Editing Quantitative Methods for Communication (Spring 2022)

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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


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== 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---but you should be ready to talk about your attempts to understand and why something is confusing.
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 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.


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


== Midterm and Final 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.


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


== Homework/Labs ==
== Homework/Labs ==


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.
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.


== SPSS Quizzes ==
== Exams ==


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


== Survey Design Project ==
== Final Project ==


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.
Students will work on a [[/Final project|Final Project]] that explains how network analysis and a network approach can benefit an organization.


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


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.
= Grades =


== Participation ==
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 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.
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.


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.
We will use the following rubric in our assessment:


Lecture classes will also typically include questions on Hotseat, which will also be used to take attendance.
* 20%: class participation, including attendance and participation in discussions and group work
* 20%: Labs and homework assignments
* 25%: Exam
* 35%: Final Project


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.
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.


= Grades =
My interpretation of grade levels (A, B, C, D/F) is the following:


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:
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.
* Engage thoughtfully with an ambitious final project.
* 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.
* Write reflections that grapple meaningfully with lessons learned as well as challenges.
* Complete all or nearly all homework assignments at a high level.


{| class="wikitable"
B: Reflects strong work. Work at this level will be of consistently high quality. Students reaching this level of achievement will:
|+ Assignment points
* Be more safe or consistent than the work described above.
|-
* Ask meaningful questions of peers and engage them in fruitful discussion.
|Midterm Exam
* Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week.
| 100 points
* Compose complete and sufficiently detailed reflections.
|-
* Complete many of the homework assignments.
|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:
C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement
will:
* Turn in and complete the final project on time.
* Be collegial and continue discussion, through asking simple or limited questions.
* 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.


<div style=display:inline-table>
D/F: These are reserved for cases in which students do not complete work or participate. Students may also be
{| class="wikitable"
impeding the ability of others to learn.
|-
| 484-500 points
| A+
|-
|464-483 points
| A
|-
|449-463 points
| A-
|-
|434-448 points
| B+
|-
|414-433 points
| B
|-
|400-413 points
| B-
|}
</div>
<div style=display:inline-table>
{| class='wikitable'
|-
|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
|-
|}
</div>


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


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


* 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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'''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/Ak9GYuFGer89k6G47 this very brief survey]
* Take [https://forms.gle/ANqbnAXxivexukgB7 this very brief survey]


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


'''Concepts:'''
'''Concepts:'''
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* Read the entire syllabus (this document)
* Read the entire syllabus (this document)


'''Readings (on Brightspace):'''  
'''Readings:'''  
* Variables in non-experimental studies
 
* Conceptualizing
 
* Writing RQs
'''Class Schedule:'''


'''Concepts:'''
* Constructs
* Hypotheses
* Research Questions
* Independent/Dependent Variables


==== January 14 ====
==== January 14 ====


'''Lab 1: Intro to SPSS'''
'''Lab 1'''


== Week 2: Surveys ==
== Week 2: Surveys ==


==== January 18 ====
==== January 18 ====
'''Assignment Due:'''


'''Readings (before class):'''  
'''Readings (before class):'''  
* Scales of Measurement
 


'''Concepts:'''
'''Concepts:'''
* Scales




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'''Readings (before class):'''  
'''Readings (before class):'''  
* How to improve online survey response rates;
 
* Survey questions 101: do you make any of these 7 question-writing mistakes?


'''Concepts:'''
'''Concepts:'''
* Selection effects
* Survey Project Assigned


====January 21====
====January 21====


'''Lab 2: Entering and Modifying Data in SPSS'''
'''Assignment Due:'''
* Lab 1: Intro to SPSS
* Completed Homework Survey


== Week 3: Descriptive Statistics ==
== Week 3: Descriptive Statistics ==
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'''Readings (before class):'''  
'''Readings (before class):'''  
* Salkind Chapter 2
 
* Salkind Chapter 4


'''Concepts:'''
'''Concepts:'''
* Distributions
* Mean
* Median
* Mode


====January 27====
====January 27====
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'''Readings (before class):'''  
'''Readings (before class):'''  
* Salkind Chapter 3
 
* Interquartile Range
* SD and normal curve


'''Concepts:'''
'''Concepts:'''
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====January 28====
====January 28====


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


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


== Week 4: Z-scores and correlation ==
== Week 4: Z-scores and correlation ==
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====February 1====
====February 1====


'''Assignment Due:'''
'''Readings (before class):'''  
* SURVEY PROJECT QUESTIONS DUE in Lecture


'''Readings (before class):'''
* Salkind Ch. 8 pp. 149-165
* Standard scores and the normal curve


