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141 2. Data Transcription Transcripts refer to data verbatim that must be done before any data analysis is conducted. The verbatim is basically the information that was said during an interview or focus group discussion. This data is usually transcribed from a recorder or otherwise handwritten notes. These notes can be edited and expanded while the information from the field is still fresh. These transcripts place emphasis on specificity of the script focusing on who said what and conveyance of gestures or others responses that may not have been captured on tape. 3. Coding This is the process of assigning and organising meaning to the data both quantitatively and qualitatively. Assigning of codes makes interpretation of answers easier. For example, questions about how much one drinks alcohol could have coded responses for each level (e.g. 1=none; 2= once a week; 3= 3-4 times a week; 4=daily etc.). This helps organise and interpret descriptive data such as answers to open– ended questions about experiences, attitudes or opinions. Qualitative data may have to be reduced before coding. 4. Data cleaning This is an essential step that checks for and corrects errors that may have arisen in the data entry. In a technologically-driven research world, some software have in-built programmes that deal with errors such as inconsistencies between data items, data omissions and values entered that may be out of range. One way of cleaning the data is to sample through the questionnaires and check for consistency for sample cases since it may not be feasible to do it for all the entered data. Focus Box 1: Data Quality Main points dealing with data that are commonly referred to aspects of data quality (Peersman, 2014). These include: 1. Validity: Data measure what they are intended to measure. 2. Reliability: Data are measured and collected consistently according to standard definitions and methodologies; the results are the same when measurements are repeated. 3. Completeness: All data elements are included (as per the definitions and methodologies specified). 4. Precision: Data have sufficient detail. 5. Integrity: Data are protected from deliberate bias or manipulation for political or personal reasons. 6. Timeliness: Data are up to date (current) and information is available on time