Quantitative Methods for Communication (Spring 2022): Difference between revisions

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{{Old Class}}
= Course Information =
= Course Information =
:'''COM 304: Quantitative Methods for Communication Research'''
:'''COM 304: Quantitative Methods for Communication Research'''
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:'''Email:''' jdfoote@purdue.edu
:'''Email:''' jdfoote@purdue.edu
:'''[[User:Jdfoote/OH|Office Hours]]:''' Thursdays; 2:00–4:00pm and by appointment
:'''[[User:Jdfoote/OH|Office Hours]]:''' Thursdays; 2:00–4:00pm and by appointment


:'''Graduate TA:''' Grace Lee
:'''Graduate TA:''' Grace Lee
:'''Email:''' lee3416@purdue.edu
:'''Email:''' lee3416@purdue.edu
:'''Office Hours:''' Thursdays; 10:30–11:45am and by appointment


:'''Graduate TA:''' Yihan Jia
:'''Graduate TA:''' Yihan Jia
:'''Email:''' jia110@purdue.edu
:'''Email:''' jia110@purdue.edu
:'''Office Hours:''' Tuesdays and Thursdays; 11:00am–12:00pm and by appointment


<div style="float:right;">__TOC__</div>
<div style="float:right;">{{toclimit|limit=3}}</div>


= Course Overview and Learning Objectives =
= Course Overview and Learning Objectives =
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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!
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!


Course Description and Objectives:
 
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:   
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:   
# 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.;  
# 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.;  
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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.
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 =
= Required resources and texts =
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== Readings ==
== Readings ==


* Required texts:  
Required texts:  
* Salkind, N. J.  (2017). Statistics for people who (think they) hate statistics  
* Salkind, N. J.  (2017). Statistics for people who (think they) hate statistics (6th ed.).  Los Angeles, CA: Sage.  
(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.
** Note: I believe that you should be fine with one edition newer or older than this one, too


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.  
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.


== Technology ==  
== Technology ==  
Smart phone or laptop to complete in-class Hotseat participation questions.
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 =
= Course logistics =
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The lectures sections will be led by Dr. Foote and will be Tuesdays and Thursdays in Lawson 1142. The lab sessions will be on Fridays and will be led by Grace and Yihan, the TAs of the course.
The lectures sections will be led by Dr. Foote and will be Tuesdays and Thursdays in Lawson 1142. The lab sessions will be on Fridays and will be led by Grace and Yihan, the TAs 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.
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 ==
== Getting Help ==
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= Assignments =
= Assignments =


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.


There will be multiple types of assignments, designed to encourage learning in different ways.
In order to encourage your learning, the course includes several types of assignments.


== Participation ==
== Midterm and Final Exams ==


I expect you to be an active member of our class. This includes paying attention in class, participating in activities, and being actively engaged in learning, thinking about, and trying to understand the material.
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.
 
To make sure that everyone has an opportunity to participate and to encourage you to do the assignments, I will randomly select students to discuss readings or to explain portions of homework assignments and labs.


== Homework/Labs ==


== Discussion Questions ==
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.


In order to make sure that we are prepared to have a productive discussion, you are required to submit one or two discussion questions that you think would be interesting to discuss on Monday by noon. Post your questions on the shared Google Doc at https://docs.google.com/document/d/1AK4MhWLVwDuxqvTwLomN7hu4aLpo59n5qXgEap8SBNc/edit?usp=sharing; try to group similar questions together.
== SPSS Quizzes ==


Questions should engage with the readings and either connect to other concepts or to the "real world". Here are some good example questions:
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.


* The readings this week talked a lot about how network ties get created. I made a list of my closest friends and I realized that most of them only became friends after we happened to be in the same groups over and over again. What role does repetition have in forming ties?
== Survey Design Project ==
* I was confused by the reading on social capital. What's the difference between social capital and power? And if they are the same, then why not just call it "network power"?
* Imagine you were asked to analyze the network of a big company to help them to identify people who deserve a raise. What measures would you use to identify them? What would you not use?


Some weeks will also include more practical homework (mostly data manipulation and visualization in R). On those weeks, portions of our discussions will center around going over homework questions and identifying places where folks are still confused.
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.


== Homework/Labs ==
== Survey Analysis Project ==


There will be a number of homework assignments. At the beginning of the class, these will be designed to help you to grasp foundational network concepts. As the class progresses, more and more of them will be analyzing and visualizing networks in R.
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.


== Exams ==
== Participation ==


There will be one take home exam. It will assess your understanding of core communication and social networks concepts.
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.


== Final Project ==
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.


Students will work on a [[/Final project|Final Project]] that explains how network analysis and a network approach can benefit an organization.
Lecture classes will also typically include questions on Hotseat, which will also be used to take attendance.


A number of intermediate assignments through the semester will help you to gain the skills and data necessary to be successful.
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.


= Grades =
= Grades =


This course will follow a "self-assessment" philosophy. I am more interested in helping you to learn things that will be useful to you than in assigning grades. In general, I think that my time is much better spent in providing better feedback and in being available to work through problems together.
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.


