2017 STATA SUMMER SCHOOL

Date of Appearance: 
Mar 11, 2017
Duration of the course: 
Jul 3, 2017 to Jul 8, 2017
Application Deadline: 
Jun 26, 2017

2017 STATA SUMMER SCHOOL

Date of Appearance: 
Mar 11, 2017
Duration of the course: 
Jul 3, 2017 to Jul 8, 2017
Application Deadline: 
Jun 26, 2017

Course Details

Summer Schools
Credits or certification after course completion: 
Certificate of attendance
Study Options: 
Full-Time

Course Fees

Not specified.

Location of Course

Location: 
London
United Kingdom

COURSE OVERVIEW

Our 2017 Stata Summer School, comprising a series of 1 and 2-day courses will take place between 3-8 July 2017 at Cass Business School, London.

Now in its 6th year, our London Stata Summer school provides a very popular and flexible course framework allowing attendance at any course separately, or the entire school. The School provides a great opportunity for students, academics and professionals to expand their data analysis and statistical skills learn from professionals pioneering research at the forefront of their specialist fields.

The courses forming the Summer School are:

  • Course 1: An Introduction to Stata
  • Course 2: An Introduction to Stata Graphics
  • Course 3: Advanced Data Management in Stata
  • Course 4: An Introduction to Stata for Medical Statistics - 6 & 7 July
  • Course 5: An Introduction to Meta-Analysis - 8 July
  • Course 6: An Introduction to Time Series Analysis using Stata - 8 July

 

COURSE AGENDA

COURSE 1: AN INTRODUCTION TO STATA

Date: 3 July 2017
Delivered by: Tim Collier, London School of Hygiene & Tropical Medicine
Learning Ratio: Theory 20% Demonstration 30% Practical 50%

 

This one-day introductory course is for people interested in using Stata for research. No prior knowledge of Stata is required.

 

COURSE OUTLINE:

  • Brief overview of Stata’s statistical, graphical and data management capabilities
  • Introduction to the Stata working environment
  • Using Stata via the Graphical User Interface and the command window
  • Understanding Stata’s command syntax
  • Helping you to help yourself – introducing Stata’s online help facilities
  • Working efficiently with do-files
  • Saving results output in a log file

 

COURSE 2: AN INTRODUCTION TO STATA GRAPHICS

Date: 4 July 2017
Delivered by: Tim Collier, London School of Hygiene & Tropical Medicine
Learning Ratio: Theory 20% Demonstration 30% Practical 50%

 

This one-day introductory course is intended for people who would like to be able to produce publication-quality graphs using Stata. Some experience of using Stata and some level of statistical knowledge would be helpful though not essential.

 

OVERVIEW:

Stata enables the production of a wide range of publication-quality graphs. All Stata’s graphics features can be accessed through the Graphical User Interface (GUI) making it simple to produce eye-catching graphs. With Stata’s integrated Graph Editor you can change anything about your graph; you can modify, add or remove titles, lines, text, marker symbols and much more. The Graph Editor features a record and playback facility which enables sets of changes to be saved and then applied to a series of graphs. Stata has a series of built-in graph styles, but it is also possible to create your own style that can easily be applied to any graph. Producing graphs using the command syntax in do-files enables easy reproduction of graphs and can save time when creating similar graphs.

 

COURSE OUTLINE:

  • Introduction to Stata graphics
  • Resources for learning Stata graphics
  • Producing graphs using the Graphical User Interface
  • Producing graphs using the command syntax in do-files
  • Editing graphs using Stata’s Graph Editor
  • Combining graphs
  • Graph schemes

 

COURSE 3: ADVANCED DATA MANAGEMENT IN STATA

Date: 5 July 2017
Delivered by: Tim Collier, London School of Hygiene & Tropical Medicine
Learning Ratio: Theory 20% Demonstration 30% Practical 50%

 

This one-day course is intended for people who are reasonably familiar with Stata (e.g. have attended the one-day introduction to Stata course) but who would like to develop their data management skills and work more efficiently.

