L6 | INFER AND EXPLAINS PATTERNS AND THEMES

• INFER

  • To derive a conclusion from facts or premises
• THEME

  • Specific and distinctive quality, characteristics, or concern
• DATA

  • factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.
• ANALYSIS

  • detailed examination of anything complex in order to understand its nature or to determine its essential features 
  • a thorough study
• INFERRING

  • process of deriving  an idea or a conclusion based on preceding facts or data.
  • using observation and background to reach a logical conclusion. 
  • very important for research data analysis since you will interpret data and give your inferences and explanation depending on the patterns and themes of the data you gathered.
• PATTERNS

  • repeated sequences or designs.
  • repeated actions that are done regularly, hence becoming patterns.
• THEMES

  • generated when similar issues and ideas expressed by participants within qualitative data are brought together by the researcher into a single category or cluster.
  • may be labeled by a word or expression taken directly from the data or by one created by the researcher because it seems to best characterize the essence of what is being said.
• STRATEGIES TO INFER AND EXPLAIN DATA

  1. THEMATIC ANALYSIS
  2. QUALITATIVE DATA ANALYSIS
• THEMATIC ANALYSIS

  • widely used method of analysis in qualitative research.
  • Braun and Clarke (2006)
  • foundational method of analysis that needed to be defined and described to solidify its place in qualitative research. 
  • step-by-step process which were then identified.
• THEMATIC ANALYSIS STEPS:

  1. FAMILIARIZATION WITH THE DATA
  2. CODING
  3. SEARCHING FOR THEMES
  4. REVIEWING THEMES
  5. DEFINING AND NAMING THEMES
  6. WRITING UP
• FAMILIARIZATION WITH THE DATA

  • involves reading and re-reading the data, to become immersed and intimately familiar with its content.
• CODING

  • generating succinct labels (codes) that identify important features of the data that might be relevant to answering the research question. 
  • involves coding the entire dataset, and after that, collating all the codes and all relevant data extracts, together for later stages of analysis.
• SEARCHING FOR THEMES

  • examining the codes and collated data to identify significant broader patterns of meaning (potential themes).
  • involves collating data relevant to each candidate theme, so that you can work with the data and review the viability of each candidate theme
• REVIEWING THEMES

  • checking the candidate themes against the dataset, to determine that they tell a convincing story of the data, and one that answers the research question.
  • Themes are typically refined, which sometimes involves them being split, combined, or discarded.
• DEFINING AND NAMING THEMES

  • developing a detailed analysis of each theme, working out the scope and focus of each theme, determining the ‘story’ of each.
  • involves deciding on an informative name for each theme.
• WRITING UP

  • weaving together the analytic narrative and data extracts, and contextualizing the analysis in relation to existing literature.
• 5 STEPS FOR QUALITATIVE DATA ANALYSIS

  1. THEMATIC ANALYSIS
  2. CONTENT ANALYSIS
  3. NARRATIVE ANALYSIS
  4. GROUNDED THEORY
  5. DISCOURSE ANALYSIS
• THEMATIC ANALYSIS

  • Identifies themes or patterns in the data (e.g., Braun and Clarke's method).
• CONTENT ANALYSIS

  • Quantifies the presence of certain words, phrases, or concepts in the data.
• GROUNDED THEORY

  • Builds a theory grounded in the data through constant comparison and iterative coding.
• NARRATIVE ANALYSIS

  • Focuses on the stories or accounts shared by participants
• DISCOURSE ANALYSIS

  • Examines language use in social contexts to understand meaning, power, and communication