The university still requires grades, so you will be leading the evaluation of your work. This will be completed with me in three stages, at the end of weeks 5, 10, and 16. In each stage, you will use [[Self Assessment Reflection|this form]] to reflect on what you have accomplished thus far, how it has met, not met, or exceeded expectations, based both on rubrics and personal goals and objectives. At each of these stages you will receive feedback on your assessments. By the end of the semester, you should have a clear vision of your accomplishments and growth, which you will turn into a grade. As the instructor-of-record, I maintain the right to disagree with your assessment and alter grades as I see fit, but any time that I do this it will be accompanied by an explanation and discussion. These personal assessments, reflecting both honest and meaningful reflection of your work will be the most important factor in final grades.
There are 500 points in the class, distributed across assignments as follows:


We will use the following rubric in our assessment:
{| class="wikitable"
|+ 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
|-
|}


* 20%: class participation, including attendance and participation in discussions and group work
Your final course grade will be calculated using the following scale:  
* 20%: Labs and homework assignments
* 25%: Exam
* 35%: Final Project


The exam will be graded like a normal exam and the score will make up 25% of your grade. For the rest of the assignments (and the other 75% of your grade), I will provide feedback which will inform an ongoing conversation about your work.
<div style=display:inline-table>
 
{| class="wikitable"
My interpretation of grade levels (A, B, C, D/F) is the following:
|-
 
| 484-500 points
 
| A+
A: Reflects work the exceeds expectations on multiple fronts and to a great degree. Students reaching this level of achievement will:
|-
* Do what it takes to learn the principles and techniques of social networks, including looking to outside sources if necessary.
|464-483 points
* Engage thoughtfully with an ambitious final project.
| A  
* Take intellectual risks, offering interpretations based on synthesizing material and asking for feedback from peers.
|-
* Share work early allowing extra time for engagement with others.
|449-463 points
* Write reflections that grapple meaningfully with lessons learned as well as challenges.
| A-
* Complete all or nearly all homework assignments at a high level.
|-
 
|434-448 points
B: Reflects strong work. Work at this level will be of consistently high quality. Students reaching this level of achievement will:
| B+
* Be more safe or consistent than the work described above.
|-
* Ask meaningful questions of peers and engage them in fruitful discussion.
|414-433 points
* Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week.
| B
* Compose complete and sufficiently detailed reflections.
|-
* Complete many of the homework assignments.
|400-413 points
 
| B-
C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement
|}
will:
</div>
* Turn in and complete the final project on time.
<div style=display:inline-table>
* Be collegial and continue discussion, through asking simple or limited questions.
{| class='wikitable'
* Compose reflections with straightforward and easily manageable goals and/or avoid discussions of challenges.
|-
* Not complete homework assignments or turn some in in a hasty or incomplete manner.
|384-399 points
 
| C+
D/F: These are reserved for cases in which students do not complete work or participate. Students may also be
|-
impeding the ability of others to learn.
|364-383 points
| C
|-
|349-363 points
| C-
|-
|334-348 points
| D+
|-
|314-333 points
| D
|-
|300-313 points
| D-
| < 300 points
| F
|-
|}
</div>


== Extra Credit for Participating in Research Studies ==
== Extra Credit for Participating in Research Studies ==


If you feel like you need to earn extra credit in order to earn the grade that you would like, the course is signed up for extra credit through the Brian Lamb School of Communication Research Participation System.
he course is signed up for extra credit through the Brian Lamb School of Communication Research Participation System.


* You earn a ½ percent credit for every half-hour that you participate in a study. The maximum percent that you can earn for this course is 2% (or 10 total points), which will be added to your total course points (500).
* Please review the instructions before you sign up for studies; to view the instructions go to https://www.cla.purdue.edu/communication/research/participation/students.html
* Please review the instructions before you sign up for studies; to view the instructions go to https://www.cla.purdue.edu/communication/research/participation/students.html
* You can sign up to participate in studies by logging into http://purdue-comm.sona-systems.com/.
* You can sign up to participate in studies by logging into http://purdue-comm.sona-systems.com/.
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== Week 1: Introductions and the network perspective ==
== Week 1: Variables, Research Questions, and Hypotheses ==


August 24
==== January 11 ====


'''Assignment Due:'''  
'''Assignment Due:'''  
* [[/Discord Signup|Sign up for Discord]] and introduce yourself
* [[/Discord Signup|Sign up for Discord]] and introduce yourself
* Take [https://forms.gle/ANqbnAXxivexukgB7 this very brief survey]
* Take [https://forms.gle/Ak9GYuFGer89k6G47 this very brief survey]


'''Required Readings:'''  
'''Required Readings:'''  
* None
* Five Big Words (on Brightspace)


'''Concepts:'''
'''Concepts:'''
* Class overview and expectations — We'll walk through this syllabus.
* Class overview and expectations — We'll walk through this syllabus.
* What are social networks?
* Goals of Quantitative Research
* Why study networks?




August 26
==== January 13 ====


'''Assignment Due:'''  
'''Assignment Due:'''  
* Read the entire syllabus (this document)
* Read the entire syllabus (this document)


'''Readings:'''  
'''Readings (on Brightspace):'''
* Variables in non-experimental studies
* Conceptualizing
* Writing RQs
 
'''Concepts:'''
* Constructs
* Hypotheses
* Research Questions
* Independent/Dependent Variables
 
==== January 14 ====
 
'''Lab 1: Intro to SPSS'''


== Week 2: Surveys ==


'''Class Schedule:'''
==== January 18 ====
* Network simulation activity (bring a computer)
* Start work on [[/Homework 1|Homework 1]]


== Week 2: Network representations  ==
'''Readings (before class):'''
* Scales of Measurement


August 31
'''Concepts:'''
* Scales


'''Assignment Due (on Monday):'''
* Install R and RStudio on your computer. [https://techvidvan.com/tutorials/install-r/ This tutorial] should help you to succeed.
* [[Communication and Social Networks (Fall 2021)/Homework 1|Homework 1]]
* [[#Discussion Questions|Discussion questions]] (Due Monday at noon!)


