This blog posting by Dr. Randy McCarthy discusses best practices in organizing/naming conventions for data files. These suggestions are probably more applicable to teaching graduate students than undergraduates. They are also the sorts of tips and tricks we use in practice but rarely teach in the classroom (but maybe we should).
Included in Randy's recommendations:
1) Maintain consistent naming conventions for frequently used variables (like scale items or compiled scales that you use over and over again in your research). Then create and run the same syntax for this data for the rest of your scholarly career. If you are very, very consistent in the scales you use and the data analyses your run, you can save yourself time by showing a little forethought.
2) Keep and guard a raw version of all data sets.
3) Annotate your syntax. I would change that to HEAVILY annotate your syntax. I even put the dates upon which I write code so I can follow my own logic if I have to let a data set go for a few weeks/months.
My only additions to the list would be a trick I learned in graduate school: Every time I compile a scale, I name it @scalename. The @ sign sticks out among variable names and reminds me that this is a compiled scale (and potentially flawed). And every time I compile a scale in SPSS, I definitely do so using syntax and I save my work (just to ensure that I have a record of what I did).
Also, have a mindful back up system for data sets. I use and love Dropbox. I also like Google drive but I am use Dropbox they way most people use My Computer on their hard drives, so it is easier for me to use.
Doe anyone else have any similar tips? Feel free to comment or email me (hartnett004@gannon.edu) if you have any ideas and I'll include them in this post.
Included in Randy's recommendations:
1) Maintain consistent naming conventions for frequently used variables (like scale items or compiled scales that you use over and over again in your research). Then create and run the same syntax for this data for the rest of your scholarly career. If you are very, very consistent in the scales you use and the data analyses your run, you can save yourself time by showing a little forethought.
2) Keep and guard a raw version of all data sets.
3) Annotate your syntax. I would change that to HEAVILY annotate your syntax. I even put the dates upon which I write code so I can follow my own logic if I have to let a data set go for a few weeks/months.
My only additions to the list would be a trick I learned in graduate school: Every time I compile a scale, I name it @scalename. The @ sign sticks out among variable names and reminds me that this is a compiled scale (and potentially flawed). And every time I compile a scale in SPSS, I definitely do so using syntax and I save my work (just to ensure that I have a record of what I did).
Also, have a mindful back up system for data sets. I use and love Dropbox. I also like Google drive but I am use Dropbox they way most people use My Computer on their hard drives, so it is easier for me to use.
Doe anyone else have any similar tips? Feel free to comment or email me (hartnett004@gannon.edu) if you have any ideas and I'll include them in this post.
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