Qualitative Data Analysis: An introduction

1. Qualitative Data Analysis: An introduction Carol Grbich Chapter 20: Coding

2. Coding Coding derives from and is maintained by: • Grounded theory methods • Computer management of data

3. Coding When to use • When using qualitative computer management programs • or if, when and as decided by the researcher. Advantages: one way of breaking into the data set Disadvantages : can decontextualise data

4. Questions for the researcher • If not undertaking grounded theory or using computer management do you need to code? • Is coding an essential and mandatory part of undertaking qualitative data analysis? • Can coding be undertaken without thematic analysis? • Where does thematic analysis fit? and can it be undertaken without coding? • Have the terms ‘codes’ and ‘themes’ simply become different terms for results from an identical process? It is impossible to give finite responses to these questions because it is really up to you the researcher to decide what terms you are planning to use, to explain how you are using them and to make your processes and justifications transparent to the reader.

5. Stages of qualitative data analysis • The process is an iterative or recursive one involving becoming familiar with your completed database through moving backwards and forwards across it , reading and re reading it until you are sure of what it contains. • Remembering your research aim, the research questions, your theoretical frameworks and the literature you have reviewed; go though the transcribed database and block/underline/colour key segments and write descriptive comments alongside in the margins. Let the data speak for itself • These identified segments are then matched with like segments and grouped. • Within these groupings, overarching labels are attached and sub groupings identified. • These groupings are then conceptualised and linked more directly with literature and theory as you move to data display and writing up.

6. Coding Process • This is a labelling process where aspects of the data are coded (assigned a letter, a number or phrase tag) for identification, amalgamation and ease of retrieval What skills do you need to code? • A broad view: - the capacity to see your data in the wider context rather than become bogged down in every quotable quote or story. • Theoretical sensitivity - the capacity to link your data to theory – interpretation • A love of orderingbut the capacity to understand that not all data will fit neatly into your developing coding system • An unfazeable personality when your messy data ends up fitting into many codes • A good memoryto recall where you have come across something similar in another part of the data

7. What is usually coded? • themes • theoretical concepts • key words • Participants’ narratives/stories, behaviours, values. interpretations, situations, and relationships, states of mind • events • policies • methodological issues • researcher’s views • settings/environments • metaphors and similes or setting related language • strategies

8. How are codes identified? Ask these questions of your data to understand better what is going on in the environment and to facilitate code formation: • What is going on? • What are people doing? • What is the person saying? • What do these actions and statements take for granted? • How do structure and context serve to support, maintain, impede or change these actions and statements? (Charmaz 2003: 94-95)

9. Coding: options • Code everything of relevance and develop themes/categories or major codes • Develop themes from transcribed/collated data via thematic analysis then code all your data • Summarise your data, edit and present in narratives or case studies • Don’t code -just query the data on the basis of your predefined research question and develop some overall themes