MC 501 · Fall 2026 · Graduate

Research Methods for Mass Communications

You will run an original two-part content analysis to a standard approaching publishable quality, and finish with a conference-ready White Paper and poster published at a public address.

Start here: your first week

You do not have to read this whole page today. Do these five things and you are current.

  1. Turn on the Graduate edition toggle in the textbook. It is the control in the lower corner of any page, and it reveals required material you are graded on.
  2. Read the Week 1 row of the schedule, including the assigned reading. The reading comes before the session, not after.
  3. Sign the Syllabus Contract in Blackboard. It is due at the end of Week 1 and it is worth 25 points.
  4. Skim what makes this graduate work. Four specific things separate MC 501 from MC 451, and all four are graded.
  5. Bookmark this page. It is the live syllabus. A printed copy goes stale; this does not.

Nothing needs to be installed yet. Software comes in Week 2, with a walkthrough.

How to reach me

  • Office hours are the primary support channel. Drop in during the times above in Dunham Hall 1017, or schedule an appointment.
  • Email (aleith@siue.edu) is preferred for time-insensitive and confidential communication, such as grades or accommodations. Expect a response within 24 hours on weekdays.
  • Course discussion (Blackboard) is for student-to-student questions. I participate periodically; bring instructor-directed questions to office hours or email.

In all communication, clearly stated needs save us both time. State what you have tried, what is not working, and what kind of response you are hoping for.

What this course is

MC 501 is the graduate-level research methods course in mass communication. Students explore quantitative and qualitative methods, including surveys, experiments, content analysis, and in-depth interviews, with emphasis on designing, executing, and communicating original research at a level approaching publishable quality.

Across the semester students complete a two-part content analysis project: a qualitative study that develops coding variables through immersion, and a quantitative study that tests relationships in the coded data. The final deliverable is a professional White Paper suitable for conference submission or client delivery, published to GitHub Pages, paired with an academic conference poster. Graduate students read the textbook with the graduate-edition toggle on; the graduate extension in every chapter, assigned peer-reviewed readings and advanced analytical prompts, is required material.

Course objectives and goals, in full

By the end of this course, students will be able to:

  • Understand and apply the principles and laws of freedom of speech and press, particularly within the context of the United States, while gaining a global perspective on freedom of expression, including the right to dissent, monitor power, and petition for redress of grievances.
  • Demonstrate an understanding of the historical role of professionals and institutions in shaping mass communications and critically evaluate their impact on the development of media systems.
  • Recognize and analyze the impact of diversity in mass communications, including but not limited to gender, race, ethnicity, and sexual orientation, within both domestic and global contexts, and appreciate the significance of this diversity in shaping media content and audience reception.
  • Apply ethical principles in conducting and presenting research, ensuring that all research practices adhere to standards of truth, accuracy, fairness, and respect for diversity.
  • Design and execute original research independently, developing robust research questions and applying appropriate methodologies at a level approaching publishable quality.
  • Conduct rigorous research, evaluate information using appropriate methods, and communicate research findings clearly and in an ethically responsible way to both academic and professional audiences.

The course also aims to:

  • Equip students to engage with mass communication research critically, so they can both conduct independent study and assess the research of others.
  • Foster an appreciation for the role of mass communications in reflecting and shaping societal values, particularly concerning diverse populations.
  • Prepare students to apply digital tools and technologies in their research, so they can navigate and contribute to the evolving landscape of mass media research.
  • Develop students’ ability to communicate research findings effectively to both academic and non-academic audiences, using appropriate forms and styles.

What makes this graduate work

You read the textbook with the Graduate edition toggle on, which reveals a required graduate extension in every chapter. Graduate work is distinguished on four dimensions.

Depth of engagement. You read the textbook’s graduate extensions (collapsible callout blocks with assigned peer-reviewed readings) as required work. Your journal entries are 450 to 500 words and explicitly engage the assigned graduate reading. Entries that address only the chapter content receive a maximum of 7 of 10 points.

