QUALITATIVE DATA
What is Qualitative Data?
How is it Collected?
Characteristics
Examples that Apply to Instructional Design
How is the Data Analyzed?
Purpose
Purpose
Understanding Experiences: To capture the depth of human experiences, emotions, and challenges that statistics alone cannot convey. Defining Concepts: To explore and define ideas, thoughts, and meanings related to a topic. Enhancing Quantitative Data: To complement quantitative data, providing context and explaining "why" numerical findings occur. Informing Action: To understand attitudes, beliefs, and motivations to inform policies and actions effectively.
5 Common Ways to Analyze Qualitative Data
Hover over the magnifying glasses
Thematic Analysis
Content Analysis
Discource Analysis
Narrative Analysis
Grounded Theory
Definition
Qualitative data refers to non-numerical information that describes qualities, characteristics, or attributes of something.
Collections Methods
Qualitative data is collected through interviews, focus groups, observations, case studies, document analysis, and surveys.
Examples that Apply to Instructional Design
TEXT Interview transcripts: Learners describing what parts of a training felt confusing or most useful. Open-ended survey responses: Comments on how an online module supported (or failed to support) workplace performance.
AUDIO and VISUALS Audio recordings: Learner feedback sessions where participants explain difficulties with using an LMS. Photographs: Screenshots of learner-created projects (e.g., art assignments, mind maps, infographics).
OBSERVATIONS Social interactions: Observing patterns of participation in online forums (e.g., who initiates vs. who only replies) Environmental contexts: Documenting workplace conditions (like background noise or time pressure) that affect learning transfer
Characteristics
Non-numeric (qualities or characteristics) Descriptive (information about behaviors, experiences, feelings, and opinions) Subjective (reflects individual perspectives, emotions, and motivations) Exploratory (uncover the "why" and "how" behind phenomena)
QUALITATIVE DATA
Kara Lanier
Created on September 1, 2025
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Transcript
QUALITATIVE DATA
What is Qualitative Data?
How is it Collected?
Characteristics
Examples that Apply to Instructional Design
How is the Data Analyzed?
Purpose
Purpose
Understanding Experiences: To capture the depth of human experiences, emotions, and challenges that statistics alone cannot convey. Defining Concepts: To explore and define ideas, thoughts, and meanings related to a topic. Enhancing Quantitative Data: To complement quantitative data, providing context and explaining "why" numerical findings occur. Informing Action: To understand attitudes, beliefs, and motivations to inform policies and actions effectively.
5 Common Ways to Analyze Qualitative Data
Hover over the magnifying glasses
Thematic Analysis
Content Analysis
Discource Analysis
Narrative Analysis
Grounded Theory
Definition
Qualitative data refers to non-numerical information that describes qualities, characteristics, or attributes of something.
Collections Methods
Qualitative data is collected through interviews, focus groups, observations, case studies, document analysis, and surveys.
Examples that Apply to Instructional Design
TEXT Interview transcripts: Learners describing what parts of a training felt confusing or most useful. Open-ended survey responses: Comments on how an online module supported (or failed to support) workplace performance.
AUDIO and VISUALS Audio recordings: Learner feedback sessions where participants explain difficulties with using an LMS. Photographs: Screenshots of learner-created projects (e.g., art assignments, mind maps, infographics).
OBSERVATIONS Social interactions: Observing patterns of participation in online forums (e.g., who initiates vs. who only replies) Environmental contexts: Documenting workplace conditions (like background noise or time pressure) that affect learning transfer
Characteristics
Non-numeric (qualities or characteristics) Descriptive (information about behaviors, experiences, feelings, and opinions) Subjective (reflects individual perspectives, emotions, and motivations) Exploratory (uncover the "why" and "how" behind phenomena)