Research Services offers tutorials and workshops on a variety of topics. Each semester, we present a series of tutorials. If you have suggestions, please contact us (researchservices@bc.edu). We will also give customized tutorials, and we are available for advanced topics and consulting. 

Tutorial Descriptions

Creating Web-Based Surveys with Qualtrics

Qualtrics offers a fairly intuitive graphical user interface to create complex surveys without complicated programming or coding. Qualtrics offers extensive documentation, free online tutorials, an extensive library of surveys and options for encryption and anonymity, and excellent customer support. Qualtrics also offers built in social media sharing functions and an accessibility checker. Working within pre-defined templates, you can use many different types of questions, including text, multiple checkboxes, sliders, single-answer radio buttons, and Likert scales. Qualtrics offers extensive branching functionality.

Once the survey is completed, data can be downloaded into a format that can be used with a variety of quantitative and qualitative analysis programs. Qualtrics also offers foreign language functionality.

This tutorial will demonstrate how to create a survey in Qualtrics and also include a section on research protections and informed consent with respect to online survey development, distribution, and analysis. Boston College faculty, students, researchers, and administrative staff may create their own Qualtrics accounts in advance of the tutorial by logging on at bostoncollege.qualtrics.com (with your BC credentials). If possible, please complete this short BC Qualtrics Terms of Use Survey before attending the tutorial.

Research Services staff are available to meet with members of the Boston College community to discuss individual Qualtrics projects.

Individual consultations or customized class consultations are available by emailing researchservices@bc.edu or dalgin@bc.edu.

Presented by Rani Dalgin

 

September 20, 2021  3:00 - 4:30pm  Zoom*

* We will email you the Zoom link the day before the tutorial.

A Gentle Introduction to Programming

The aim of the tutorial is to help researchers with no prior .programming experience gain a basic understanding of the ways to leverage computer code to accomplish a specific goal. In this gentle introduction, we will rely primarily on visual tools and visual examples to establish an intuitive understanding of logic and code. We will then replicate our example in an abstract programming language (Python) to solidify the understanding by drawing parallels and highlighting differences between the two implementations. No prior knowledge of a programming language is required. Appreciation of water-loving cats is preferred.

Special Instructions

To view the materials, you must register for the tutorial following the standard Research Services tutorial registration procedure. By September 15, 2021 you will receive a link to the videos and materials, and an invitation to join a discussion forum on google hangouts where you can post comments or ask questions. 

Prepared by Simon Goshev

 

September 15, 2021 10:00 - 11:00am (Asynchronous)

Introduction to BC’s Linux Cluster

This tutorial is intended to be an introduction to the Linux cluster at Boston College. Currently the user can access two clusters. An overview, the primary components, and examples of how to use BC’s Linux cluster. This hands-on tutorial will cover:

  • Overview of the two Linux cluster system at Boston College
  • The hardware architecture
  • Management of Linux Cluster
  • How to remote access of the cluster
  • Common Unix/Linux commands
  • How to use software modules and PBS queuing system
  • How to submit jobs to clusters
     

Presented by Wei Qiu.

 

September 23, 2021 12:00 - 1:30pm  Zoom*

* We will email you the Zoom link the day before the tutorial.

Introduction to Data Management with R

Designed to get people with zero experience over the initial hump of using R for statistical work. This series of five, 2-hour sessions will cover: searching for missing data, listwise deletion, re-coding, sorting, summarizing, and creating basic data visualizations using R.

Taught by Larry Kaplan, a PhD candidate in Measurement, Evaluation, Statistics and Assessment in the Lynch School.

The 5 parts:

  • Part 1: The R ecosystem. The R studio dashboard, data types, data structures (e.g. variables, vectors, data frames, lists), storing data into data structures, importing data from CSV files (excel).