Methodological formality. Your codebook includes a formal reliability protocol with planned sample size, target alpha thresholds, and revision triggers. Your research proposal is a structured sentence outline that could serve as the front half of a conference paper.

Final product format. You produce a White Paper: a formal Quarto Book with an executive summary, a structured literature review (15 or more sources), an explicit limitations analysis, and professional formatting suitable for conference submission or client delivery, paired with an academic conference poster (a shared requirement with MC 500).

500-level activity. At least one-third of class meeting time is engaged in 500-level activity: the required graduate-extension readings and the discussion built around them, and the heavier graduate deliverables (Research Proposal, Extended Codebook & Reliability Protocol, and the conference-ready White Paper with its paired poster). Your ethics work includes the non-human-subjects IRB determination you would actually file, building familiarity with the protocol process for doctoral study. Statistics coverage goes deeper by depth (assumptions, diagnostics, and model interpretation), not by additional techniques.

How the course works

The course runs in five roles. The Journalist is a throughline, not a stage: a weekly reading journal that runs the length of the term. The other four are sequential, and each hands you the materials for the next. Those four track the textbook’s five parts.

Points by phase, out of 1,025
Phase When Points What you hand in
I. The Journalist Weeks 1 to 15 150 Weekly Reading Journal with graduate extensions (10 pts per week for 14 teaching weeks, plus 10 pts consistency)
II. The Architect Weeks 1 to 4 100
  • GitHub Profile (25)
  • Syllabus Contract (25)
  • Librarian Visit Report (50)
III. The Builder Weeks 5 to 10 325
  • Annotated Manuscripts, 3 articles (25)
  • Research Proposal, sentence outline (75)
  • Topic Selection and RQs (25)
  • Definitions Practice (25)
  • Extended Codebook & Reliability Protocol (75)
  • Sampling Plan & Pilot, 20 to 30 items (75)
  • CITI Ethics Certification (25)
IV. The Analyst Weeks 11 to 13 250
  • Data Wrangling in R (50)
  • Describing Data in R (100)
  • Inferencing Data in R (100)
V. The Publisher Weeks 15 to 17 200 White Paper (200), plus the paired conference poster
Total 1,025

The White Paper (200 points)

The white paper follows IMRaD: Introduction, Methods, Results, Discussion, in that order, with a short abstract in front and references at the close. It uses APA 7th edition throughout, reports effect sizes and power statistics, and carries the transparency markers a reviewer looks for.

Component Points Full marks means
Introduction and framing 25 States the question and why it is worth asking, and situates it in the theoretical literature rather than gesturing at it.
Methods 55 Repeatable in detail, and carries the three transparency markers: a data provenance statement, an intercoder reliability report giving the kappa value with the training protocol and threshold applied, and the pre-registration disclosure with its OSF URL.
Results 40 Reports the findings without interpretation, including effect sizes and the power statistics behind the design. Figures are labelled and captioned.
Discussion 40 Interprets the findings, situates them within the theoretical literature, distinguishes statistical significance from practical size, and states the limits.
Abstract and references 15 The abstract summarises all four sections. References are complete and in APA 7th edition.
Reproducibility and publication 15 The document renders from source with no pasted numbers, and is live at a public address.
Reflection 10 One specific paragraph naming what proved harder than expected, what the data could not answer, and what a second attempt would change.

The paired conference poster is a separate requirement shared with MC 500.

Grading

Grade Points Percentage
A 922 to 1025 90 to 100%
B 820 to 921 80 to 89%
C 717 to 819 70 to 79%
D 615 to 716 60 to 69%
F below 615 below 60%

Rubrics are checklists. You start at full credit and lose full or partial points for each item that is incorrect or missing. You are welcome to raise point disputes during office hours. In special cases, opportunities to address mistakes for partial credit are provided.

Feedback timeline. Grades with feedback are posted within one week of the due date for most assignments. Find your grades under My Grades in the Blackboard course menu.

