1 / 8

Coding and Managing Data: Key Points and Methods

Learn the importance of coding data and the methods for coding quantitative and qualitative data. Discover how computer software can assist in coding and subsequent analysis.

rluisa
Télécharger la présentation

Coding and Managing Data: Key Points and Methods

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 17 Coding and Managing Data

  2. Key points • Before analysis can start, data need to be coded • The methods for coding differ for quantitative and qualitative data • For both, computer software can greatly assist both coding and the subsequent analysis

  3. Why code data? • Analysis is ultimately based on comparison: • between groups of cases • between the same cases at different points of time • To make comparisons, the data need to be organised and categorised • This may be easy for a pre-coded questionnaire • just translate the answers to numbers for statistical analysis • But for other data (qualitative data, including open questions), the coding will result in a set of text segments, each labelled or tagged with a word or phrase

  4. Coding quantitative data • Coding produces a data matrix: • rows: one row for each case (respondent) • columns: one column for each variable (answer) • cells: each cell of the matrix holds a value (may be a ‘missing value’) • The first step is to develop a coding frame • a guide showing how every possible answer is to be translated into a number • Codes must be • mutually exclusive • exhaustive • applied consistently throughout

  5. Creating a code book • A code book for quantitative data should have, for every variable: • question number and wording • variable name for the analysis program • column location of the data for that variable • values that the variable can take, and which answers these values represent • missing values • range of valid values

  6. Coding qualitative data • Extract the first batch (say, 20) of the relevant segments from the data, e.g. • the answers to open questions in an interview schedule • the sentences or paragraphs related to a particular topic in fieldnotes • Sort these segments into categories • Repeat with the next batch • the categories may need to be adapted to deal with the new data, or new categories may need to be added • When no categories are being modified or added, stop categorising segments

  7. Coding qualitative data (2) • Write a rule for each category, defining which segments should go into that category. • Assemble these coding instructions into a code book • This process may be made easier by using a CAQDAS (Computer Aided Qualitative Data Analysis Software) program

  8. Summary • It is necessary to code both qualitative and quantitative data before analysis can start • Coding involves assigning codes (numbers or labels) to every data item • Codes need to be mutually exclusive, exhaustive and consistent • A code book defines which codes should be applied to data items

More Related