1. Software for data management: The contribution of Stata
Dr Karen Robson, Senior Research Fellow, The Geary Institute, University College Dublin, Ireland

2. Getting acquainted with Stata
StataCorp develops and distributes Stata, software for statistical analysis.
Stata is available for Windows, Macintosh, and Unix computers.
Stata is used by medical researchers, biostatisticians, epidemiologists, economists, sociologists, political scientists, geographers, psychologists, social scientists, and other research professionals needing to analyze data. Gaining popularity in the social and medical sciences
Particularly useful for handling large-scale longitudinal data

3. Stata SE (for large data sets)
can analyze datasets with as many as 32,766 variables, and the only limit on observations is the amount of RAM on your computer
can handle string variables with a maximum length of 244 characters
can handle matrices up to 11,000 x 11,000.
requires at least 512 megabytes of RAM and 80 megabytes of disk space

4. Stata/Intercooled (the standard one)
can analyze datasets with as many as 2,047 variables, and the only limit on observations is the amount of RAM on your computer
can handle string variables with a maximum length of 244 characters
can handle matrices up to 800 x 800.

5. Small Stata
A smaller, student version of Stata (for educational purchases only)

6. Stata MP
The fastest version of Stata (for dual-core and multicore/multiprocessor computers)
Stata/MP is the fastest and largest version of Stata.

7. Resources
StataCorp website (

9. Resources StataCorp website (www.stata.com)
Timberlake website (

11. Resources StataCorp website (www.stata.com)
Timberlake website (
UCLA Stata “portal” (

13. Resources StataCorp website (www.stata.com)
Timberlake website (
UCLA Stata “portal” (statcomp.ats.ucla.edu/stata)
Statalist (

15. Resources StataCorp website (www.stata.com)
Timberlake website (
UCLA Stata “portal” (statcomp.ats.ucla.edu/stata)
Statalist (
Stata Journal (

18. As well, available Dec 2008

19. Launching Stata OS contingent Default window preferences
Window preferences fully adjustable
Auto memory set

23. Comparing with SPSS
Start up differences

26. Comparing with SPSS
Start up differences
With data file open

29. Comparing with SPSS Start up differences With data file open
Viewing data
data viewer, data editor

31. Comparing with SPSS Start up differences With data file open
Viewing data
data viewer, data editor
Viewing variables

33. Comparing with SPSS Start up differences With data file open
Viewing data
data viewer, data editor
Viewing variables
Viewing output/commands
output window buffer, log files

36. Comparing with SPSS Start up differences With data file open
Viewing data
data viewer, data editor
Viewing variables
Viewing output/commands
output window buffer, log files
Syntax and “do files”

38. Variable window
INPUT
Stata command window
Do file
Pull-down menu
Review window
Computation
RESULTS
Output window
Log file

39. Advantages and disadvantages of Stata
User driven
Free STBs
Dedicated journal
Web active
Memory requirements
Backward compatible
Change!
SPSS dominance
Orientated to writing syntax/code
Pull-down windows debate! Now in version 8 forward

40. Advantages and disadvantages of Stata
Easier code
Easier data handling
Clarity of operations/ feedback
Results table function
Before version 8, limited graphics
Now, complex graphics
Variable labelling
Editing of output

41. Advantages and disadvantages of Stata
Copy and paste
Nested/master do files
Flexible terminology
Setting types of data
Interactive help
Switch output (log file) on/off

43. Overview of analytic techniques
Too numerous to mention!
Comprehensive manuals
A selection:
All types of regression
Survey package
Epidemiological package
Multilevel modelling
Time series functions
Cluster analysis

44. Data
Data files .dta
Stat/Transfer software

46. Stata – using wide and long file formats
Wide file formats (everything you add goes to the right of the existing data)
Long file formats (everything you add goes underneath the existing data)

47. MERGE
Data 1
Data 2
APPEND

48. Data 1 (indi)
‘master’
Data 2 (indj)
‘using’
_merge values 1 3 2