Late work. Late submissions (after Friday 11:59 PM) lose 10% of the assignment grade per business day, for up to one week. After one week, the assignment receives a zero unless an accommodation has been arranged in writing before the original due date. Non-emergency extensions must be requested before the deadline. If you are experiencing an emergency, contact me as early as possible; I work with students case by case when contacted proactively, but I cannot extend deadlines retroactively.

Resubmission. Assignments worth 50 or more points may be revised and resubmitted within one week of grade posting. The final grade is the average of the original and the resubmission.

Graduate extension readings are required, not optional. Your weekly journal must engage with both the chapter content and the assigned graduate reading. Entries that address only the chapter content receive a maximum of 7 of 10 points.

Required graduate readings

Each week has one required reading, assigned below and discussed in class. Read it before the session; your weekly journal engages it. These are the sources behind each chapter’s graduate extension. Items marked Library are available through the SIUE Library (Lovejoy) with your student login; everything else is open access. Weeks 14, 16, and 17 (break, work session, and finals) have no assigned reading.

Week Reading Access
1 Munafo, M. R., et al. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1, 0021. Open
2 Wilson, G., et al. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), e1005510. Open
3 Nosek, B. A., et al. (2018). The preregistration revolution. PNAS, 115(11), 2600-2606. Open
4 Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159. Library
5 Gelman, A., & Loken, E. (2014). The statistical crisis in science. American Scientist, 102(6), 460-465. Open
6 Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267. Open
7 Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1(1), 77-89. Library
8 Lombard, M., Snyder-Duch, J., & Campanella Bracken, C. (2002). Content analysis in mass communication. Human Communication Research, 28(4), 587-604. Library
9 Munafo et al. (2017), revisit: the sections on reporting and usability. Open
10 Hayes & Krippendorff (2007), revisit: computing alpha for your pilot. Library
11 Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1-23. Open
12 Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science. Frontiers in Psychology, 4, 863. Open
13 Lakens, D., et al. (2018). Justify your alpha. Nature Human Behaviour, 2, 168-171. Open
15 Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values. The American Statistician, 70(2), 129-133. Open

Materials and technology

Textbook. This course uses an Open Educational Resource (OER): From Vibes to Variables: A Field Guide to Open Media Science (3rd Ed.), free and online at aura-lab-siue.github.io/v2v. There is no textbook to purchase. Turn on the “Graduate edition” toggle (the control in the lower corner of any page), which reveals a required graduate extension in every chapter (assigned peer-reviewed readings and advanced analytical prompts). Your choice is remembered across pages. For you it is required material.

Software. All software is free and open-source. Installation guides are on the setup pages.

  • VS Code: your single workspace for writing, R, Quarto, and Git
  • R: statistical computing environment, run inside VS Code
  • Quarto: scientific and technical publishing
  • Git and GitHub: version control and your White Paper
  • GitHub Desktop (optional): a visual Git interface

What you need to have. A reliable computer and internet connection are required.

  • A desktop or laptop computer for the R analysis in Phases IV and V; this work cannot be completed on a phone or tablet.
  • A current version of a supported browser (Chrome, Edge, Firefox, or Safari), kept updated.
  • Cloud storage (OneDrive, Google Drive, or similar) for backing up your project.
  • VS Code, R, and Git installed and working by the end of Week 2. The setup pages include a walkthrough.

Technical problems are not an excuse for missed deadlines, but they can often be resolved quickly. See Getting help.

Artificial intelligence

AI coding assistants (for example, GitHub Education Copilot) are permitted in this course as supervised aids, with disclosure. This is a course-specific policy; unless a course states otherwise, SIUE’s default is that AI use is prohibited.

  • Permitted: using AI to remember syntax, scaffold code you understand, or draft prose you then revise, provided you can explain everything you submit and you disclose the assistance.
  • Disclosure is required: when AI shaped work you submit, note in your weekly journal what you used it for and how you checked its output. Graduate work carries a higher expectation that AI-assisted analysis is independently verified.
  • Not permitted: submitting AI-generated work as your own without disclosure; using AI to fabricate citations, data, or results; or using AI to circumvent a learning objective. These are academic dishonesty.

