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- –
- 2 Days
- Stata
Overview
Designed for professionals and researchers taking their first steps with Stata, this course provides a clear and supportive introduction to data analysis.
No extensive background in statistics or prior experience with statistical software is required. You’ll begin by exploring Stata’s interface, building confidence as you learn how to manage, clean, and prepare datasets for meaningful analysis.
From there, the course guides you through the core principles of data analysis and visualisation, helping you turn raw data into clear, insightful outputs. You’ll then progress to two of the most widely used analytical techniques—linear regression and logistic regression.
Through practical, hands-on exercises using carefully selected health research datasets, you’ll not only learn how to run these analyses in Stata, but also gain a solid understanding of the statistical concepts behind them. By the end of the course, you’ll be equipped with the skills and confidence to apply these methods in your own work.
Course Highlights
- Comprehensive Coverage: From fundamental concepts to advanced dynamic models.
- Practical Learning: Real-world case studies and hands-on exercises with Stata.
- Expert Insights: Gain clarity on complex topics like endogeneity and serial correlation.
- Interactive Format: Live Q&A sessions to address individual questions and challenges.
Agenda
Day 1: Getting started with Stata
Stata Basics
- Loading & saving data, Importing data from other formats
- User Interface; Click and go; Command line; Do files, Syntax of Stata commands
- Managing Projects, Data, Memory
- Altering Data Structure
- Transforming variables and creating new variables
- Storage Types and Working with String Variables
- Working with Dates
- Group-Level Characteristics
- Getting help and online resources
- Essential Descriptive Statistics
Day 2: Data analysis
Graphics
- Histograms; Boxplots
- Kernel density functions
- Bivariate graphs: Scatter plots & Line graphs
- Formatting graphs
- Overlaying multiple plots
Linear Regression
- Ordinary Least Squares in Stata
- Interpretation of results
- Model diagnostics
- Graphing actual and fitted values
Logistic Regression
- Short-comings of the linear probability model
- Theory of logistic regression
- Maximum likelihood estimation
- Interpretation of coefficients: odds ratios; marginal effects
- Multinomial logistic regression
Prerequisites
Principal texts for pre- and post-course reading:
Alan C. Acock. 2018. A Gentle Introduction to Stata, Sixth Edition. Texas: Stata Press.
Angrist, Joshua & Jörn-Steffen Pischke (2014) Mastering ’metrics: The path from cause to effect. New Jersey: Princeton University Press.
Course Timetable
Terms
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Temporary, time limited licences for the software(s) used in the course will be provided. You are required to install the software provided prior to the start of the course.
- Payment of course fees required prior to the course start date.
- Registration closes 1-calendar day prior to the start of the course
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
