The first step in data preparation is to edit the collected raw data to detect errors and omissions that would compromise quality standards. The editor is responsible for making sure the data are accurate, consistent with other data, uniformly entered, and ready for coding. In survey work, it is common to use both field and central editing.
Coding is the process of assigning numbers and other symbols to answers so that we can classify the responses into categories. Categories should be appropriate to the research problem, exhaustive of the data, mutually exclusive, and unidimensional. The reduction of information through coding requires that the researcher design category sets carefully, using as much of the data as possible. Codebooks are guides to reduce data entry error and serve as a compendium of variable locations and other information for the analysis stage. Software developments in survey construction and design include embedding coding rules that screen data as they are entered, identifying data that are not entered correctly.
Closed questions include scaled items and other items for which answers are anticipated. Precoding of closed items avoids tedious completion of coding sheets for each response. Open-ended questions are more difficult to code since answers are not prepared in advance, but they do encourage disclosure of complete information. A systematic method for analyzing open-ended questions is content analysis. It uses preselected sampling units to produce frequency counts and other insights into data patterns.
"Don't know" replies are evaluated in light of the question's nature and the respondent. While many DKs are legitimate, some result from questions that are ambiguous or from an interviewing situation that is not motivating. It is better to report DKs as a separate category unless there are compelling reasons to treat them otherwise. Missing data occur when respondents skip, refuse to answer, or do not know the answer to a questionnaire item, drop out of the study, or are absent for one or more data collection periods. Researcher error, corrupted data files, and changes to the instrument during administration also produce missing data. Researchers handle missing data by first exploring the data to discover the nature of the pattern and then selecting a suitable technique for replacing values by deleting cases (or variables) or estimating values.
Data entry is accomplished by keyboard entry from precoded instruments, optical scanning, real-time keyboarding, telephone pad data entry, bar codes, voice recognition, OCR, OMR, and data transfers from electronic notebooks and laptop computers. Database programs, spreadsheets, and editors in statistical software programs offer flexibility for entering, manipulating, and transferring data for analysis, warehousing, and mining.