You are accountable for everything you submit, including whatever role AI played in producing it. See the GitHub Education Copilot guide for how to use it responsibly.

Participation and interaction

Success in this course requires attendance and active participation. Regular engagement with the readings, activities, and discussions is essential.

Regular and Substantive Interaction (RSI) is required under U.S. Department of Education regulations. In this course, RSI takes the form of direct instruction and in-class activities, weekly office hours as a live support channel, substantive instructor participation in discussions, and personalized written feedback on graded work on the timeline above. Your part is to engage on a weekly cadence: read the assigned chapters and graduate extensions, participate in class, and submit work on time.

Getting help

You do not need to know how the university is organized to get help from it. Find your situation on the left and start at the office on the right. If you are not sure, ask me and I will point you.

Where to go, by what you need
If you need Start here
A deadline you cannot meet Email me before the deadline. I work with students case by case when contacted early; I cannot extend a deadline that has already passed.
An accommodation for a disability ACCESS, Student Success Center 1203. myaccess@siue.edu, 618-650-3726. You do not need a diagnosis in hand to start the conversation.
Help with the writing itself The Writing Center. Bring a draft at any stage, including a blank page.
Help with the course content My office hours first. Then the Tutoring Resource Center and Supplemental Instruction.
Finding sources Lovejoy Library. Ask for the Mass Communications subject librarian by name; that is what they are there for.
A computer, software, or login problem ITS: 618-650-5500, help@siue.edu. Check ITS System Status first, and search the ITS KnowledgeBase.
To talk to someone about your mental health Counseling and Health Services, Student Success Center 0222, 618-650-2842. Appointments through Cougar Care (SIUE login).
Food, housing, or money to be short Financial Aid and the Kimmel Belonging and Engagement Hub, Morris University Center 2027, 618-650-3180. Tell me too, if it is affecting your work in this course.
To report harassment or discrimination Office of Equal Opportunity, Access, and Title IX Coordination (EOA), Rendleman 3316.
Help choosing courses or a major Academic Advising, and the Career Development Center, 618-650-3708.

Services for students needing accommodations

It is the policy and practice of SIUE to create inclusive learning environments. If aspects of the instruction or design of this course create barriers to your inclusion or to accurate assessment of achievement, please contact Accessible Campus Community and Equitable Student Support (ACCESS) as soon as possible. Register with ACCESS online at siue.edu/access or in person in the Student Success Center, Room 1203. Email myaccess@siue.edu or call 618-650-3726.

Accommodations are not a favor and they are not remedial. Tell me what you need and we will make it work.

Cougar Care

College life can be challenging, and I support students prioritizing their mental health. Counseling is available at the Student Success Center, Room 0222. Make an appointment at cougarcare.siue.edu or call 618-650-2842.

Full list of academic and student services

University policies

Academic integrity

The expectations and academic standards in the Student Academic Code (3C2) apply to all courses at the University, regardless of modality. Plagiarism (the use of another person’s words or ideas without credit) and cheating will not be tolerated and may lead to failure on an assignment, in the class, or dismissal from the University, per the SIUE academic dishonesty policy (3C1). Cite your data sources and collaborators.

Diversity and inclusion

SIUE is committed to respecting everyone’s dignity at all times. Our classroom must be a place where students and instructors feel safe and supported. Systems of oppression permeate our institutions and our classrooms; all students and instructors share responsibility for affirming inclusion, equity, and social justice. Racism, sexism, classism, ableism, heterosexism, xenophobia, and other social pathologies will not be tolerated, and violations will be enforced in line with the SIUE Student Conduct Code. The Kimmel Belonging and Engagement Hub is a resource for students.

Pregnancy and newly parenting

This course follows SIUE’s Newly Parenting Policy (3C15). Students may request accommodations through the Office of Equal Opportunity, Access, and Title IX Coordination (EOA).

