Quantitative Methods for Communication (Spring 2023)

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Course Information[edit]

COM 304: Quantitative Methods for Communication Research


Location: WALC 3090
Class Hours: Tuesdays and Thursdays; 10:30-11:20 AM


Location: BRNG B286
Section 003: 12:30-1:20 PM F
Section 006: 11:30-12:20 PM F


Professor: Jeremy Foote
Email: jdfoote@purdue.edu
Office Hours: Thursdays; 1:00–3:00pm and by appointment

Graduate TA: Hsuen-Chi (Hazel) Chiu
Email: chiu101@purdue.edu
Office Hours: Monday and Wednesday; 9:30–10:30am and by appointment

Course Overview and Learning Objectives[edit]

Welcome to COM 304: Quantitative Methods for Communication! We are excited to have you in the class. Nearly all communication jobs involve quantitative research in some way; in this course, we will provide you a foundation for doing quantitative communication research.

I know that for many Communication majors even thinking of math and statistics is traumatic, but we will work hard to provide the resources that you need to succeed and we will take things one step at a time. You can do this!

This course introduces students to a range of social-scientific research methods used to investigate human communication. By the end of this course, you will be able to:

  1. Explain the types of research questions, methods, and analyses used by scholars who conduct social-scientific studies of communication, as well as by practitioners in fields such as marketing and consumer research, political polling, etc.;
  2. Critically evaluate quantitative research reports, including those you may read in other courses at Purdue as well as those described in the popular media, appearing in business reports, grant applications, and so forth;
  3. Design and conduct basic research studies about communication-related topics.

The course is organized into three components which are addressed simultaneously throughout the semester: (1) Research Design, (2) Statistics, and (3) Statistical Software.

The Research Design component focuses on the process of planning research, considering the range of choices researchers must make in order to conduct useful studies. This component will not only help you conduct research, it will make you a more critical research consumer.

The Statistics component is concerned with analyses by which numerical data can be synthesized, described, and interpreted. This component provides a strong conceptual introduction to statistics—with a limited amount of math—and will help you to be confident in analyzing basic numerical data for almost any purpose.

The Software component is closely allied with the Statistics component. This component focuses on basic applications of the Statistics Package for the Social Sciences (SPSS)—a powerful, but user-friendly computer program—and will give you an immediately marketable skill (something to put on the resume). This course should be of use to students with a number of goals, including those: (a) who are contemplating graduate study in communication or related fields; (b) whose current or future career may require them to answer questions by collecting and analyzing data (e.g., advertising, human relations, marketing, public relations); and (c) who want to develop their skills at critically evaluating research and knowledge claims made by “experts” on communication issues.

Required resources and texts[edit]


Required texts:

  • Salkind, N. J. (2017). Statistics for people who (think they) hate statistics (6th ed.). Los Angeles, CA: Sage.
    • Note: I believe that you should be fine with one edition newer or older than this one, too. Just make sure that the topic matches up with what the syllabus says.

You also will be assigned readings from online resources; these readings are listed on the course schedule of this syllabus (below) and links are provided in each lecture’s folder on the course Brightspace site. Readings from the text and online resources will be covered in the midterm and final exams.


Smart phone or laptop to complete in-class Hotseat participation questions.

We will be using the statistical software SPSS for most of the labs. SPSS is loaded on all Purdue lab computers. In a pinch, you may be able to access it via goremote (https://goremote.itap.purdue.edu) but in general using the lab computers will be simpler and faster.

Course logistics[edit]

Note About This Syllabus[edit]

Although the core expectations for this class are fixed, I may make some adjustments to the details of readings and assignments. As a result, there are three important things to keep in mind:

  1. Although details on this syllabus will change, I will not change readings or assignments less than one week before they are due. If you plan to read more than one week ahead, contact me first.
  2. Because this a wiki, you will be able to track every change by clicking the history button on this page. I will also summarize these changes in an announcement on Brightspace that should be emailed to everybody in the class if you have notifications turned on.
  3. I will ask the class for voluntary anonymous feedback. Please let me know what is working and what can be improved.

Class Sessions[edit]

There are two types of class sessions: lecture sessions and lab sessions.

The lectures sections will be led by Dr. Foote and will be Tuesdays and Thursdays in WALC 3090. The lab sessions will be on Fridays and will be led by Hazel, the TA of the course.

I expect you to come to the lecture sessions prepared, having read the material. It is fine to have questions---indeed, one of the goals of these sessions is to identify things that are confusing and to clear up misconceptions---but you should be ready to talk about your attempts to understand and why something is confusing.

