3 classes matched your search criteria.

Fall 2021  |  POL 3085 Section 001: Quantitative Analysis in Political Science (22842)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
UMNTC Liberal Education Requirement
Times and Locations:
Regular Academic Session
 
09/07/2021 - 12/15/2021
Tue, Thu 08:15AM - 09:30AM
UMTC, West Bank
Blegen Hall 5
Enrollment Status:
Closed (61 of 60 seats filled)
Also Offered:
Course Catalog Description:
POL 3085 teaches students how to study politics scientifically and introduces them to how to use quantitative analysis to answer political questions. The first part of the class covers how to formulate a theory (a possible answer to a question), specify testable hypotheses (what you would see if the theory is correct or incorrect), and set up a research design to test those hypotheses. In the second part of the class, we cover quantitative data analysis, beginning from preliminary statistical analysis to multivariate linear regression. There is no mathematical or statistical background required for this course. By the end of the class, students should be able to ask and answer political questions using quantitative data and fluently evaluate statistical analyses of political phenomena in the media and many academic articles.
Class Notes:
http://classinfo.umn.edu/?jlsumner+POL3085+Fall2021
Class Description:
While the POL 3085 lecture focuses on teaching students research design, statistics, and the statistical software program R, the lab is dedicated strictly to teaching and reinforcing understanding of the statistical software. No computer science background or previous programming experience is necessary. We will teach you all you need to know.
Who Should Take This Class?:
This class is ideal for anyone interested in conducting quantitative research or evaluating quantitative research (note: 'reading the news' qualifies). It does not require you to be a "math person"* (* there is no such thing as a "math person") -- all mathematical backgrounds and perceived ability levels can thrive in this class.
Learning Objectives:
The lab section is solely dedicated to understanding the statistical software program R. Learning statistical software is good for several reasons: not only does it give you practical tools for manipulating and analyzing data and making cool graphics, but it also teaches you algorithmic thinking, which is a good skill set for life.
Grading:

Grades are based on points.


For Fall 2020, all work will be group-based. This serves a few purposes. First, collaborating in research projects is increasingly common in political science as a field, in large part because it allows people to bring their different strengths to the table to create a better end product than one person could alone. Second, it means no one necessarily needs to be operating at 100% effort all the time. This is a nice feature usually -- since life happens! -- but especially with the uncertainty of COVID-19, working in teams means that if anyone gets sick or has caretaker responsibilities, the person can take some time to take care of themselves while the team continues.


Each team will produce a final research project. Homework will also be team-based, and team members will have the opportunity to explain who did what part of the assignment. Students will take frequent quizzes to evaluate their own individual knowledge.


Assessment:

Grading for this class will be based on six items:

1. Lecture quizzes. There will be five (5) quizzes throughout the semester. The dates the quizzes will be assigned are posted on the syllabus. You can take each quiz as many times as you want and only your highest grade is recorded. You must complete each quiz within one week of its assignment date. Quizzes are not timed and are all administered on Google Forms, with a link to the quiz on Canvas. Each quiz is worth 15 points.

2. Lab quizzes. There will be a quiz assigned at the end of every lab section to assess your knowledge of R. You can take each quiz as many times as you want and only your highest grade is recorded. You must complete each quiz within one week of its assignment date. Quizzes are not timed and are all administered on Google Forms, with a link to the quiz on Canvas. Each lab quiz is worth 5 points.

3. Paper chunks. Your co-author group will have to complete and submit eight ``paper chunks'' throughout the semester. Each chunk is a draft of a section of your final paper. I emphasize that these are drafts -- they are not expected to be perfect, and should reflect your best effort at completing the task at the time that it is assigned. Each paper chunk is worth fifteen points.

4. Check-Ins. Co-author teams are required to send at least one person (and preferably more) to meet with me during office hours to check in at least six times per semester (at least once every 2-3 weeks). These check-ins are worth five points each.

5. Final presentation. Near the end of the semester, your group will present your project to the class and to the ``public'', both to practice communicating about research and to get feedback for your final paper. More detailed information about these presentations will be provided later on. The final presentation is worth 50 points.

6. Final paper. For your final paper, you will edit those paper chunks with the feedback I have given you, and you will incorporate them into a paper, which you will turn in as your final project. Although final papers can seem intimidating, remember that you will be be working on this paper bit by bit throughout the semester. Ideally, by the end of the semester, all you need to do is copy and paste and edit. The final paper is worth 50 points.

