Friday, March 30, 2012

Big Data: Quantitative Analysis Workshops

The Office of Faculty Enhancement and the Quantitative Analysis Faculty Learning Community introduce 

Big Data: Quantitative Analysis Workshops
coming in April 2012.  These workshops are designed for faculty who are interested in learning more about recent developments in quantitative analysis and would like to learn how these techniques could be relevant to their research. The Learning Community has organized the two workshops below.

Friday, April  6, 2012
1:00p.m. - 3:00 p.m.
Social Sciences Building (51), Room 1202
Register by emailing
In this workshop, Dr. Lakshmi Goel, Assistant Professor of Information Systems, Coggin College of Business, will provide a basic introduction to Structural Equation Modeling (SEM) as a second-generation statistical technique for analysis. Participants will contrast SEM with first generation techniques, such as regression, and will discuss covariance and partial least squares based approaches to SEM. Then, using AMOS software and a sample dataset, participants will engage in a hands-on example of SEM. Researchers in the Social and Behavioral Sciences, Health Sciences, and in disciplines that deal with large datasets with many interrelated variables particularly will be interested in attending.

Thursday, April  12, 2012
3:00 p.m. - 5:00 p.m.
College of Education Building (57), Room 2520
In this workshop, Dr. Dan Richard, Associate Professor, Psychology, will introduce meta-analysis as a way to summarize large bodies of research and to conduct program evaluation. Participants will discuss both fixed-effects and random-effects approaches to meta-analysis, and will review standard metrics in meta-analysis (using standardized mean differences and correlation coefficients) as well as raw metrics. Then, using SPSS and an example dataset, participants will conduct a small meta-analysis and produce plots to illustrate results as well as check for publication bias. Researchers in the Sciences, Education, Health Sciences, and disciplines where research on a topic is conducted across labs and programs especially will be interested in attending.

For further information, contact Dan Richard at or Albert Loh at

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