'''Lecture Video (before class):'''
====January 20====
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819548/View Network Data and Network Types Lecture] [19:18]


'''Readings (before class):'''  
'''Readings (before class):'''  
* James M. Cook, [http://www.umasocialmedia.com/socialnetworks/wp-content/uploads/2016/08/WhatIsASocialNetwork.pdf What is a Social Network?]
* How to improve online survey response rates;
* James M. Cook, [http://www.umasocialmedia.com/socialnetworks/wp-content/uploads/2016/09/IndividualsVersusNetworks.pdf Individuals versus Networks]
* Survey questions 101: do you make any of these 7 question-writing mistakes?
* (Optional/skim) Freeman, L. C. (2000). [https://www.cmu.edu/joss/content/articles/volume1/Freeman.html Visualizing social networks]. Journal of social structure, 1(1), 4.
 
'''Concepts:'''
* Selection effects




'''Concepts:'''
* Survey Project Assigned
* Complex systems and networks
* Individual and collective behavior


====January 21====


September 2
'''Lab 2: Entering and Modifying Data in SPSS'''


'''Class Schedule:'''
'''Assignment Due:'''
* Go through [https://ncase.me/polygons/ Parable of the Polygons] by Nicky Case
* Lab 1: Intro to SPSS
* Start work on [[/R Lab 1|R Lab 1]]
* Completed Homework Survey


== Week 3: How are communication networks formed? ==
== Week 3: Descriptive Statistics ==




September 7
====January 25====


'''Assignment Due (on Monday):'''
'''Readings (before class):'''  
* [[/R Lab 1|R Lab 1]]
* Salkind Chapter 2
** [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/7337413/View Video explanation]
* Salkind Chapter 4
* [[#Discussion_Questions|Discussion Questions]]


'''Lecture Video:'''
'''Concepts:'''
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819550/View Edge Creation] [12:43] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_3/lecture/tie_formation.html Slides]]
* Distributions
* Mean
* Median
* Mode


'''Readings:'''
====January 27====
* Monge, P. R., & Contractor, N. S. (2003). [https://purdue.brightspace.com/d2l/le/content/208700/viewContent/5245859/View Theories of communication networks]. Oxford, UK: Oxford University Press. (pp. 298--314) - On Brightspace under Content > Readings
* Feld, S. L. (1981). [https://www.jstor.org/stable/2778746 The focused organization of social ties]. American Journal of Sociology, 86(5), 1015–1035.
* McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). [https://www-jstor-org.ezproxy.lib.purdue.edu/stable/2678628 Birds of a Feather: Homophily in Social Networks]. Annual Review of Sociology, 27, 415–444.


''Note:'' This week involves reading two academic articles. [[#Reading_Academic_Articles|Read this]] to understand my expectations and some tips for reading and understanding these articles.
 
'''Readings (before class):'''
* Salkind Chapter 3
* Interquartile Range
* SD and normal curve


'''Concepts:'''
'''Concepts:'''
* Exposure, formation, maintenance, decay
* Homophily
* Reciprocity
* Triadic closure


* [https://jeremydfoote.com/Communication-and-Social-Networks/week_3/lecture/week_3.html Class Slides]
====January 28====


'''Lab 3: Modifying data in SPSS'''


September 9
'''Assignment Due:'''
* Lab 2
* SPSS Homework Survey Data File


== Week 4: Z-scores and correlation ==


'''Supplementary R lectures (watch before class):'''
====February 1====
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819552/View Why R + Programming principles lecture] [12:53]
 
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819553/View ggraph explanation video] [12:14]
'''Assignment Due:'''
** [https://jeremydfoote.com/Communication-and-Social-Networks/week_6/ggraph_walkthrough.html webpage for ggraph explanation video]
* SURVEY PROJECT QUESTIONS DUE in Lecture


'''Class Schedule:'''
'''Readings (before class):'''  
* [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/week_4/creating_networks.Rmd R Lab 2]
* Salkind Ch. 8 pp. 149-165
* Standard scores and the normal curve


== Week 4: Small group networks ==
'''Concepts:'''
* Standardization
* Z-scores


September 14
====February 3====
 
'''Readings (before class):'''
* Salkind Chapter 5
 
'''Concepts:'''
* Correlation
* r
 
====February 4====
 
'''Lab 4: Descriptive Stats and z scores'''


'''Assignment Due:'''
'''Assignment Due:'''
* [[#Discussion Questions|Discussion questions]]
* Lab 3
* [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/week_4/creating_networks.Rmd R Lab 2] (right-click, save to your computer, and open in RStudio)
 
** [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819549/View Homework explanation video]
== Week 5: Reliability and Validity ==
 
====February 8====


'''Lecture video:'''
'''Readings (before class):'''  
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/7373077/View Networks in small groups] [14:43] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_4/lecture/small_groups.html Slides]]
* Salkind Chapter 6
* Reliability and its relationship to Validity


'''Concepts:'''
* Validity
* Generalizability
* Replication


'''Readings:'''
====February 10====
* Krackhardt, D., & Hanson, J. R. (1993). [https://hbr.org/1993/07/informal-networks-the-company-behind-the-chart Informal networks: The company behind the chart]. Harvard business review, 71(4), 104-111.
* Katz, N., Lazer, D., Arrow, H., & Contractor, N. (2004). [https://libkey.io/libraries/228/articles/5387888/full-text-file?utm_source=api_559 Network theory and small groups]. Small Group Research, 35(3), 307–332.


