Learn Bayesian Inference, a core methodology of statistical reasoning, and the Hayashi Quantification Method specialized for categorical data analysis. Using SPSS Bayesian Statistics, you will practice various statistical analyses including binomial proportions, mean differences, correlation coefficients, regression models, and ANOVA, while mastering Quantification Methods 1–4 to derive internal insights from categorical variables.

Learn multi-level data analysis using Structural Equation Modeling (SEM) bootstrapping techniques and Hierarchical Linear Modeling (HLM). This is an advanced course designed to help you grow into a statistical expert capable of analyzing complex research data, covering everything from advanced AMOS features to the practical application of HLM. You will master both theory and practice through real-world research cases in the fields of education, psychology, sociology, and health.

The 65th Open House is about showing how data analysis is enriched by changing the type of variables. It mainly covers scoring, binning, and optimal scaling. The 66th Open House is about learning in detail about the concept and understanding of marginal effects and subgroup analysis.

The 63rd Open House explains the concept of effect size and SPSS's effect size output. It also covers the concept of sample size and power and SPSS's power analysis method. The 64th Open House learns in detail about the extension and evolution of linear regression.

You will learn through hands-on practice everything from the basic usage of KNIME to advanced data analysis techniques, and acquire analysis skills that can be applied to actual data.

Learn about PSM using KoreaPlus SPSS.

Learn about Text Analytics using KoreaPlus SPSS.