SSRC Workshop in Methods (WIM): 2016-2017

Spring 2017

Friday, February 3, 2017

Dr. Olga Scrivner, "Interactive Visual Data Analysis with Shiny Applications: Interactive Text Mining Suite and Language Variation Suite"

1:30-3pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)

The main objective is to introduce researchers to user-friendly analytical tools. ITMS and LVS are two web-based tools for visualization and quantitative analysis.  In contrast to existing software programs (e.g., SAS, SPSS, and Tableau), these two applications are built in R and require no installation or programming skills.

This hands-on workshop will provide an overview of available statistical and text-mining techniques in these tools. You will learn how to import csv, text and pdf files, create plots, and run statistical analysis, including conditional trees and random forest tests. You will also learn about natural language pre-processing techniques, such as stopwords removal and stemming. Finally, you will be able to perform topic modeling and cluster analysis.

Olga Scrivner is a Visiting Lecturer in Spanish and Portuguese Department. Her research interests lie at the intersection of linguistics, text mining and data visualization.

Flyer | Add to my calendar: Google; IU Calendar; iCal (.ics) | Download exercise files (.zip) | Presentation slides: part one, part two (pdf)

Friday, February 10, 2017

Dr. Patricia Mabry, "Strategies for Strengthening Grant Proposals"

2-4pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)

Dr. Patricia Mabry is the Executive Director for the IU Network Science Institute (IUNI).  Prior to joining IU in October of  2015, she spent 15 years at the National Institutes of Health. There, among other things, she established an interdisciplinary program in systems science, and served as a member of the Big Data to Knowledge Executive Committee. Because she was in the Office of the Director for the majority of her tenure at NIH, and because of the interdisciplinary focus of her program, she worked with investigators across a broad spectrum of disciplines, and has worked with most of the 27 Institutes and Centers that make up the NIH.  Through these experiences, she came to realize that there were a common core of issues that many investigators shared in common. In this presentation Dr. Mabry will share the compilation of the advice she gave in response to these issues. By attending this presentation, you will: 1) learn to decode NIH-speak, 2) learn how the NIH operates 3) learn specific strategies for strengthening your proposal, 4) hear important differences across funding agencies, 5) learn about peer review 6) learn how to fit your work into funding opportunities, and much more. Even a seasoned investigator is likely to learn something new!

Dr. Mabry is the Executive Director of the Indiana University Network Science Institute (IUNI) and is a Senior Research Scientist in the IU-Bloomington School of Public Health. Through interdisciplinary collaboration, educational offerings, methodological innovation, theoretical development, and provision of supercomputing and IT resources, IUNI nurtures 21st century network science among over 150 affiliated IU faculty. Prior to joining IU in October of 2015, Dr. Mabry had a 15-year career at the National Institutes of Health serving in the National Cancer Institute’s Tobacco Control Research Branch, the Office of Behavioral and Social Sciences Research (OBSSR) and the Office of Disease Prevention (ODP). At NIH Dr. Mabry established and led a systems science program in the behavioral and social sciences and served on the Big Data to Knowledge (BD2K) Executive Committee. She also led ODP’s portfolio analysis tool development team. Dr. Mabry launched her post-doctoral career at the Medical University of South Carolina where she divided her time between teaching behavioral aspects of medicine to medical students, delivering psychological services, and conducting research in tobacco control. She also worked in a small business developing personalized, computer-delivered, behavior change programs (e.g., smoking cessation, dietary and physical activity goal-setting and adherence). Her expertise spans obesity, tobacco control, diabetes, mood disorders, systems science, scientific rigor, and big data. Her work has been published in Science, the American Journal of Public Health, the American Journal of Preventive Medicine, Nicotine & Tobacco Research, and PLoS Computational Biology. Dr. Mabry is a Fellow of the Society of Behavioral Medicine and was a 2008 recipient the Applied Systems Thinking Prize. Dr. Mabry holds a Ph.D. in clinical psychology from the University of Virginia.

