Southern Utah University

Course Syllabus

Southern Utah University
Southern Utah University
Fall Semester 2025

Research Methods in Political Science Lab (Online)

POLS 2005-30I

Course: POLS 2005-30I
Credits: 1
Term: Fall Semester 2025
Department: PSCJ
CRN: 33251

Course Description

This lab provides an engaged learning experience with emphasis on collecting, analyzing, and interpreting data using spreadsheets and statistical software to apply critical-analysis concepts learned in POLS 2000 - Research Methods in Political Science. (Spring) [Pass/Fail] Co-requisite(s): POLS 2000 Registration Restriction(s): None

Instructor Information

Office Hours: MWF 12:30pm - 3:00pm
Additional Office Hours by appointment
Office: GC 406A

Required Texts

Required Texts and Materials


Most of the materials we utilize in this course are free/open source. The only textbook you will be required to purchase is the DSS textbook (see point 1 below).

  1. Textbook 1 (DSS, hereafter): Laudmet, Elena and Kosuke Imai. 2022. Data Analysis for Social Science: A Friendly Introduction. 1st Edition. Princeton University Press. Princeton, NJ. ISBN: 9780691199436.
  2. Student Resources available from DSS, downloaded here: LINK. (We will be using files from the DSS folder)
  3. Textbook 2 (MD, hereafter) (free, available online): Ismay, Chester, Albert Y Kim and Arturo Valdvia. Statistical Inference via Data Science: A ModernDrive into R and the TidyVerse. 2nd Edition. CRC Press. Available here: LINK
  4. Computer access, with administrative permissions to download new software like R Studio. If you do not have access to a computer capable of utilizing R Studio, you may schedule time in one of SUU's several on-campus computer labs. The list of labs with R already downloaded is available here: LINK
  5. R, a statistical programing language. (More details will be given on R in the lecture videos) Download here: R and R Studio
  6. R Studio, a program that reads the R programming language. (More details will be given on R Studio in the lecture videos) Use link above to download.
  7. MikTeX, a text processing program that will allow us to write and share assignments in a consistent way. Download here: LINK

Additional Free Resources:

  1. Coding Help
    • Hands on Coding with R: LINK
    • R CheatSheets: LINK
  2. Data Visualization 
    • Data Visualization: A Practical Introduction: LINK
    • Fundamentals of Data Visualization: LINK
    • ggplot2 Textbook: LINK
  3. R Markdown Help
    • R Markdown Tutorial: LINK
    • R Markdown Textbook: LINK
  4. Additional Data Science Resources
    • R for Data Science: LINK
    • Introduction to Modern Statistics (free PDF): LINK
    • Causal Inference: What is it?: LINK

Learning Outcomes

Successful students will:

  • Acquire and/or further develop knowledge of basic statistical tools, such as the histograms, average, standard deviation, normal approximation, scatterplots, correlation, simple and multiple regression, sample survey, and hypothesis tests;
  • Understand basic concepts in probability theory, such as conditional probability, the law of averages, the expected value, and the standard error;
  • Develop knowledge of how to use basic programming in R to implement statistical tools;
  • Learn how to evaluate empirical arguments;
  • Explain data and analyses in a clear and convincing way.

Course Requirements

Grading Policy


The final course grade will be determined based on the following breakdown: 
  • Lab Assignments: 100% (5 total)
  • See Syllabus for POLS 2000 for further details.


Lab Assignments


As part of the class, we’ll learn about statistical computing in R. Five computing exercises will be due throughout the semester that will walk students through computation. Think of this as the "practical" or "applied" portion of the course. Students will need to pass the lab assignments with an average of 70% in order to receive a passing grade for POLS 2005. Each of these is worth 7% of the final grade. A late penalty of 10% per day will be assessed. Labs will be submitted as PDF's from a knitted R Markdown file (instruction will be given on how to do this).


 

Assignment Due Dates

All assignments, quizzes, and labs in this course are due on Sunday nights (11:59pm). I schedule it this way to give students the maximum amount of time possible to complete readings, quizzes, and assignments throughout the week at your own pace and according to your own schedule. HOWEVER this means that in most weeks, multiple assignments will be due at the same time. Students are responsible for planning ahead to ensure that all assignments are completed by the deadlines listed in the syllabus. In other words, you should not wait until Sunday to START the assignments/readings/labs for a particular week, and instead should pace yourself throughout the week. 

Course Outline

The schedule is tentative and subject to change. The learning goals below should be viewed as the key concepts you should grasp after each week. The schedule below is copied from the schedule for POLS 2000. Lab assignments are bolded.