 

OVERVIEW:

As well as statistical and graphical capabilities Stata also has an excellent, flexible and wide ranging suite of tools for data management. In this one-day course we will looks at how to handle data of different types, e.g. continuous, categorical, string and dates. We will look at how to change the shape of a dataset e.g. transpose or collapse to create a summary dataset. We will also learn some simple programming tools that will help you save time in your research.

 

COURSE OUTLINE:

  • Introduction to course and brief review of Stata basics
  • Introduction to how Stata is organised
  • Loading data into Stata from non-Stata formats
  • Useful functions for creating summary variables
  • Dealing with string variables and dates in Stata
  • Changing the shape of your data
  • Creating summary datasets
  • Using Stata’s system variables for data management tasks
  • Some simple programming tools for saving time

 

COURSE 4: AN INTRODUCTION TO STATA FOR MEDICAL STATISTICS

Date: 6 - 7 July 2017
Delivered by: Tim Collier & Tim Clayton, London School of Hygiene & Tropical Medicine
Learning Ratio: Theory 20% Demonstration 30% Practical 40%

 

This introductory medical statistics course is intended for people who are reasonably familiar with Stata (e.g. have attended the one-day introduction to Stata course) but who would like to develop their statistical analysis skills.

 

OVERVIEW:

In this two-day course we will look at how to use Stata to analyse data that typically arises in medical research including continuous outcomes e.g. blood pressure, binary outcomes e.g. dead / alive and time-to-event outcomes e.g. time to myocardial infarction. We will look at how to fit appropriate models in Stata and how to interpret the resulting output. We will look at how to adjust for multiple explanatory variables, allow for interactions and how to obtain model predictions.

 

COURSE OUTLINE:

  • Introduction to course and data
  • Principles of statistical analysis
  • For each of continuous, binary and time-to-event outcomes:
  • Fitting models in Stata and understanding the output
  • Including categorical explanatory variables
  • Fitting multiple explanatory variables
  • Fitting interactions
  • Comparing models and model selection
  • Obtaining model predictions
  • Reporting results
  • For time-to-event outcomes:
  • Setting up survival data in Stata

 

COURSE 5: AN INTRODUCTION TO META-ANALYSIS

Date: 8 July 2017
Delivered by: Prof. Aurelio Tobías, Spanish Scientific Research Council
Learning ratio: 30% theory, 20% demonstration and 50% practical

 

A one-day course that is aimed at both academics and practitioners, with a basic knowledge of Stata, who are interested in applying meta-analysis using Stata commands designed for this purpose.

 

OVERVIEW:

Meta-analysis is a statistical technique for combining the findings from independent studies. This one-day course introduces the main statistical techniques for meta-analysis and shows how to do it in practice using the Stata commands metan, metareg and mvmeta.

 

COURSE OUTLINE:

  • Basic Stata commands for meta-analysis
  • Effect sizes based on binary and continuous data
  • Fixed vs. random effects models for meta-analysis
  • Testing for heterogeneity
  • Subgroup analysis and meta-regression
  • Practical exercises using the metan and metareg commands

 

COURSE 6: AN INTRODUCTION TO TIME SERIES ANALYSIS USING STATA

Date: 8 July 2017
Delivered by: Prof. Giovanni Urga & Dr. Elisabetta Pellini, Cass Business School, City University
Learning ratio: 50% theory, 20% demonstration and 30% practical.

 

This one day course provides a theoretical introduction and a practical guide to most used univariate and multivariate time series analysis techniques. Practical demonstration of the alternative techniques will be illustrated using Stata and with economic and financial time series.

 

SESSION 1 & 2

  • Stochastic processes and time series. Stationarity, autocorrelation function, white noise processes. Unit root nonstationarity and main unit root tests.
  • Univariate time series models: Moving Average (MA), Autoregressive (AR), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models.
  • Empirical application 1: Analysis of the features of time series, model selection and estimation in practice.
  • Univariate time series models: Moving Average (MA), Autoregressive (AR), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models.
  • Empirical application 1: Analysis of the features of time series, model selection and estimation in practice.

SESSION 3 & 4

  • Multivariate time series models: vector autoregressions (VAR).
  • Models of nonstationary time series: Cointegration theory, Engle and Granger two-step method and Johansen’s approach.
  • Empirical application 2: Modelling long-run relationships in economics.

Class Size: 
20 - 30 students

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