Course Syllabus

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  Course Syllabus & Information

 

 Stat E-100: Introduction to Quantitative Methods for the Social Sciences and Humanities
Syllabus for Spring 2018

 

Please Note: The syllabus is REQUIRED reading for the course. You will be expected to understand the policies and assignments discussed in the syllabus. To view an older version of the course syllabus as a pdf, click here. Note that the online version of the syllabus is the latest version.

Overview: This course introduces the basic concepts of data analysis and statistical computing, both increasingly used in the social sciences and the humanities. The emphasis is on the practical application of quantitative reasoning, visualization, and data analysis. The goal is to provide students pragmatic tools for assessing statistical claims and conducting their own basic statistical analyses. Topics covered include basic descriptive measures, measures of association, sampling and sample size estimation, and simple linear regression. Assignments are based on real-world data and problems in a wide range of fields in the social sciences and humanities, including psychology, sociology, education, and public health.

 

I. Course Mechanics

Instructor: Ethan Fosse (Ph.D., Harvard)
Office Hours: By appointment
Email: efosse@princeton.edu or fosse.ethan@gmail.com 

Course Lectures: Course lectures will be delivered online via weekly modules. New modules will be released on the course website every Thursday at 5pm Eastern Standard Time (EST). Students will have the week to complete all material at their own pace (abiding by the due dates for assignments).

Weekly Sections: There will be weekly online sections during which teaching assistants will review topics from the lectures, outline additional example problems, and answer questions about the semi-weekly missions. Sections will be scheduled throughout the week for students to attend at their convenience. Although attendance at specific sections is not mandatory, students are strongly encouraged to attend at least one section per week if they have questions about the course material. More details will be available as the semester begins.

Teaching Assistant Office Hours: Each teaching assistant will also hold office hours online. Information can be found here.

Assigned Teaching Assistant: You will be assigned a teaching assistant for the duration of the course after the semester starts. You may attend any online section (or all sections) that fit your schedule, but your assigned teaching assistant is your first contact for grading concerns, section issues, and questions about the missions or exams. To find out more about the teaching staff, click here. To find your assigned TA, click here.

 

II. Course Resources 

Course Website: The course website will have additional information on the course mechanics, missions, lecture materials, and the teaching staff. Also note that all exams will be taken entirely online and the missions will be submitted entirely through course website. It will be updated as the semester starts. The course website may be accessed here (make sure you log in so you can view the website): https://canvas.harvard.edu$CANVAS_COURSE_REFERENCE$/assignments/syllabus

Required Textbooks: There are two required textbooks for the course, one for learning statistical concepts and another for learning how to use statistical software:

  • For learning statistical concepts, the required textbook is OpenIntro Statistics, 3rd Edition by David M. Dietz, Christopher D. Barr, and Mine Cetinkaya-Rundel published in 2015. It is available online for free as a pdf or for less than $10 from Amazon: https://www.openintro.org/stat/textbook.php?stat_book=os

Statistical Software: This course is focused primarily on helping you learn basic statistical concepts. However, learning statistical concepts is generally aided by analyzing data using statistical software. Because of its popularity and applicability, this course will focus on using R with RStudio. R is the underlying programming language, while RStudio is a graphical user interface that makes working with R much easier. Both are free, open-source, and used widely by statisticians. To install R with RStudio, go to the following link and click on the installer for your computer’s operational system: https://www.rstudio.com/products/rstudio/download/ If

Technical and Installation Issues: If you run into any technical or installation issues with R or RStudio, don't fret! The Harvard University Information Technology (HUIT) Support Desk will help you troubleshoot any technical or installation issues. Simply call (617-495-7777) or email (ithelp@harvard.edu) for help.

IMPORTANT: It is up to you to have R and RStudio installed before the official start of the course. The causes of any installation issues are highly dependent on the specifics of your operating system, permissions settings, Internet access, and a large number of other factors, some of which may be technical in nature. Although we provide some additional resources on installing R and RStudio, you should direct ALL questions to the HUIT Support Desk, who are trained to help you with installing computer software for courses.

 

III. Grading and Course Requirements

For all students, your grade will be based on the following:

  • Participation in module questions that will count for 10% of your grade
  • Regularly-assigned missions that will count for 20% of your grade
  • Two online exams that will count for 40% of your grade
  • A final project that will count for 30% of your grade

Module Questions (10%): Accompanying each week’s lectures are a set of module questions to test your understanding of the material. You will not be evaluated on whether or not you obtain the right answer on these questions, only that you answered them. If you answer all of the questions, then you will obtain a full participation grade.

Missions (20%): Missions are assigned and submitted online through the course website. Each mission focuses on a particular set of questions around a particular topic or theme. Details on the missions will be published on the course website as the semester starts. Late missions are not accepted for any reason. Working with other students on the missions is allowed and encouraged but only as long as you hand in your own work and do not simply copy the work of someone else. For the missions you will be graded on whether or not you have given the correct answer for each question. As a courtesy we will drop your lowest-scoring mission at the end of the semester. If you do not complete a mission then you will be given a score of zero. The course website in Canvas will inform you of which mission is dropped with the message: "This assignment is dropped and will not be considered in the total calculation."

