The evaluation of applicants to job requirements is one of the most basic roles of the modern HR team, yet one that continues to represent an enormous challenge. When one thinks about the outcomes of a quality selection process – high individual performance, solid team fit, long tenure, high engagement, and commitment to the organization’s values, purpose, and mission – the pressure builds to get it right. With companies hiring hundreds (or thousands) of new employees every year, and the need to hire people who will “stick” with the company the ability to be consistently “right” in the selection process can be a differentiator in the achievement of corporate goals. Such a relative make-or-break proposition for candidate evaluation methods calls out the need to create capabilities, processes, and systems that are consistent generators of quality hires across an organization.
While there are dozens of techniques to assess job candidates, best practice consistently calls for a multi-faceted process that leverages a variety of tools and techniques to evaluate a given candidate's suitability for a particular role. The trick is to understand what types of candidate evaluation methods exist, what they are designed to measure, how well they measure those, and which ones can be combined to provide as objectively comprehensive an understanding of the candidate's strengths and weaknesses relative to the job requirements.
The starting point is to define the job requirements and detail those in a job description, job posting, or success profile. To properly screen (and ultimately select) candidates for a job, it is important to evaluate a combination of factors to ensure that the chosen candidates are the best fit for the role and the organization. Here are some key factors that candidate evaluation methods can leverage to choose for consideration:
Assessing job candidates involves evaluating their skills, qualifications, and suitability for a specific role within an organization. There are various candidate evaluation methods and techniques available, and most often a combination of these methods is used to make well-informed hiring decisions. Here are some of the common ways to assess job candidates:
The first step is often reviewing the candidates' resumes to assess their qualifications, education, work experience, and relevant skills against the requirements stated in the job description. This can be conducted manually with a visual scan or automatically with an automated applicant tracking system (ATS). Most typically, key concepts such as required skills, experiences, education and training, and industry match are considered.
Most organizations use detailed application forms that all candidates must fill out, which may include specific questions related to their qualifications, years and type of experience, and skills. Key to reviewing these are consideration of tenure, types of roles held, job progressions/promotions within a given employer, reasons for leaving, etc. These are also considered legal documents, as they are signed and dated with a personal certification of their accuracy.
Increasingly popular as efficient screening tools, candidates are asked to record responses to pre-recorded questions, giving employers flexibility in evaluating candidates' responses at their convenience. They review candidates’ verbal and non-verbal cues, vocabulary, voice tone, and facial/eye/hand movements to assess (often using artificial intelligence) personality, communication skills, problem-solving skills, professionalism, etc.
Candidates are observed while working in groups on tasks or activities designed to simulate real work scenarios. This method assesses teamwork, leadership, communication, and problem-solving skills. These are particularly valuable when assessing candidates whose work will involve heavy project and team-based participation.
Contacting the candidate's professional references, such as previous employers or colleagues, who can provide insights into the candidate's work ethic, skills, and behavior in a professional setting. While companies have become increasingly reluctant to provide such feedback on former employees, automated versions sent to former colleagues and managers have demonstrated greater success.
Verifying candidates' education, work history, criminal record, and other relevant background information to ensure accuracy and authenticity. These are most often outsourced to third-party providers.
Organizations often customize their assessment methods based on the nature of the job, the industry, and their specific hiring goals. A combination of these methods can provide a comprehensive view of a candidate's suitability for a position.
The use of Artificial Intelligence (AI) for screening and selecting candidates for jobs is becoming increasingly available and common, particularly with automated pre-screening technologies. While AI use is receiving a lot of attention in both the business and general press, estimates vary as to its current level of adoption in corporate settings. Current estimates range from 24% of businesses using AI to evaluate talent to a range-topping out as high as 50% of organizations leveraging this technology.
While they can offer several advantages and disadvantages as candidate evaluation methods, AI applications are still considered to be emerging capabilities that must be used with great care. A review of the potential promise and perils is important to understand:
Incorporating Artificial Intelligence into the hiring process can yield significant benefits, but it's crucial to strike a balance between automation and human judgment. Combining AI's efficiency with human insight and empathy can lead to a more effective and inclusive candidate selection process. Regular monitoring, bias mitigation, and adapting AI systems based on feedback are essential for a successful implementation.
The use of any tool or technique in the screening and selection of job candidates will be influenced by several factors, including governmental laws and regulations, company values, acceptance by hiring managers, and comfort by candidates. The primary issues that are raised with any kind of pre-hire candidate evaluation method tend to fall into three categories: 1) its validity, 2) the length of time it takes to administer them, and 3) the use of AI and other technologies.
In general, the most important of those three is the validity of the tool or method, and the extent to which it actually measures (or predicts) what it is intended to. In other words, if an assessment is designed to measure the extent of cultural fit or predict subsequent high performance, it must do just that – and be statistically proven. This means that before implementing a tool or method, it should be subjected to formal testing on the targeted population. This usually (and ideally) involves having a psychologist set up and run a test of the tool on high-performing employees compared to lower-performing peers. The results of such a test are then subjected to statistical analysis with the outcome providing insights into the extent to which the test successfully separates the high from the average/below average employees. Another option is to use a formal assessment that has been validated on a large set of employees from a range of companies, as many psychometric assessments are. While this option is somewhat less impactful, its use is nonetheless legally defensible and operationally credible.
Secondly, the length of time that an assessment or test takes is a consideration, particularly when presented online. Completion rates are essential process measures for any pre-employment test, and as such should be taken into consideration when deploying a test such as a psychometric assessment that in some cases can take 30-60 minutes to complete. Longer online tests often have higher abandonment rates, leading to an often unnecessary loss of potentially high-quality job candidates.
A test that takes a long time to complete can negatively impact the candidate experience, with candidates complaining that their time was ill-spent. In the same way, when numerous assessment techniques are leveraged, the total elapsed length of time that the candidate spends in the screening and selection process can create negative reactions for a process that might take weeks to complete. The number, process, and types of evaluation techniques should thus be chosen carefully. The time to fill (TTF) metric often drives candidate (and hiring manager) satisfaction, and to that end, the total average length of the process should be monitored for reasonableness.
Thirdly, special considerations need to be applied to the use of Artificial Intelligence applications to avoid negative perceptions or outcomes. Core to this (and as with the use of any tool, test, or technology) is that AI should be thought of and used only as a tool to support and supplement human decision-making. With that in mind, best practice calls for transparency, with the inclusion of appropriate disclaimers in the assessment instructions to build trust and ensure that candidates feel like they are being treated fairly.
Educate recruiters and hiring managers about the potential drawbacks of AI in hiring, and the need to exercise caution and judgment when reviewing the results of any such assessment. And finally, plan on continuously monitoring AI systems for bias and take steps to mitigate it. Use outcome measures such as hiring/rejection rates by job role and work with the vendor to have the algorithm(s) reevaluated and updated on a regular (e.g., quarterly, semi-annually) basis.
In any case, the use of all pre-employment assessment techniques should be reviewed by an organization’s legal counsel for compliance with federal, state, and local laws and regulations (including those in any non-U.S. locale).
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