Course Syllabus

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  Course Syllabus & Information

Stat E-100:  Introduction to Quantitative Methods for the Social Sciences and Humanities
Tentative Syllabus for Spring Semester 2016
(Last Updated January 5, 2015)

Please Note: The syllabus is required reading for the course. You will be expected to understand the policies and assignments discussed in the syllabus.

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: Tuesdays 7:00-9:00 pm EST online via Big Blue Button
Email: efosse@fas.harvard.edu or fosse.ethan@gmail.com

Head Teaching Assistant: Mark Ouchida (M.Ed., A.L.M.)
Office Hours and Location: Thursdays 9:00-10:00 am EST online via Big Blue Button
Email: mark_ouchida@harvard.edu

Course Lectures: Course lectures will be delivered online via weekly modules. New modules will be released on the course website every Thursday at 5pm, starting on Thursday, January 28, 2016. 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 optional weekly online sections during which teaching assistants will review topics from the lectures, outline additional example problems, and answer questions about the weekly problem sets. More details will be available as the semester begins.

Teaching Assistant Office Hours: Each teaching assistant will also hold weekly office hours online. More details will be available as the semester begins. 

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 issues, section issues, and questions about the problem sets or exams.

  

II. Course Resources

Course Website: The course website will have additional information on the course mechanics, problem sets, lecture materials, and the teaching staff. Also note that all exams will be taken entirely online and that problem sets 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/courses/8323

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

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/

 

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 problem sets that will count for 20% of your grade
  • Two online exams 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.

Problem Sets (20%): Problem sets are assigned nearly every week and submitted online through the course website. Details on the problem sets will be published on the course website as the semester starts. Late problem sets are not accepted for any reason. Working with other students on the problem sets 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 these problem sets, you will be graded on whether or not you have written the correct answer.

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 48-hour time period. Each exam will consist of two parts: first, a set of multiple choice questions; second, a set of questions in which you will be asked to enter a numerical answer. The exams are “open book” in that you can use any textbook, notes, problem sets, 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. 

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. Details on the final project will be given near the beginning of the semester.
  • 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. Details on the final project will be given near the beginning of the semester.

 

IV. Tentative Course Schedule

The tentative course schedule is given below. It is subject to change as the semester begins. The due dates for problem sets, 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 Topic

Textbook Readings

Problem Sets Due

Jan 28

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

Feb 4

Categorical Data

OI: Ch. 1: Pp. 43-48

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

None

Feb 11

Numerical Data

OI: Ch. 1: Pp. 28-37

SG: Ch. 3: Pp. 27-34

Problem Set #1

Feb 18

Probability Tables and Relative Risk

OI: Ch. 2: Pp. 76-101

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

Problem Set #2

Feb 25

Correlation Analysis

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

SG: Ch. 5: Pp. 45-46

Problem Set #3

March 3

Simple Linear Regression

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

SG: Ch. 5: Pp. 47-48

Problem Set #4

March 10

Basics of Sampling

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

SG: Ch. 3: Pg. 35

Problem Set #5

March 17

Spring Recess

None

None

March 24

 

March 31

Exam 1

 

Sampling Distribution

None

 

OI: Ch. 4: Pp. 175-197

SG: None

None

April 7

Tests for Means

OI: Ch.5: Pp. 219-239

SG: Ch. 3, Pp. 36-37

Problem Set #6

April 14

Tests for Proportions

OI: Ch. 6: Pp. 274-286

SG: Ch. 4: Pp. 41-42

Problem Set #7

April 21

Tests for Contingency Tables

OI: Ch. 6: Pp. 297-302

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

Problem Set #8

April 28

Inferences for Correlation and Simple Linear Regression

OI: Ch. 7: Pp. 351-355

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

Problem Set #9

May 5

Course Review

None

Problem Set #10

May 12

Exam 2

None

None

 

V. Other Course Policies

Collaboration: You may discuss the problem sets with other students, but you must write 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


Students with Disabilities: Harvard University has made a commitment to creating an accessible academic and campus community. If you have a disability, we ensure that you have equal opportunity to participate in, contribute to, and benefit from our academic and residential programs. Additional information may be found here: https://www.extension.harvard.edu/resources-policies/resources/disability-services-accessibility

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Course Summary:

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