Part 1: September 7, 2021   10:00am - 12:00pm  Zoom*

  • Part 2: Commands, tables & changing values. default viewing of descriptive states and basic plots, If/Then/Else, For Loops, Length function, saving projects.

Part 2: September 9, 2021   10:00am - 12:00pm  Zoom*

  • Part 3: Searching & deleting cases in data frames. R libraries, using the %in% and %>% functions to identify cases, deleting rows and/or columns, changing row and/or column names.

Part 3: September 14, 2021   10:00am - 12:00pm  Zoom*

  • Part 4: Re-coding & ordering cases in data frames. Exploring the functions: order, cbind, ifelse, mean, sd, filter, group_by, mutate, summarize, n(), str_sub, str_split.

Part 4: September 21, 2021   10:00am - 12:00pm  Zoom*

  • Part 5: Visualizations with ggplots. Bar plots, histograms, and scatter plots (this is ambitious so hopefully we can get to all of this).

Part 5: September 23, 2021   10:00am - 12:00pm  Zoom*

* We will email you the Zoom link the day before the tutorial.

Introduction to Geographic Information Systems (GIS)

Geographic Information System (GIS) is used to conduct spatial analysis and visualize data in the form of maps. Esri’s ArcGIS Online is a popular cloud-based GIS mapping software that is used in academia and industry for spatial analysis. This tutorial is a beginner level introduction to GIS and will cover the following topics:

  • Overview of GIS
  • Examples of GIS applications
  • Introduction to ArcGIS Online
  • Demonstration of ArcGIS Online workflow
     

Presented by Awanti Acharya

 

September 14, 2021  2:00 - 3:30pm Zoom*

* We will email you the Zoom link the day before the tutorial.

Introduction to Machine Learning

Machine learning is a data analysis method of getting computers to act without being explicitly programmed. It based on the algorithms that use statistics to build models and find patterns in massive amounts of data. Machine Learning is extensively used in a wide variety of applications and changing our day-to-day life. This tutorial is for beginners to learn and will cover:

  • Introduction/Definition
  • Where and Why Machine Learning is used
  • Types of Learning
  • Supervised Learning
  • Unsupervised Learning
     

Presented by Yixin Pan

 

September 10, 2021 1:00 - 2:00pm Zoom*

* We will email you the Zoom link the day before the tutorial.

Introduction to REDCAP (Research Electronic Data Capture)

This tutorial is geared towards Boston College Principal Investigators, researchers, and research project team managers. REDCap tands for Research Electronic Data Capture. REDCap is a web based, data collection, database management system that was originally developed at Vanderbilt University, initially for medical research. REDCap is now overseen by a consortium of academic research partners in the United States and throughout the world. Boston College is part of the REDCap consortium.

In this introduction to REDCap we will discuss:

  • How to request a REDCap project at Boston College
  • How to make sure that your REDCap project complies with the mandates of your project's IRB approval
  • How to create basic data collection forms
  • An introduction to best practices for setting up your REDCap project
  • We will discuss additional REDCap functionality including offline
    survey capabilities, text to voice capability, potential for using
    twilio.com SMS services (for an additional fee), improved field
    calculations, repeating forms and more
  • How to enter data into REDCap
  • How to control REDCap user access rights
  • How to export your data
     

Research Services staff are available to meet with members of the Boston College community to discuss individual REDCap projects. Individual consultations or customized class consultations are available by emailing researchservices@bc.edu or dalgin@bc.edu.

If possible, prior to the tutorial, please fill out the BC REDCap Terms of Use survey described on this google doc and indicate that you will be attending the REDCap Tutorial.

Presented by Rani Dalgin

September 13, 2021  3:00 - 4:30pm  Zoom*

* We will email you the Zoom link the day before the tutorial.

Introduction to Regression

As the most common methodology in statistical analysis regression is an important tool for any modern researcher. This course is intended as an introduction to standard or linear regression. We will focus on estimation methods, identifying and validating model assumptions. We will also focus on hypothesis testing for regression estimates and statistical model building. We will use R software but the goal of the course is to learn concepts and is not intended as a tutorial for any specific software. Note: The mixed modeling course is a natural sequel to Introduction to Regression. 

