Are you wondering how to tell the difference between data that are based on numbers (quantitative) and those based on sensations and experiences (qualitative)?

Many business owners find it difficult to make the distinction… which can lead to frustration, overwhelm… and eventually to a business that is vulnerable to threats that could cause irreparable harm.

To solve this problem, I recommend that you understand your business data — meaning the facts and figures that represent:

• how and when your services or products are used,
• the experiences of your customers, and
• the feedback from your customers and employees—especially Foundational Staff.

Using these sources of data, you can better interpret the changes that could be affecting your business and take steps to adjust your decisions accordingly. This awareness of unknowns is called Strategic Risk Intelligence.

The Differences Between Quantitative and Qualitative

To see the differences between quantitative data (money, time, speed, etc.) and qualitative data (feedback, story, experiences, etc.), take a look at this:

Comparison Chart

The chart below shows some differences between Quantitative Data (center column) and Qualitative Data (right column).

(Scroll down to see this chart in paragraph form.)

Criteria	Quantitative Data	Qualitative Data
Definition	Numerical calculations and measurements	Sensations, feelings, and experiences
Purpose	1) Identify cause-and-effect relationships 2) Compare differences 3) See trends and develop predictions for the future	1) Understand the larger context 2) Interpret social interactions 3) Find larger themes
Examples	money, time, speed, frequency, movement, height, length, area, volume, weight, temperature, humidity, pressure, sound level, quality, degree, intensity, event, item, categories (age, gender, occupation), positioning, and status.	colors, textures, smells, tastes, appearance, beauty, desirability, feelings, intuition, sensations, choices, significance, inspiration, encouragement, loyalty, capability, strength, fitness, well-being, values, and beliefs.
View of Reality	Objective; single reality	Both subjective and objective; multiple realities
Reasoning	Deductive (make a specific conclusion)	Inductive (find patterns)
Method	Fact-based	Perception-based
Objectives	Describe, explain, and predict	Explore, discover, and create
Focus	Narrow perspective; testing a specific hypothesis	Wide perspective; examine breadth and depth of a situation
Observation Environment	In controlled conditions to isolate the root causes	In natural environment to observe behavior and relationships
Methodology	Systematic	Open-ended
Structure	Highly structured; low flexibility	Less structure; high flexibility
View of Behavior	Orderly, predictable, and collective	Dynamic, situational, and individual
Type of thinking	Logical thinking	Intuitive thinking
Measurement	Defined measurements	Unstructured variables
Collection formats	Precise measurements of money, time, height, length, volume, etc.	Open-ended, intuitive examination and monitoring
Collection tools	Direct (Structured survey, Phone call, Meeting, Face-to-face discussion, Online form) and Indirect (Spreadsheet, Statistical evaluations, Analytics and reports, Comparison of past, current, and projected data, Direct mail or package)	In-person (Interview, Narrative story, Group discussion, Unstructured survey, Focus group, Observation, Description) and Digital (online or e-mail survey, web forum, chat, online community)
Data type	Numbers and statistics	Words, objects, images
Data analysis purpose	Identify statistical relationships	Identify patterns, features, and themes
Data analysis types	Descriptive Statistical tools (mean, median, mode, frequency distribution, standard deviation, variance analysis) and Inferential Analysis tools (surveys, experiments, hypothesis testing, T-Test, ANOVA, chi-square, z-score)	Flexible collection such as – Content Analysis – Narrative Analysis – Autobiography – Case Study – Ethnography – Framework Analysis – Discourse Analysis – Grounded Theory
Key Questions	What happened? How did it happen? When is it happening? How often is it happening? How many times does it occur in a week or month? Who is it happening to? Where is it happening? What does the data tell us?	Why is this happening? To what degree? What can we feel and sense? What’s the emotional impact? What are the root causes? How can we avoid it? What can we learn?
Results	Generalizable findings that can be applied broadly	In-depth perspective with definite findings that can be applied to a particular group
Interpretation	Compare results with previous predictions and research to make decisions	Consider the context, narrative, and descriptions to make decisions
Pros	– Broadly examine a situation and make predictions – Brings awareness of shifts and trends – Avoid researcher bias – Results are logical and clear	– Provides a deeper understanding of the data – Exposes difficult realities – Reveals complexities and a change in perception – Fewer participants are required
Cons	– Uses rigid data-collection – Not adaptable to changing conditions – Requires a large sample size – Loses the “flavor” of narrative data	– Very time consuming – Takes a lot of effort and emotional energy – Can be swayed by bias – Difficult to interpret the results
When to Use	– When there is a defined question – When a problem can be measured precisely – With a large sample size – When hard data is needed – When an overall summary is needed	– When a problem cannot be measured precisely – When there is an open-ended question – With the sample size is small – When intuition is needed – When first-hand examples are useful

 

Comparison Chart Detail

Here is a duplicate of the above chart, with all of the information grouped together.

What is Quantitative Data?

