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Related Experiment Video

Updated: Jun 7, 2026

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Published on: October 5, 2020

The Bootstrap Discovery Behaviour (BDB): a new outlook on usability evaluation.

Simone Borsci¹, Alessandro Londei, Stefano Federici

¹ECoNA-Interuniversity Centre for Research on Cognitive Processing in Natural and Artificial Systems, Sapienza University of Rome, Rome, Italy. simone.borsci@gmail.com

Cognitive Processing

|November 4, 2010

Summary

This summary is machine-generated.

A new Bootstrap Discovery Behaviour (BDB) model for usability evaluations suggests more experts and users are needed than the ROI model. This enhances the representativeness of findings, despite increased costs.

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Area of Science:

Human-Computer Interaction
Usability Engineering
Statistical Modeling

Background:

The Return on Investment (ROI) model by Nielsen and Landauer (1993) is debated for its limitations in estimating the optimal number of evaluators for interface usability.
Existing models often fail to account for a comprehensive set of variables crucial for accurate usability problem identification.

Purpose of the Study:

To propose and validate an alternative usability evaluation model, the Bootstrap Discovery Behaviour (BDB) model, which incorporates a wider range of variables.
To compare the BDB model's estimation of necessary evaluators against the traditional ROI model.

Main Methods:

The study adapted the bootstrap statistical model (Efron, 1979) to account for interface properties and the probability of sample discovery behavior representing the population.
Two experimental groups (users and experts) evaluated a website (http://www.serviziocivile.it).
The BDB model was applied to the usability problems identified by these groups.

Main Results:

The BDB model estimated that 13 experts and 20 users are required to identify 80% of usability problems.
This contrasts with the ROI model's estimation of 6 experts and 7 users for the same problem identification rate.
The BDB model necessitates a higher number of participants, leading to increased usability evaluation costs.

Conclusions:

The BDB model offers a more robust estimation of the number of experts and users needed for comprehensive usability evaluations.
While increasing costs, the BDB model provides a higher probability of representing the entire population of experts and users.
The findings advocate for the adoption of the BDB model for more reliable usability assessments.