PUBL0055: Introduction to Quantitative Methods, Lecture notes of Quantitative Techniques

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PUBL0055: Introduction to Quantitative Methods

Lecture 1: Introduction

Jack Blumenau and Benjamin Lauderdale

Lecture Outline

Course Outline

Logistics

Quantitative Methods and Research Design

Introduction to Quantitative Data

Conclusion

What is this course?

• This is not a course on statistics
  • A statistics course would focus on the theory and derivation of statistical methods
  • We will discuss some theory at a basic level, but will not concern ourselves with the derivation
• This is a course on applied quantitative research methods
  • Focus on the developing intuition about quantitative methods
  • Focus on using these methods to answer social science questions
• This course is different to other similar courses
  • Stronger focus on causality, data vizualisation, and application
  • Less focus on sampling, statistical inference and uncertainty

What is in this course?

1. Introduction
2. Causality
3. Describing Quantitative Data
4. Regression I (Prediction)
5. Regression II (Specification)
6. Regression III (Causality)
7. Panel Data
8. Sampling, Uncertainty and Confidence Intervals
9. Hypothesis Testing & Uncertainty in Regression
10. Additional Topics / Summing Up

Week 6 is reading week. There will be no lecture, but you will have a midterm assessment.

Why should you take quantitative research methods?

You will learn…

• …to apply a wide range of quantitative methods to answering your potential research questions
• …the types of questions that can (and cannot) be answered using quantitative analysis
• …to make more persuasive arguments using quantitative data
• …to evaluate the quantitative evidence others present in their work
• …some transferable skills

Logistics

Moodle

• In addition to the course website, Moodle access is essential for this course - Lecture slides and recordings - Links to office hours signups - Assessments
• Students will be automatically enrolled in Week 1, but you can get access sooner by enrolling manually: - Enrollment key for PUBL0055 – regression

Lecturers

Jack Blumenau

• E-mail: [email protected]
• Office Hours: Sign up via link on Moodle Benjamin Lauderdale
• E-mail: [email protected]
• Office Hours: Sign up via link on Moodle

Introduction or Advanced?

We offer two quantitative methods modules at the MSc level:

Introduction Advanced Term One Two Pre-requisites (methods) None One prior course Pre-requisites (R) None None Substantive focus Intro to quant methods Causal inference

On most of the MSc programmes, it is possible for you to take both this course and the advanced course.

Which course should I take?

• This course has no pre-requisites: we will assume that you have no prior experience in either quantitative methods, or in coding
• The Advanced course requires you to have at least one prior course in quantitative methods/econometrics up the level that we cover on this course
• If you are unsure which course to take
  1. Take this quiz

2. Book an office hour appointment to speak to Jack

Textbook

• New book which includes many social science examples and focuses on R code implementations
• We recommend buying this book, although some copies are available in the library
• We will provide additional notes on some topics

Advice on reading for this course

Statistical readings can be intimidating and on this course you should focus on an in depth reading of the textbook, rather than a broad and shallow reading of multiple sources.

1. Do the required reading before lecture
2. Do not expect to understand everything the first time
3. If overwhelmed, focus on the text, not the equations
4. After lecture, re-read to maximize understanding

Lectures and classes

Lectures

• Lecturers will alternate weeks
• Lectures will be 60-80 minutes per week
• Lecture recordings will be uploaded by Friday of the preceding week. Seminars
• One hour seminar slots
• Wednesday 4pm & 5pm
• Thursday 9am, 10am, 11am, 3pm, 4pm, 5pm
• Friday 9am, 10am
• Seminar attendance is mandatory

Homework

• The instructions and code you need for the seminars and homework will be available on the course website
• You should work through the exercises before your scheduled seminar time
• The site also includes useful information about the course, quantitative methods, and coding in R
• Each seminar includes a homework exercise which focusses on implementing the skills you have learned on new data
• Solutions will be posted on the Wednesday following the Friday class
• These homeworks are not assessed, but they will be very similar in style to the assessments!