Qualitative data analysis
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Qualitative Data Analysis. Step 1: Determining Questions. “Start-up” questions are general questions that help to frame the initial qualitative research project. Emergent questions develop during the research process. Both “start-up” and “emergent” questions guide qualitative data analysis.

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Qualitative Data Analysis

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Qualitative data analysis

Qualitative Data Analysis

Step 1 determining questions

Step 1: Determining Questions

  • “Start-up” questions are general questions that help to frame the initial qualitative research project.

  • Emergent questions develop during the research process.

  • Both “start-up” and “emergent” questions guide qualitative data analysis.

Step 2 unitizing textual data

Step 2: Unitizing Textual Data

  • Textual data must be unitized (broken-down into parts).

  • Units must be heuristic.

  • A unit of textual data is also called a concept.

    • First order concepts

    • Second order concepts

  • Textual units are not generally standardized.

Step 3 developing coding categories

Step 3: Developing Coding Categories

  • Coding categories are developed through an iterative process.

  • There are several types of coding processes:

    • Housekeeping coding

    • Research process coding

    • Analytic coding

      • Constant Comparative Method

      • Analytic Induction

Processes of categorizing

Processes of Categorizing

  • Read all of your data completely in order to immerse yourself in them.

  • Identify your first unit of data.

  • Identify your second unit of data.

  • Make a basic judgment of similarity-difference between these two units.

  • Proceed with the similarity-judgment task for all your units of data.

  • Develop category labels and descriptions (memoing) for each of your categories.

  • Start over.

Step 4 plugging holes

Step 4: Plugging Holes

  • Additional data are collected to clarify connections between units of data in a given category.

  • Additional data are collected when there are few textual units in a category that the researcher find important.

  • Additional data are collected when logic suggests that particular categories are missing.

Step 5 checking

Step 5: Checking

  • Negative case analysis is a process in which the researcher “tests” categories against new data – searching for units that are deviant or discrepant.

  • Member checks are conducted by reviewing the researcher’s categories with informants.

  • Triangulation is used when the researcher applies her coding categories to other types of data or when additional researchers derive coding categories from the same data set.

Step 6 finding exemplars

Step 6: Finding Exemplars

  • Not all of the data will be reported in the final qualitative research report, exemplars are chosen to represent those data.

  • Exemplars are examples in the data that best illustrate or “bring to life” the categories.

  • Exemplars should be chosen for their “thick description” (Geertz, 1973) to enable the transferability of the study.

Step 7 integrating coding categories

Step 7: Integrating Coding Categories

  • The relationships between coding categories are mapped.

  • This is a meta-coding activity.

Models of qualitative data analysis

Models of Qualitative Data Analysis

The developmental research sequence drs

The Developmental Research Sequence (DRS)

  • The DRS is used to understand semantic relationships and rules of a given speech community.

  • The study of semantic relationships is MOST often featured.

  • There are four activities involved in the DRS:

    • Domain Analysis

    • Taxonomic Analysis

    • Componential Analysis

    • Theme Analysis

Grounded theory development gtd

Grounded-Theory Development (GTD)

  • GTD MOST often features the study of rules.

  • The goal of GTD is inductive theory construction.

  • There are four primary activities involved in GTD:

    • Open Coding

    • Axial Coding

    • Process Analysis

    • Selective Coding

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