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 ( We will also give customized tutorials, and we are available for advanced topics and consulting. 

Tutorial Update

All fall tutorials have ended. Stay tuned for our spring tutorial offerings in January 2021.


Tutorial Descriptions

Introduction to REDCAP (Research Electronic Data Capture)

This tutorial is geared towards Boston College Principal Investigators, researchers and research project team managers. REDCap stands 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 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 or

Advanced Linux Command Shell

The command interpreter in Unix and Linux, and in the MacOS terminal is called the shell. The shell interpreter provides various features beyond the basic execution of the entered command with arguments. These include:

  • Input and output data stream redirection
  • Pattern matching of file names
  • Expression substitution
  • Variable setting and evaluation
  • Command history

Used skillfully, these features can help the user reduce the amount of typing required to accomplish tasks.

There are several flavors of command shell: the Bourne shell (sh), C shell (csh), Tenex C shell (tcsh), Korn shell (ksh), Bourne-again shell (bash), Z shell (zsh) and others. This tutorial will cover the features these shells have in common and their differences.

Linear Mixed Effects Models

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.

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, please register for the tutorial as usual. Shortly after registration, 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.

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 this website: with their 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 or

Introduction to BC’s Linux Cluster

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

  • Overview of the two Linux cluster system at Boston College
  • The hardware architecture
  • How to remotely access of the cluster
  • Common Unix/Linux commands
  • How to submit jobs to clusters

Introduction to Geographic Information Systems

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 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

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 any specific software.  Note: The mixed modeling course is a natural sequel to Introduction to Regression.

Power Analysis

A well-designed research study will be able to detect the hypothesized effect if it actually exists. This tutorial will briefly outline the concept of statistical power and how to get it. In addition to sample size there are many ingredients which can increase the chance of a successful study. These include choice of experimental design, outcome measure and the proposed data analysis. We will discuss how each of these factors affects statistical power and review methods to optimize it.


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 and will cover:

  • Introduction/Definition
  • Where and Why Machine Learning is used
  • Types of Learning
  • Supervised Learning
  • Unsupervised Learning
  • Introduction to Scikit-Learn(Python)