Bita Akram Julia Zochodne Open coding Bita Akram Julia Zochodne

Qualitative research A research method used in many disciplines Social sciences Marketing Aims to understand The human behavior The reason behind this behavior Answering questions like: Why/how/when/where/what Methods used for qualitative research interviwe Participant observation field notes Journals Documents Photographs

Qualitative data analysis “Analysis is the search patterns in data and for ideas that help explain why those patterns are there in the first place”[3] qualitative data analysis techniques Interpretive techniques Coding Recursive abstraction Mechanical techniques

Open coding Analytic Process Data should be observed thoroughly for: Important concepts are highlighted Their properties and the dimensions of these properties are identified Data should be observed thoroughly for: Ideas Thoughts meanings Links you to the idea Cyclic procedure

conceptualizing Naming a phenomena Grouping similar happenings based on their common properties Classifying similar objects Defining suitable actions regarding the common properties of a class There are more than one way to classify an object based on Researcher point of view Research context Conceptualization happens by The meaning a phenomenon reflects in researcher’s mind “In vivo”: The name is directly taken form participants data It is necessary to be innovative in looking at phenomena

memos The analysis a researcher perform on data based on her Thoughts Interpretations Questions This archived analysis is kept as a direction for conducting further research An example: Looking at a key word in data from different perspectives Looking at a key word in data based on different meanings in different contexts Analyze the key word based on pervious investigations

Patterns Patterns are frequent happening of an action or behavior in human affairs A research primarily tries to find patterns in data Patterns leads us to finding the theory lay behind a specific behavior Patterns have following characteristics: likeness Predictable variation Frequency Order Relations dependancy Cause and effect

categorization Arranging things in systematic manner Moving from words in data to more general and abstract view Grouping codes based in their shared characteristics Different shared characteristics might be considered based on our perspective Naming a category: Answering what is going on here based on the mutual characteristics of codes in the same category Established names Using in vivo codes Coming up whith new names

Category properties and dimensions A category can be defined based on its properties and characteristics Dimension: Defining the position of a property on a specified range A subcategory inherits the general properties of its parent category while the its properties are restricted to a specific range

Example Of a concept,properties and dimentions Concept: Color Properties: Shade, Intensity and Hue Dimenstions: The particular range defined for each of these properties [4]

Theory Theory is the overall research goal based on: Relations between categories and subcategories The simplified procedure schematic of reaching to a theory [1]

What to code Social life recorded in data Participant activities Perception Artifacts produced by participants As a novice code every piece of data As an expert you probablu know what parts to ommit

How to code Leave enough space Pre-coding Between paragraphs Use three columns Data Real time coding Final coding Pre-coding Circle/highlight/bold potential words/phrases/sentences Title/organizational framework Evidence for your theory Start coding while collecting data Separate them from original data Keep your research goal in front of you

How to code Cont. Code using one method before the other to see the influence Codes should be categorized in a minimum number of categories preserving the essence of their subset Keep a record of all of your coding cycles Your codes Their content Data example Can be used as a Standard for team Can help in categorization

Coding manually vis. electronically Learning basics of coding and qualitative analysis Complex instructions Multiple functions Coding electronically (CAQDAS) Storing Organizing Reconfiguring It is good to it first manually and after mastering the work use a software

Most current versions of caqdas ATLAS.ti:www.atlasti.com MAZQDA:www.maxqda.com Nvivo:www.qsrinternational.com

Capabilities of caqdas Apply what you can do manually Assign more than one code to a block Sub-coding Pattern coding Organize evolving and complex coding for user reference Hierarchies Networks Auto-coding Eliminate similarities

Team coding Depends on the size of the project Joint Research Involving more than one way perspective and interpretation of data in research Ideas are built on top of each other Team coding can occur by: Inviting a participant to the team Coding each others data to double check their reality There should be a minimum range of 85%-90% of agreement between coworkers

Solo coding Depends on the size of the project Single coding Seek peer support Involve participants Start coding from initial stages Maintain a reflective journal

Necessary Personality attributes Have cognitive skills Organized Flexible Creative Ethical Embracing extensive vocabulary Exercise perseverance Be able to deal with ambiguity

references [1] Saldaña, Johnny. The coding manual for qualitative researchers. No. 14. Sage. (2012). [2] Strauss, A. and Corbin, J. Basics of Qualitative Research, 2nd Edition. Sage Publications. (1998). [3] http://en.wikipedia.org/wiki/Qualitative_research#Data_analysis [4] http://warrenmars.com/visual_art/theory/colour_wheel/hsv_cylinder.png