This project analyzes the frequencies of 16 personality types to uncover insights using Power BI. It offers a simplified version of the Myers-Briggs framework with interactive visualizations, allowing users to discover their personality type and assess compatibility between two types.
Goal
This project aimed to uncover insights on the frequency of personality types and provide a simplified tool for users to assess their own personality types. Additionally, it includes a feature to assess compatibility between two personality types, offering an engaging and interactive experience.
Data
The data used in this project was sourced from the 16Personalities Website and 16Personalities Institute. It primarily focuses on the frequency of personality types among test takers, providing a foundation for understanding the distribution of types within the larger population.
Approaches Used
PowerBI:
Data Visualization
Interactive Dashboards
Buttons and Bookmarks
Quick Navigation
(Click on a section to jump directly to it!)
Personality Type Frequencies: Dashboard
Personality Type Frequencies: Key Insights
Personality Trait Frequencies: Dashboard
Personality Trait Frequencies: Key Insights
Personality Type Test
Creating a Compatibility Measure: Step by Step Instructions
Personality Type Comparisons and Compatibility: Dashboard
Final Insights
Personality Type Frequencies: Key Insights
The dashboard reveals interesting insights into the distribution of personality types. The most common roles are Sentinels and Explorers, followed by Diplomats and Analysts. When examining the gender distribution, Analysts and Explorers are more male-dominated, with Analysts showing the strongest male presence. In contrast, Sentinels and Diplomats tend to be more female-dominated. These findings highlight distinct gender trends across the different roles within the Myers-Briggs framework.
Personality Trait Frequencies: Intro
The “Personality Trait Frequencies” dashboard allows users to explore the distribution of different personality traits across four aspects: Energy, Mind, Nature, and Tactics. By selecting one of the buttons at the top, users can focus on specific aspects to reveal insights related to trait frequencies, role distribution, and gender dominance. The Identity aspect has been excluded, as it only affects the subtype and not the personality type.
Personality Trait Frequencies: Key Insights
- Trait Frequency Comparison:
Energy: Females tend to lean slightly more toward Extraverted, while males are slightly more Introverted, though the averages are fairly close overall.
Mind: There is a significant difference, with Observant traits being favored by both males and females, significantly more than Intuitive.
Nature: Feeling is more prevalent overall, with a stark gender divide—Females heavily favor Feeling, and Males are predominantly Thinking.
Tactics: Both genders show a preference for Judging over Prospecting, but the gap is not as large.
- Role Distribution:
Energy: The role distribution is fairly even, though Diplomats tend to be more Extraverted, and Sentinels are more Introverted.
Mind: Explorers and Sentinels are all Observant, while Analysts and Diplomats are entirely Intuitive.
Nature: Diplomats are all Feeling, while Analysts are all Thinking. Explorers and Sentinels show a mix of both traits.
Tactics: Sentinels are entirely Judging, while Explorers are all Prospecting, with Analysts and Diplomats split between the two.
- Gender Dominance Across Personality Types:
Energy, Mind, and Tactics: The gender distribution is close to a 50/50 split for all these traits.
Nature: The gender divide is more prominent here, with Feeling types being female-dominated and Thinking types being male-dominated across all 8 personality types.
Discover Your Personality Type
The interactive dashboard below is a simplified take on the 16 Personalities test, built using five key aspects of personality: Energy, Mind, Nature, Tactics, and Identity. Simply select the traits that resonate most with you, and the results will reveal your personality type, subtype, role, description, and frequency. This tool is a fun, condensed version of the original Myers-Briggs framework and is not meant to replace a full personality assessment.
Creating a measure to assess compatibility between personality types
In this section, I created a compatibility calculator with a measure I created using DAX.
Step 1: Creating A Column to Combine Personality Type and Subtype
To ensure users select the full personality type (including its subtype) in the slicer, we need to create a new column that merges the Personality Type and Subtype into a single value. This will make filtering and comparisons more accurate.
Full_Personality_Type = personalitytypedata[Identity] & “ “ & personalitytypedata[Personality_Type] & “ “ & “(” & personalitytypedata[SubType] & “)”
Now that we have a combined column, we can use it in our slicers to allow users to select a specific personality type with its subtype.
Step 2: Creating Two Independent Tables for Slicers
To allow users to select and compare two different personality types, we need to create two independent tables that will serve as the source for our slicers. These tables will not be linked to the main dataset, preventing them from filtering each other out.
