Short answer

Incorporate standardized readability assessments into your design evaluation process to ensure visualizations are easily understood by their intended audience.

Field
User-Centred Design
Source
IEEE Transactions on Visualization and Computer Graphics (2024)
Method
Instrument development and validation
Evidence
Strong effect

A new instrument, PREVis, offers a standardized way to measure how easily users perceive they can understand data visualizations. This user-centred design research insight is drawn from a 2024 study published in IEEE Transactions on Visualization and Computer Graphics. Using Instrument development and validation, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Incorporate standardized readability assessments into your design evaluation process to ensure visualizations are easily understood by their intended audience.

Study
User-Centred DesignRecentStrong effect

PREVis: A Validated Instrument for Measuring Perceived Readability in Data Visualizations

A new instrument, PREVis, offers a standardized way to measure how easily users perceive they can understand data visualizations.

IEEE Transactions on Visualization and Computer Graphics · 2024

01

Key Findings

  • 01The PREVis instrument is a validated tool for measuring perceived readability in data visualizations.
  • 02Perceived readability can be broken down into four key dimensions: understandability, layout clarity, readability of data values, and readability of data patterns.
02

Application

Design takeaway

Incorporate standardized readability assessments into your design evaluation process to ensure visualizations are easily understood by their intended audience.

How to apply

Use the PREVis questionnaire in user testing sessions to gather feedback on the clarity and understandability of your data visualizations.

Project actions

  • 01When evaluating your design, consider using a survey to ask users about their perception of its readability.
  • 02Think about the different aspects that make a visualization easy to understand, like clear labels and uncluttered layouts.
03

Method & Evidence

AimTo develop and validate an instrument for measuring the perceived readability of data visualizations.
MethodInstrument development and validation
ProcedureThe researchers developed the PREVis instrument through a rigorous process, including defining the construct of perceived readability, item generation, and validation. The final instrument consists of 11 items across four dimensions: understandability, layout clarity, readability of data values, and readability of data patterns.
ContextData visualization design and evaluation

Variables

IVType of data visualization, design elements (e.g., color, layout)
DVPerceived readability score (as measured by PREVis)
CVUser demographics, task complexity, visualization complexity
04

Strengths & Limitations

Strengths

  • +Rigorous development and validation process for the instrument.
  • +Addresses a gap in existing methods for assessing visualization readability.

Limitations

The availability and applicability of the PREVis instrument might be limited to specific types of visualizations or user groups.

Reliability & validity

The study reports on the validation of the PREVis instrument, suggesting it has good reliability and validity for measuring perceived readability.

Think critically

How might perceived readability differ from actual task performance, and what are the implications of this difference for design evaluation?

05

Design Principles

"Perceived readability is a critical user-centred metric for effective data visualization."

Effective data visualization is crucial for clear communication of information. By providing a validated tool to assess perceived readability, designers and researchers can better understand user comprehension and iterate on designs to improve clarity and impact.

06

What This Means for Your Design

This study created a new survey to ask people how easy they think it is to read and understand different charts and graphs.

How to use in your project

  • 1.You can use the PREVis instrument (or a modified version) to gather quantitative data on user perception of your design's readability.
  • 2.This data can support your design decisions and demonstrate how your design addresses user needs for clarity.
07

Add to My Project

08

Quick Cite

Paragraph starter

The PREVis instrument provides a validated method for assessing perceived readability in data visualizations, focusing on understandability, layout clarity, and data value/pattern readability. This approach can be valuable in evaluating the clarity of my design by providing quantitative user feedback on how easily they perceive the information can be interpreted.

09

Source

IEEE Transactions on Visualization and Computer Graphics

PREVis: Perceived Readability Evaluation for Visualizations

journal · 2024

View source

Questions About This Research

What does the research say about previs: a validated instrument for measuring perceived readability in data visualizations?
Incorporate standardized readability assessments into your design evaluation process to ensure visualizations are easily understood by their intended audience. Evidence: IEEE Transactions on Visualization and Computer Graphics (2024).
Why does "PREVis: A Validated Instrument for Measuring Perceived Readability in Data Visualizations" matter for design?
Effective data visualization is crucial for clear communication of information. By providing a validated tool to assess perceived readability, designers and researchers can better understand user comprehension and iterate on designs to improve clarity and impact.
How can designers apply this research?
Incorporate standardized readability assessments into your design evaluation process to ensure visualizations are easily understood by their intended audience.
What were the main findings?
The PREVis instrument is a validated tool for measuring perceived readability in data visualizations.. Perceived readability can be broken down into four key dimensions: understandability, layout clarity, readability of data values, and readability of data patterns.
What research method was used?
Instrument development and validation.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2024 journal from IEEE Transactions on Visualization and Computer Graphics.
What should I do differently in my next project?
Use the PREVis questionnaire in user testing sessions to gather feedback on the clarity and understandability of your data visualizations.
What are the limitations?
The instrument measures *perceived* readability, which may not always align perfectly with actual task performance. The specific context and user expertise can influence perceived readability.