Definition

Numerical calculations and measurements

Purpose

1) Identify cause-and-effect relationships

2) Compare differences

3) See trends and develop predictions for the future

Examples

• money
• time
• speed
• frequency
• movement
• height
• length
• area
• volume
• weight
• temperature
• humidity
• pressure
• sound level
• quality (yes, you can measure the quality of processes and outcomes using numbers!)
• degree
• intensity
• event
• item
• categories (age, gender, occupation)
• positioning
• status

View of Reality

Objective; single reality

Reasoning

Deductive (make a specific conclusion)

Method

Fact-based

Objectives

Describe, explain, and predict

Focus

Narrow perspective; testing a specific hypothesis

Observation Environment

In controlled conditions to isolate the root causes

Methodology

Systematic

Structure

Highly structured; low flexibility

View of Behavior

Orderly, predictable, and collective

Type of thinking

Logical thinking

Measurement

Defined measurements

Collection formats

Precise measurements of money, time, height, length, volume, etc.

Collection tools

Direct

• Structured survey
• Phone call
• Meeting
• Face-to-face discussion
• Online form

Indirect

• Spreadsheet
• Statistical evaluations
• Analytics and reports
• Comparison of past, current, and projected data
• Direct mail or package

Data type

Numbers and statistics

Data analysis purpose

Identify statistical relationships

Data analysis types

Descriptive Statistical tools

• Mean
• Median
• Mode
• frequency distribution
• standard deviation
• variance analysis

Inferential Analysis tools

• Surveys
• Experiments
• hypothesis testing
• T-Test
• ANOVA
• chi-square
• z-score

Key Questions

• What happened?
• How did it happen?
• When is it happening?
• How often is it happening?
• How many times does it occur in a week or month?
• Who is it happening to?
• Where is it happening?
• What does the data tell us?

Results

Generalizable findings that can be applied broadly

Interpretation

Compare results with previous predictions and research to make decisions

Pros

• Broadly examine a situation and make predictions
• Brings awareness of shifts and trends
• Avoid researcher bias
• Results are logical and clear

Cons

• Uses rigid data-collection
• Not adaptable to changing conditions
• Requires a large sample size
• Loses the “flavor” of narrative data

When to Use

• When there is a defined question
• When a problem can be measured precisely
• With a large sample size
• When hard data is needed
• When an overall summary is needed

 

Here is the graphic once again that explains the differences:

 

Next, we will take a look at the other type of data, qualitative — which has to do with experiences and emotions.

 

What is Qualitative Data?

Definition

Sensations, feelings, and experiences

Purpose

1) Understand the larger context

2) Interpret social interactions

3) Find larger themes

Examples

• colors
• textures
• smells
• tastes
• appearance
• beauty
• desirability
• feelings
• intuition
• sensations
• choices
• significance
• inspiration
• encouragement
• loyalty
• capability
• strength
• fitness
• well-being
• values
• beliefs

View of Reality

Both subjective and objective; multiple realities

Reasoning

Inductive (find patterns)

Method

Perception-based

Objectives

Explore, discover, and create

Focus

Wide perspective; examine breadth and depth of a situation

Observation Environment

In natural environment to observe behavior and relationships

Methodology

Open-ended

Structure

Less structure; high flexibility

View of Behavior

Dynamic, situational, and individual

Type of thinking

Intuitive thinking

Measurement

Unstructured variables

Collection formats

Open-ended with intuitive examination and monitoring

Collection tools

In-person

• interview
• narrative story
• group discussion
• unstructured survey
• focus group
• observation
• description

Digital

• online or e-mail survey
• web forum
• chat
• online community

Data type

Words, objects, images

Data analysis purpose

Identify patterns, features, and themes

Data analysis types

Flexible collection such as

• Content Analysis
• Narrative Analysis
• Autobiography
• Case Study
• Ethnography
• Framework Analysis
• Discourse Analysis
• Grounded Theory

Key Questions

• Why is this happening?
• To what degree?
• What can we feel and sense?
• What’s the emotional impact?
• What are the root causes?
• How can we avoid it?
• What can we learn?

Results

In-depth perspective with definite findings that can be applied to a particular group

Interpretation

Consider the context, narrative, and descriptions to make decisions

Pros

• Provides a deeper understanding of the data
• Exposes difficult realities
• Reveals complexities and a change in perception
• Fewer participants are required

Cons

Very time consuming

Takes a lot of effort and emotional energy

Can be swayed by bias

Difficult to interpret the results

When to Use

• When a problem cannot be measured precisely
• When there is an open-ended question
• With the sample size is small
• When intuition is needed
• When first-hand examples are useful

 

Also check out these articles on how you can apply data in business decision-making:

• Interpreting the Quantitative Data (Numbers) in Your Business
• Interpreting the Qualitative Data (Experiences and Emotions) in Your Business

 

Interested in hearing how you can reverse a toxic workplace? Find out more here.

 

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Grace LaConte is a business consultant, writer, workplace equity strategist, and the founder of LaConte Consulting. Her risk management tools are used around the globe, and she has successfully reversed toxic work environments for clients in the healthcare and non-profit fields. Grace specializes in lactation law compliance & policy development, reducing staff turnover after maternity leave, and creating a participatory work culture.

Find more at laconteconsulting.com, or connect with her on Instagram and Twitter @lacontestrategy.