Workshops

The Statistical Consulting Service (SCS) at the University of Connecticut is pleased to announce the SCS 2019 Fall Semester Workshop Series:

  • “A Practical Introduction to Variable Selection in R” on November 13, 2019;
  • “A Practical Introduction to Structural Equation Modeling in R” on March 8, 2019.

 

Information about past workshops is available here.


Workshop 1: A Practical Introduction to Variable Selection in R

Presented by: Shuang Yin, Xiaomeng Li, & Jinjian Mu

Presentation Slides: Variable_Selection_Workshop

Abstract:

Variable selection, also known as feature screening, is getting much attention in many research areas, especially for large ‘omics data sets. This workshop will introduce why we should do variable selection and some basic variable selection methods including stepwise, forward and backward regression. The Least absolute shrinkage and selection operator (LASSO) method will also be covered as a widely used variable selection method. Furthermore, this workshop will include the elastic net method, which is a combination of the ridge regression and the LASSO method. All the methods will be implemented in R.

 

Outline:

  1. Why use variable selection?
  2. Stepwise forward and backward regression
  3. The LASSO method
  4. The Elastic Net in R

 

Location: McHugh Hall (MCHU) 201

Time & Date: 11:00 AM – 12:00 PM, November 13, 2019

Registration: Please register at the following link: https://forms.gle/yWs6JqVnstyLo6va6

 

Workshop 2: A Practical Introduction to Structural Equation Modeling in R

Presented by: Yulia Sidi, Renjie Chen, & Joochul Lee

 

Presentation Slides: SEM_Workshop

 

Abstract:

Structural equation modeling, also known as SEM, is increasingly being used in research, particularly in the social sciences. SEM allows for the estimation of complex relationships between measured variables and latent constructs. In this workshop, we will introduce the components of the SEM framework. We will also go over a practical implementation of SEM in R, using the lavaan package. Specifically, we will focus on factor and mediation analyses.

 Outline:

  1. Latent variable and the basic elements of an SEM
  2. Practical demonstration of Factor Analysis in R using lavaan
  3. Extension of Factor Analysis – Mediation

 Location: Biology Physics Building (BPB) 130

Time & Date: 10:00 AM – 11:00 AM, November 15, 2019

Registration: Please register at the following link: https://forms.gle/yWs6JqVnstyLo6va6

 

 


Previous 2019 Workshops

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Workshop 3: Perfect the Imperfect Data – How to Deal with Missing Data in Practice

Abstract:

No data would be perfect because of imperfect design or data collection process. Missing data is often inevitable in practice. In order to help researchers handle missing data properly, cause, consequence, analysis methods and prevention suggestions of missing data will be all introduced. In this workshop. Case studies in SPSS will be presented as well.

Outline:

    1. Types of Missing Data

 

    1. Consequence of Missing Data

 

    1. Analysis of Missing Data

 

  1. Preventing Missing Data

Location: Pharmacy/Biology Building (PBB) 129

Date: 11:00 AM – 12:00 PM, April 5, 2019

Live streaming is available at https://ait.uconn.edu/live-streaming/.

Registration: http://merlot.stat.uconn.edu/www/consulting/workshops/register.php.


Workshop 1: Can Dead Fish be Alive? – (Math-Free Workshop to) Modern Statistical Methods for Testing Multiple Hypotheses

Abstract:

If brain cells of a dead fish were tested, can we conclude if it’s dead? When everyone thought running multiple tests would better support our claim, turns out, it rather leads to false conclusions; hence, the name “multiplicity problem”. An overview of classical and modern multiplicity adjustment methods will be introduced controlling Family-wise Error Rate (FWER) and False Discovery Rate (FDR). Software details in R will be demonstrated. Real examples and applications will be presented. Don’t revive a dead fish!

Outline:

    1. What is “multiplicity”?

 

    1. When does “multiplicity” arise?
      • (Example 1) Can dead fish be alive?
      • (Example 2) Can one drug cure multiple diseases?

 

    1. How can it be handled?
      • Procedures (Brace yourself for namedropping!)
      • Dead fish revisited
      • Drug test revisited

 

  1. How else? (Modern method)
    • Parallel Gate Keeping

Location: Pharmacy/Biology Building (PBB) 129

Date: 11:00 AM – 12:00 PM, February 15, 2019

Registration: The first workshop has ended.


Workshop 2: Statistical Guarantee for Conducting a Successful Study – A Math-Free Workshop for Power Calculation

Abstract:

Sample size calculation plays an important role in study design and grant proposal preparation. An overview of power analysis will be given. Two case studies will be presented to demonstrate the magic of power calculation for the survival model and the linear mixed effect model using SAS and R.

Outline:

    1. Basic elements of power analysis

 

    1. Design of a survival study based on the Log-Rank test

 

  1. The linear mixed effect model: a path for designing a longitudinal study

Location: Pharmacy/Biology Building (PBB) 129

Date: 11:00 AM – 12:00 PM, March 8, 2019

Registration: The second workshop has ended.