This workshop offers a crash course on graphics and regression models in R. Topics covered include multiple regression, model comparison, mixed-effects models, and the construction of graphical displays using the ggplot2 package. This is an intermediate workshop appropriate for those already familiar with R.
This accelerated introduction will help you quickly get up to speed with the R statistical computing language. We will introduce the basics of the R language syntax, data import and management, package installation, and basic programming concepts including functions, classes and methods, iteration, and debugging. No prior R knowledge is required, but previous experience statistical or technical computing (e.g., Matlab, SAS, Stata, Python, and of course R) will be helpful.
Interested in using Twitter for academic research? Using R and Python, we will learn how to use the Twitter API and other tools to gather tweets, social networks and other available data and investigate a framework for analyzing it. Prerequisites: basic programming techniques (you can write a for loop).
In this workshop, we will provide a hands-on introduction to Matlab, including basic programming concepts such as data structures, functions and loops, as well as applicationsof these concepts to presenting stimuli and reading in data from external tools such as Qualtrics.
Get an introduction to SAS, one of the more frequently used statistical packages in business. With hands-on exercises, explore SAS's many features and learn how to import and manage datasets and and run basic statistical analyses. This is an introductory workshop appropriate for those with little or no experience with SAS.
This hands-on class will provide a comprehensive introduction to graphics in Stata. Topics for the class include graphing principles, descriptive graphs, linear regression, factor variables, and post-estimation graphs. This is an introductory workshop appropriate for those with only basic familiarity with Stata. Prerequisite: a general familiarity with Stata (such as taking the Introduction to Stata workshop).
This class will introduce common data management techniques in Stata. Topics covered include basic data manipulation commands such as: recoding variables, creating new variables, working with missing data, and generating variables based on complex selection criteria. Participants will be introduced to strategies for merging datasets (adding both variables and observations), and collapsing datasets. This workshop is intended for users who have an introductory level of knowledge of Stata software.
This class will provide a hands-on introduction to Stata. You will learn how to navigate Stata’s graphical user interface, create log files, and import data from a variety of software packages. We will also share tips for getting started with Stata including the creation and organization of do-files, examining descriptive statistics, and managing data and value labels. This workshop is designed for individuals who have little or no experience using Stata software.
This introduction to the popular ggplot2 R graphics package will show you how to create a wide variety of graphical displays in R. Topics covered included aesthetic mapping and scales, faceting, and themes. This is an intermediate level workshop appropriate for those already familiar with R. Participants should be familiar with importing and saving data, data types (e.g., numeric, factor, character), and manipulating data.frames in R.
This hands-on, intermediate R course will guide users through a variety of programming functions in the open-source statistical software program, R. This workshop covers blocks, loops, program flow, functions,S3 classes and methods, and debugging in R. This workshop is intended for those already comfortable with using R for data analysis who wish to move on to writing their own functions. Prerequisite: basic familiarity with R, such as acquired from an introductory R workshop.
This hands-on, intermediate R course will demonstrate a variety of statistical procedures using the open-source statistical software program, R. Topics covered include multiple regression, multilevel models, and multiple imputation. We expect that users enrolled in this course are already familiar with the statistical processes that we cover and are interested in learning how to run these procedures in R. Prerequisite: Basic familiarity with R, such acquired through an introductory R workshop.
Our team of consultants can run trainings, give presentations to groups or hold office hours on site. If you have ideas for ways we can help your research, please get in touch: firstname.lastname@example.org