SOCI/CRIM 3040, Quantitative Research Methods, introduces students to foundational concepts, principles, and practices in contemporary quantitative social science. Core topics include research computing, data collection and processing, descriptive and inferential statistics, measurement, visualization, and statistical modelling. Research ethics, transparency and reproducibility, and effective communication are emphasized throughout.

This course follows the Faculty of Humanities and Social Sciences' Quantitative Reasoning Course Guidelines (see www.mun.ca/hss/qr).



Contact Information

Professor John McLevey (he/him)

Office: AA 4055
Office Hours: 3:00-4:00 PM Tuesdays and Thursdays or by appointment
Email: mclevey@mun.ca

I will reply to course related e-mail within 24 hours between Monday and Friday. I do not check or respond to e-mails on weekends or holidays. Please use your @mun.ca email account for all course-related correspondence, not Brightspace email.

Graduate Assistant (GA)

Atinuke Tiamiyu (she/her), PhD Student
Office Hours: By appointment
Email: aotiamiyu@mun.ca



Required Materials

Jamovi (Statistics Software)

Jamovi is free/open source statistics software that provides an accessible interface for statistical analysis. We'll use Jamovi throughout this course for all lab assignments and hands-on exercises. Here are three ways you can use Jamovi:

  1. LABNET Computers at MUN: Jamovi is pre-installed on computers in MUN's LABNET facilities, for example in our class lab (C2003). These computers are available during lab sessions and open lab hours.
  2. Your Personal Computer: Jamovi can be downloaded and installed on your own laptop or desktop computer. This allows you to work on assignments at home and bring your laptop to class if you prefer.
  3. Jamovi Cloud: If you don't have a suitable computer or can't access the LABNET facilities, you can use Jamovi Cloud through your web browser. A free account is available with limitations on storage and compute resources, but it provides full access to Jamovi's statistical analysis capabilities and is generally sufficient for course requirements.

Download and Setup Instructions

If you'd like to install Jamovi on your personal computer, follow these steps below. The installation process is straightforward and typically takes 5-10 minutes. No special configuration is required.

  1. Visit www.jamovi.org/download.html
  2. Click the download button for your operating system
  3. Once the download completes, open the installer file
  4. Follow the on-screen installation prompts
  5. Launch Jamovi to verify it opens correctly

We'll cover Jamovi basics in Week 3 of the course and then you'll develop your skills over the course of the semester. No prior experience with statistics software is required.

Textbook

Learning Statistics with Jamovi by Danielle Navarro and David Foxcroft (2025) is available as a free online textbook at davidfoxcroft.github.io/lsj-book.



Schedule

Date Topic Required Reading / Notes
Tues., Jan. 6 — Class cancelled (John sick)
Thurs., Jan. 8 — Class cancelled (John sick)
Tues., Jan. 13 Introduction Ch. 1
Thurs., Jan. 15 Statistics & Design Ch. 2 Sections 2.1-2.4
Tues., Jan. 20 Research Design Ch. 2 Sections 2.5-2.7
Thurs., Jan. 22 Jamovi Ch. 3
Tues., Jan. 27 Central Tendency Ch. 4 Section 4.1, 4.2
Thurs., Jan. 29 Variability Ch. 4 Section 4.3-4.6
Tues., Feb. 3 Graphs Ch. 5
Thurs., Feb. 5 Tables & Data Processing Ch. 6 and Prelude
Tues., Feb. 10 Probability Ch. 7 Sections 7.1-7.3
Thurs., Feb. 12 Probability Ch. 7 Sections 7.4-7.7
Tues., Feb. 17 Sampling & Estimation Ch. 8 Section 8.1
Thurs., Feb. 19 Sampling & Estimation Ch. 8 Sections 8.2-8.6
Tues., Feb. 24 — READING WEEK
Thurs., Feb. 26 — READING WEEK
Tues., Mar. 3 Hypothesis Testing Ch. 9 Sections 9.1-9.5
Thurs., Mar. 5 Hypothesis Testing Ch. 9 Sections 9.6-9.10
Tues., Mar. 10 Categorical Data Analysis Ch. 10 Section 10.1
Thurs., Mar. 12 Categorical Data Analysis Ch. 10 Sections 10.2-10.9
Tues., Mar. 17 Comparing Means Ch. 11 Sections 11.1-11.4
Thurs., Mar. 19 Comparing Means Ch. 11 Sections 11.5-11.11
Tues., Mar. 24 Correlation, Regression Ch. 12 Sections 12.1-12.4
Thurs., Mar. 26 Correlation, Regression Ch. 12 Sections 12.5-12.12
Tues., Mar. 31 Factor Analysis Ch. 14 Sections 15.1, 15.2
Thurs., Apr. 2 Factor Analysis Ch. 14 Sections 15.3-15.6
Tues., Apr. 7 Exam Review —

