About the summer school
Graduate students, faculty, post doctoral fellows, staff, and others are invited to attend the KU Stats Camp. This year, we are offering three weeks of sessions about R, Stata, and Structural Equation Modeling. KU Stats Camp begins in the last week of May and continues through the first two weeks of June. Participants are encouraged to enroll for all sessions in each week.
Registration is open to KU students, faculty, and staff, as well as corporate employees or affiliates of other universities. The daily price of attendance for KU affiliates is $50, while the daily price for others is $70.
Summer school agenda
Day 1—Installation & Getting to Know R
Instructor: Dr. Paul Johnson, CRMDA Director and Professor, Political Science
Installation
R documentation, interacting with the R help system, packages
User interfaces: Comparing Windows R, Emacs, Notepad++, Macintosh
Line aRt: Illustrating functions, create publication quality line art and graphic displays
Day 2—Establishing a Replicable Research Process
Instructor: Dr. Paul Johnson, CRMDA Director and Professor, Political Science
Data impact and Recode data: Wrestling with numerical, text, and factor variables
Graphical exploration and presentation scatterplots, barplots, boxplots, etc.
Exporting tables for presentations in documents: cross tabulations regression, and other tables
Day 3—Statistical Analysis the R Way
Instructor: Dr. Paul Johnson, CRMDA Director and Professor, Political Science
Regression & ANOVA
Structural Equation Modeling
Moderation and Mediation
Day 4—R Toolkit for Interacting with Data
Instructor: Dr. Paul Johnson, CRMDA Director and Professor, Political Science
Matrix Algebra with R
Iteration concepts in R: for, lapply
Subsetting data, processing subsets and merging results
Creating R functions to customize analysis
Day 5—Monte Carlo Programming and Power Analysis Instructors: Dr. Paul Johnson and Dr. Ben Kite
Monte Carlo simulation in R
Power analysis: definition and implications
Using Monte Carlo simulation to estimate power
Day 6—An Introduction to Stata for Statistical Analysis
Instructor: Dr. Jacob Fowles, CRMDA and School of Public Affairs & Administration
The Stata interface: point & click, menus, command line, and the do-file editor
- Finding help, including web sources
- Editing data within Stata, the project, data, and variable views
- Managing collaborative projects
- Reproducibility and documentation options within Stata
Day 7—Reliable and Reproducible Workflows Using Stata
Instructor: Dr. Jacob Fowles, CRMDA and School of Public Affairs & Administration
Common syntax and structure for writing code
- Alternatives for editing do-files
- Workflow concepts, customized do-file template that facilitates project organization
- Importing, organizing, recoding, and labeling variables
- Generating descriptive plots and tables
Day 8—Automating Common Tasks in Stata
Instructor: Dr. Jacob Fowles, CRMDA and School of Public Affairs & Administration
- Installing and using prepared Stata packages
- Estimating quantitative models and capturing estimation output
- Stata macros
- Produce “pretty” output (summary statistics, regressions results, etc.), using the estout suite of command and putexcel commands
Day 9—Data Visualization in Stata
Instructor: Dr. Jacob Fowles, CRMDA and School of Public Affairs & Administration
- Programs for creating plots, charts, and graphs
- Customizing graphics using Stata’s suite of graphing commands
- Stata’s “margins” and “marginsplot” command for visualizing regression results
Day 10—Introduction to Structural Equation Modeling
Instructor: Dr. Edgar Merkle, University of Missouri, Department of Psychological Sciences
- Factor Analysis Overview
- Confirmatory Factor Analysis By Example
- Visualizing SEM with Path diagrams
- Diagnostics for Estimated Models
Day 11—Using Mplus and R Instructor: TBA
- Introducing the CRMDA SEM Code Repository
- Mplus
- Using R for basic SEM: the lavaan package
Day 12—Interaction Effects in SEM
Instructor: Dr. Holger Brandt, University of Kansas, Department of Psychology
- Lavaan overview and Product Indicators
- The R package nlsem
- Latent moderate structural equations (LMS)
- Graphical illustrations
Day 13—Extensions to semiparametric approaches
Instructor: Dr. Holger Brandt, University of Kansas, Department of Psychology
- Structural equation mixture modeling (SEMM)
- SEMM with the R packages nlsem and plotSEMM
- Robust alternatives for non-normality
Day 14—Bayesian alternatives and multilevel SEM
Instructors: Dr. Holger Brandt, University of Kansas, Department of Psychology
- (Short) introduction to Bayesian modeling
- Introduction to stan (a Bayesian analysis framework) and rstan (a R package for usage of stan)
- Interaction models with regression and the multilevel framework in stan
- Multilevel SEM with interaction effects in stan
For more information click "Further official information" below.
This opportunity has expired. It was originally published here:
http://crmda.ku.edu/statscamp