Presented by Matt Gregas

 

September 15, 2021  10:00am – 12:00pm  Zoom*

* We will email you the Zoom link the day before the tutorial.

Introduction to Stata 1: Getting Started, Descriptive Stats, and Do Files

Stata is a powerful, yet easy to use statistical package. This hands-on tutorial is designed as an introduction for beginning users who are just getting started using Stata. The emphasis in this tutorial is on exploring the data, cleaning the data for research purposes, and generating descriptive statistics

  • Loading data
  • Data manipulation
  • Descriptive statistics
  • Do-files and log files
     

Presented by Praveen Saini

 

October 5, 2021  10:00 - 11:30am   Zoom*

* We will email you the Zoom link the day before the tutorial.

Introduction to Stata 2: Graphing, Dataset Combining, Linear Regression

Stata is a powerful, yet easy to use statistical package. This hands-on tutorial is designed as an introduction for beginning users who are just getting started using Stata. The emphasis in this tutorial is on basic graphing, merging data, and linear regression.

  • Basic graphing and graph editor
  • Combining multiple datasets
  • Linear Regression in Stata
     

Presented by Praveen Saini

 

October 26, 2021  10:00 - 11:30am  Zoom*

* We will email you the Zoom link the day before the tutorial.

Linear Mixed Effects Modeling

This tutorial is a brief introduction to linear mixed effects (LME) modeling, also known as multilevel modeling or hierarchical linear modeling. LME models are essential for researchers handling either longitudinal (repeated measures) data or data that is 'clustered' (e.g. students nested within classrooms, and classrooms nested within schools). Many familiar methods such as ANOVA or regression assume that all observations are recorded independently; Clustered data and data with repeated measures violate this assumption. LME modeling is an extension of regression that accounts for the correlated data structure inherent in repeated-measures and clustered designs. In this tutorial, we introduce the model, discuss when and why this method should be used, and how to interpret results in common statistical programs. This tutorial is appropriate for anyone with a background in linear regression. Those wanting a refresher may consider attending the Research Services tutorial on regression immediately preceding this tutorial.

Presented by Melissa McTernan

September 15, 2021  12:00 - 1:30pm  Zoom*

* We will email you the Zoom link the day before the tutorial.

MATLAB Parallel Computing Workshop - Part 1

MATLAB Parallel Server™ lets you scale MATLAB® programs and Simulink® simulations to clusters and clouds. This two-part, hands-on workshop is designed to introduce users to parallel computing constructs in MATLAB and to show them how they can scale their jobs to the HPC clusters on campus.

  • Introduction to parallel computing in MATLAB Parallelism using constructs such as parfor, parfeval, and spmd
  • Accelerating code using GPUs
  • Scaling to clusters and clouds
  • Self-paced exercises

Presented by Damian Pietrus, Mathwork
 

September  21, 2021  1:00 - 3:00pm Webex*

* We will email you the Webex link a few days before the tutorial.

MATLAB Parallel Computing Workshop - Part 2

MATLAB Parallel Server™ lets you scale MATLAB® programs and Simulink® simulations to clusters and clouds. This two-part, hands-on workshop is designed to introduce users to parallel computing constructs in MATLAB and to show them how they can scale their jobs to the HPC clusters on campus.

  • Scaling MATLAB to the Sirius cluster – users will follow along as instructors demonstrate how to configure MATLAB to submit to the   cluster
  • Running single- and multi-node MATLAB jobs
  • Differences between parpool and batch job submission
  • Non-interactive submission via qsub
     

Presented by Damian Pietrus, Mathwork


September 28, 2021  1:00 - 3:00pm Webex*

* We will email you the Webex link a few days before the tutorial.