As yet another alternative, we might want to separate nonacademic concerns into those involving administrative matters and those dealing with campus facilities. Table 14-3 Shows how the first ten responses would be coded in that event.
As these few examples illustrate, there are many possible schemes for coding a set of data your choices should match your research purposes and reflect the logic that emerges from the data themselves. Often, you’ll find yourself modifying the code categories as the coding process proceeds. Whenever you change the list of categories, however, you must review the data already coded to see whether changes are in order.
Like the set of attributes composing a variable, and like the response categories in a closed-ended questionnaire item, code categories should be both exhaustive and mutually exclusive. Every piece of information being coded should fit into one and only one category. Problems arise whenever a given response appears to fit equally into more than one code category or whenever it fits into no category: Both signal a mismatch between your data and your coding scheme.
If you’re fortunate enough to have assistance in the coding process, you’ll to train your coders in the definitions of code categories and show them how to use those categories properly. To do so, explain the meaning of the code categories and give several examples of each. To make sure your coders fully understand what you have in mind, code several cases ahead of time. Then ask your coders to code the same cases without knowing how you code the same cases without knowing how you coded them. Finally, compare your coders’ work with your own. Any discrepancies will indicate an imperfect communication of your coding scheme to your coders. Even with perfect agreement between you and your coders, however, it’s best to check the coding of at least a portion of the cases throughout the coding process.
If you’re not fortunate enough to have assistance in coding, you should still obtain some verification of your own reliability as a coder. Nobody’s perfect, especially a researcher hot on the trail of a