Why throw away your non-normal research data using casewise, listwise, or pairwise deletion to "fix" missing data problems? Or why "average it away" with mean/median/mode replacement? Discounted 4-session live online course instructing on the use of 4 different data imputation techniques suitable for data that is not multivariate normal. You receive training on the professional VIMGUI software (available through R), as well as unrestricted, permanent use of the software itself. VIMGUI supports the following contemporary data imputation techniques: (1) Hot Deck imputation; (2) k-nearest neighbor; (3) individual, regression-based imputation; and (4) iterative, model-based, stepwise regression imputation (irmi algorithm). Course registration includes R-Courseware community user account through December of 2014. VIMGUI also provides extensive missing data visualization capabilities so you can see the 'missingness' data patterns to choose the most appropriate imputation approach. If you want to learn how to perform statistical analyses; data analyses and/or data mining; graphical presentations of data; and/or programming with open-source R software for your school work or for your job, please consider this opportunity. Included R-Courseware user account has 1300+ analytics, statistical, and data mining video and materials files on "hands on" research methods techniques. Visit http://tinyurl.com/vimgui Geoff Hubona Educational Research and Methodology Listserv ----------------------------------------------------------------- List Service Info http://listserv.uconn.edu To cancel your subscription address click please do the following: SEND an email to the following address: mailto (colon) listserv (at) listserv (dot) uconn (dot) edu Your email should contain only the message UNSUB EDRESMETH-L. Address problems with your subscription to: [log in to unmask] -----------------------------------------------------------------