During this course you will learn: ✓ SPSS ✓ R Studio ✓jamovi/jasp ✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS ✓ Simple and multiple linear regression and Logistic regression ✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers) ✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means) ✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman) ✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters) ___________________________________ ✓ My courses are based on exercises with the essentials of the course to remember. ✓ Working method for better understanding. ✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.

During this course you will learn: ✓ SPSS, jamovi, jasp ✓ R Studio ✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS ✓ Simple and multiple linear regression and Logistic regression ✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers) ✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means) ✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman) ✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters) ___________________________________ ✓ My courses are based on exercises with the essentials of the course to remember. ✓ Working method for better understanding. ✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.