Events

We usually hold research colloquia and software workshops on Fridays and Saturdays, but there may be variations.  Please take note of the dates and times for the particular events.

Up-Coming Presentations and Workshops:

DDI: What the Data Documentation Initiative means for you

Larry Hoyle, Senior Scientist, KU Institute for Policy & Social Research
Friday, September 29, 2017 -
3:00pm to 4:00pm
Watson Library, Room 455

Abstract: The Data Documentation Initiative (https://www.ddialliance.org) is an international effort to develop standard methods for recording data and tracking changes in storage formats and coding. A new version of the DDI standard is nearing completion. This talk will discuss where the DDI project has been and where it is likely to go.

Weekly Colloquium

Using R and Lavaan for SEM

CRMDA Staff
Saturday, October 21, 2017 -
1:00pm to 4:00pm
Watson Library, Room 455

SEM-in-R!

This seminar will extend the R methods for regression analysis to Structural Equation Models (SEM). We will introduce model syntax, with examples of how to run a path analysis, confirmatory factor analysis, and latent regression models, as well as how to extract various kinds of output. Advanced topics include comparing nested models, using equality constraints across multiple groups or repeated measures, how to handle categorical indicators, and how to run models when data are missing.

These topics will be addressed through hands-on activities with example data sets and R code templates that you can take and re-use for your future projects. Additionally, time will be available during breaks and for a short period after the seminar for individual consultations.

Saturday Seminar
R
SEM

Penalized quantile regression

Benjamin S. Sherwood, KU School of Business
Friday, November 17, 2017 -
3:00pm to 4:00pm
Watson Library, Room 455
Abstract: Quantile regression is a method for estimating conditional quantiles. It is a more robust method than least squares and provides a more complete description of a conditional distribution. Penalized quantile regression shrinks estimators towards zero and the penalties I am interested allow for simultaneous estimation and variable selection. I will provide an introduction to quantile regression and then discuss penalized quantile regression with both the Lasso (L1 norm) penalty and non-convex penalties (SCAD and MCP). I'll include a brief tutorial into my R package, rqPen, with some discussion about quirks and challenges of penalized quantile regression. 
Weekly Colloquium

For Historical Records, see:


CRMDA Calendar

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