Khadijah

Sources (briefly):

• Creswell (2013) – Academic book

• Harvard Business Review – Article

• SHRM.org – HR website

Customers are the only group more crucial to a company’s success than management and employees, since they are the source of its revenue. Ironically, if a company places customers’ interests before shareholders, it may be a better long-term investment. If feasible, it’s a good idea to try being a customer. Say you’re considering investing in an airline that has reined in costs, beat earnings estimates in three consecutive quarters, and plans to buy back shares. When you try to actually use the airline, however, you find the website bug-ridden, the customer service representatives cranky, the extra fees petty, and your fellow passengers resentful. The negative experience tells you that the company has a lack of priority for its customers and to be careful investing in the airline.

1- It is studying words or text like interviews or notes to find meaning or patterns.

2- It is using words to understand situations, like in business or cases.

3-It is looking at customer opinions from reviews or social media.

What is similar in these definitions?

They all use words, not numbers.

They all look for patterns or themes.

They use things like interviews and notes.

What is different?

One focuses on research.

One talks about business situations.

One is about customer opinions and marketing.

1. Oxford defines qualitative data analysis as analyzing text, audio, or images to find patterns and construct meaning.

2. Miles and Huberman define it as reducing, displaying, and verifying non-numerical data to identify patterns and explanations.

3. GetThematic defines it as gathering, organizing, and interpreting unstructured data to uncover themes and insights.

   Similarities:
   All focus on non-numerical data.

   Differences:
   Oxford and GetThematic stress the purpose, while Miles and Huberman stress the process.

   Keywords:
   Patterns, themes, interpretation, meaning, and non-numerical data.

   https://www.oxfordreference.com

• Similarities:
  All definitions emphasize that qualitative data analysis deals with non-numerical data (texts, interviews, observations).
  They aim to extract patterns, meanings, or insights.

• Differences:
  The academic definition highlights theory and interpretation.
  The business definition focuses on practical application in work settings.
  The HR definition emphasizes human behavior and decision-making.

• All focus on non-numeric, descriptive data.

• Aim to understand human experiences, behaviors, or perceptions.

• Depend on themes, coding, and interpretation.

  
  

  Differences:

  • Academic sources → emphasize theory building and systematic approaches.

  • Business sources → emphasize solving practical problems and decision-making.

  • Psychology sources → emphasize emotions, feelings, and human perspectives.

  • Education sources → emphasize learning environments and students’ experiences.

1 (Academic book):

Qualitative data analysis is a systematic process of examining texts, interviews, or observations in order to identify patterns, themes, and relationships to understand social phenomena

2 (Business article):

“Qualitative data analysis involves categorizing and coding non-numerical data such as opinions and observations to extract insights that support decision-making in business environments.”

3 (HR resource site):

Qualitative data analysis is the use of methods such as coding and comparison to understand employee experiences and to identify trends or issues in the workplace.

Similarities:

• All emphasize understanding phenomena (social, organizational, or workplace).

• Mention coding/classifying data.

• Highlight patterns and themes as key outcomes.

Differences:

• Context:

• Academic /social phenomena.

• Business /decision-making.

• HR /employee experiences & workplace trends.

• Purpose: differs depending on the field.

Common keywords:

• Patterns

• Themes

• Coding

• Interpretation

• Understanding

References:

Miles, M. B., Huberman, A. M., & Saldaña, J. — Qualitative Data Analysis: A Methods Sourcebook. This is a foundational academic book that details methods like coding, themes, displays, and interpretation.

“Qualitative Data Analysis: Step-by-Step Guide (Manual vs …)” — GetThematic. Defines qualitative data analysis as gathering, organizing, and interpreting non-numerical data to uncover patterns, themes, and insights.

“How to use and assess qualitative research methods” — Biomed Central article by L. Busetto et al. (2020). This includes definitions and describes the nature of phenomena, context, coding, transcripts, etc.

Fatima ahmed

H00596301

Similarities:

• All focus on non-numeric, descriptive data.

• Aim to understand human experiences, behaviors, or perceptions.

  
  

  Differences:

  • Academic sources → emphasize theory building and systematic approaches.

  • Business sources → emphasize solving practical problems and decision-making.

1- Qualitative data analysis is the process of gathering, organizing, and interpreting non-numerical data to uncover patterns, themes, and insights.

2- Qualitative data analysis (QDA) is the process of organizing, analyzing, and interpreting qualitative research data — non-numeric, conceptual information, and user feedback — to capture themes and patterns, answer research questions, and identify actions to improve your product or website.

3- Qualitative data analysis, in essence, is the systematic examination of non-numerical information to uncover patterns, themes, and insights.

Similarities:

1- They all say qualitative data analysis works with non-numerical data (words, feedback, observations.

2- They mention organizing, interpreting, analyzing, arranging the data.

3- They all talk about finding patterns, themes, or insights

Difference:

1- Some emphasize user feedback or product/website improvement (the business-oriented ones).

2- One definition (from Contentsquare) explicitly includes “identify actions” — i.e. not just understanding but using the findings practically.

3- Some definitions mention “systematic” or structured examination (Six Sigma one uses “systematic”).

