Course outline
Thecourse introduces the core methods of latent variable modeling (LVM), including structural equation models (SEM) and Latent growth models (LGM), which uses in the structural equation modeling framework to estimate growth trajectories. These are a family of statistical approaches that explore complex relationships between and among latent (unobserved) and observed variables, as well as the longitudinal analysis technique to estimate growth over a period of time. The course will cover LVM with cross-sectional data in single and multiple-group settings and the methodology of LGM to investigate systematic change and growth and inter-individual variability in the change.
The course emphasizes the empirical applications of LVM and LGM techniques to broadly address relevant questions in the different disciplines. Course lectures, readings, and assignments will reflect this applied focus. At the end of the course, the student is expected to understand the essential issues within SEM and LGM. The students should be able to carry out analysis independently.
Lecturer
Fan Y. Wallentinis a Professor of Statistics at Uppsala University, Sweden. She received her Ph.D. in Statistics in 1997. She is a recipient of the Arnberg Prize from the Swedish Royal Academy of Sciences. Dr. Wallentin’s research program is on the theory and applications of latent variable modeling and other types of multivariate statistical analysis, particularly their applications in the social and behavioral sciences. She has published research articles in several leading statistics and psychometrics journals. She has taught courses related to latent variable modeling in Sweden, USA, China, and several European countries. She has broad experience in statistical consultation for researchers in social and behavioral sciences.
Prerequisites
This course suits students interested in analyzing the causal relationships among latent variables. The participants are expected to understand statistics, economics, and other social sciences with quantitative orientations. Students with their own research projects and data that can apply the methodology from the course are especially welcome.
Register the course
Please scan this QR code to register. Registration is open tillNov11,12:00.
本课程受中央财经大学引智项目支持。
Schedule and contents
WeekOne |
2022-11-12, 15.00 – 18.00 |
Measurement Theory |
2022-11-13, 15.00 – 18.00 |
Measurement Models |
2022-11-16, 15.00 – 17.00 |
Computer lab exercise |
Week Two |
2022-11-19, 15.00– 18.00 |
Structural equation models for cross-sectional data |
2022-11-20, 15.00– 18.00 |
Structural equation models for multiple samples |
2022-11-23, 15.00 – 17.00 |
Computer lab exercise |
WeekThree |
2022-11-26, 15.00– 18.00 |
Structural equation models for categorical data |
2022-11-27, 15.00– 18.00 |
Latent variable models for longitudinal data |
2022-11-27, 15.00– 17.00 |
Computer lab exercise |
Week Four |
2022-12-03, 15.00– 18.00 |
Latent growth curve models |
2022-12-04, 15.00 – 18.00 |
Final computer exam |