Important End of Term Dates

Assessment


You'll complete ten short online quizzes (4% each), two lab assignments, and one final examination. These assessment components are designed to evaluate both ongoing engagement with course material and cumulative learning outcomes.

Quizzes (40%)

You'll complete ten quizzes administered through Brightspace, each worth 4% of your final grade. These quizzes consist of multiple-choice and true/false questions and are open-book, so you can consult course materials while completing them. Quizzes emphasize content from assigned readings as well as material covered in lectures and labs. This format encourages regular engagement with course content while developing your ability to apply concepts and locate relevant information efficiently.

Lab Assignments (20%)

You'll complete two lab assignments designed to provide hands-on experience with statistical analysis using Jamovi. These assignments will be completed across multiple lab sessions, with time provided in class to work through exercises. You'll submit lab assignments twice during the term as collections of completed exercises. Each submission is worth 10% of your final grade. These assignments provide opportunities to apply theoretical concepts to real data and develop proficiency with statistical software.

Final Exam (40%)

There will be an cumulative, in-person, final exam. It will assess your understanding of course material, including key concepts, theoretical frameworks, and analytical methods covered throughout the term. The final exam questions will be very similar to the quiz questions you answer throughout the term, but unlike the quizzes it is closed-book and invigilated.



Policies

Attendance and Participation

You are much more likely to succeed in this course if you show up regularly. Class sessions builds directly on previous work, and collaborative learning activities cannot be replicated outside class. If you must miss a session:

Technology and Device Policy

Please use devices responsibly and avoid non-course related activities during class where possible.

Academic Integrity

Students are expected to adhere to the principles of academic integrity as outlined in the University's Academic Integrity Policy. Academic misconduct includes, but is not limited to, plagiarism, cheating, misrepresentation, and unauthorized collaboration. All violations will be reported to the appropriate authorities and may result in failure of the assignment, course, or more severe sanctions. Please consult www.mun.ca/student/supports-and-services/student-conduct-and-academic-integrity/

All work submitted must be your own, though collaboration and discussion are encouraged during class sessions and office hours. When working with code and data analysis:

Course Policy on Generative Artificial Intelligence (GenAI)

The use of generative artificial intelligence (GenAI) tools in this course is permitted with the following guidelines:

Note that violations of academic integrity could result in automatic failures of the assignment or the course, depending on severity. All cases will be reported.

Class Cancellations

Changes to the schedule due to weather, illness, or other circumstances will be communicated via Brightspace announcements and email to your @mun.ca accounts.

Accessibility and Accommodations

Memorial University is committed to supporting students with disabilities. Students who may need accommodations are encouraged to contact the Glenn Roy Blundon Centre for Students with Disabilities for a confidential consultation as early as possible in the semester.

Contact Information

I am committed to ensuring all students can succeed in this course and will work with you to implement approved accommodations effectively.

Respectful Learning Environment

Memorial University is committed to creating a respectful, inclusive learning environment free from harassment and discrimination. All students, faculty, and staff have the right to learn and work in an environment that promotes dignity and respect. This is a shared responsibility.



Resources for Students

Student Well-being and Support

Student Wellness and Counselling Centre

Student Success Centre

Sexual Harassment and Misconduct Reporting

Financial Support

Research and Library Support

QEII Library

Student Life (ASK)

Additional Academic Support

If you are struggling with course material, personal issues, or need additional support, please reach out during office hours or via email. Early communication allows us to address challenges before they become overwhelming. The university provides extensive support services designed to help you succeed academically and personally.