I will do my best to post lecture slides to Brightspace before class.

Getting Help[edit]

Your first place to look for help should be each other. By asking and answering questions on Discord, you will not only help to build a repository of shared information, but to reinforce our learning community.

I will also hold office hours on Thursdays, from 1-3.

I will also check Discord at least once a day. I encourage you to post questions there, and to use it as a space where we can help and instruct each other. In general, you should contact me there. I am also available by email. You can reach me at jdfoote@purdue.edu. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours.


Your success in this class depends most on you being engaged in the learning process. It is essential that you: (a) study the readings in advance of class; (b) attend class and stay focused on the day’s material; (c) complete all of the lecture and SPSS homework assignments, and talk to me or your TA if you have questions about these assignments, and (d) plan in advance for the completion of projects and studying for exams. In short, it is essential that you take significant responsibility for your own learning. If you do so, you may find that Quantitative Methods is not only challenging but rewarding.

In order to encourage your learning, the course includes several types of assignments.

Midterm and Final Exams[edit]

Students will complete a midterm and a final exam. Each exam will cover material from lectures, discussion, and readings. They will assess your knowledge of research methods and statistics, but not your use of SPSS. Review sheets will be distributed before each exam. The midterm and final exam each are worth 100 points. Please do not make travel arrangements that interfere with the scheduled exams.


Students will complete 10 SPSS homework assignments over the course of the class. They will be assigned in Friday labs and are due in the next lab, when lab begins. Each SPSS homework assignment is worth 5 points, for a total of 50 points.

SPSS Quizzes[edit]

There will be two quizzes pertinent to the SPSS component of the course. These quizzes will assess your ability to use SPSS and interpret the program output. They will be given during Friday lab sessions at the middle and end of the semester. Each SPSS quiz is worth 50 points. Please do not make travel arrangements that interfere with the scheduled quizzes.

Survey Design Project[edit]

To build your knowledge of survey design, you will work in a group on a survey development project. The project involves developing research and survey questions; the project and your contribution to the group together are worth 50 points.

Survey Analysis Project[edit]

To encourage synthesis of knowledge and skills across all of the course components, you will work in a group on the collection and analysis of data from the Survey Design Project. The project involves the collection, analysis, and “write-up” of data. Detailed descriptions of this project will be provided as the semester progresses. This project is worth 80 points.


I expect you to be an active member of our class. This includes reading the material before class, paying attention in class, participating in activities, and being actively engaged in learning, thinking about, and trying to understand the material.

To make sure that everyone has an opportunity to participate and to encourage you to do the assignments, I will randomly select students to answer questions about the concepts from readings or to explain portions of homework assignments and labs.

Lecture classes will also typically include questions on Hotseat.

Please do not come to class if you have COVID symptoms (or symptoms of other airborne diseases!). In order to align the incentives so that people don't show up sick, we will track attendance but lecture and lab participation will be self-graded out of a total of 20 points.


I really believe that students and instructors should be on the same team, with the goal of learning. I see grades as a tool to motivate learning. Our goal will be to provide feedback that helps you to learn.

There are 500 points in the class, distributed across assignments as follows:

Assignment points
Midterm Exam 100 points
Final Exam 100 points
Survey Design Project 50 points
Survey Analysis Project 80 points
SPSS Quiz 1 50 points
SPSS Quiz 2 50 points
Lab Homework 50 points
Participation 20 points

Your final course grade will be calculated using the following scale:

484-500 points A+
464-483 points A
449-463 points A-
434-448 points B+
414-433 points B
400-413 points B-
384-399 points C+
364-383 points C
349-363 points C-
334-348 points D+
314-333 points D
300-313 points D- < 300 points F

Extra Credit for Participating in Research Studies[edit]

he course is signed up for extra credit through the Brian Lamb School of Communication Research Participation System.


NOTE This section may be modified throughout the course to meet the class's needs. Check back in weekly.

Week 1: Variables, Research Questions, and Hypotheses[edit]

January 10[edit]

Assignment Due:

  • Sign up for Discord and introduce yourself
  • Take the intro survey (link in Brightspace announcement)

Required Readings:

  • Five Big Words (on Brightspace)


  • Class overview and expectations — We'll walk through this syllabus.
  • Goals of Quantitative Research

January 12[edit]

Assignment Due:

  • Read the entire syllabus (this document)

Readings (on Brightspace):

  • Variables in non-experimental studies
  • Conceptualizing
  • Writing RQs


  • Constructs
  • Hypotheses
  • Research Questions
  • Independent/Dependent Variables

January 13[edit]

Lab 1: Intro to SPSS

Week 2: Surveys[edit]

January 17[edit]

Readings (before class):

  • Scales of Measurement


  • Scales

January 19[edit]

Readings (before class):

  • How to improve online survey response rates;
  • Survey questions 101: do you make any of these 7 question-writing mistakes?


