is the distribution of the test statistic? The Cohen's d effect size for the scheme of two dependent groups.

2. One-way analysis of variance (ANOVA)
Nomenclature. Factor and dependent variable.
Data requirements (what conditions should be met to use ANOVA).
F test (used in ANOVA). The test statistic of the F test. What is meant by within-group and between-group variability. How the test statistic is calculated. What values indicate that a factor differentiates the means of the dependent variable. Based on what distribution is the p-value (statistical significance) calculated. What is the distribution of the F-Snedecor statistic. The degrees of freedom and what they mean.
Effect size in the ANOVA test, what it means, what information it gives. Thresholds according to Cohen,
Continuation of ANOVA analysis - post-hoc tests. Effect sizes for post-hoc test.

3. Simple and multiple linear regression.
Relationships between correlation and regression analysis. The difference between the two.
Assumptions of the regression model - what variables can we use? What relationship should they have?
Simple (univariate) regression analysis.
Nomenclature - regression; dependent variable and independent variable; predictor; explained/explained variable, explanatory/explanatory variable; linear combination; regression residuals; predicted value notation
What is prediction based on? How to do it.
Interpretation of regression coefficients, standardized and non-standardized,
Regression analysis as a statistical model - model assumptions, regression coefficients (interpretation)
Recording of regression results in accordance with the APA. Diagnostics of the regression model - What should have a normal distribution? Dependent variable, independent variable, something else?
R square adjusted, explained variation, Tests of statistical significance of regression coefficients.

4. Exploratory factor analysis
Nomenclature. What are observable variables and what are latent variables? Correlation matrix and spherical matrix. Testing of assumptions: the Bartlett test. KMO measure. Determinant of the correlation matrix - what number can the determinant of the matrix be? What does E mean in the notation under the table of the correlation matrix?
Eigenvalues of a correlation matrix - what number can the eigenvalue be?
Scatter plot - construction (what is on the horizontal axis and what is on the vertical axis). What does a scatter plot look like, and what can it not look like?
Criteria for determining the number of factors (based on the percentage of explained variation, Catell's criterion/slope graph and Kaiser's criterion).
Rotation - what is the difference for and what types are there
Factor loadings and cross-loadings - their interpretation and selection of questionnaire items for the final version.

5 Bootstrap methods.
What is it and what is it used for? A sampling scheme with and without replacement. The number of elements in a bootstrap sample.

6. Power analysis - what is it and what is it used for? Error of the 1st, 2nd type, effect size and sample size - the relationship between them. Dependence of test power on effect size and sample size. Winner's curse and statistical oversignificance (overpowered studies) and their implications for the scientific literature.