Week 01 (8/27 - 8/31): Course Intro, Syllabus Quiz

  • READ: Syllabus, DSS Chp1, MD Chp1
  • Syllabus Quiz due Sunday, 8/31 at 11:59pm

Week 02 (9/1 - 9/7): Causality and Theory

  • READ: DSS Chp 2, MD Chp 2 and Chp 3
  • Lab 1 due Sunday, 9/7 at 11:59pm
  • Problem Set 1 due Sunday, 9/7 at 11:59pm

Week 03 (9/8 - 9/14): Correlations, Histograms + Visualization

  • READ: DSS Chp 3, MD Chp 4
  • Lab 2 due Sunday, 9/14 at 11:59pm
  • Problem Set 2 due Sunday, 9/14 at 11:59pm
  • Research Proposal due Sunday, 9/14 at 11:59pm

Week 04 (9/15 - 9/21): Prediction, Linear Regression

  • READ: DSS Chp 4, MD Chp 5
  • Lab 3 due Sunday, 9/21 at 11:59pm
  • Problem Set 3 due Sunday, 9/21 at 11:59pm

Week 05 (9/22 - 9/28): Causality with Observational Data

  • READ: DSS Chp 5, MD Chp 6 
  • Lab 4 due Sunday, 9/28 at 11:59pm
  • Problem Set 4 due Sunday, 9/28 at 11:59pm
  • Initial Data Analysis for Research Project due Sunday, 9/28 at 11:59pm

Week 06 (9/29 - 10/5): Probability

  • READ: DSS Chp 6
  • Lab 5 due Sunday, 10/5 at 11:59pm
  • Problem Set 5 due Sunday, 10/5 at 11:59pm

Week 07 (10/6 - 10/12): Uncertainty

  • READ: DSS Chp 7
  • Research Project due Sunday, 10/12 at 11:59pm

Instructor's policies on late assignments and/or makeup work

The Late Policy takes effect after the 11:59pm deadline. Meaning a 12:00am submission will count as 1 day late. 
Late policies for each assignment type are detailed below:
 

Syllabus Quiz

The Syllabus Quiz will be accepted until the final day of instruction. A late penalty of 10\% per day late will be assessed. 

Research Assignment

Points are not awarded for submitting the proposal and initial data analysis. Instead, a 10% reduction in the final grade will result for each deadline missed. As the Research Assignment is due on the final day of instruction, a late submission will not be accepted.

Labs

Late Lab Assignments will be accepted until the final day of instruction. A late penalty of 10\% per day late will be assessed.

Problem Sets

Problem Sets will not be accepted later than 3 days after the deadline, as solutions will be posted on Canvas. A late penalty of 10% per day late will be assessed.

Attendance Policy

This course is fully delivered asynchronously, meaning there are no specified times that the student is expected to view the videos. Students are responsible for planning ahead by checking the syllabus for upcoming readings and assignments. Students are responsible for all assigned readings. Therefore, it is imperative that students complete all readings and integrate them into the course assignments as applicable. The assignment deadlines are "hard" deadlines, so plan your week accordingly. It is recommended that you you watch the lecture videos early in the week, especially the lab videos.

AI Policy

Generative AI can be useful for difficult coding tasks. Therefore, this course adopts the following AI Policy:

The use of generative AI may only be used in the Research Assignment for advanced coding support. Students are required to specify and provide proper citation for any instance where AI was used to generate code in the Research Assignment. Generative AI MAY NOT BE USED to produce any written aspect of the text. All written text must be your own work.

Generative AI MAY NOT BE USED in any capacity for the Problem Sets or the Lab Assignments.

Suspected use of AI will result in a grade of 0 for the assignment.

Extra Credit

There are only two opportunities for extra credit in this course.
  1. An optional lab will be made available, due by the end of the 4th week of class, worth the amount of one lab (5%).
  2. Winners of the figure/graphic competition will also receive extra credit (3% for 1st place, 2% for 2nd place, 1% for 3rd place).

No other extra credit opportunities will be made available.

Class Policies

Students are responsible for checking formatting requirements prior to submission. If assignment files are illegible, corrupted, uploaded to the wrong location, blank, or ``wrong versions", they will be treated as late and the Late Policy will apply. To avoid issues, be sure to review your assignments after submission. Links to externally stored files (e.g. GoogleDocs) will not be accepted

Syllabus Change Policy

Except for changes that substantially affect implementation of the evaluation (grading) statement, this syllabus is a guide for the course and is subject to change with advance notice. 

ADA Statement

Students with medical, psychological, learning, or other disabilities desiring academic adjustments, accommodations, or auxiliary aids will need to contact the Disability Resource Center, located in Room 206F of the Sharwan Smith Center or by phone at (435) 865-8042. The Disability Resource Center determines eligibility for and authorizes the provision of services.

If your instructor requires attendance, you may need to seek an ADA accommodation to request an exception to this attendance policy. Please contact the Disability Resource Center to determine what, if any, ADA accommodations are reasonable and appropriate.

