Skip to main content

Reproducible data analysis in Stata

Course description

A scientific article typically provides the first table presenting characteristics for the study population. The characteristics include for example frequencies for categorical variables, mean and standard deviations for continuous variables, p values etc. There should also include tables typically presenting results out of several statistical analyses. 

The ideal reproducible data analysis requires that the whole working process from data cleaning to the published results should be reproducible without any copy-and-paste because it is well-known that copy-and-paste Stata output can be not only tedious and time-consuming but also error prone. Therefore, workout a sequential Stata do-files automatically export both the descriptive and the analytical output is crucial not only to align with the principles of reproducible data analysis but also make the working process much more efficient.

Aim

The major aim of the course is to make at least part of data analysis to be reproducible - automatically export descriptive and analytical output to either Excel tables or word text avoiding any manual copy-paste. 

Content

The course will introduce tools and Stata programming language automatically producing Excel tables of characteristics and analytical results. 
In the section of results, it typically describes and highlights major findings. For doing so, this section typically combines the text and some of descriptive and/or analytical results (such as odds ratio, hazard ratio etc. together with 95% confidence interval) from the current study. The course will introduce Stata commands how to incorporate analytical results into text avoid manual copy-paste. 
Finally, a few tricks/tips will be introduced to facilitate the process.

Prerequisite 

The participants should have at least already participated in introduction course on Stata.

 

Teaching arrangement

Lectures + exercises in computer

 

Intended learning outcome

At the end of the course, students should be able to:

  • Write Stata codes automatically exporting results to Excel tables.
  • Write State codes automatically incorporate results into word document.

 

Course fee

The course is free of charge for PhD students enrolled in Universities that have joined the "Open market agreement" NorDoc.

For other participants there is a course fee of

DKK 4800

EUR 643

Graduate Programme

Public Health

Venue

SDU, Odense

Course director

Associate professor Chunsen Wu, SDU

ECTS credits

1,8 ECTS

Register for this course

Sign up here!

The PhD programme Faculty of Health Sciences University of Southern Denmark

  • Campusvej 55
  • Odense M - DK-5230
  • Phone: 6550 4949

Last Updated 31.10.2024