- Time-consuming 
- Difficulty in identifying patterns 
- Hard to remember details 


1) Large Quantity of data → Superficial Analysis

Too many details confuse people to know which one is useful, which ended up repeating participants response without any analysis

2) Rich data → descriptions of too many details

Contradicting information from different or same participants, which lead to indefinite analysis 

3) Contradicting data → not definitive 

When there are no goals, researchers can get lost in details and report wrong information 

4) No goals → misdirected analysis 

- Qualitative data can be overwhelming and without a systematic process

Claremont Graduate University

Qualitative Data

Rosala, M. (2019, September 29). How to Analyze Qualitative Data from UX Research: Thematic Analysis. https://www.nngroup.com/articles/thematic-analysis/

References: Thematic Analysis

- Captures participants’ thoughts, beliefs, and self-reported needs from interviews, focus groups, and diary studies 

Qualitative attitude data

- Observation about people’s behavior from contextual inquiry and ethnographic approaches  


Qualitative behavioral data

Challenges and Issues in Qualitative Data

Three sources of qualitative data:

- Traditional period (1900s-1940s)
- Modernist phase (1950s-1970s)
- Period of blurred genres (1970 - mid 1980s)
- Time of crisis of representation (mid 1980s - mid-1990s)
- Postmodern and post experimental stages (mid-1990s -present)

History - 5 stages in which qualitative methods were developed: 


- Subjective in nature 
- There are common mistakes that can impact the validity, trustworthiness, and dependability of results 
- Problems can happen in data collection and transcription processes 
- Preventing the common pitfalls can establish higher trustworthiness and methodological confidence

Qualitative Research Methods

Terms to be reconsidered in Qualitative Research

- Quantification is difficult
- Nature of qualitative is iterative

Why Qualitative Data is more difficult than Quantitative Data

- Cognitive Processes
- Gap between experiences and language
- Language Barrier

Why Language is a major limitation of Qualitative Data

While deductive reasoning begins with a premise that is proven through observations, inductive reasoning extracts a likely (but not certain) premise from specific and limited observations. Based on this definition, deductive analysis users predetermined categories to sort the data, and inductive analysis immerses researchers in the data and allows them to suggest new groups to researchers.

Deductive analysis vs. Inductive analysis

- Lightweight data analysis
- Heavyweight data analysis

Two Typical Analysis Plans

Task Analysis is the method of learning about how users go about finishing tasks. This study was introduced by instructional design (design training), ergonomics (analyzing how users use models to better suit user experience), and human factors. TA is critical to improving user experience and goals.

Task Analysis

Qualitative Surveys

The relative weaknesses of quantitative methodology are often the primary strengths of qualitative research. Qualitative approaches excel at generating novel and interesting research questions and formulating new hypotheses that might otherwise be overlooked. Furthermore, qualitative research is particularly strong in providing thick, detailed descriptions of human behavior as it occurs in real-world contexts and successfully capturing the subjective, lived experiences of specific individuals and groups.

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