The Statistical Consulting Services (SCS) Spring 2018 Workshop Series
The Statistical Consulting Services (SCS) is pleased to announce three FREE workshops in April, 2018 covering power analysis for ANOVA and repeated measures ANOVA, data visualizations with R Shiny��, and missing data in surveys!
Session 1: Power Analysis for ANOVA and Repeated Measures ANOVA
When: Tuesday, April 10, 5:00 p.m. – 6:00 p.m.
Where: LH 302
Session 2: Data Visualizations with R Shiny
When: Wednesday, April 18, 12:00 p.m. – 1:00 p.m.
Where: LH 302
Session 3: Missing Data in Surveys
When: Thursday, April 26, 4:00 p.m. – 5:00 p.m.
Where: LH 205
Who: Any UConn or UCHC faculty, students, or affiliated persons are welcome to attend.
Registration: http://merlot.stat.uconn.edu/www/consulting/workshops/register.php. Participants can sign up for one or multiple sessions. Registration will be closed whenever the cap number (75 for each session) is reached or by April 9, 2018 for Session 1, April 17, 2018 for Session 2, and April 25, 2018 for Session 3.
Session 1: Power Analysis for ANOVA and Repeated Measures ANOVA
Power analysis is routinely used to determine if the sample size is sufficiently large to detect the treatment effects for a given significance level. This workshop will cover two commonly used designs: ANOVA and rANOVA. The fundamentals of power analysis will be presented. Several motivating examples will be given. GPower will be introduced and illustrated during the workshop.
Session 2: Data Visualizations with R Shiny
Shiny is an R package that makes it easy to build interactive web apps from R. Shiny combines the computational power of R with the interactivity of the modern web. This workshop will introduce the basics of R Shiny, including the basic structure of a shiny app and how to make interactive visualizations. Participants are expected to have some beginner level coding experience in R.�
Session 3: Missing Data in Surveys
Missing data are frequently encountered in surveys. This workshop will provide a brief overview of survey data and introduce types of missing data mechanisms. Several statistical methods in analyzing missing data will be discussed. Illustrative case studies will be presented in analyzing missing data using SPSS.
For more information, contact: Tracy Burke at tracy.burke@uconn.edu