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CODING AND CODEBOOKS FOR QUANTITATIVE DATA

Quantitative data processing depends crucially on having a useful codebook. A codebook for quantitative data spells out exactly how to transform observations into numbers that can be manipulated statistically and searched for patterns.

A good codebook is worth a lot in data analysis and it’s worth more every year. It tells you (and others) what you have in your data—what variables you’ve studied, what you’ve called those variables, and how you’ve stored information about them. You simply can’t analyze quantitative data without a good, clear codebook.

Just as important, neither can anyone else. You can’t share your data with other researchers unless you give them a codebook they can use. Six months after you finish anything but the simplest projects (those with only half a dozen or fewer variables), even you won’t recognize your own data without a codebook. And if you want to reanalyze your data several years after a project has ended, or compare data you (or someone else) collected in 2005 with data you collect now, you won’t be able to do so unless you have a good codebook handy.

 
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