'''Readings (before class):'''
* Sampling


'''Concepts:'''
'''Concepts:'''
* Informal networks
* Networks and group outcomes


== Week 5: Ego networks and network perception ==
====February 11====


September 21
'''Lab 5: Scatterplots and correlation'''
 
'''Assignment Due:'''
* Lab 4


'''Assignment Due:'''
* [[#Discussion Questions|Discussion questions]]
* Turn in your [[Self Assessment Reflection]] on Brightspace


== Week 6: Sampling (cont'd) ==


'''Lecture:'''
====February 15====
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819551/View Ego networks and network perceptions lecture] [17:14] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_5/lecture/ego_nets.html Slides]]


'''Readings:'''
'''Assignment Due:'''
* Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. University of California. ([https://faculty.ucr.edu/~hanneman/nettext/C9_Ego_networks.html Chapter 9])
* Survey project
* Marsden, P. V. (1987). [https://www-jstor-org.ezproxy.lib.purdue.edu/stable/2095397 Core Discussion Networks of Americans]. American Sociological Review, 52(1), 122–131.
* [https://hbr.org/2016/05/research-you-have-fewer-friends-than-you-think Research: You Have Fewer Friends than You Think]. (2016, May 12). Harvard Business Review.
* Smith, E. B., Menon, T., & Thompson, L. (2012). [https://pubsonline-informs-org.ezproxy.lib.purdue.edu/doi/full/10.1287/orsc.1100.0643 Status Differences in the Cognitive Activation of Social Networks]. Organization Science, 23(1), 67–82.


'''Readings (before class):'''
* Sampling distribution
* Sampling error


September 23
'''Concepts:'''


'''Class Schedule:'''
* [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/week_6/power_visualization.Rmd R Lab 3]


== Week 6: Power, centrality, and hierarchy ==
====February 17====


Due to Dr. Foote's illness, Sept. 28 discussion moved to Sept. 30
'''Review for Midterm'''


September 28


'''Assignment Due:'''
====February 18====
* [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/week_6/power_visualization.Rmd R Lab 3] (Right-click, save, open in RStudio, and knit)
** [https://jeremydfoote.com/Communication-and-Social-Networks/week_6/ggraph_walkthrough.html Introduction to tidygraph and ggraph]. Now that you've been at it for a while review this walkthrough that I wrote to help you to figure out how all of the different pieces work in tidygraph and ggraph.
* [[#Discussion Questions|Discussion questions]]


'''Video lecture:'''
'''Lab 6: Reliability and Cronbach's alpha'''
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/7413870/View Centrality measures] [18:44] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_6/lecture/centrality.html Slides]]


'''Readings:'''  
'''Assignment Due:'''
* Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. [https://faculty.ucr.edu/~hanneman/nettext/C10_Centrality.html Chapter 10: Centrality and Power]
* Lab 5
* Healy, K. (2013). [https://kieranhealy.org/blog/archives/2013/06/09/using-metadata-to-find-paul-revere/ Using Metadata to find Paul Revere].
* [https://www.youtube.com/watch?v=0unzqsPaPk8 Centrality measures]. Matthew Jackson. From [https://www.youtube.com/channel/UCCnG8fKY45aH73ahmGK2xcg Social and Economic Networks course]
* [https://www.youtube.com/watch?v=q8oBWwS2wAQ Centrality Eigenvector Measures]. Matthew Jackson
* (Optional) Holliday, Audrey, Campbell, & Moore, (2016). [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4898141/ Identifying well-connected opinion leaders for informal health promotion]


'''Class Schedule:'''


== Week 7: Experimental Design ==


September 30


'''Class Schedule:'''
====February 22====


== Week 7: Social Capital, structural holes, and weak ties ==
'''MIDTERM EXAM'''




October 5
====February 24====


'''Assignment Due:'''  
'''Readings (before class):'''  
* [[#Discussion Questions|Discussion questions]]


'''Lecture Video:'''
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819554/View Capital and Social Capital] [16:02] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_7/lecture/social_capital.html Slides]]


'''Readings:'''  
'''Concepts:'''
* Granovetter, M. S. (1973). [https://www-jstor-org.ezproxy.lib.purdue.edu/stable/2776392?sid=primo&seq=1#metadata_info_tab_contents The Strength of Weak Ties]. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469
* Kadushin, C. (2012).  [https://ebookcentral.proquest.com/lib/purdue/reader.action?docID=829477&ppg=175 Networks as Social Capital], in Kadushin, C. (2012). Understanding Social Networks. Theories, Concepts and Findings. Oxford: Oxford University Press.
* Putnam, R.D. (1995). [https://muse.jhu.edu/article/16643 Bowling Alone: America's Declining Social Capital]. Journal of Democracy 6(1), 65-78.
* (Optional) Bourdieu, P. (1986). [https://www.marxists.org/reference/subject/philosophy/works/fr/bourdieu-forms-capital.htm The forms of capital]. In J. Richardson (Ed.) Handbook of Theory and Research for the Sociology of Education (New York, Greenwood), 241-258.
* (Optional) Rainie, L. and Perrin, A. (2019). [https://www.pewresearch.org/fact-tank/2019/07/22/key-findings-about-americans-declining-trust-in-government-and-each-other/ Key findings about Americans’ declining trust in government and each other]. Pew Research Center.
* (Optional) Burt, R. S. (2000). [https://www.sciencedirect.com/science/article/pii/S0191308500220091 The network structure of social capital]. Research in Organizational Behavior, 22, 345–423.


