IN PERSON – 5-day Statistics Short Course
An intermediate 5-day course introducing several popular data mining approaches such as regression based methods (ridge and lasso regularized regression, regression splines), tree methods (random forests, boosted trees), and support vector machines, and their application to empirical data. The course combines lectures and hands-on practice using R.
- Review of linear regression and the least squares criterion
- Regularization methods (ridge regression, lasso, elastic net)
- Regression splines
- Prediction error and k-fold cross validation
- Tree methods to predict categorical or continuous outcomes (CART, random forest, boosting
- Support vector machines for classification
In the age of rapidly increasing data collection endeavors it has become more and more important to understand how to find structure in data, especially when substantive theory about structural relations between the collected variables is not yet fully developed. This short course starts with briefly outlining the key differences and similarities between standard parametric modeling (e.g., linear regression) and data mining approaches. The course provides basic insights into a number of popular methods such as regression methods (ridge regression and the lasso, regression splines), tree methods (CART, random forests, boosting), and support vector machines. The emphasis is on a conceptual understanding of these methods and their appropriate application to empirical data. Importantly, these methods are useful not only for large data collections, but also more generally for exploratory analyses when the substantive theory to design and fit suitable parametric models (e.g. SEM) is not available. Data mining (aka statistical learning) is used in a wide variety of fields including but not limited to public health, education, biology, and the different social sciences.
Participants are invited to discuss potential data mining applications to their particular field of interest during individual consultations with the instructor scheduled at the end of the second and third day.
Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.
Instructor: Gitta Lubke, Ph.D.
Gitta Lubke is a Full Professor in the Department of Psychology/Quantitative Area at the University of Notre Dame. Her research interests are in data mining and general latent variable modeling. In addition to the challenges of analysing complex human behavior such as psychiatric disorders, she is interested in the analysis of genetic data. Related areas of expertise include mixture models, twin models, multi-group factor analysis and measurement invariance, longitudinal analyses, and the analysis of categorical data.
APA Continuing Education Credits:
This course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.
Materials, downloads, recorded course video viewable for up to one year.