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MEAFA Workshop on Quantitative Analysis Using Stata

Venue: University of Sydney
Sydney, Australia
Dates: February 10–14, 2014
Register online

Workshop description

You may attend any one or any combination of the following days:

Monday, February 10:

Working efficiently with Stata by Demetris Christodoulou, MEAFA General Convener

This day assumes no previous knowledge of Stata 13. It describes the environment of Stata and syntactic features. It demonstrates ways of working efficiently with Stata, including the use of logs and do-files. It discusses key principles and presents tools for developing work that is reproducible and verifiable. It also explains how Stata understands data, related precision, and the physical limitations of working with large data. The day will benefit those who are new to Stata or have limited experience with Stata 13. It will also benefit more experienced users who wish to attain a more structural understanding of Stata from first principles.

Tuesday, February 11:

Programming by Demetris Christodoulou, MEAFA General Convener

This day assumes working knowledge of Stata 13 but no knowledge of programming with Stata or any other software. By the end of this day, you will be able to produce efficient, tractable, and automated routines for data management, statistical analysis, econometric estimation, and the creation of tables and graphs. The day covers key programming tools (macros, scalars, loops, saved results) and the fundamentals of building your own commands in Stata. This day is appropriate for those who wish to attain a deeper knowledge of Stata and achieve the aforementioned attributes in their work. This day assumes knowledge of all the material presented on the first day.

Wednesday, February 12:

Monte Carlo simulation by Demetris Christodoulou, MEAFA General Convener

This day assumes good knowledge of Stata 13 and reasonable knowledge of statistics. Monte Carlo (MC) simulation describes the process of generating repeated random sampling for imitating real situations through the use of reasonable probabilistic assumptions. MC simulation is appropriate for evaluating complex deterministic formulations that are characterized by significant uncertainty. The focus is on the application of MC simulation. The principles of MC simulation will be demonstrated through a wide variety of applications. This day assumes knowledge of all material presented on the first day and some programming tools from the second day.

Thursday–Friday, February 13–14:

Multilevel/mixed models with Stata 13 by Yulia Marchenko, Director of Biostatistics, StataCorp

These two days assume working experience with Stata and reasonable knowledge of statistics. Mixed models contain both fixed effects analogous to the coefficients in standard regression models and random effects not directly estimated but instead summarized through the unique elements of their variance–covariance matrix. Mixed models may contain more than one level of nested random effects, and hence, these models are also referred to as multilevel or hierarchical models, particularly in the social sciences. Stata’s approach to linear mixed models is to assign random effects to independent panels where a hierarchy of nested panels can be defined for handling nested random effects. If you have no or little experience with Stata 13, then you are advised to attend at least the first day. The StataCorp website gives a detailed coverage of multilevel/mixed capabilities.

For more information, including a detailed program, or to register, visit sydney.edu.au/business/research/meafa/activities/pdworkshop/2014.





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