Flyer | Add to my calendar: Google; IU Calendar; iCal (.ics)

Thursday, February 23, 2017

UITS IT Training, "SAS: The Basics" (registration required)

5:30-8:30pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)


SAS is a popular and powerful application that is used for data management and analysis in both industry and academia. This workshop is intended for those that are new to SAS or those who need a refresher of basic topics. Participants should have experience with Microsoft Windows file structure and basic statistical concepts. Working knowledge of Microsoft Word and EXCEL, as well as familiarity with basic programming concepts/logic, will be very helpful. Participants will learn about the SAS windowing environment, how to use the import wizard to get data into SAS, how to use DATA steps to create new variables or subset a dataset, and how to use basic PROC steps to examine data and compute descriptive statistics.

Register here | Add to my calendar: GoogleIU CalendariCal (.ics)


Friday, February 24, 2017

NaLette Brodnax, "Introduction to Python and Introduction to Using APIs with Python"

1:30-4:30pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)


Part I: Introduction to Python (1:30pm)

Python is a widely used, general purpose programming language.  Part I of the workshop will introduce the basic elements of Python that are commonly used for data cleaning, analysis, visualization, and other applications. Participants will also learn how to set up a "development environment" for Python on their personal computer. Computers with Python pre-loaded are available in the SSRC on a first-come, first-served basis. This workshop is intended for social scientists who are new to programming. No experience is required.

Part II: Introduction to Using APIs with Python (3pm)

An application programming interface (API) is a tool that allows computers to communicate and share information. For social scientists, APIs can be useful for accessing data or services from firms, organizations, or government agencies. Part II of the workshop will introduce the use of APIs to obtain data from sources such as Survey Monkey, Twitter, or This workshop is intended for social scientists who are new to working with APIs, but have some familiarity with Python or have attended the Introduction to Python workshop.

NaLette Brodnax is a data scientist and joint PhD candidate at Indiana University Bloomington in the School of Public and Environmental Affairs and the department of Political Science.  Her fields of specialization include public policy, policy analysis, and quantitative research methodology.

Flyer | Add to my calendar: GoogleIU CalendariCal (.ics)

Thursday, March 2, 2017

UITS IT Training, "SPSS: The Basics" (registration required)

5:30-8:30pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)


SPSS is a powerful statistics application. This workshop, which is intended for people who already have a basic understanding of statistics, introduces SPSS for performing common basic statistical analyses. Participants will learn the basic features of SPSS and how to use it to generate t-tests, linear regression, and descriptive statistics, and to interpret results.

Register here | Add to my calendar: GoogleIU CalendariCal (.ics)


Friday, March 3, 2017

Mia Stephens, "Data Visualization and Analysis with JMP, Statistical Discovery Software from SAS"

1-3pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)


JMP is an easy-to-use, standalone statistics and graphics software from SAS Institute.  It includes comprehensive capabilities for every academic field, and its interactive point-and-click interface and linked analyses and graphics make it ideal for research and for use in statistics courses, from the introductory to the advanced levels.  JMP runs on Windows and Macintosh operating systems and also functions as an easy, point-and-click interface to SAS®, R, MATLAB and Excel.  Come and see how to use JMP for data summary, analysis, visualization and predictive modeling, with an emphasis on what’s new in our latest release, JMP 13. 


  • JMP basics:  Menus, help, navigation and the JMP interface, saving and sharing your work, getting data into JMP
  • Data summary and graphics:  Dynamic graphing with Graph Builder, geographic mapping, filtering data, creating tabular summaries, and other visualization tools
  • Basic data analysis (univariate, bivariate, and multivariate): Distribution, Fit Y by X, and Fit Model
  • Analyzing survey and unstructured text data:  Categorical analysis and Text Explorer
  • Resources for learning and teaching with JMP:  Learning Library, case study library, webinars, books with JMP, teaching modules and simulators
  • Other topics (upon request):  Multivariate analysis, predictive modeling, mixed models, time series, integration with other programs (SAS, R, Excel,…), and more.

Mia Stephens is a member of the JMP Academic Team. Prior to joining SAS, she was an adjunct professor of statistics at University of New Hampshire and a statistical trainer and consultant with the North Haven Group. A co-author of four books and several papers, she has developed training materials, taught and consulted within a variety of industries.

Flyer | Add to my calendar: GoogleIU CalendariCal (.ics)


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 ( 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.

Flyer | Materials on Box | Add to my calendar: GoogleIU CalendariCal (.ics)

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
Flyer | Register on the MathWorks website:

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.