'''Concepts:'''
'''Concepts:'''
* Standardization
* Z-scores


====February 3====
====February 3====


'''Readings (before class):'''  
'''Readings (before class):'''  
* Salkind Chapter 5
 


'''Concepts:'''
'''Concepts:'''
* Correlation
* r


====February 4====
====February 4====


'''Lab 4: Descriptive Stats and z scores'''
'''Lab 4'''
 
'''Assignment Due:'''
* Lab 3


== Week 5: Reliability and Validity ==
== Week 5: Reliability and Validity ==
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'''Readings (before class):'''  
'''Readings (before class):'''  
* Salkind Chapter 6
 
* Reliability and its relationship to Validity


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


====February 10====
====February 10====


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


'''Concepts:'''
'''Concepts:'''
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'''Lab 5: Scatterplots and correlation'''
'''Lab 5: Scatterplots and correlation'''


'''Assignment Due:'''
* Lab 4


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


====February 15====
====February 15====


'''Assignment Due:'''
'''Readings (before class):'''  
* Survey project


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


'''Concepts:'''
'''Concepts:'''
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'''Lab 6: Reliability and Cronbach's alpha'''
'''Lab 6: Reliability and Cronbach's alpha'''
'''Assignment Due:'''
* Lab 5




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==== March 1 ====
==== March 1 ====
Final Project Assigned


'''Readings (before class):'''  
'''Readings (before class):'''  
* Cialdini EPH Experiment (pp. 599-601)
* True Experimental Designs




'''Concepts:'''
'''Concepts:'''
* "True" experiments
*


====March 3====
====March 3====


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


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


'''Concepts:'''
'''Concepts:'''
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'''Readings (before class):'''  
'''Readings (before class):'''  
* Salkind Chapter 9 pp. 192-194
 
* Confidence interval for the mean


'''Concepts:'''
'''Concepts:'''


====March 11====
====March 11====


'''Lab 9: Final Project Data Collection'''
'''Lab 9'''


====SPRING BREAK MARCH 14-18====
====SPRING BREAK MARCH 14-18====
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'''Readings (before class):'''  
'''Readings (before class):'''  
* Salkind Chapter 7
 
* Salkind Chapter 9 pp. 188-192
* Salkind Chapter 10


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




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'''Readings (before class):'''  
'''Readings (before class):'''  
* Chapter 17
 
* Computation of Two-way Chi-Square


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




====March 25====
====March 25====


'''Lab 10: Survey Analysis Project: Data Entry'''
'''Assignment Due:'''
* Final Project data must be gathered


== Week 11: Significance tests ==
== Week 11: Significance tests ==
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'''Readings (before class):'''  
'''Readings (before class):'''  
* Cramer’s Phi
 


'''Concepts:'''
'''Concepts:'''
* Chi-Square: Effect Size


====March 31====
====March 31====


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


'''Concepts:'''
'''Concepts:'''
* Testing ''r'' for statistical significance


==== April 1 ====
==== April 1 ====


'''Lab 11: Two-way Chi-squared'''
'''Assignment Due:'''
* Lab 10


== Week 12: t-tests ==
== Week 12: t-tests ==
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'''Readings (before class):'''  
'''Readings (before class):'''  
* Salkind Chapter 11
 


'''Concepts:'''
'''Concepts:'''
* t-tests for independent groups


====April 7====
====April 7====


'''Readings (before class):'''  
'''Readings (before class):'''  
* Introduction to Effect Size
 


'''Concepts:'''
'''Concepts:'''
* Cohen's ''d''


====April 8====
====April 8====


'''Lab 12: t-test and Pearson's r'''
'''Assignment Due:'''
* Lab 11




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


* Salkind Chapter 9, pp. 177-186


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


====April 14====
====April 14====


Final project review + Q&A
'''Readings (before class):'''


====April 15====


'''No Lab: Workday for Final Project'''
'''Concepts:'''


'''Assignment Due:'''
* Lab 12


== Week 14 ==
====April 15====
 
====April 19====
 
Review for SPSS Quiz 2
 
====April 21====
 
Open Topic: Buffer Day / Out-of-class workday
 
====April 22====
 
'''SPSS Quiz 2'''


== Week 15 ==




 
== Week 14-15: Final Project ==
====April 26====
 
Out-of-class work day for final project
 
====April 28====
 
Review for Final exam
 
'''Assignment Due:'''
* '''Final Project'''
 
====April 29====
 
'''No Lab'''


== Week 16: Final Exam ==
== Week 16: Final Exam ==
May 5, 1:00 pm - 3:00 pm


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= Policies =
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