  • Selection effects

  • Survey Project Assigned

January 20[edit]

Lab 2: Entering and Modifying Data in SPSS

Assignment Due:

  • Lab 1: Intro to SPSS
  • Completed Homework Survey

Week 3: Descriptive Statistics[edit]

January 24[edit]

Readings (before class):

  • Salkind Chapter 2
  • Salkind Chapter 4


  • Distributions
  • Mean
  • Median
  • Mode

January 26[edit]

Readings (before class):

  • Salkind Chapter 3
  • Interquartile Range
  • SD and normal curve


January 27[edit]

Lab 3: Modifying data in SPSS

Assignment Due:

  • Lab 2
  • SPSS Homework Survey Data File

Week 4: Z-scores and correlation[edit]

January 31[edit]

Assignment Due:


Readings (before class):

  • Salkind Ch. 8 pp. 149-165
  • Standard scores and the normal curve


  • Standardization
  • Z-scores

February 2[edit]

Readings (before class):

  • Salkind Chapter 5


  • Correlation
  • r

February 3[edit]

Lab 4: Descriptive Stats and z scores

Assignment Due:

  • Lab 3

Week 5: Reliability and Validity[edit]

February 7[edit]

Readings (before class):

  • Salkind Chapter 6
  • Reliability and its relationship to Validity


  • Validity
  • Generalizability
  • Replication

February 9[edit]

Readings (before class):

  • Sampling


February 10[edit]

Lab 5: Scatterplots and correlation

Assignment Due:

  • Lab 4

Week 6: Sampling (cont'd)[edit]

February 14[edit]

Assignment Due:

  • Survey project

Readings (before class):

  • Sampling distribution
  • Sampling error


February 16[edit]

Review for Midterm

February 17[edit]

Lab 6: Reliability and Cronbach's alpha

Assignment Due:

  • Lab 5

Week 7: Experimental Design[edit]

February 21[edit]


February 23[edit]

Readings (before class):


February 24[edit]

Lab 7: Review for SPSS Quiz 1

Week 8: Causality[edit]

February 28[edit]

Final Project Assigned

Readings (before class):

  • Cialdini EPH Experiment (pp. 599-601)
  • True Experimental Designs


  • "True" experiments

March 2[edit]

Readings (before class):

  • Establishing Cause and Effect
  • Internal Validity
  • External Validity


March 3[edit]

Lab 8: SPSS Quiz 1

Week 9: Central Limit Theorem and standard errors[edit]

March 7[edit]

Readings (before class):

  • Salkind Ch. 9, pp. 186-187
  • Probability and Normal Curve & Standard Error of Mean (same PDF)


March 9[edit]

Readings (before class):

  • Salkind Chapter 9 pp. 192-194
  • Confidence interval for the mean


March 10[edit]

Lab 9: Final Project Data Collection


Week 10: Testing hypotheses[edit]

March 21[edit]

Readings (before class):

  • Salkind Chapter 7
  • Salkind Chapter 9 pp. 188-192
  • Salkind Chapter 10


  • The null hypothesis
  • Hypothesis testing
  • Statistical significance
  • One sample z-test

March 23[edit]

Readings (before class):

  • Chapter 17
  • Computation of Two-way Chi-Square


  • Chi-squared tests

March 24[edit]

Lab 10: Survey Analysis Project: Data Entry

Assignment Due:

  • Final Project data must be gathered

Week 11: Significance tests[edit]

March 28[edit]

Readings (before class):

  • Cramer’s Phi


  • Chi-Square: Effect Size

March 30[edit]

Readings (before class):

  • Salkind Chapter 15


  • Testing r for statistical significance

March 31[edit]

Lab 11: Two-way Chi-squared

Assignment Due:

  • Lab 10

Week 12: t-tests[edit]

April 4[edit]

Readings (before class):

  • Salkind Chapter 11


  • t-tests for independent groups

April 6[edit]

Readings (before class):

  • Introduction to Effect Size


  • Cohen's d

April 7[edit]

Lab 12: t-test and Pearson's r

Assignment Due:

  • Lab 11

Week 13: Errors and Ethics[edit]

April 11[edit]

Readings (before class):

  • Salkind Chapter 9, pp. 177-186


  • Type I and Type II errors
  • Statistical power

April 13[edit]

Final project review + Q&A

April 14[edit]

Optional office hour for SPSS Quiz 2

Assignment Due:

  • Lab 12
  • Do practice quiz before lab time

Week 14[edit]

April 18[edit]

Group meetings on Discord. Come prepared with 3-4 questions you would like to ask about your final project. Each group will have 10 minutes with a member of the teaching team.

