Data coding

Data coding is an aspect of data processing and refers to an analytical process in which data, in both quantitative form (such as questionnaires results) or qualitative (such as interview transcripts) are categorised to facilitate statistical analysis.

Coding means the transformation of data into a form understandable by computer software. The classification of information is an important step in preparation of data for computer processing with statistical software.

One code should apply to only one category and categories should be comprehensive. There should be clear guidelines for coders (individual who do the coding) so that code is consistent.

Some studies will employ multiple coders working independently on the same date. This minimizes the chance of errors from coding and increases the reliability of data.

Quantitative approach
For quantitative analysis, data is coded usually into measured and recorded as nominal or ordinal variables.

Questionnaire data can be pre-coded (process of assigning codes to expected answers on designed questionnaire), field-coded (process of assigning codes as soon as the date flows, usually during fieldwork, post-coded (coding of open questions on completed questionnaires) or office-coded (done after fieldwork). Note that some of the above are not mutually exclusive.

In social sciences, spreadsheets such as Excel and more advanced packages such as PSPP/SPSS, DAP/SAS and MiniTab are often used.

Qualitative approach
For disciplines in which a qualitative format is preferential, including ethnography, humanistic geography or phenomenological psychology a varied approach to coding can be applied. Iain Hay (2005) outlines a two-step process beginning with basic coding in order to distinguish overall themes, followed by a more in depth, interpretive code in which more specific trends and patterns can be interpreted.

The process can be done manually, which can be as simple as highlighting different concepts with different colours, or fed into a software package. Qualitative software packages include for example Atlas.ti, QDA Miner and NVivo.

Mixed methods
For those interested in mixed methods and both qualitative and quantitative analysis, the RQDA package within R (programming language) is a potential resource. Operating its own Graphical User Interface (GUI) in a separate window from R, RQDA can be used to perform character level coding. Through traditional R commands, some of this data can be analyzed using quantitative tools.