Sas - Introduction to working under change management
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The document provides an overview of good data management practices using statistical packages such as SPSS, Stata, R, and SAS, highlighting the importance of syntax and documentation in data cleaning and variable creation. It emphasizes never overwriting original data and offers guidance on safeguarding work through multiple backups and proper file organization. The text also includes examples of creating and recoding variables while maintaining the integrity of original datasets.
Sas - Introduction to working under change management
- 4. Four Statistical Packages • SPSS • Stata • R • SAS
- 5. • Point and Click • Command Line • Programs (the best way) Three Ways to Work
- 6. Outline • Sermon on SYNTAX • Cleaning data and creating variables • Never overwrite original data • Practices that will help you keep track of your work • Safeguarding your work
- 7. A Sermon on SYNTAX • Command line and Point and Click – Advantages: • Quick, may require less learning – Disadvantages: • Takes longer the second time – you must wade through the point and click menu rather than just change a word • You do not have a record of what you have done
- 8. SPSS The King of Point and Click
- 9. You can point and click to get files, create variables, change variable values, and do analysis, and end up without a record of what you have done. You will be sorry.
- 10. Or, you can use Point and Click as an aid as you write programs. You can copy syntax created by Point and Click into your program. In SPSS programs are written in a Syntax Window and they have the extension of .sps when you save them.
- 11. You can modify SPSS defaults so that commands will be reflected in the log. This allows you to copy commands from your log into your program file. These changes also make debugging easier.
- 12. You will find information about how to modify SPSS at the following URL.
- 13. STATA
- 14. You can point and click, issue commands on the command line, or create .do files. “.do” files can store your programs.
- 15. R
- 16. With R you can point and click, issue commands on the command line, or create .R files. “.R” files store your programs. Results from P&C are reflected so you can copy them into your program.
- 17. SAS
- 18. SAS allows some point and click, but immediately offers an editor where you can write your programs. SAS programs end with the .sas extension, and are text files. SAS features an enhanced editor with cool color coding that makes it easier to write and debug programs.
- 19. Never clean data in the data view
- 20. Scenario 1: You get a data set and find errors in it. You change the values in the data window. You save it with point and click, over-writing your original data. Later you try to recall what changes you made, when and why. Of course you can’t. You can’t even be sure that you made the “corrections” for the proper cases. You can’t look back at older data sets to confirm what you did. You sit there sweating.
- 21. Scenario 2 same as Scenario 1 : You save it with point and click, over-writing your original data and, while you are saving the file, 1) Your computer goes down because of a power outage OR 2) There is a brief interruption in the network HALF OF YOUR DATA SET IS LOST. You cry.
- 22. Scenario 3: You get a data set and find errors in it. You write a program that: 1) gets the original data 2) makes changes in values with SYNTAX 3) Includes comments about the changes 4) saves the new file in a different name Science marches forward.
- 23. Creating Variables and Recoding is not the same as Cleaning Data • You always want clean data • You may not always want the recoded or created variables • Make new variables, but keep the old ones. (don’t over-write) Use the original to check the new
- 24. Examples of Recoding/Creating • Creating a series of dummies from a categorical variable • Creating an index from a series of scale variables • Creating a dichotomous or categorical variable from a continuous variable • Always consider MISSING VALUES
- 25. Sample SPSS Program * CleanNew.sps . * 10/10/05 created dummy for male . Get file = ‘dirty.sav’ . * Cleaning data, PJG, looked at survey form, educ for ID=1 should be 16, 10/9/05 . If id = 1 educ = 16 . * Create a dummy variable from “gender”. If gender = ‘m’ male = 1 . If gender = ‘f’ male = 0 . If gender = ‘’ male = -9 . Missing values male (-9) . Variable label male ‘Male’ . Value labels male 1 ‘Male’ 0 ‘Female’ . Save outfile = ‘CleanNew.sav’ / drop gender .
- 26. Summary for Cleaning and Creating variables • Use syntax (programs) to create and clean variables • Document when and why in your programs • Save new file – do not over-write the old
- 27. It may be months between the time that you finish a paper, submit it, and get to revise it for publication.
- 28. What you will need to know: • The origin of your variables: – What is the source for each variable – How were they created? • What programs created your final tables? • What program files created the file you used for your final tables?
- 29. Create a Directory for the Project • For example, c:MA_Thesis • Store all of the programs and data in that directory and subdirectories
- 30. Naming Conventions • For every data file you have, you should have a program file with a corresponding name. • When you have finished your paper, create a program file for each table. For example: table1.sas table2.sas
- 31. Document your work • Write comments in your program. • Put a file in your directory called a_note, readme, or something similar that includes a brief description of the project and important information.
- 32. Safeguarding your work • Multiple backups – not all stored in the same basket • Worry about the future – Keep up with formats (cards, tapes, floppy disks, CDs, what next? ) – Store in portable formats
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