For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| PSY4007 | Multivariate analysis and statistical learning | 3 | 6 | Major | Bachelor/Master | Psychology | - | No | |
| This course covers principles and practice of multivariate data analysis in psychology and related fields. Applications of multiple regression, logistic regression, principal component analysis, factor analysis, cluster analysis and other multivariate techniques for psychological research will be illustrated. In addition, supervised learning and unsupervised learning will be discussed in relation to the multivariate techniques. This course is designed for graduate students or 3rd- or 4th-year undergraduate students in psychology. | |||||||||
| PSY5136 | Structural Equation Modeling and Related Methods | 3 | 6 | Major | Master/Doctor | 1-4 | Psychology | - | No |
| This course is an introduction to structural equation modeling(SEM) which is quite broad and deep as a multivariate method. Students are required to understand the basic theory of SEM and to analyze multivariate data with hypotheses of linear structural relations. Also they are trained to be able to understand published articles demonstrating use of SEM as the major analytic method. | |||||||||
| PSY5188 | Structural Equation Modeling | 3 | 6 | Major | Master/Doctor | Psychology | Korean | Yes | |
| This course covers fundamentals of latent variable modeling, path analysis, and structural equation modeling, combining theoretical and practical perspectives. The course is designed to provide details of structural equation modeling, from the statistical underpinnings to how to conduct various types of structural equation analyses. | |||||||||
| SIC5020 | Longitudinal Data Analysis | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This course covers empirical frameworks for drawing causal inferences from longitudinal data. Topics include longitudinal study design, exploring longitudinal data, random effects and fixed effects models; and quasi-experimental research design such as diff-in-diffs regression, propensity score matching, and regression discontinuity design. | |||||||||
| SIC5021 | Social Big Data Analysis | 3 | 9 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course aims to provide the students with a knowledge and skill about how to collect, save and analyze online text data. Specifically it seeks to help students scrap and crawl text data via online news sites, blogs, and SNS and analyze the data using unsupervised machine learning techniques. It focuses on how to use beginners or intermediate levels of natural learning process (NLP) techniques and how to visualize the corpus. The analyzes center around probabilistic topi models in different levels, ranging from LDA, to DTM and ETM. This course is designed to help students apply the techniques obtained to the data the students themselves crawl and write a research note that could potentially be submitted for journal publication. | |||||||||
| SIC5022 | Predictive Modeling using Regression Analysis | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This course offers an introduction to predictive analytics and statistical learning using regression techniques. Students will be exposed to technical aspects of regression analysis, model selection, regularization, and data pre-processing, and learn how to use a programmable software in estimating and validating predictive models. This course prepares students for a more advanced course in machine learning. | |||||||||
| SIC5028 | Machine Learning with Python | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course aims that students implement machine learning algorithms with Python programming. In the beginning of this course, students will learn the basics about Python programming. In the latter part, students will implement various machine learning algorithms such as supervised and unsupervised learning with Python so that they could exactly understand the algorithms. | |||||||||
| SIC5034 | Hierarchical Linear Modeling | 3 | 6 | Major | Master/Doctor | 2-8 | Convergence for Social Innovation | English | Yes |
| The purpose of this course is to develop the skills necessary to identify an appropriate technique, estimate models, and interpret results for independent research and to critically evaluate contemporary social research using hierarchical linear modeling. Social research focuses on issues that examine the relationship between individuals and the social contexts in which they work, live, or learn. This involves multilevel research, which investigates individuals within groups. In multilevel research, the nature of the data structure is hierarchical. For example, in educational research, the data typically consists of schools and pupils within these schools. In this example, pupils are nested within schools. When analyzing multilevel data, we need special statistical skills and techniques, because single-level analysis of multilevel data brings about misleading standard errors and significance tests. The hiearchical linear modeling addresses this issue, accurately dealing with a hierarchical data set, often individuals within groups. This course will be applied in the sense that we will focus on estimating models and interpreting the results, rather than understanding in detail the mathematics behind the techniques. | |||||||||
| SIC5035 | Multivariate Regression Analysis | 3 | 6 | Major | Master/Doctor | 1-8 | Convergence for Social Innovation | Korean | Yes |
| Introduction to data analysis via linear models. Topics include basic assumptions of the linear model, methods for transforming data, estimation and interpretation of the classical linear model, derivations of the estimators of interest, and diagnostics of results and/or potential fixes for violations of assumptions. This course lays the foundations for more advanced statistical modeling techniques used in data science and academic research. | |||||||||
| SIC5038 | Longitudinal Categorical Data Analysis | 3 | 6 | Major | Master/Doctor | 1-8 | Convergence for Social Innovation | Korean | Yes |
| This course will cover the foundations of longitudinal categorical data. Upon successful completing of this course, students will be able to (a) understand the types of hypotheses and research questions for which categorical data analytical produces are used, (b) perform number of cross sectional and longitudinal analytical procedures including regression with binary, ordinal, and multinomial outcomes, survival analysis, (first- and second-order) growth curve modeling with categorical data, and (c) read and evaluate research articles regarding testing of for which cross-sectional and longitudinal categorical data analytcial procedures are used. The course topics are as follows: Review of basic regression model. Introduction to Logistic and Profit Regression. Introduction to Count Data. Introduction to Latent Growth Model. Latent Class (Transition) Model. Growth Mixture Model with categorical data. Introduction to Survival Analysis. | |||||||||
| SOC5061 | Elementary/Intermediate Statistics | 3 | 9 | Major | Master/Doctor | Sociology | Korean | Yes | |
| This class provides the Graduate-level, social-science majoring students with a variety of Elementary and Intermediate levels of statistics, besides Advanced statistics, which include descriptive and a variety of inferential statistics (e.g., z-test, t-test, χ2-test, F-test, Simple Regression, Multiple Regression, Logistic Regression, etc.). | |||||||||
| SOC5062 | Factor Analysis / Covariance Structure Analysis | 3 | 9 | Major | Master/Doctor | Sociology | - | No | |
| This class provides the Graduate-level, social-science majoring students with a variety of Advanced Statistics (besides Elementary & Intermediate Statistics), which includes, most importantly, Factor Analysis (EFA & CFA) and Covariance Structure Analysis. | |||||||||