Cognition & Emotion Lab @ TTU - Lab Manual
Welcome to the Cognitive & Emotion Lab in the College of Media & Communication at Texas Tech University. We are really glad to have you in the lab and look forward to the awesome things we will accomplish together. We hope that your time in the lab helps you develop multiple skills (e.g., programming, data analysis, writing) as you learn about communication science. We work hard in the lab, and take our research seriously. But we also like to have fun, and want this to be a space for friendships and imagination!
This lab manual was originally inspired by a mentoring workshop that a friend of the lab (Richard Huskey) attended facilitated by Professor Stephanie Robert from the University of Wisconsin-Madison, and by a Blue Sky Workshop at the International Communication Association Meeting in 2018 that centered around graduate mentorship that Justin helped prepare. It borrows heavily from ideas presented in those workshops and discussions, but also from the RILAB Lab Manual and from Richard's lab as well. In fact, some key sections are lightly edited from these Lab manuals. Like our lab, this document is a work in progress and will evolve over time. Want to add something, or see an idea better articulated? Then talk with Justin!
General Lab Information
Writing Priorities and Guidelines
- Writing Priorities: How we organize our writing priorities to maximize our effort.
- Authorship Guidelines: How we decide authorship/contributorship on manuscripts.
- Lab Expectations: Everything you need to know about working in the lab.
- Mentor and Mentee Expectations: What is expected of you, and what you can expect of the PIs in this lab.
- Helpful Articles for Trainees: Here is a (completely non-exhaustive) list of helpful articles for anyone interested in a career in science.
- Lab Resources: Details for our lab workstations (both in the CCR and in the Science Communication Collaborative).
- Lab Storage Practices: Details for how our lab practices safe storage procedures for all of our experiment files, raw data, cleaned data, analysis scripts, and output.
Scheduling and Communication
- Calendar: We are in the process of creating an Outlook group calendar. Please contact Justin for access.
- Slack: Our lab uses Slack for general communication.
- Recruiting Using Sona: We will use Sona to recruit undergraduate subjects from the TTU CoMC subject pool.
- Recruiting Using Acquity: We will use Acquity to recruit from the general public.
- Scheduling Data Collection: Every study must be scheduled well ahead of time using a standard set of steps.
- Contributing to the Lab Wiki: How you can add to the Wiki.
- Conference Publications: Conference publications and talks that have come out of the lab.
- Journal Articles and Book Chapters: Refereed articles and chapters from the lab.
Research Best Practices and Standard Programs
- How to organize a lab binder: What to include in every lab binder.
Standard Programs and Experiment Examples
- TTL Signals: How to send and receive TTL triggers.
Continuous Response Measurement
Behavioral/Big Lab Studies
- IRB: How to complete IRB training at TTU.
Version Control and Coding Best Practices
- Styleguide: How to write code in the lab.
- Git: A quick introduction on how to use Git and GitHub for version control.
- GitHub: We have a CEL Lab GitHub repo. Please use it as you see fit, but you can also create your own repositories and then add all of the lab members and the lab Git account as collaborators.
- Integrating RStudio and Github for version control: We use RStudio for programming and running data analysis using R. The software is also powerful enough to handle version control via Git (and is how Justin edits his Git repos as well).
- Introduction to Unix: Learn how to use the Unix Shell
- Shell Basics: New to a shell? Here is a crash course on how to use a shell in a Unix environment.
- Writing in LaTeX: We write papers (typically) in ShareLatex. Here are some resources that will help get your started using LaTeX as a writing environment.
- Using R for Data Analysis: This is a list of helpful R resources.
- Using Python for Social Science: This is a list of helpful Python introductions and resources.