Social network analysis is becoming increasingly popular in social, educational, and psychological sciences. This interactive course intends to provide participants with a detailed introduction, practical examples, and demonstration of analyzing social network data using the free software R. Topics covered include (1) Network Data; (2) Network Visualization; (3) Network Statistics; (4) Basic and Advanced Network Models. Especially, we will cover classical models such as Stochastic Block Model, Exponential Random Graph Model, Latent Space Model, and the newly developed techniques such as Latent Factor Space Modeling and Network Mediation Analysis.
Modeling longitudinal data is one of the most active areas of research in social and behavioral sciences because longitudinal research provides valuable insights into change and causal relationships. The application of Bayesian methods in longitudinal research has gained increasing popularity. Taught by four quantitative researchers who are active in developing Bayesian methods and longitudinal data analytical methods, this workshop will teach participants how to analyze longitudinal data using Bayesian statistics. We will introduce the basic idea of Bayes' theorem first and move on to models including multilevel models, growth curve models, growth mixture models, and longitudinal structural equation models. Concrete examples will be provided to illustrate how to compute, report, and interpret Bayesian modeling results with empirical psychological data. Additionally, we will teach the application of Bayesian robust methods for nonnormal data ignorable and non-ignorable missing data.
This workshop teaches researchers how to conduct a statistical power analysis to determine the sample size for a planned study when applying structural equation modeling. Participants will learn statistical power analysis for mediation analysis, path analysis, and structural equation modeling using both traditional methods and Monte Carlo methods. Free software WebPower (https://webpower.psychstat.org) will be used to illustrate how to conduct power analysis in practice.
- Understanding Machine Learning Concepts
- Data Preprocessing and Feature Engineering
- Model Selection and Evaluation
- Implementing Machine Learning Algorithms
- Hyperparameter Tuning
- Model Deployment and Integration
- Ethical Considerations and Bias in Machine Learning
- Continuous Learning and Model Maintenance
You'll learn the fundamentals of data analysis with Python. Through the workshop, you will understand the six steps of data analysis processes as well as know how to read data from sources like CSV files and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.
This Training is design to introduce the foundamental on how to shape and transform your data before the data analysis using Power Query Editor. You will discover how to navigate this intuitive tool and get to grips with Power BI’s Data, Model, and Report views. You’ll load multiple datasets in the Data view, build a data model to understand the relationships between your tables in Model view, and create your first bar graph and interactive map visualization in Report view. You’ll also practice using Power Query Editor to prep your data for analysis.
Through hands-on exercises, you’ll learn how to change and format a wide range of visualizations, before moving on to sorting data and creating hierarchies—making it possible for you to drill into your reports.