Exam Format:
Enter information here.
Class Format:
Most weeks student will be expected to watch a lecture video before lab. The class period will be dedicated to going through specific activities related to that content and answering student questions.
Workload:
Weekly attendance and weekly pass/fail quizzes.
Textbooks:
https://bookstores.umn.edu/course-lookup/22842/1219
Instructor Supplied Information Last Updated:
25 March 2021

Fall 2021  |  POL 3085 Section 002: Quantitative Analysis in Political Science (22843)

Instructor(s)
Class Component:
Discussion
Credits:
4 Credits
Grading Basis:
A-F or Audit
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
UMNTC Liberal Education Requirement
Times and Locations:
Regular Academic Session
 
09/07/2021 - 12/15/2021
Thu 10:10AM - 11:00AM
UMTC, West Bank
Carlson School of Management L-114
Enrollment Status:
Closed (31 of 30 seats filled)
Also Offered:
Course Catalog Description:
POL 3085 teaches students how to study politics scientifically and introduces them to how to use quantitative analysis to answer political questions. The first part of the class covers how to formulate a theory (a possible answer to a question), specify testable hypotheses (what you would see if the theory is correct or incorrect), and set up a research design to test those hypotheses. In the second part of the class, we cover quantitative data analysis, beginning from preliminary statistical analysis to multivariate linear regression. There is no mathematical or statistical background required for this course. By the end of the class, students should be able to ask and answer political questions using quantitative data and fluently evaluate statistical analyses of political phenomena in the media and many academic articles.
Class Description:
While the POL 3085 lecture focuses on teaching students research design, statistics, and the statistical software program R, the lab is dedicated strictly to teaching and reinforcing understanding of the statistical software. No computer science background or previous programming experience is necessary. We will teach you all you need to know.
Who Should Take This Class?:
This class is ideal for anyone interested in conducting quantitative research or evaluating quantitative research (note: 'reading the news' qualifies). It does not require you to be a "math person"* (* there is no such thing as a "math person") -- all mathematical backgrounds and perceived ability levels can thrive in this class.
Learning Objectives:
The lab section is solely dedicated to understanding the statistical software program R. Learning statistical software is good for several reasons: not only does it give you practical tools for manipulating and analyzing data and making cool graphics, but it also teaches you algorithmic thinking, which is a good skill set for life.
Grading:

Grades are based on points.


For Fall 2020, all work will be group-based. This serves a few purposes. First, collaborating in research projects is increasingly common in political science as a field, in large part because it allows people to bring their different strengths to the table to create a better end product than one person could alone. Second, it means no one necessarily needs to be operating at 100% effort all the time. This is a nice feature usually -- since life happens! -- but especially with the uncertainty of COVID-19, working in teams means that if anyone gets sick or has caretaker responsibilities, the person can take some time to take care of themselves while the team continues.


Each team will produce a final research project. Homework will also be team-based, and team members will have the opportunity to explain who did what part of the assignment. Students will take frequent quizzes to evaluate their own individual knowledge.


Assessment:

Grading for this class will be based on six items:

1. Lecture quizzes. There will be five (5) quizzes throughout the semester. The dates the quizzes will be assigned are posted on the syllabus. You can take each quiz as many times as you want and only your highest grade is recorded. You must complete each quiz within one week of its assignment date. Quizzes are not timed and are all administered on Google Forms, with a link to the quiz on Canvas. Each quiz is worth 15 points.

2. Lab quizzes. There will be a quiz assigned at the end of every lab section to assess your knowledge of R. You can take each quiz as many times as you want and only your highest grade is recorded. You must complete each quiz within one week of its assignment date. Quizzes are not timed and are all administered on Google Forms, with a link to the quiz on Canvas. Each lab quiz is worth 5 points.

3. Paper chunks. Your co-author group will have to complete and submit eight ``paper chunks'' throughout the semester. Each chunk is a draft of a section of your final paper. I emphasize that these are drafts -- they are not expected to be perfect, and should reflect your best effort at completing the task at the time that it is assigned. Each paper chunk is worth fifteen points.

4. Check-Ins. Co-author teams are required to send at least one person (and preferably more) to meet with me during office hours to check in at least six times per semester (at least once every 2-3 weeks). These check-ins are worth five points each.

5. Final presentation. Near the end of the semester, your group will present your project to the class and to the ``public'', both to practice communicating about research and to get feedback for your final paper. More detailed information about these presentations will be provided later on. The final presentation is worth 50 points.

6. Final paper. For your final paper, you will edit those paper chunks with the feedback I have given you, and you will incorporate them into a paper, which you will turn in as your final project. Although final papers can seem intimidating, remember that you will be be working on this paper bit by bit throughout the semester. Ideally, by the end of the semester, all you need to do is copy and paste and edit. The final paper is worth 50 points.