Key Words / Concepts That Appear Often:

1- Non-numerical / qualitative

2- Organizing / arranging / gathering

3- Interpreting / analyzing

4- Patterns / themes

5- Insights

6- Systematic

Reference:

https://getthematic.com/insights/qualitative-data-analysis/

https://contentsquare.com/guides/qualitative-data-analysis/methods/

https://www.6sigma.us/six-sigma-in-focus/qualitative-data-analysis/

https://www.6sigma.us/six-sigma-in-focus/qualitative-data-analysis/

Similarities:

• Relies on interpretation rather than measurement.

• Focuses on why and how instead of how many.

• Involves coding, categorizing, and theme development.

• Provides deep insights into social or human behavior.

  
  

  Differences:

  • Social science → explains culture and social contexts.

  • Healthcare → focuses on emotions, patient needs, and challenges.

2) Business / UX article — Contentsquare

Definition (in simple words): Qualitative data analysis (QDA) is the process of organizing, analyzing, and interpreting non-numeric data (like user feedback and text) to find themes and patterns and to answer research questions.

Source: Contentsquare (QDA guide).

3) HR / professional practice — SHRM (examples of collecting qualitative data in HR)
Definition (in simple words, based on HR guidance): In HR practice, qualitative data analysis means collecting and making sense of people’s words (interviews, focus groups, open answers) to understand reasons, feelings, and ideas that numbers alone cannot show.
Source: SHRM article about tools to collect qualitative data (HR context).

Similarities (what these definitions share)

All three say we work with non-numeric data (words, observations, feedback).

All describe a process: collecting/organizing and then interpreting the data to get meaning (not only storing it).

All focus on finding patterns or themes that help explain behaviour, opinions, or experiences.

Differences (how they differ)

• The academic source (Miles & Huberman) emphasizes rigorous steps for valid interpretation and methods for displaying and reducing data (more depth about method).

• The business/UX source (Contentsquare) stresses using analysis to answer research questions and to inform product or design decisions (action oriented).

• The HR view focuses on using qualitative analysis to understand people in the workplace (reasons behind survey answers, interviews, exit interviews), often to guide HR decisions.

Keywords / concepts that appear in most definitions

• Non-numeric / qualitative data (words, interviews, observations).

• Organizing / structuring the data.

• Coding or grouping parts of the data (labeling text).

• Patterns / themes (finding repeated ideas).

• Interpreting / insights (making meaning and drawing conclusions).

1- (Academic textbook):
Qualitative Data Analysis is the process of examining non-numerical data like words, interviews, or observations to find patterns and themes.

2- (Business article):
Qualitative Data Analysis means studying written or spoken information to understand ideas, experiences, and behaviors of people.

1- (HR website):
Qualitative Data Analysis is about organizing and interpreting text data to make sense of employee feedback, opinions, and workplace culture.

Similarities:

• All talk about non-numerical data (like words, text, interviews).

• All mention finding patterns or themes.

• All focus on interpretation and meaning.

Differences:

• Textbook is more general and academic.

• Business article focuses on behavior and experiences.

• HR website focuses on workplace and employees.

Keywords in most definitions:

• Patterns

• Themes

• Interpretation

• Non-numerical data

Qualitative Data Analysis means looking carefully at words, stories, or pictures to find important ideas and patterns.

2. From a Business Article:

It’s the process of studying non-number information like customer reviews or interviews to understand what people think and feel.

3. From a Health Website:

It helps researchers understand people’s thoughts, feelings, and experiences by examining detailed information, not just numbers.

Similarities:

• All say it deals with non-numerical data (words, stories, feelings).

• All focus on finding patterns or important ideas.

• All try to understand people’s behavior or experiences.

Differences:

• Some focus on how to analyze data carefully (school book).

• Others focus on using it to improve business or health services.

Common Keywords:

• Patterns

• Interpretation

• Non-numerical data

• Understanding people

Qualitative Data Analysis means looking carefully at words, stories, or pictures to find important ideas and patterns.

2. From a Business Article:

It’s the process of studying non-number information like customer reviews or interviews to understand what people think and feel.

3. From a Health Website:

It helps researchers understand people’s thoughts, feelings, and experiences by examining detailed information, not just numbers.

Similarities:

• All say it deals with non-numerical data (words, stories, feelings).

• All focus on finding patterns or important ideas.

• All try to understand people’s behavior or experiences.

Differences:

• Some focus on how to analyze data carefully (school book).

• Others focus on using it to improve business or health services.

Common Keywords:

• Non-numerical data

• Understanding people

• Interpretation

• Patterns

1. School/Academic Source:

“Qualitative Data Analysis means looking carefully at words, stories, or pictures to find important ideas and patterns.”
— Adapted from: Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications.

2. Business Article:

“It’s the process of studying non-number information like customer reviews or interviews to understand what people think and feel.”
— Adapted from: Adogy. (n.d.). Qualitative Data Analysis. Retrieved from https://www.adogy.com/terms/qualitative-data-analysis

3. Health Website:

“It helps researchers understand people’s thoughts, feelings, and experiences by examining detailed information, not just numbers.”
— Adapted from: Springer Publishing. (n.d.). Qualitative Data Analysis. Retrieved from https://connect.springerpub.com/content/book/978-0-8261-8206-7/part/part04/chapter/ch13