Data Science For Health Researchers: An Introduction to Stata

Data Science For Health Researchers: An Introduction to Stata

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360,00 €
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2 Days
Online
Stata

Overview

This course is for professionals and researchers who are new to Stata.

 

The course assumes only limited statistical knowledge and experience of using statistical software. Course participants will be introduced to Stata’s interface before being shown how to manage and prepare datasets for analysis. The fundamentals of data analysis and visualization will also be taught. Then, the participants will be introduced to two of the main data analysis tools: linear regression and logistic regression. Participants will be taught the statistical theory behind these methods, and they will apply these methods to specially chosen datasets using examples from health research.

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

Subject to minor changes

Morning Session

Afternoon Session

Q&A with Instructor

10am-12pm (London time)

2pm-4pm (London time)

4pm-4:30pm (London time)

10am-12pm (London time)

2pm-4pm (London time)

4pm-4:30pm (London time)

 

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.

 

Tuteurs

Student Testimonials