Online Examinations (40%): There will be two exams during the course, each worth 20% of your overall grade. Each exam will last 2 hours and will be offered online on the course website during a specified time period over several days. You will not need a proctor to take the online exams. Each exam will consist of three parts: first, a set of multiple choice questions; second, a set of questions in which you will be asked to enter a numerical answer; and third, a small set of questions that require you to critically evaluate the interpretation of statistical analyses. The exams are “open book” in that you can use any textbook, notes, missions, or lecture slides to help you answer the questions. The exams are designed to test your knowledge of basic statistical concepts, not your ability to use R. To the extent R will be tested, you will only be asked to interpret standard statistical output in the form of tables and graphs.

  • Exam 1: Released at 5pm Eastern Standard Time (EST) on Thursday March 22. Due at 11:59pm EST on Sunday March 25.
  • Exam 2: Released at 5pm Eastern Standard Time (EST) on Thursday May 10. Due at 11:59pm EST on Sunday May 13.

Practice Exams: We will provide a Practice Exam 1 and a Practice Exam 2. These exams are identical in form as the actual exams. The Practice Exam 1 and Practice Exam 2 are released approximately one week before the actual Exam 1 and Exam 2, respectively.

Final Project (30%): Both undergraduate and graduate students are expected to conduct a final project, with differing requirements:

  • For undergraduate students, the final project is a short paper (no more than 5 pages) in which you analyze a dataset using one of the methods discussed in the course. Students are expected to use one of the datasets used during the course, although you may use another dataset if desired. The final project will be submitted online through the course website. 
  • For graduate students, the final project will consist of a paper (no more than 10 pages) describing the analysis of a dataset using one the methods discussed in the course. You may use one of the datasets used in the course or another dataset. The final project will be submitted online through the course website. 

IMPORTANT: You do not have to use R or RStudio when writing your final project. You may use any statistical software, including but not limited to SAS, SPSS, Stata, Excel, and Python.

 

IV. Tentative Course Schedule

The tentative course schedule is given below. It is subject to change as the semester begins. The due dates for missions, assigned textbook readings, and lecture topics are subject to change. In the table below, OI refers to the OpenIntro Statistics while SG refers to the Student’s Guide to R.

 

Date

Lecture

Readings &  Due Dates

1/25

Module 1: Introduction to Data

OI: Ch. 1: Pp. 7-13, 15-20

SG: Pp. 5-7, 11-12; Ch. 1: Pp. 13-14; Ch. 2: Pp. 15-16, 20-25

None

2/1

Module 2: Categorical Data

OI: Ch. 1: Pp. 43-48

SG: Ch. 4: Pg. 39 only; Ch. 6: Pp. 55, 57   

None

2/8

Module 3: Numerical Data

OI: Ch. 1: Pp. 28-37

SG: Ch. 3: Pp. 27-34

Mission #1

2/15

Module 4: Probability Tables and Relative Risk

OI: Ch. 2: Pp. 76-101

SG: Re-read Ch. 6: Pp. 55, 57

Mission #2

2/22

Module 5: Correlation Analysis

OI: Ch. 1: Pp. 26-27; Ch. 7: Pp. 338-339

SG: Ch. 5: Pp. 45-46

Mission #3

3/1

Module 6: Simple Linear Regression

OI: Ch. 7: Pp. 331-338, 340-348

SG: Ch. 5: Pp. 47-48

Mission #4

3/8

Module 7: Basics of Sampling

OI: Ch.1: Pp. 20-23; Ch. 3: Pp. 127-141; Ch. 4: Pp. 168-174

SG: Ch. 3: Pg. 35

Mission #5

3/15

Spring Recess

None; Practice Exam 1 Released

3/22

Exam 1

None

None

3/29

Module 8: Sampling Distribution

OI: Ch. 4: Pp. 175-197

SG: None

None

4/5

Module 9: Tests for Means

OI: Ch.5: Pp. 219-239

SG: Ch. 3, Pp. 36-37

Mission #6

4/12

Module 10: Tests for Proportions

OI: Ch. 6: Pp. 274-286

SG: Ch. 4: Pp. 41-42

Mission #7

4/19

Module 11: Tests for Contingency Tables

OI: Ch. 6: Pp. 297-302

SG: Ch. 4: Pp. 42-44; Ch. 10: Pp. 75-76

Mission #8

4/26

Module 12: Inferences for Correlation and Simple Linear Regression

OI: Ch. 7: Pp. 351-355

SG: Re-read Ch. 5: Pp. 47-48

Mission #9

5/3

Module 13: Course Review

None; Practice Exam 2 Released

Final Project

5/10

 Exam 2

None

None

V. Other Course Policies

Collaboration: You may discuss the missions with other students, but you must give the final answer yourself. Solutions prepared by copying or paraphrasing someone else’s work are not acceptable. All computer output you submit must come from work that you have done yourself.

Academic Integrity: Harvard University expects students to understand and maintain high standards of academic integrity. Breaches of academic integrity are subject to review and may be grounds for disciplinary action. Please review the examples of violations of academic integrity at the following link: https://www.extension.harvard.edu/resources-policies/student-conduct/academic-integrity

Course Summary:

Course Summary
Date Details Due