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Computer Adaptive Testing (CAT)

CAT is the future of assessment due to the recognised benefits of speed, accuracy, and candidate experience.

What's all the fuss about CAT?

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Shorter tests

Compared to fixed form tests, CATs provide a comparable level of accuracy in roughly half the number of questions. This means that CATs can be completed in half the time, without sacrificing precision.

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Better candidate experience

Candidates are only shown questions which are suited to their level of ability, so they don’t get put off by overly difficult questions or bored by overly easy questions. This means a greatly improved candidate experience.

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Increased test security

CATs employ question banks that contain hundreds of unique questions, making it very unlikely that any two candidates will see the same questions. This means CATs are harder to cheat, ensuring the integrity of the results.

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Frequent Updating

Because CATs employ question banks, new questions are added on an ongoing basis, keeping the content fresh, relevant, and engaging. This means that CATs are constantly and consistently improved over time.

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Customisable content

CAT allows items to be added to the item bank (or dropped out) very easily without negatively affecting the performance of the test. This means test experiences can be customised to suit different presentation requirements.

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Accurate results

CAT algorithms administer a unique and optimal set of questions to every candidate based on their estimated ability. This increases the accuracy and precision of the assessments, quickly achieving a high level of accuracy.

What are CAT tests?

The principle behind CAT is simple: Ask candidates the right questions, not the same questions. As candidate’s progress through the test, CAT algorithms estimate their ability in real time. They then use this information to adjust the difficulty to the test, ensuring that optimally difficult questions are administered.

This provides each candidate with a unique testing experience, designed to reach the maximum level of precision in the minimum amount of time. By adopting this approach, CATs show a greater level of accuracy, become much harder to cheat on, and provide a better candidate experience.

Compare this to traditional fixed form tests

A one-size-fits-all approach simply doesn’t work with psychometrics. Give easy questions to high performers, they get them all right. Give hard questions to low performers, they get them all wrong. In both cases, it was a waste of time giving them a test at all.

More worryingly, if every candidate sees the same questions, what happens if an unscrupulous candidate leaks those questions? For fixed form tests, this means the test is now ruined, as anyone can simply download the questions and figure out the answers. CATs however, are protected from this kind of cheating, making them far more suitable for online testing.

Principles of adaptive testing

  • If a candidate answers a question incorrectly, the next question automatically selected from the item bank will be slightly easier and receive a lower weighting.
  • By homing in on the candidate level of ability, CATs administer the most informative questions available, reaching a higher level of accuracy in less time.
  • By reducing administration time, you prevent candidates getting frustrated, fatigued or just plain bored with the testing process, improving candidate experience.
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  • If a candidate answers a question correctly, the next question automatically selected from the item bank will be slightly harder and receive a larger weighting.
  • Candidates are never bored by overly easy questions, or stressed out by excessively difficult questions, instead everyone receives an optimal testing experience.
  • Proving an optimal testing experience ensures that candidates perceive the assessment as fair and creditable, independent of how well they perform.

CAT methodology

Adaptive tests are the future of personalised, targeted, and fair assessments.

Each time a candidate answers a question (item), our Item Response Theory algorithm selects the best item to show the candidate next. A more difficult item is shown in response to a correct answer, and a less difficult item is shown in response to an incorrect answer.

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