CSSCR Courses



To sign up for our free courses, scroll to the bottom of this page!

Spring 2016

Introduction to Stata

Description:

Learn the basics of Stata so that you can understand do/log files, reading help files, and how to write code in order to perform data cleaning and statistical analysis. No experience in statistical programming necessary.

Instructor: Laine Rutledge
Date: Wednesday, April 20, 2016
Time: 4:00pm to 4:50pm
Place: Savery 121

Introduction to Excel

Description:

Learn the fundamentals of using Excel including: data entry, basic computation, elementary charts and graphs, and some statistical functions. Prior knowledge of Excel is not necessary.

Instructor: Katelyn Stickel
Date: Friday, April 22, 2016
Time: 4:00pm to 4:50pm
Place: Savery 121

Writing faster code in R

Description:

While many routine analytical techniques are easy to implement in R and require trivial computation time, conducting more intensive analyses often requires developing code largely from scratch that is more computationally demanding, e.g. analyses of large datasets involving multiple steps; automated procedures that are intended to be run across multiple samples; resampling or Monte Carlo simulations involving large numbers of iterations; etc. In such contexts, thoughtful coding can sometimes mean the difference between writing code that takes hours or days to run, versus seconds, minutes, or hours. In this workshop, we will look at coding habits that improve computation time, including taking redundant steps out of for-loops, parallel processing, etc.

Instructor: Will Brown
Date: Monday, April 25, 2016
Time: 9:30am to 10:20am
Place: Savery 121

Introduction to Qualitative Research and ATLAS.ti

Description:

This course provides a brief introduction to computer software for qualitative data analysis, including a comparison of two options, ATLAS.ti and the cloud-based Dedoose. The class will then provide a practical introduction to working in ATLAS.ti, covering basic terminology and functionality of the program. This will include importing documents (text and other media types), coding and annotating documents, and a brief introduction to analysis and query tools.

Instructor: Carolina Johnson
Date: Monday, April 25, 2016
Time: 4:00pm to 4:50pm
Place: Savery 117

Using R with Big Data

Description:

This course is for those who have some familiarity with R. We will talk about challenges and strategies when using R with big data.

Instructor: Stephanie Lee
Date: Wednesday, April 27, 2016
Time: 12:00pm to 12:50pm
Place: Savery 121

Introduction to SAS

Description:

This introductory class will cover basic features and some data analysis procedures of SAS. The topics include: an overview of the SAS system; how to read/enter data, modify, explore and manage data; as well as some statistical procedures for regression analysis, such as general linear model and logistic regression.

Instructor: Tina Tian
Date: Wednesday, April 27, 2016
Time: 3:00pm to 4:15pm
Place: Savery 121

Regression with Stata

Description:

This course will cover the basics of regression analysis using Stata. Using a sample research question, we will explore the data, specify a linear model, and test the assumptions underlying OLS regression to come up with a better model. The latter part of the course will be devoted to running and interpreting logistic regression. Attendees will be assumed to have basic familiarity with Stata.

Instructor: Myong Hwan Kim
Date: Monday, May 9, 2016
Time: 11:30am to 12:20pm
Place: Savery 117

R Graphics Using ggplot2

Description:

This class is an introduction to the popular graphics package ggplot2. We will discuss the structure of the ggplot2, including the basic elements of the underlying grammar and how plots can be built-up in a layered fashion. We will then use ggplot2 to produce number of different plots, such as line graphs, histograms, and boxplots. Attendees will be assumed to have basic familiarity with R.

Instructor: Colin Beam
Date: Wednesday, May 11, 2016
Time: 2:00pm to 2:50pm
Place: Savery 121

Instrument Refinement Across Platforms

Description:

When utilizing measurement tools, an extremely important, but often overlooked part of the methodological process is evaluating the instrument itself for robustness. This class will survey (pun intended) the various procedures which can be used to do dimension reduction. The aim is to provide an overview of the different programs that can be used to do this, from the most basic and accessible (Excel) to more intermediate (SPSS) to the more sophisticated (IRTPRO). Additionally, it will demonstrate how to get similar metrics, such as reliability, from different programs (ie, R vs STATA vs SPSS), highlighting when and why one might be a better choice than another.

Instructor: Gabby Gorsky
Date: Friday, May 20, 2016
Time: 10:00am to 10:50am
Place: Savery 121

Introduction to SPSS

Description:

This class introduces SPSS basic structure and layout. We will go over how to import, manage, and record data as well as do some descriptive statistics and simple analyses like t-test, correlation, and regression.

Instructor: Shin Lee
Date: Tuesday, May 31, 2016
Time: 2:30pm to 3:20pm
Place: Savery 121



Click here to register for courses.

Note: If you have trouble with this link, log out of your non-UW Google Apps Google account
OR
right-click the link and open it in a new private/incognito window.