For the categorisation process which I am currently working through with regards to the CoP defintions, it is vital that I define the mehodology behind my approach and explain why I chose that particular methodology. Today I have been researching the different forms of analysis I may use and they include:

  • Thematic Analysis
  • Frequency Analysis
  • Categorisation
  • Content Analysis

 

Thematic Analysis

(Reference: wikipedia)

  • It is one of the most common forms of analysis in qualitative research
  • It emphasizes pin-pointing, examining and recording patterns (or themes) withtin data
  • Themes are patters across data sets that are important to the description of a phenomenon and are associated to a specific research question
  • The themes become the categories for analysis
  • Thematic analysis is performed through the process of coding in 6 phases, to create established, meaningful patterns:
    • familiarisation with data
    • geerating initial codes
    • searching for themes among codes
    • reviewing themes
    • defining and naming themes
    • producing the final pattern
  • Thematic analysis goes beyond simply counting phrases and words in a text – it moves to identifying explicit and implicit ideas within the data
  • Coding the data recognises important moments in the data and encodes it prior to interpretation
  • The interpretation of these codes can include
    • comparing theme frequencies
    • identifying theme co-occurence
    • graphically displaying relationships between different themes
  • This form of analysis is usually used to analyze transcribed in-depth interviews that can be 2-hours in length, resulting in nearly 40 pages of transcribed data per respondent
  • Thematic analysis consists of reading transcripts, identifying possible themes, comparing and contrasting themes and building theoretical models
  • It focuses on the human experience subjectively. This approach emphasizes participant’s perceptions, feelings and experiences as the paramount object of study

Reflexivity Journals

  • the process of documenting close reflections of potential findings and implications of the research study
  • they can be useful for reflecting on emergent patterns, themes and concepts
  • they are detailed records of the development of each of their codes and potential themes
  • changes made to themes and connections between themes are incorported into the final report to assist the reader in understanding decisions that were made throughout the coding process
  • ensure that notes written in the journal are different from the data
  • the use if italics, bold words and adding brackets will assist in showing distinctions between data and journaling
  • avoid abbreviations
  • keep a log of concerns with the research, theoretical framework, central research questions, goals and major issues that help focus on the coding process

Coding Process

Questions to consider when coding:

  • What are people doing? What are they trying to accomplish?
  • How exactly do they do this? What specific means or strategies are used?
  • How do members talk about and understand what is going on?
  • What assumptions are they making?
  • What so I see going on here? What did I learn from note-taking?
  • Why did I include them?

Sample Size Considerations

  • depends on the type of data collection and size of the projects (6 to 400+)

(Reference: designresearchtechniques.com)

The Process of Thematic Analysis:

  1. Collect Data
    • field diaries
    • observational data
    • pictures/video (transcribed data)
    • historical data
    • questionnaire statements
    • transcripts
    • audio recordings (transcribed data)
  2. Coding Data
    • by hand or through a software programme
    • the researcher will code every 2 to 3 lines of text with handles that identify key words, concepts, images and reflections
    • a code should be clear and consise – what is it, what are its boundaries and when does it occur?
    • codes become the foundation for the themes that are going to be used by the researcher
    • the following elements should be addressed when coding:
      • what am I going to call it?
      • how am I going to define it?
      • how am I going to recognise it in the data?
      • what do I want to exclude?
      • what is an example?
  3. Code Validation
    • the codes should be developed and reviewed by more than one person
  4. Themes/Frameworks Identification
    • the researcher identifies themes and sub themes from the code book (journal): patterns that have emerged from the coded data
  5. Information Consolidation, finalize theme names
    • the researcher finalizes the name of each theme, writes its description and illustrates it with a few quotations from the original text to help communicate its meaning

Frequency Analysis

(Reference: wikipedia)

  • the study of the frequency of letters or groups of letters in a ciphertext
  • the method is used as an aid to breaking classical ciphers

Categorisation

Reference: Wikipedia)

  • the process in which ideas and objects are recognized, differentiated and understood
  • it implies that objects are grouped into categories, usually for some specific purpose
  • ideally a category illuminates a relationship between the subjects and objects of knowledge
  • it is fundamental in language, prediction, inference, decision making and in all kinds of environmental interaction
  • the 3 general approaches to categorisation are:
    • Classical Catgorisation
    • Conceptual Clustering
    • Prototype Theory

Content Analysis

(Reference: terry.uga.edu)

It is a research technique used to make replicable and valid inferences by interpreting and coding textual material. By systematically evaluating texts (e.g. documents, oral communication and graphics), qualitative data can be converted in quantitative data.

(Reference: Wikipedia)

  • it can refer to methods for studying and/or retreiving meaningful information from documents
  • it can also refer to a family of techniques for studying the “mute evidence” of texts and artifacts
  • There are 5 types of texts in content analysis:
    • written text, such as books and papers
    • oral text, such as speech and theoretical performance
    • iconic text, such as drawings, paintings and icons
    • audio-visual text, such as TV Programs, movies and videos
    • hypertexts which are texts found on the internet

(Reference: journals.sagepub.com)

3 Approaches to Qualitative Content Analysis:

  • conventional
    • coding categories are derived directly from the text data
  • directed
    • analysis starts with a theory or relevant research findings as guidance for inital codes
  • summative
    • involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context

(Reference: libweb.surrey.ac.uk)

The 10 steps of content analysis:

1) Copy and read through the transcript – make brief notes in the margin when interesting or relevant information is found

2) Go through the notes made in the margins and list the different types of information found

3) Read through the list and categorise each item in a way that offers a description of what it is about

4) Identify whether or not the categories can be linked any way and list them as major categories (or themes) and / or minor categories (or themes)

5) Compare and contrast the various major and minor categories

6) If there is more than one transcript, repeat the first five stages again for each transcript

7) When you have done the above with all of the transcripts, collect all of the categories or themes and examine each in detail and consider if it fits and its relevance

8) Once all the transcript data is categorised into minor and major categories/themes, review in order to ensure that the information is categorised as it should be.

9) Review all of the categories and ascertain whether some categories can be merged or if some need to them be sub-categorised

10) Return to the original transcripts and ensure that all the information that needs to be categorised has been so.

The process of content analysis is lengthy and may require the researcher to go over and over the data to ensure they have done a thorough job of analysis

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