'''Class Schedule:'''
====February 25====
* R Review
** Go through the [https://raw.githubusercontent.com/jdfoote/Communication-and-Social-Networks/fall-2021/resources/tidygraph_tutorial.Rmd Tidygraph Tutorial] in groups


== Week 8: Small worlds  ==
'''Lab 7: Review for SPSS Quiz 1'''


October 12
== Week 8: Causality  ==


OCTOBER BREAK - NO CLASS
==== March 1 ====


October 14
Final Project Assigned


'''Assignment Due:'''
'''Readings (before class):'''  
* [[#Discussion Questions|Discussion questions]] - Just one question this week
* Cialdini EPH Experiment (pp. 599-601)
* True Experimental Designs


'''Lecture Video:'''
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/6819555/View Small worlds video] [18:45] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_8/lecture/small_worlds.html Slides]]


'''Readings:'''  
'''Concepts:'''
* [https://www.youtube.com/watch?v=TcxZSmzPw8k The Science of Six Degrees of Separation][video][9:22]
* "True" experiments
* Travers, J. and Milgram, S. (1969). [https://www-jstor-org.ezproxy.lib.purdue.edu/stable/2786545  An experimental study of the small world problem]. ''Sociometry'', 32(4):425-443
*  
* (Optional but short) Dodds, P. S., Muhamad, R., & Watts, D. J. (2003). [https://science-sciencemag-org.ezproxy.lib.purdue.edu/content/301/5634/827 An Experimental Study of Search in Global Social Networks]. ''Science'', 301(5634), 827.


====March 3====


'''Class Schedule:'''
'''Readings (before class):'''  
* Establishing Cause and Effect
* Internal Validity
* External Validity


== Week 9: Scale-free networks and the friendship paradox ==
'''Concepts:'''


October 19
====March 4====


'''Assignment Due:'''  
'''Lab 8: SPSS Quiz 1'''
* [[/Social Search Assignment|Social Search Assignment]]
* [[#Discussion Questions|Discussion questions]]


'''Lecture Video:'''
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/7524997/View Scale-free networks and the Friendship Paradox][18:21] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_9/lecture/scale_free_and_friend_paradox.html Slides]]


'''Readings:'''
== Week 9: Central Limit Theorem and standard errors ==
* Feld, Scott L. (1991), [https://www-jstor-org.ezproxy.lib.purdue.edu/stable/2781907 Why your friends have more friends than you do]. American Journal of Sociology, 96 (6): 1464–1477. https://doi.org/10.1086%2F229693
* [https://www.youtube.com/watch?v=tP2MLp7GL7Q Early Detection of an Outbreak using the Friendship Paradox]
* [https://www.youtube.com/watch?v=c867FlzxZ9Y Networks are everywhere with Albert-László Barabási]


(Optional)
====March 8====
* Christakis, N. A., & Fowler, J. H. (2010). Social Network Sensors for Early Detection of Contagious Outbreaks. PLOS ONE, 5(9), e12948. https://doi.org/10.1371/journal.pone.0012948


October 21
'''Readings (before class):'''
* Salkind Ch. 9, pp. 186-187
* Probability and Normal Curve & Standard Error of Mean (same PDF)


'''Class Schedule:'''
'''Concepts:'''
* [[/Six Degrees of Wikipedia Activity|Six Degrees of Wikipedia Activity]]


====March 10====


== Week 10: Social influence and diffusion ==
'''Readings (before class):'''
* Salkind Chapter 9 pp. 192-194
* Confidence interval for the mean


October 26
'''Concepts:'''


'''Weekly lecture:'''
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/7547996/View Social Influence and Contagion][22:12] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_10/lecture/influence_and_diffusion.html Slides]]


'''Assignment Due:'''
====March 11====
* Turn in your [[Self Assessment Reflection]] on Brightspace
* [[Communication and Social Networks (Fall 2021)/Dutch School Data Visualization challenge|Dutch School Data Visualization
Challenge]]
* [[#Discussion Questions|Discussion questions]]


'''Readings:'''  
'''Lab 9: Final Project Data Collection'''
* Chapter 4, "[https://web.archive.org/web/20191019100528/http://everythingisobvious.com/wp-content/themes/eio/assets/EIO_chapter4.pdf Special People]", in Watts, D. J. (2011). Everything is Obvious: Once you know the answer. New York, NY: Crown Business.
* [https://youtu.be/D9XF0QOzWM0 Duncan Watts on Common Sense]
* [Optional] Centola, D., & Macy, M. (2007). [https://www-journals-uchicago-edu.ezproxy.lib.purdue.edu/doi/full/10.1086/521848 Complex Contagions and the Weakness of Long Ties]. American Journal of Sociology, 113(3), 702–734.
* [Optional] Christakis, N. A., & Fowler, J. H. (2012). [https://onlinelibrary-wiley-com.ezproxy.lib.purdue.edu/doi/full/10.1002/sim.5408 Social contagion theory: Examining dynamic social networks and human behavior]. Statistics in Medicine, 32, 556–577.