PersonalityType1 =
SELECTCOLUMNS(
personalitytypedata,
“Personality Type”, [Personality_Type],
“Energy”, [Energy],
“Mind”, [Mind],
“Nature”, [Nature],
“Tactics”, [Tactics],
“Identity”, [Identity],
“SubType”, [SubType],
“Role”, [Role],
“Full Personality Type”, [Full_Personality_Type]
)
Repeat this step to create an identical PersonalityType2 Table.
Step 3: Creating Measures to Retrieve Selected Personality Types
Now that we have two independent slicers for personality selection, we need to extract the values chosen by the user. This will allow us to compare traits and calculate compatibility.
Creating a DAX measure to retrieve the selected personality types from each slicer:
SelectedType1 = SELECTEDVALUE(PersonalityType1[Full_Personality_Type]) // for the first slicer
SelectedType2 = SELECTEDVALUE(PersonalityType1[Full_Personality_Type]) // for the second slicer
Step 4: Creating a Measure to Count Matching Personality Aspects
Now that we can retrieve the selected personality types, we need to compare their five key aspects—Energy, Mind, Nature, Tactics, and Identity—to determine how similar they are.
The following DAX formula counts how many aspects match between the two selected types:
MatchingAspects =
VAR Type1 = LOOKUPVALUE(personalitytypedata[Energy], personalitytypedata[Full_Personality_Type], [SelectedType1])
VAR Type2 = LOOKUPVALUE(personalitytypedata[Energy], personalitytypedata[Full_Personality_Type], [SelectedType2])
VAR Mind1 = LOOKUPVALUE(personalitytypedata[Mind], personalitytypedata[Full_Personality_Type], [SelectedType1])
VAR Mind2 = LOOKUPVALUE(personalitytypedata[Mind], personalitytypedata[Full_Personality_Type], [SelectedType2])
VAR Nature1 = LOOKUPVALUE(personalitytypedata[Nature], personalitytypedata[Full_Personality_Type], [SelectedType1])
VAR Nature2 = LOOKUPVALUE(personalitytypedata[Nature], personalitytypedata[Full_Personality_Type], [SelectedType2])
VAR Tactics1 = LOOKUPVALUE(personalitytypedata[Tactics], personalitytypedata[Full_Personality_Type], [SelectedType1])
VAR Tactics2 = LOOKUPVALUE(personalitytypedata[Tactics], personalitytypedata[Full_Personality_Type], [SelectedType2])
VAR Identity1 = LOOKUPVALUE(personalitytypedata[Identity], personalitytypedata[Full_Personality_Type], [SelectedType1])
VAR Identity2 = LOOKUPVALUE(personalitytypedata[Identity], personalitytypedata[Full_Personality_Type], [SelectedType2])
VAR Matches =
IF(Type1 = Type2, 1, 0) +
IF(Mind1 = Mind2, 1, 0) +
IF(Nature1 = Nature2, 1, 0) +
IF(Tactics1 = Tactics2, 1, 0) +
IF(Identity1 = Identity2, 1, 0)
RETURN Matches
Step 5: Creating a Compatibility Label Measure
With the number of matching personality aspects calculated, the next step is to translate the match count into a compatibility label. This makes the results more user-friendly and visually appealing.
The following DAX measure assigns a descriptive label based on the number of matching aspects:
CompatibilityLabel =
VAR Matches = [MatchingAspects]
RETURN
SWITCH(
TRUE(),
Matches = 5, “Perfect Match! 🔥”,
Matches = 4, “Great Match! 😊”,
Matches = 3, “Good Match! 🙂”,
Matches = 2, “Decent Match 🤔”,
Matches = 1, “Not Ideal 😕”,
Matches = 0, “No Match at All ❌”
)
Step 6: Organizing the Dashboard on PowerBI
Now that we’ve built all the necessary measures and slicers, the final step is to design an intuitive and user-friendly dashboard that allows users to compare personality types effectively.
What to include in the dahsboard:
Two slicers (Personality Type 1 and 2)
A card visual showing the compatibility label
Additional tables/charts/cards for more detailed comparison between the two selected personality types.
Final Insights:
Most Common Roles: Sentinels and Explorers are the most frequent, followed by Diplomats and Analysts.
Gender Distribution:
Analysts and Explorers are predominantly male, with Analysts showing the strongest male presence.
Sentinels and Diplomats are more female-dominated.
Trait Preferences:
Females show a strong preference for Feeling, while Males lean heavily towards Thinking.
Observant traits are more common overall compared to Intuitive traits.
Role and Trait Associations:
All Diplomats are Feeling, and all Analysts are Thinking.
All Sentinels are Judging, and all Explorers are Prospecting.
These insights reveal distinct trends in both personality role distribution and gender associations, offering a deeper understanding of how the Myers-Briggs framework plays out in real-world data.