Multilevel Statistical Models
Enrollment limited: first come, first served
Limited to 25 participants.
Participants welcome at individual sessions (series)
Prereq: some knowledge of statistics, survey analysis
This course explicates basic principles for assessing causal effects in multilevel linear statistical models (synonyms: hierarchical linear models or mixed models) used to model hierarchical data structures and clustered data. The 1st session of each week will present an example from research practice and the 2nd session of that week will replicate the analysis. Although each weekly session builds on the knowledge gained from learning the material presented in earlier sessions, participants are welcome to drop in or drop out at their discretion.
Contact: Bob Smith, 8-403, x3-5122, firstname.lastname@example.org
Sponsor: Cambridge-MIT Institute
First week topics cover meta-analysis of studies of health care evaluation research.
Mon Jan 9, Thu Jan 12, 11am-12:00pm, 8-404
Second week topics cover the effects of computerization on employee discontent
Tue Jan 17, Thu Jan 19, 11am-12:00pm, 8-404
Third week topics cover sources of global human development.
Mon Jan 23, Thu Jan 26, 11am-12:00pm, 8-404
Fourth week topics cover assessments of comprehensive reform of elementary schools.
Mon Jan 30, Thu Feb 2, 11am-12:00pm, 8-404
Latest update: 07-Nov-2005