Technology and privacy

This course uses Blackboard; review the Anthology Blackboard Privacy Statement for how your data is handled. The course also uses GitHub; review GitHub’s privacy policy before creating an account.

Recordings of class content

Any recordings of class sessions are for the educational use of students enrolled in this course only. Students may not copy, share, or distribute recordings without the written permission of the instructor. Office hours are not recorded, so that conversations about grades, accommodations, or personal circumstances stay private.

Subject to change

All material, assignments, and deadlines are subject to change with prior notice. Watch the course announcements and Blackboard regularly. This page is the live version of the syllabus; if a printed copy and this page disagree, this page is correct.

Weekly schedule

Lecture decks for every meeting are posted at MC 501 Lecture Decks.

MC 501 meets once weekly (Wed 6:00 to 8:50 PM). Each 170-minute block covers a full week of material and adds the graduate layer: the required graduate-extension readings, the discussion built around them, and the heavier graduate deliverables (see What makes this graduate work). The course covers descriptive and inferential statistics (t-test, ANOVA, regression); graduate work goes deeper. The White Paper and its paired academic conference poster are assigned Week 13 and due at Finals.

ImportantWeek 5 and Week 9 are the pinch points

Week 5 stacks the Annotated Manuscripts with CITI Certification, and Week 9 carries the Extended Codebook and Reliability Protocol (75 pts) one week before the Sampling Plan and Pilot (75 pts). Those four weeks in the middle of the term carry 200 points between them. Start the CITI certification in Week 1; it is the one item here that is pure clock time and can be finished early.

Week Wed Topics Due
1 Aug 26 Orientation; Ch 1 The Science of Storytelling (Reading: Munafo et al. (2017)) Syllabus Contract
2 Sep 2 Ch 2 The Open Workspace + project setup (Reading: Wilson et al. (2017)) GitHub Profile
3 Sep 9 Ch 3 Ethics; Ch 4 Intelligence Gathering (librarian visit) (Reading: Nosek et al. (2018))
4 Sep 16 Ch 4 annotating; Ch 5 Theory as a Lens (Reading: Cohen (1992)) Librarian Visit Report
5 Sep 23 Ch 5 variables & hypotheses; Ch 6 Research questions (Reading: Gelman & Loken (2014)) Annotated Manuscripts (3) · CITI Certification
6 Sep 30 Ch 6 the prospectus; Ch 7 Structured Listening (Reading: Lakens (2022)) Topic Selection / RQs
7 Oct 7 Ch 7 manifest vs. latent; Ch 8 operationalization (Reading: Hayes & Krippendorff (2007)) Research Proposal
8 Oct 14 Ch 8 measurement, reliability & validity; building the codebook (Reading: Lombard et al. (2002)) Definitions Practice
9 Oct 21 Ch 9 [R] coding the codebook; reading data vs. the codebook (Reading: Munafo et al. (2017), revisit) Extended Codebook & Reliability Protocol
10 Oct 28 Ch 10 The Sample; the pilot; kappa / alpha (Reading: Hayes & Krippendorff (2007), revisit) Sampling Plan & Pilot
11 Nov 4 Ch 11 [R] Wrangling the Data (Reading: Wickham (2014)) Data Wrangling [R]
12 Nov 11 Ch 12 [R] Describing: descriptive statistics + graphics (Reading: Lakens (2013)) Describing Data [R]
13 Nov 18 Ch 13 [R] Inferential: t-test, ANOVA, regression; White Paper + poster assigned (Reading: Lakens et al. (2018)) Inferencing Data [R]
14 Nov 25 Thanksgiving Break, no class
15 Dec 2 Ch 14 The Publisher; white-paper + poster studio (Reading: Wasserstein & Lazar (2016))
16 Dec 9 Work Session: open lab / support
17 Dec 16 Finals Week White Paper (+ conference poster)

Schedule subject to change with notice.