====SPRING BREAK MARCH 14-18====


October 28
== Week 10: Testing hypotheses ==
* Troubled Lands


====March 22====


== Week 11: Communities and Core-periphery ==
'''Readings (before class):'''
* Salkind Chapter 7
* Salkind Chapter 9 pp. 188-192
* Salkind Chapter 10


November 2
'''Concepts:'''
* The null hypothesis
* Hypothesis testing
* Statistical significance
* One sample z-test


'''Assignment Due:'''
* [[#Discussion Questions|One discussion question]]
* Submit two exam questions on Brightspace
* [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/week_11/groups_in_networks.Rmd Finding and visualizing groups in networks] (Right-click, save, and open in RStudio).


'''Video Lecture:'''
====March 24====
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/7556414/View Communities and Core-periphery] [23:15] [[https://jeremydfoote.com/Communication-and-Social-Networks/week_11/lecture/communities_in_networks.html Slides]]


'''Readings:'''
'''Readings (before class):'''  
* Girvan, M., & Newman, M. E. (2002). [https://www.pnas.org/content/pnas/99/12/7821.full.pdf Community structure in social and biological networks]. Proceedings of the National Academy of Sciences.
* Chapter 17
* Barberá, P., Wang, N., Bonneau, R., Jost, J. T., Nagler, J., Tucker, J., & González-Bailón, S. (2015). [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143611 The critical periphery in the growth of social protests]. PLoS ONE.
* Computation of Two-way Chi-Square
* (Optional) Hanneman, R. A., & Riddle, M. (2005). [http://faculty.ucr.edu/~hanneman/nettext/C11_Cliques.html Cliques and sub-groups]. In Introduction to social network methods. University of California.


'''Class Schedule:'''
'''Concepts:'''
* Chi-squared tests


== Week 12: Technology and networks ==


November 9


====March 25====
'''Lab 10: Survey Analysis Project: Data Entry'''


'''Assignment Due:'''
'''Assignment Due:'''
* [[#Discussion Questions|Discussion questions]]
* Final Project data must be gathered


'''Lecture Video:'''
== Week 11: Significance tests ==
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/7947991/View Technology and networks] [19:38]


'''Readings:'''
====March 29====
* Pariser, E. [https://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles Beware Online Filter Bubbles TED talk]
 
* Fletcher, R. [https://reutersinstitute.politics.ox.ac.uk/risj-review/truth-behind-filter-bubbles-bursting-some-myths The truth behind filter bubbles: Bursting some myths].
'''Readings (before class):'''  
* Bail, C. [https://www.youtube.com/watch?v=nwRm_ssTarE Should we break our echo chambers?]
* Cramer’s Phi
* Cohen, M. [https://www.psychologytoday.com/intl/blog/finding-love-the-scientific-take/202012/context-collapse Context Collapse]


(Optional)
'''Concepts:'''
* Kleinberg, J. (2012). [https://link-springer-com.ezproxy.lib.purdue.edu/chapter/10.1007%2F978-3-642-29952-0_8 The Convergence of Social and Technological Networks]. In M. Agrawal, S. B. Cooper, & A. Li (Eds.), Theory and Applications of Models of Computation.
* Chi-Square: Effect Size
* Chris Bail, et al. (2018). [https://www.pnas.org/content/115/37/9216 Exposure to opposing views on social media can increase political polarization]. PNAS.


====March 31====


November 11
'''Readings (before class):'''
* Salkind Chapter 15


* How does the Internet work?
'''Concepts:'''
* Testing ''r'' for statistical significance


== Week 13: Collective behavior ==
==== April 1 ====


November 16
'''Lab 11: Two-way Chi-squared'''


'''Assignment Due:'''
'''Assignment Due:'''
* [[#Discussion Questions|One discussion question]]
* Lab 10
* Keep working on the [[Communication_and_Social_Networks_(Spring_2020)/Final_project | final project]]


'''Readings:'''
== Week 12: t-tests ==
* Becker, J., Brackbill, D., & Centola, D. (2017). [https://doi.org/10.1073/pnas.1615978114 Network dynamics of social influence in the wisdom of crowds]. Proceedings of the National Academy of Sciences, 201615978.
* [https://youtu.be/sdI-b5mfjH4 Video discussion with Dr. Becker] (watch after reading paper)
* Do [http://ncase.me/crowds/ The Wisdom or Madness of Crowds Simulation]


November 18
====April 5====


* Take-home exam is due
'''Readings (before class):'''
* Salkind Chapter 11


== Week 14: Networks and collaboration ==
'''Concepts:'''
* t-tests for independent groups


November 23
====April 7====


Asynchronous class - Happy Thanksgiving!
'''Readings (before class):'''
* Introduction to Effect Size


'''Assignment Due:'''  
'''Concepts:'''
* 1 Discussion Question
* Cohen's ''d''


====April 8====


'''Lecture video:'''
'''Lab 12: t-test and Pearson's r'''
* [https://purdue.brightspace.com/d2l/le/content/335123/viewContent/8035051/View Networks and Collaboration][17:19]


'''Readings:'''  
'''Assignment Due:'''
* Read the [https://en.wikipedia.org/wiki/The_Wealth_of_Networks Wikipedia Article about The Wealth of Networks]
* Lab 11
* Skim section two of Benkler, Y. (2002). [https://www-jstor-org.ezproxy.lib.purdue.edu/stable/1562247 Coase’s Penguin, or, Linux and "The Nature of the Firm."] The Yale Law Journal, 112(3), 369.


