RESEARCH QUALITATIVE ANALYSIS

Overview of Qualitative Data

Distinction between quantitative and qualitative data.
- Quantitative data focuses on numerical data and can often be generalized.
- Qualitative data (explored in this section) pertains to non-numerical facts, particularly related to human experiences and interpretations.

Empirical Nature: Qualitative data is empirical and derives from real-world experiences.
Lack of Statistical Application: No need for statistics as the focus is on words and narratives of individuals rather than quantifiable measures.

Qualitative research aims to explore:
- People's feelings and attitudes.
- Individual experiences in specific contexts.
Inductive Approach: The research is exploratory and context-sensitive.

Analysis is context-sensitive; findings from one sample cannot be generalized.
Unlike quantitative research, qualitative findings are tied to specific contexts.

Iterative and Reflexive: Analysis begins during data collection.
- Researchers interpret while collecting data, such as during interviews.
Instruments of Research: The researcher is considered an instrument in gathering qualitative data; thus, their position can influence the interpretation.

Inputs: Include various resources for analysis, such as transcripts from interviews and recordings.
Data Collection Methods: Data can be collected via:
- Interviews (audio or video formats).
- Focus groups.
Outputs: The analysis leads to outputs, such as the creation of transcripts.

Transcription Importance: Converts audio/video data into text, making it analyzable.
Process Overview:
- Transcriptions should include spoken words and any non-verbal cues (e.g., gestures).
- Must follow ethical considerations, such as informed consent and data protection.
De-identification: Essential to remove personal identifiers, naming participants as numbers (e.g. Participant 1).

Definition: A reflexive method to identify and interpret patterns of meaning within qualitative data.
Aim: Summarizes patterned meanings while retaining participants' rich accounts.
Phases of Thematic Analysis (Broad and Clarke, 2006):
1. Familiarization: Researcher reviews data for common themes.
2. Labeling Features: Systematically label interesting data features.
3. Defining Themes: Develop and name the identified patterns.
4. Reporting: Provide insights supported by data examples.

Purpose: Generate new theory from qualitative data based on participants' accounts rather than testing existing theories.
Main Characteristics:
- Emerges closely from empirical data.
- Employs structured sampling and constant comparison for theoretical integration.
- Aimed at producing explanatory models of processes observed.
Comparison with Thematic Analysis:
- Thematic Analysis focuses on describing and interpreting patterns without needing to build new theories.
- Grounded Theory explicitly seeks theory development.

Thematic Analysis:
- Descriptive and interpretive without the necessity for theory building.
- Emphasis on revealing rich thematic accounts related to research questions.
Grounded Theory:
- Explicit pursuit of theory development and explanation.

Reflexivity:
- Importance of researcher self-awareness about their background, values, and positionality while conducting research.
- Reflexivity strengthens credibility and reduces bias in data analysis.
Use of Technology:
- Researchers might utilize software for thematic analysis to ease the process of identifying themes in large amounts of data.
Future Research Directions: Consider implications for data interpretation based on researcher positionality, psychological aspects, and socio-economic influences among participants.

Informed Consent: Necessary to obtain permission from participants prior to interviews and data collection.
Data Sensitivity: Special measures must be in place to secure and de-identify sensitive information.