“Introduction to Quantitative Statistical Methods”
Welcome! This website is meant to provide students with additional and useful material for PO8006, Introduction to Quantitative Statistical Methods
This space will be used to publish tutorials’ slides and extra-material such as useful papers or web-pages. This webpage only supplements the information from the lectures by offering additional material.
Should you have further queries, please do get in touch with me!
I prepared this Google Calendar with all the main deadlines and dates for the course. Feel free to add it to your Calendars.
Communication and Office Hours
For questions about the lectures or tutorials you can either send me an email or join the Slack channel I have set up for the course. Slack is a workspace hub with amazing chat features that allow for a quick and efficient communication. The idea behind this is to create a virtual space where you can ask questions that me and your colleague will be able to see as well (use emails for private communication!). For a fancier description on slack click HERE.
How to join? Just click HERE and use to TCD email to log in. Please avoid using nicknames.
In general, I scheduled my office hours on Tuesdays from 15 to 16 (ONLY upon request). While I am certainly happy to meet students, I would encourage you to bring up doubts, questions or issues of sort during tutorials.
Get and install STATA
This course is based on STATA. It is relatively easy to use. Stata is available on the computers in the Beckett Lab and the Ussher Lab, and possibly other computer labs on campus. A cheap student version of the software package is available directly from Stata’s Website. You are free to use alternative (and free) statistical software such as R or JASP but it is your responsibility both to learn the package (i.e., no assistance will be guaranteed by either the instructors or the teaching assistant) and to use the package to complete the module’s assignments.
Online Support and Resources
While we will cover a lot of the STATA functions in the tutorials, for your homework and research project you will very likely need additional functions.
- UCLA Tutorials for R, Stata and SAS: Very useful tutorials for the most important statistical analyses.
- Swirl: Simulates an R workspace allowing you to learn R in R.
- Moderndive: A great interactive introduction to data visualisation and modelling in R.
- Stackoverflow: Here you will find almost all answers to specific questions.
Information on submitting assignments
Throughout the course you will need to submit 8 assignments. You are free to conduct the homework with STATA or an alternative statistical software (for example SPSS or R, but not Excel).
Some general rules:
The assignments must be typed into a LaTeX or Word/Open Office document and submitted as a PDF via Turnitin. Screenshots of the STATA output are not sufficient as you will need to describe and interpret the results and procedures.
If you include tables, do not use a screenshot, but “export” it (we will discuss this in class). Please save figures appropriately in high resolution (We recommend PDF as vector graphic formats have the best possible quality).
Add the contents of the Stata do file/SPSS Syntax file/R script file at the end of your document. It is good academic practice to present the full code and replication script.
Useful links for each tutorial
Below I post a selection of useful links for each tutorial. If you found additional material that might be useful, either open a pull request on GitHub or let me know via email/slack.
- Pseudo R Squares
- Interpretation of Dummy Variables (intuitive examples)
- Interpret Odds-Ratio in Logistic Regression
- Logistic Regression in STATA
- Data Manipulation with Stata, an example
- Handling Data Files with Stata
- Cleaning Data in Stata - University of Toronto
- Regression with Dummy Vars in STATA
- Interaction Effects: Food and condiment example
- More on interaction effects
- Student’s t-test explained
- Calculate t-test by hand
- Hypothesis Testing - Z test two-tailed example (VIDEO)
- Hypothesis Testing - T test one-tailed example (VIDEO)
- How (not) to interpret confidence intervals
- Difference between standard deviation and standard error
- Simulate the distribution of sample means
- Creating a grouped variable from a continuous variable
- Mathematically transforming a variable
- Describing a continuous variable: measures of central tendency and dispersion; histogram
- Describing a categorical (ordinal) variable: frequency table; bar chart
Handouts are partially based on the excellent notes by Silvia Decadri who taught this course in previous years.