== Week 15: Networked racism ==


November 30
== Week 13: Errors and Ethics ==


'''Assignment Due:'''
====April 12====
* Rough draft of [[/Final project|Final Project]] on Brightspace and sent to your "peers"


'''Readings (before class):'''


'''Readings:'''
* Salkind Chapter 9, pp. 177-186
* Fernandez, R. M., & Fernandez-Mateo, I. (2006). [https://journals-sagepub-com.ezproxy.lib.purdue.edu/doi/pdf/10.1177/000312240607100103 Networks, Race, and Hiring]. American Sociological Review, 71(1), 42–71. '''Read the introduction (pp. 42–47) and the Summary and Conclusion (pp. 65–67)'''
* Sunstein, C. R. (1991). Why markets don’t stop discrimination. Social Philosophy and Policy, 8(02), 22–37. https://doi.org/10.1017/S0265052500001114


'''Concepts:'''
* Type I and Type II errors
* Statistical power


December 2
====April 14====


No class - work on Final Project
Final project review + Q&A


== Week 16: Network Visualization Principles ==
====April 15====


December 7
'''No Lab: Workday for Final Project'''


'''Assignment Due:'''
'''Assignment Due:'''
* Peer feedback on final project
* Lab 12
 
== Week 14 ==
 
====April 19====
 
Review for SPSS Quiz 2
 
====April 21====
 
Open Topic: Buffer Day / Out-of-class workday
 
====April 22====
 
'''SPSS Quiz 2'''


'''Class Schedule:'''
== Week 15 ==
* Review principles of good network visualizations
** Put examples at https://padlet.com/jdfoote1/networks (I will explain in class)
* Work on final projects




December 9


No class - work on Final Project
====April 26====


== Week 16.5: Finals week  ==
Out-of-class work day for final project


====April 28====
Review for Final exam


'''Assignment Due:'''
'''Assignment Due:'''
* [[Communication and Social Networks (Fall 2021)/Final project|Final Project]] - Due Wednesday, December 15
* '''Final Project'''
* Turn in your [[Final self reflection]] on Brightspace
 
====April 29====


'''No Lab'''


<!-- Bikerack
== Week 16: Final Exam ==


* Skim [https://kateto.net/network-visualization Static and dynamic network visualization with R] by Katya Ognyanova
May 5, 1:00 pm - 3:00 pm
* Show family networks
* Introduction to RStudio
** R files - Download [https://raw.githubusercontent.com/jdfoote/Communication-and-Social-Networks/master/activities/r_example.R example file here].
** R Notebook files - Download [https://raw.githubusercontent.com/jdfoote/Communication-and-Social-Networks/master/activities/r_markdown_example.Rmd example file here].
* Start [https://campus.datacamp.com/courses/network-analysis-in-r/ Network Analysis in R], chapter 1
* Use R to create an accurate network image of the family network you created for Homework #3.  Include node labels for each family member.
** If you get stuck, [https://youtu.be/isBm5RTslow this video] may help.
** Use [https://kateto.net/network-visualization Static and dynamic network visualization with R] to figure out how to make it look nice!
* Troubled Lands Activity
* Answer questions about DataCamp
* Review principles of good network visualizations
* Find and assess networks visualizations ([https://padlet.com/jdfoote1/networks padlet is here])
* Begin visualization challenge
** Right click on [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/activities/network_visualization_examples_and_assignment.Rmd THIS LINK], save it, and open it in RStudio.
* [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/week_10/lecture/ Slides]
* [https://youtu.be/5EOHaU_R94o Weekly lecture] on social influence and network diffusion
* [https://youtu.be/sdI-b5mfjH4 Interview with Josh Becker] (skim his article below first).
* [https://youtu.be/d3C2r7gPfBU Great video about homophily in networks]
* [https://youtu.be/MzA12DkQGBw Answering questions about R]
* [https://github.com/jdfoote/Communication-and-Social-Networks/raw/fall-2021/activities/school_data_example.Rmd Example with code for the Dutch School assignment]
* [https://www.youtube.com/watch?v=prCmVEUTxQE Video explaining my example]
* [https://youtu.be/mOtVC0N-ItA Networks in Organizations lecture]
-->


= Policies =
= Policies =

Latest revision as of 22:07, 2 January 2023

This is an old version of the course and is kept for information only. Some links may be broken or may point to content that has been updated since this course was taught

Course Information[edit]

COM 304: Quantitative Methods for Communication Research

Lecture

Location: LWSN 1142
Class Hours: Tuesdays and Thursdays; 9:30-10:20 AM

Recitations

Location: BRNG B286
Section 002: 1:30-2:20 PM F, with Yihan Jia
Section 003: 12:30-1:20 PM F, with Grace Lee
Section 006: 11:30-12:20 PM F, with Grace Lee
Section 007: 2:30-3:20 PM F, with Yihan Jia

Instructors[edit]

Professor: Jeremy Foote
Email: jdfoote@purdue.edu
Office Hours: Thursdays; 2:00–4:00pm and by appointment


Graduate TA: Grace Lee
Email: lee3416@purdue.edu
Office Hours: Thursdays; 10:30–11:45am and by appointment


Graduate TA: Yihan Jia
Email: jia110@purdue.edu
Office Hours: Tuesdays and Thursdays; 11:00am–12:00pm 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]

Readings[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.