Academic Credit

According to the federal definition of a Carnegie credit hour: A credit hour of work is the equivalent of approximately 60 minutes of class time or independent study work. A minimum of 45 hours of work by each student is required for each unit of credit. Credit is earned only when course requirements are met. One (1) credit hour is equivalent to 15 contact hours of lecture, discussion, testing, evaluation, or seminar, as well as 30 hours of student homework. An equivalent amount of work is expected for laboratory work, internships, practica, studio, and other academic work leading to the awarding of credit hours. Credit granted for individual courses, labs, or studio classes ranges from 0.5 to 15 credit hours per semester.

Academic Freedom

SUU is operated for the common good of the greater community it serves. The common good depends upon the free search for truth and its free exposition. Academic Freedom is the right of faculty to study, discuss, investigate, teach, and publish. Academic Freedom is essential to these purposes and applies to both teaching and research.

Academic Freedom in the realm of teaching is fundamental for the protection of the rights of the faculty member and of you, the student, with respect to the free pursuit of learning and discovery. Faculty members possess the right to full freedom in the classroom in discussing their subjects. They may present any controversial material relevant to their courses and their intended learning outcomes, but they shall take care not to introduce into their teaching controversial materials which have no relation to the subject being taught or the intended learning outcomes for the course.

As such, students enrolled in any course at SUU may encounter topics, perspectives, and ideas that are unfamiliar or controversial, with the educational intent of providing a meaningful learning environment that fosters your growth and development. These parameters related to Academic Freedom are included in SUU Policy 6.6.

Academic Misconduct

Scholastic honesty is expected of all students. Dishonesty will not be tolerated and will be prosecuted to the fullest extent (see SUU Policy 6.33). You are expected to have read and understood the current SUU student conduct code (SUU Policy 11.2) regarding student responsibilities and rights, the intellectual property policy (SUU Policy 5.52), information about procedures, and what constitutes acceptable behavior.

Please Note: The use of websites or services that sell essays is a violation of these policies; likewise, the use of websites or services that provide answers to assignments, quizzes, or tests is also a violation of these policies. Regarding the use of Generative Artificial Intelligence (AI), you should check with your individual course instructor.

Emergency Management Statement

In case of an emergency, the University's Emergency Notification System (ENS) will be activated. Students are encouraged to maintain updated contact information using the link on the homepage of the mySUU portal. In addition, students are encouraged to familiarize themselves with the Emergency Response Protocols posted in each classroom. Detailed information about the University's emergency management plan can be found at https://www.suu.edu/emergency.

HEOA Compliance Statement

For a full set of Higher Education Opportunity Act (HEOA) compliance statements, please visit https://www.suu.edu/heoa. The sharing of copyrighted material through peer-to-peer (P2P) file sharing, except as provided under U.S. copyright law, is prohibited by law; additional information can be found at https://my.suu.edu/help/article/1096/heoa-compliance-plan.

You are also expected to comply with policies regarding intellectual property (SUU Policy 5.52) and copyright (SUU Policy 5.54).

Mandatory Reporting

University policy (SUU Policy 5.60) requires instructors to report disclosures received from students that indicate they have been subjected to sexual misconduct/harassment. The University defines sexual harassment consistent with Federal Regulations (34 C.F.R. Part 106, Subpart D) to include quid pro quo, hostile environment harassment, sexual assault, dating violence, domestic violence, and stalking. When students communicate this information to an instructor in-person, by email, or within writing assignments, the instructor will report that to the Title IX Coordinator to ensure students receive support from the Title IX Office. A reporting form is available at https://cm.maxient.com/reportingform.php?SouthernUtahUniv

Non-Discrimination Statement

SUU is committed to fostering an inclusive community of lifelong learners and believes our university's encompassing of different views, beliefs, and identities makes us stronger, more innovative, and better prepared for the global society.

SUU does not discriminate on the basis of race, religion, color, national origin, citizenship, sex (including sex discrimination and sexual harassment), sexual orientation, gender identity, age, ancestry, disability status, pregnancy, pregnancy-related conditions, genetic information, military status, veteran status, or other bases protected by applicable law in employment, treatment, admission, access to educational programs and activities, or other University benefits or services.

SUU strives to cultivate a campus environment that encourages freedom of expression from diverse viewpoints. We encourage all to dialogue within a spirit of respect, civility, and decency.

For additional information on non-discrimination, please see SUU Policy 5.27 and/or visit https://www.suu.edu/nondiscrimination.

Pregnancy

Students who are or become pregnant during this course may receive reasonable modifications to facilitate continued access and participation in the course. Pregnancy and related conditions are broadly defined to include pregnancy, childbirth, termination of pregnancy, lactation, related medical conditions, and recovery. To obtain reasonable modifications, please make a request to title9@suu.edu. To learn more visit: https://www.suu.edu/titleix/pregnancy.html.

Disclaimer Statement

Information contained in this syllabus, other than the grading, late assignments, makeup work, and attendance policies, may be subject to change with advance notice, as deemed appropriate by the instructor.