Time: April 1, 2016 at 9am to April 2, 2016 at 4pm
Location: Temple University Center City
Street: 1515 Market Street
City/Town: Philadelphia, PA 19102
Website or Map: http://statisticalhorizons.co…
Event Type: training, seminar
Organized By: Statistical Horizons LLC
Latest Activity: Jan 12, 2016
Taught by Tenko Raykov, Ph.D
Researchers in the behavioral, social, biomedical, business and economic disciplines often collect data that have a hierarchical structure. Patients are nested (clustered) within treatment centers, employees are nested within firms, respondents are nested within cities, students are nested within schools, and so on. As a consequence of this nesting, the observations in the data set are not statistically independent, thus violating a basic assumption of standard methods of analysis. Ignoring the nesting effect and proceeding with conventional, single-level methods of analysis (like linear regression) can yield highly misleading results. That’s because traditional methods produce standard errors that are typically too small, leading to confidence intervals that are too short and p-values that are too low.
This two-day seminar provides a thorough introduction to multilevel modeling, a statistical framework that accounts for the nesting effect and avoids these problems, as well as those associated with earlier methods of aggregation and disaggregation. Throughout the seminar, many empirical examples are drawn from the behavioral, clinical, educational and economic disciplines. The popular software package Stata is used for all the examples, along with a detailed discussion of the command syntax and interpretation of the output.
Participants in this seminar can expect to come away with:
1. A nuanced understanding of the conceptual foundations and basic mathematical formulation of the multilevel model.
2. The ability to understand, interpret and explain the output from multilevel modeling software.
3. An appreciation of the advantages and disadvantages of multilevel modeling as compared with other approaches to nested data.
4. Practical tools and strategies for developing and testing multilevel models.
5. The ability to extend the multilevel model to dichotomous outcomes.
6. A clear understanding of the differences between fixed and random effects.