Open Source Psychology/Printable version
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Why Open Source
Introduction[edit | edit source]
Many Psychology projects already benefit from Open Source Software, weather it is general open source applications, or specific software such as psychopy.
Advantages[edit | edit source]
More often than not, open-source software and publicly available resources are free of charge and more powerful than proprietary software and costly services. This also allows for more reliable and inclusive collaboration, as sharing files and information does not require that others spend money on licensing or instruction manuals. In addition, if you are the programmer-type, you can adapt these resources to your needs, and create unique solutions that would otherwise be impossible.
Having materials open source or publicly available gives people the freedom to cooperate with one another, and that's why open source material generally progresses quickly. Wikipedia, for example, is a vast repository of information today because users are able to contribute their ideas and words to something they know will be useful. And arguably, that's a motive to do well with their contributions. Authors write things they like and they find their audience willing to listen and the audience can become authors as well and contribute their talents.
Disadvantages[edit | edit source]
Though more powerful, many of these options have a steeper learning curve. But don't be discouraged! Though it may take longer to get started with the resources described in this wikibook, the payoff will be worth it!
Some files that you will collect will need to be encrypted due to your Institutional Review Board Protocol, other regulations, or you simply want to protect your data so you can safely transfer it.
Encryption is the process of encoding your data so that even if an unauthorized person intercepts the data, they will be unable to access the raw data.
De-identifying Data[edit | edit source]
If you simply want to change your data so that individuals can no longer be linked to their data, but that you still have all data points with unique user ids, you will need to de-identify your data.
Identifiable data is anything that can be used to link information back to a specific individual. This normally includes names, address, social security numbers, other identification numbers, etc., but can include much less obvious indicators of who the person is.
In R, open the 'digest' package
You will then want to 'salt' the unique values so that nobody can reverse the hash codes through brute force...
a$first <- c("Aaron","Brittany","Chris") a$last <- c("Moore","Norren","Oster")