Welcome to the Course

Hi, I’m Dr. A. P. Leith, and I’ll be your guide through this semester’s journey into research methods for mass communications.

Some professors start with “I’ve been fascinated by \(X\) since childhood.” I… didn’t. I’ve always been a passive media consumer. I’m the person who prefers to watch someone else play a video game rather than pick up the controller. There’s usually something playing in the background while I work—podcasts, TV series I’ve half-memorized, or livestreams of games I’ve watched through a dozen times.

That “observer” habit shaped my research: I notice small, strange patterns in how people communicate through technology. Once I notice them, I can’t not poke at them until I understand what’s going on.

My Research in a Nutshell

My work sits at the intersection of interpersonal communication and digital media platforms. I’ve studied:

  • Parasocial relationships and cues — how tone, pacing, and chat responses in livestreams foster felt intimacy.
  • Media and grief — how fans grieve fictional characters as if they were close others.
  • Watching as play — why many of us choose to watch games rather than play them.
  • VR and platform affordances — which features spark joy, trust, or frustration.
  • Media use during COVID — how livestreaming became a social space for tension release.
  • Virtual meetings and accessibility — how captions, avatars, and features affect engagement.

These projects usually start as “Why do people do that?” moments. They evolve into studies using interviews, surveys, content analysis, and computational text methods—the same methods you’ll practice here.

Teaching Philosophy

Research is formalized curiosity. The tools and methods you’ll learn are ways to chase a good question.

In this class, you will think critically, creatively, and practically. My role is to help you build skills and confidence to investigate your own questions about the media world—and to design research that matters to you.

That means:

  • We’ll balance theory with hands-on practice.
  • We’ll make space for trial, error, and iteration.
  • We’ll use R, RStudio, Quarto, and GitHub not just because they’re “required,” but because they enable reproducible, shareable work.

“Research is formalized curiosity. It is poking and prying with a purpose.” — Zora Neale Hurston

How to Use This Book (A 20-Minute Weekly Workflow)

  1. Skim (3–5 min): Read the learning goals at the top of the chapter and scan the section headings. Note the end-of-chapter prompts.
  2. Deep read (10–12 min): Read the body text, pausing at each callout box (see legend below).
  3. Journal (3–5 min): Choose one prompt and draft a focused response (250–400 words).
  4. Preview the lab (2–3 min): Glance at the week’s assignment so the reading connects to what you’ll do.

Feature Legend (you’ll see these throughout the book)

  • R Quick Win — a tiny, confidence-building task in R that takes ~2–5 minutes.
  • IRB Watch-Out — an ethics or compliance pitfall to consider before collecting data.
  • Design Decision — a fork in the road (e.g., survey vs. interview) and how to choose.
  • Grad Extension — an optional deeper dive or advanced deliverable for graduate students.
  • Repro Tip — a short step that improves reproducibility (naming, versioning, codebooks).

(In Quarto, these appear as callouts; in PDF/print, they appear as shaded boxes.)

A Macro View of the Course

This semester follows a simple arc:

  1. Laying the Foundations — What counts as research in mass communications? Set up the digital tools you’ll need.
  2. Designing Research — Develop a researchable question, connect it to theory, and choose a feasible, ethical method.
  3. Collecting & Managing Data — Build instruments or protocols, gather data responsibly, and keep it organized.
  4. Analyzing & Visualizing — Use R to explore data, run appropriate analyses, and make clear visuals.
  5. Communicating Results — Write plainly and precisely, with rigor that stands up to scrutiny.

[Figure: Semester arc timeline — five labeled phases running left-to-right with small icons for setup, design, collect, analyze, communicate. Beneath each phase: 2–3 example activities (e.g., “Quarto project,” “Operationalize concepts,” “Pilot survey,” “EDA + t-tests,” “Results + discussion”). The figure emphasizes that writing and ethics span all phases.]

Final Project Paths

  • Undergraduates (teams): White Paper focused on a practical, industry-facing question.
  • Graduate students (individual): Research Manuscript with fuller literature integration and reporting.

What to Expect

  • Weekly Readings & Journals Read one chapter and respond to one of three prompts in a short journal using a Quarto template submitted via GitHub.

  • Hands-On Assignments Most weeks, complete a small, applied project—clean a dataset, build a visualization, draft a method, or run an analysis. Each task connects directly to the chapter.

  • Skill Building in R, Quarto, and GitHub We proceed step-by-step. You’ll set up a project, script analyses, render reports, and version your work—without needing prior coding experience.

  • Final Project All roads lead here. You’ll combine your design, data, analysis, and writing into a coherent deliverable.

Advice for success: Treat the course like a series of small investigations. Ask precise questions, make your steps transparent, and let your evidence do the talking.

Tools & Reproducibility (Why These Matter)

  • R + RStudio — Open, free, and widely used for analysis and visualization.
  • Quarto — Turns your code and writing into clean, shareable documents.
  • GitHub — Tracks changes, backs up your work, and makes collaboration easier.

You’ll learn project-first habits: consistent file structure, named scripts, codebooks, and short README files that future-you (and collaborators) can understand. Small steps—like setting a seed, saving session info, and exporting figures from code—will make your work replicable.

Where to Get Help

  • In class/lab: Bring questions early, especially about setup and first-week friction.
  • Office hours: For deeper design choices, ethics questions, or results interpretation.
  • Peer support: Ask classmates, but cite any outside code or guides you adapt.
  • Documentation habit: When you hit a snag and fix it, add two sentences to your README explaining the fix.

Cover Image

Cover Image