Qualitative Data Analysis (QDA) Overview
Qualitative Data Analysis (QDA) involves examining non-numeric data to understand human experiences, perceptions, and behaviors. Unlike quantitative data, which focuses on numbers and statistics, qualitative data includes texts, images, audio, and video recordings.
Key Methods of QDA:
- Content Analysis: Systematically categorizing textual information to identify patterns and themes.
- Grounded Theory: Developing theories based on data collected from participants.
- Discourse Analysis: Studying communication and how language is used in social contexts.
- Thematic Analysis: Identifying and analyzing themes within qualitative data.
- Narrative Analysis: Examining stories and personal accounts to understand how people make sense of their experiences.
Common Steps in QDA:
- Data Collection: Gathering data through interviews, focus groups, observations, or document reviews.
- Data Coding: Assigning labels to segments of data to categorize and organize it.
- Identifying Themes: Looking for recurring patterns or themes within the coded data.
- Interpreting Data: Making sense of the themes and patterns to draw conclusions.
- Reporting Findings: Presenting the analysis in a structured format, often with quotes or examples to support the findings.
QDA is widely used in fields like sociology, psychology, education, and market research to gain deeper insights into complex social phenomena.
Using Functions:
TEXTJOIN
XLOOKUP
TEXTSPLIT
UNIQUE
TRANSPOSE
IF
ISNUMBER
SEARCH
LEN
TRIM
SUBSTITUTE
Theory by MS #Copilot
Pic by MS #Designer