You’ve probably questioned how valuable candidate self assessments really are. After all, expecting a candidate to honestly assess their proverbial obsession with detail, the extent of their resilience in the face of criticism, or their ability to work well with others is a big ask: First, how accurately do people really see themselves? And, more importantly, how likely are they to be honest about their weaknesses in the context of job hunting?
And yet, self assessment remains a significant tool in candidate screening. The reason? While companies can easily test hard skills and check on expertise and experience, it is far more difficult to get an accurate understanding of people's soft skills - everything from attention to detail to growth mindset to positivity. What’s more, because candidates realize what companies are looking for - they’ve heard expert advice, read about your company on Glassdoor - they are likely to answer to expectations and try and manage impressions, whether manipulatively (ie, knowing that they are not being accurate) or based on a real desire to please and meet expectations.
And yet, research shows* that soft skills are a far better predictor of performance and success at a company than hard skills. Which is why relying on such a subjective, and ultimately flawed, methodology, is so problematic.
In a nutshell, it’s about asking questions in a way that gets past a candidate’s self defenses and then analyzing and cross referencing answers to get an accurate picture of the candidate, going beyond what they want you to see.
A few ideas:
All of this is critically important to assess a candidate, but nearly impossible to achieve when working on your own: people are complex, and analyzing hundreds of data points on a stream of candidates is not a human-sized task. But the holy grail of pre-hire assessment - truly understanding candidates’ soft skills - is now possible using smart technology, powered by behavioral science. At Empirical, we design questionnaires based on deep behavioral science knowledge, analyze actual performance on a test (including factors like time lags in answering questions) vs. self reporting, and then use ethical AI to make sense of hundreds of data points, and to do so across all candidates for all positions.
Taking the proverbial “attention to detail,” as an example: Traditional scales such as Likert, ask candidates to rate themselves. While there might be some value in self perception, it is also open to distortion due to impression management, especially for more sophisticated candidates. However, if you ask questions that actually CHECK attention to detail (for example, by changing the scale in some questions from 1 being most positive to 5 being most positive), you can assess the extent to which candidates actually pay attention to detail. In addition, you can use the information on any gaps between self reporting and actual performance to identify self perception gaps across the board.
We are in a world of increasingly sophisticated candidates. This makes relying on self reporting problematic, creating a picture the candidate wants to project, or thinks you want to see. By thinking outside the (question) box, and using smart technology, you can go beyond people’s impression management and gain a real sense of who they are and how well they are likely to fit in and perform at your company.
Forget about the ideal candidate. Empirical helps you identify candidates that will excel at YOUR company. Recruiters and Hiring Managers across industries in 60 countries leverage the Empirical platform - ethical AI powered by deep behavioral expertise - to significantly impact their companies’ bottom line. With Empirical you reduce time-to-hire, eliminate bias, and ensure new hires stay and flourish. (By the way, we think you should be getting the credit for your contribution to the bottom line!)
*Schmidt, F. L., Oh, I. S., & Shaffer, J. A. (2016).
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