Exam Format:
Enter information here.
Class Format:
Most weeks student will be expected to watch a lecture video before lab. The class period will be dedicated to going through specific activities related to that content and answering student questions.
Workload:
Weekly attendance and weekly pass/fail quizzes.
Textbooks:
https://bookstores.umn.edu/course-lookup/22843/1219
Instructor Supplied Information Last Updated:
25 March 2021

Fall 2021  |  POL 3085 Section 003: Quantitative Analysis in Political Science (22844)

Instructor(s)
Class Component:
Discussion
Credits:
4 Credits
Grading Basis:
A-F or Audit
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
UMNTC Liberal Education Requirement
Times and Locations:
Regular Academic Session
 
09/07/2021 - 12/15/2021
Thu 11:15AM - 12:05PM
UMTC, West Bank
Carlson School of Management L-114
Enrollment Status:
Closed (30 of 30 seats filled)
Also Offered:
Course Catalog Description:
POL 3085 teaches students how to study politics scientifically and introduces them to how to use quantitative analysis to answer political questions. The first part of the class covers how to formulate a theory (a possible answer to a question), specify testable hypotheses (what you would see if the theory is correct or incorrect), and set up a research design to test those hypotheses. In the second part of the class, we cover quantitative data analysis, beginning from preliminary statistical analysis to multivariate linear regression. There is no mathematical or statistical background required for this course. By the end of the class, students should be able to ask and answer political questions using quantitative data and fluently evaluate statistical analyses of political phenomena in the media and many academic articles.
Class Description:
While the POL 3085 lecture focuses on teaching students research design, statistics, and the statistical software program R, the lab is dedicated strictly to teaching and reinforcing understanding of the statistical software. No computer science background or previous programming experience is necessary. We will teach you all you need to know.
Who Should Take This Class?:
This class is ideal for anyone interested in conducting quantitative research or evaluating quantitative research (note: 'reading the news' qualifies). It does not require you to be a "math person"* (* there is no such thing as a "math person") -- all mathematical backgrounds and perceived ability levels can thrive in this class.
Learning Objectives:
The lab section is solely dedicated to understanding the statistical software program R. Learning statistical software is good for several reasons: not only does it give you practical tools for manipulating and analyzing data and making cool graphics, but it also teaches you algorithmic thinking, which is a good skill set for life.
Grading:

Grades are based on points.


For Fall 2020, all work will be group-based. This serves a few purposes. First, collaborating in research projects is increasingly common in political science as a field, in large part because it allows people to bring their different strengths to the table to create a better end product than one person could alone. Second, it means no one necessarily needs to be operating at 100% effort all the time. This is a nice feature usually -- since life happens! -- but especially with the uncertainty of COVID-19, working in teams means that if anyone gets sick or has caretaker responsibilities, the person can take some time to take care of themselves while the team continues.


Each team will produce a final research project. Homework will also be team-based, and team members will have the opportunity to explain who did what part of the assignment. Students will take frequent quizzes to evaluate their own individual knowledge.


Assessment:

Grading for this class will be based on six items:

1. Lecture quizzes. There will be five (5) quizzes throughout the semester. The dates the quizzes will be assigned are posted on the syllabus. You can take each quiz as many times as you want and only your highest grade is recorded. You must complete each quiz within one week of its assignment date. Quizzes are not timed and are all administered on Google Forms, with a link to the quiz on Canvas. Each quiz is worth 15 points.

2. Lab quizzes. There will be a quiz assigned at the end of every lab section to assess your knowledge of R. You can take each quiz as many times as you want and only your highest grade is recorded. You must complete each quiz within one week of its assignment date. Quizzes are not timed and are all administered on Google Forms, with a link to the quiz on Canvas. Each lab quiz is worth 5 points.

3. Paper chunks. Your co-author group will have to complete and submit eight ``paper chunks'' throughout the semester. Each chunk is a draft of a section of your final paper. I emphasize that these are drafts -- they are not expected to be perfect, and should reflect your best effort at completing the task at the time that it is assigned. Each paper chunk is worth fifteen points.

4. Check-Ins. Co-author teams are required to send at least one person (and preferably more) to meet with me during office hours to check in at least six times per semester (at least once every 2-3 weeks). These check-ins are worth five points each.

5. Final presentation. Near the end of the semester, your group will present your project to the class and to the ``public'', both to practice communicating about research and to get feedback for your final paper. More detailed information about these presentations will be provided later on. The final presentation is worth 50 points.

6. Final paper. For your final paper, you will edit those paper chunks with the feedback I have given you, and you will incorporate them into a paper, which you will turn in as your final project. Although final papers can seem intimidating, remember that you will be be working on this paper bit by bit throughout the semester. Ideally, by the end of the semester, all you need to do is copy and paste and edit. The final paper is worth 50 points.

Exam Format:
Enter information here.
Class Format:
Most weeks student will be expected to watch a lecture video before lab. The class period will be dedicated to going through specific activities related to that content and answering student questions.
Workload:
Weekly attendance and weekly pass/fail quizzes.
Textbooks:
https://bookstores.umn.edu/course-lookup/22844/1219
Instructor Supplied Information Last Updated:
25 March 2021

ClassInfo Links - Fall 2021 Political Science Classes

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