  • 10:30-10:40
    • Hazel: Group 1
    • Jeremy: Group 2
  • 10:40-10:50
    • Hazel: Group 3
    • Jeremy: Group 4
  • 10:50-11:00
    • Hazel: Group 5
    • Jeremy: Group 6
  • 11:00-11:10
    • Hazel: Group 7
    • Jeremy: Group 8
  • 11:10-11:20
    • Hazel: Group 9
    • Jeremy: Group 10

April 20[edit]

Open Topic: Buffer Day / Out-of-class workday

April 21[edit]

SPSS Quiz 2

Week 15[edit]

April 25[edit]

Out-of-class work day for final project

April 27[edit]

Review for Final exam

Assignment Due:

  • Final Project

April 28[edit]

No Lab

Week 16: Final Exam[edit]

May 2; 7-9 pm



I try very hard to make our meeting times valuable to you and I expect that you attend. That being said, hopefully the pandemic taught us that if you feel sick, please do not come to class. If you need to miss class, then it is your responsibility to seek out support from classmates for notes, handouts, and other information.

Only I can excuse a student from a course requirement or responsibility. When conflicts can be anticipated, such as for many University-sponsored activities and religious observations, please inform me of the situation as far in advance as possible. For unanticipated or emergency conflicts, when advance notification is not possible, contact me as soon as possible on Discord or by email. In cases of bereavement, quarantine, or isolation, the student or the student’s representative should contact the Office of the Dean of Students via email or phone at 765-494-1747. Our course on Brightspace includes a link to the Dean of Students under 'Campus Resources.'

Classroom Discussions and Peer Feedback[edit]

Throughout the course, you may receive, read, collaborate, and/or comment on classmates’ work. These assignments are for class use only. You may not share them with anybody outside of class without explicit written permission from the document’s author and pertaining to the specific piece.

It is essential to the success of this class that all participants feel comfortable discussing questions, thoughts, ideas, fears, reservations, apprehensions and confusion. Therefore, you may not create any audio or video recordings during class time nor share verbatim comments with those not in class linked to people’s identities unless you get clear and explicit permission. If you want to share general impressions or specifics of in-class discussions with those not in class, please do so without disclosing personal identities or details.

Academic Integrity[edit]

While I encourage collaboration, I expect that any work that you submit is your own. Basic guidelines for Purdue students are outlined here but I expect you to be exemplary members of the academic community. Please get in touch if you have any questions or concerns.


I strongly support Purdue's policy of nondiscrimination (below). If you feel like any member of our classroom--including me--is not living up to these principles, then please come and talk to me about it.

Purdue University is committed to maintaining a community which recognizes and values the inherent worth and dignity of every person; fosters tolerance, sensitivity, understanding, and mutual respect among its members; and encourages each individual to strive to reach his or her own potential. In pursuit of its goal of academic excellence, the University seeks to develop and nurture diversity. The University believes that diversity among its many members strengthens the institution, stimulates creativity, promotes the exchange of ideas, and enriches campus life.


Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let me know so that we can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247.

Emergency Preparation[edit]

In the event of a major campus emergency, I will update the requirements and deadlines as needed.

Mental Health[edit]

If you or someone you know is feeling overwhelmed, depressed, and/or in need of mental health support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at 765-494-6995 during and after hours, on weekends and holidays, or by going to the CAPS office of the second floor of the Purdue University Student Health Center (PUSH) during business hours.


A grade of incomplete (I) will be given only in unusual circumstances. The request must describe the circumstances, along with a proposed timeline for completing the course work. Submitting a request does not ensure that an incomplete grade will be granted. If granted, you will be required to fill out and sign an “Incomplete Contract” form that will be turned in with the course grades. Any requests made after the course is completed will not be considered for an incomplete grade.

Additional Policies[edit]

Links to additional Purdue policies are on our Brightspace page. If you have questions about policies please get in touch.