Workshop in Methods (WIM)
The Workshop in Methods (WIM) was created in 2009 with the mission of providing introductory education and training in sophisticated research methods to graduate students and faculty in the social sciences at Indiana University. Our goal is to supplement statistics and methods courses across the Bloomington campus with topical workshops led by leading methodological scholars from IU and across the United States.
WIM is currently directed by Stephen Benard, working with the WIM advisory committee and the Social Science Research Commons. The initial idea for WIM began with Scott Long, who discussed his vision with Dr. William Alex Pridemore. Pridemore created WIM and directed the series until 2013.
If you would like to receive updates from WIM – including announcements of upcoming presentations – you can request to join our mailing list by sending an email to email@example.com.
Spring 2017 Upcoming Workshops
Friday, March 31, 2017
Kevin Tharp and Joanna Landrum, "Qualtrics Advanced Survey Software Tools"
2-4pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)
Qualtrics (qualtrics.com) is a software package for collecting survey data that has been widely adopted by leading research universities and major corporations. Many IU departments and centers currently use Qualtrics, with more and more discovering the software’s usefulness each year. This hands-on workshop is directed toward current users who have a basic knowledge of Qualtrics and wish to learn about more advanced features.
Among the features that we’ll cover:
- Importing contact lists
- Embedding data from your contact lists inside your survey instrument
- Creating separate survey paths and modules
- Personalizing your email invitation messages
- Coding complex skips including screening questions
We will allow time at the end to address specific questions from attendees, and also discuss what we learned from the Qualtrics Insight Summit.
Kevin Tharp works at the IU Center for Survey Research, and Joanna Landrum works at the IU Foundation. Combined, they have 30+ years of market and survey research experience and 10+ years using Qualtrics.
Friday, April 7, 2017
Karl F. Schuessler Lecture in Social Science Methodology: Professor Matthew Salganik
12-1:30pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)
The 2017 Karl F. Schuessler Lecture in Social Science Methodology, will be presented by Matthew Salganik, Professor of Sociology at Princeton University. Professor Salganik is affiliated with several of Princeton's interdisciplinary research centers: the Office for Population Research, the Center for Information Technology Policy, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. A reception will follow the talk.
Friday, April 14, 2017
Mathworks Day Seminars: Image Processing, Signal Processing, Machine Learning and Deep Learning in MATLAB
Image Processing, Machine Learning, Computer Vision and Deep Learning in MATLAB
9:30-11:30am, Social Science Research Commons Grand Hall (Woodburn Hall 200)
This seminar will be particularly valuable for anyone interested in using MATLAB to process, visualize, and quantify imagery. Rather than focus on extracting information from a few homogeneous images, we will introduce a typical real-world challenge, and discuss approaches to managing and exploring collections of widely heterogeneous images. We will also describe approaches to implementing deep learning networks in MATLAB, and will compare and contrast those approaches with more traditional computer vision and machine learning techniques.
In this presentation, we will:
- Explore and manage a range of real-world image sets
- Solve challenging image processing problems with user interfaces
- Classify images by content using machine learning techniques
- Detect, recognize, and track objects and faces in images
12-1pm Pizza lunch
Signal Processing and Machine Learning Techniques for Sensor Data Analytics
1:30-3:30pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)
An increasing number of applications require the joint use of signal processing and machine learning on time series and sensor data. MATLAB can accelerate the development of these systems by providing a full range of modelling and design capabilities within a single environment. In this session we will introduce common signal processing methods (including digital filtering and frequency-domain analysis) that help extract descriptive features from raw waveforms. We will then then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) to model the system performance. Finally, we will show how to scale the modeling to large datasets and ultimately deploy a streaming classification algorithm with automatic C code generation.
Product Highlights Include:
- Signal Processing Toolbox
- DSP System Toolbox
- Statistics and Machine Learning Toolbox
- Neural Network Toolbox
- Parallel Computing Toolbox
- MATLAB Coder
Friday, April 21, 2017
Jefferson Davis, Introduction to R
2-3:30pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)
R is a flexible, free software language for statistical computing and visualizations. Its popularity is increasing across a broad range of disciplines. This workshop will provide and introduction to using R including
- Downloading R and where to find R on IU computers
- Basic R syntax
- The Rstudio environment
- Creating and importing data
- Producing and editing graphs
- Using statistical techniques such as t-tests, simple linear regressions, and mixed models.
No prior knowledge of R is assumed. We do, however, recommend that if you are using your own laptop that you download R and RStudio from the following links:
Jefferson Davis has worked in Research Analytics for over ten years, and has extensive experience with R, Matlab, and other numerical packages. Some of Jefferson's recent projects have included: developing interfaces for computational text analysis; cluster analysis of institutional "big data" sets; and numerical simulations of resource management in fisheries.