A Wall Street Journal article featured FurstPerson and focused on the value that pre-hire assessments brought to the talent acquisition process. In the article, the reporter discusses the goal of a pre-hire assessment: to obtain accurate information about a candidate that will help a company make an accurate hiring decision. And, in a follow up blog post from the reporter, the concept of "gaming" the assessments - trying to manipulate answers to increase the chance of passing the hiring assessment - was discussed. The post noted that despite the efforts of some applicants, personality assessments are becoming harder to outsmart.
Widespread availability of resources designed to help applicants “game” a screening assessment – even if only marginally helpful – may undermine the accuracy of information gathered about a candidate, thus negatively impacting the accuracy of hiring decisions. The practical implication of “gaming” an assessment is that some people may get a job they are unlikely to perform well.
To combat the problem, progressive companies are investing in advanced assessment technologies, such as computer adaptive testing (CAT), that virtually eliminate candidates’ ability to “game” the assessment.
A CAT approach to hiring assessments differs from a more traditional machine learning model that some assessments try to apply. Rather than replace theory with raw empiricism, the CAT approach represents an algorithm-based delivery that are built on theoretical foundations. In other words, rather than base the assessment process on beliefs and observations, CAT-styled assessments evaluate the answers to an assessment through a solution designed for data and built around those theories. It adds the value of qualitative data to an otherwise theoretical and fluid concept.
Using a CAT model within the assessment process addresses two issues: cheating and assessment length. Many are concerned that applicants will attempt to cheat a personality assessment by changing their response style to fit the “ideal candidate’s” answers, rather than honest ones. With a CAT model assessment, however, the risk of candidates trying to cheat is virtually eliminated because candidates are required to choose item pairs that score on the Big Five model of personality – a standard for most personality assessments. Candidates will make choices to questions that seem unrelated, but actually end up representing the individual because the CAT’s algorithm can identify the individuals’ choices and score those choices without the concern of a candidate trying to manipulate or cheat answers. Additionally, traditional assessments can be long winded, with some going over 300 questions. Using a CAT-based system reduces the amount of questions because the algorithm use an applicant’s response on one item pair to select the next best item pair. So, rather than having to use 300+ questions to learn about a candidate, a CAT-based system can achieve the same amount of insights on each of the Big Five scales in only 7-10 questions each. And, due to the design behind the CAT’s algorithms, there is no loss in quality of data, and is actually more accurate despite being so much shorter.
The use of CAT-based assessments has a demonstrated ability to eliminate the concern of candidates trying to cheat the assessment process, while simultaneously making the process shorter and more accurate. To learn more about this innovative process, request a discussion with FurstPerson today.