Technology[edit]

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, this is my first time teaching the course and 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 Lawson 1142. The lab sessions will be on Fridays and will be led by Grace and Yihan, the TAs 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 2-4 (sign up here).

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.


Assignments[edit]

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.

Homework/Labs[edit]

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.

Participation[edit]

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, which will also be used to take attendance.

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.

Grades[edit]

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.

Schedule[edit]

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 11[edit]

Assignment Due:

Required Readings:

  • Five Big Words (on Brightspace)

Concepts:

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


January 13[edit]

Assignment Due:

  • Read the entire syllabus (this document)

Readings (on Brightspace):

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

Concepts:

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

January 14[edit]

Lab 1: Intro to SPSS

Week 2: Surveys[edit]

January 18[edit]

Readings (before class):

  • Scales of Measurement

Concepts:

  • Scales


January 20[edit]

Readings (before class):

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

Concepts:

  • Selection effects


  • Survey Project Assigned

January 21[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 25[edit]

Readings (before class):

  • Salkind Chapter 2
  • Salkind Chapter 4

Concepts:

  • Distributions
  • Mean
  • Median
  • Mode

January 27[edit]

Readings (before class):

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

Concepts:

January 28[edit]

Lab 3: Modifying data in SPSS

Assignment Due:

  • Lab 2
  • SPSS Homework Survey Data File

Week 4: Z-scores and correlation[edit]

February 1[edit]

Assignment Due:

  • SURVEY PROJECT QUESTIONS DUE in Lecture

Readings (before class):

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

Concepts:

  • Standardization
  • Z-scores

February 3[edit]

Readings (before class):

  • Salkind Chapter 5

Concepts:

  • Correlation
  • r

February 4[edit]

Lab 4: Descriptive Stats and z scores

Assignment Due:

  • Lab 3

Week 5: Reliability and Validity[edit]

February 8[edit]

Readings (before class):

  • Salkind Chapter 6
  • Reliability and its relationship to Validity

Concepts:

  • Validity
  • Generalizability
  • Replication

February 10[edit]

Readings (before class):

  • Sampling

Concepts:

February 11[edit]

Lab 5: Scatterplots and correlation

Assignment Due:

  • Lab 4


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

February 15[edit]

Assignment Due:

  • Survey project

Readings (before class):

  • Sampling distribution
  • Sampling error

Concepts:


February 17[edit]

Review for Midterm


February 18[edit]

Lab 6: Reliability and Cronbach's alpha

Assignment Due:

  • Lab 5


Week 7: Experimental Design[edit]

February 22[edit]

MIDTERM EXAM


February 24[edit]

Readings (before class):


Concepts:

February 25[edit]

Lab 7: Review for SPSS Quiz 1

Week 8: Causality[edit]

March 1[edit]

Final Project Assigned

Readings (before class):

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


Concepts:

  • "True" experiments

March 3[edit]

Readings (before class):

  • Establishing Cause and Effect
  • Internal Validity
  • External Validity

Concepts:

March 4[edit]

Lab 8: SPSS Quiz 1


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

March 8[edit]

Readings (before class):

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

Concepts:

March 10[edit]

Readings (before class):

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

Concepts:


March 11[edit]

Lab 9: Final Project Data Collection

SPRING BREAK MARCH 14-18[edit]

Week 10: Testing hypotheses[edit]

March 22[edit]

Readings (before class):

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

Concepts:

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


March 24[edit]

Readings (before class):

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

Concepts:

  • Chi-squared tests


March 25[edit]

Lab 10: Survey Analysis Project: Data Entry

Assignment Due:

  • Final Project data must be gathered

Week 11: Significance tests[edit]

March 29[edit]

Readings (before class):

  • Cramer’s Phi

Concepts:

  • Chi-Square: Effect Size

March 31[edit]

Readings (before class):

  • Salkind Chapter 15

Concepts:

  • Testing r for statistical significance

April 1[edit]

Lab 11: Two-way Chi-squared

Assignment Due:

  • Lab 10

Week 12: t-tests[edit]

April 5[edit]

Readings (before class):

  • Salkind Chapter 11

Concepts:

  • t-tests for independent groups

April 7[edit]

Readings (before class):

  • Introduction to Effect Size

Concepts:

  • Cohen's d

April 8[edit]

Lab 12: t-test and Pearson's r

Assignment Due:

  • Lab 11


Week 13: Errors and Ethics[edit]

April 12[edit]

Readings (before class):

  • Salkind Chapter 9, pp. 177-186

Concepts:

  • Type I and Type II errors
  • Statistical power

April 14[edit]

Final project review + Q&A

April 15[edit]

No Lab: Workday for Final Project

Assignment Due:

  • Lab 12

Week 14[edit]

April 19[edit]

Review for SPSS Quiz 2

April 21[edit]

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

April 22[edit]

SPSS Quiz 2

Week 15[edit]

April 26[edit]

Out-of-class work day for final project

April 28[edit]

Review for Final exam

Assignment Due:

  • Final Project

April 29[edit]

No Lab

Week 16: Final Exam[edit]

May 5, 1:00 pm - 3:00 pm

Policies[edit]

Attendance[edit]

I try very hard to make our meeting times valuable to you and I expect that you attend. That being said, we are in the midst of a pandemic, so if you feel sick or think that you might have COVID, 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.


Nondiscrimination[edit]

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.


Accessibility[edit]

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.


Incompletes[edit]

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.

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