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.

What factors are best to evaluate candidates for a job?

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:

• Skills and qualifications/job fit: The candidates' skills, qualifications, and relevant experience that directly pertain to the job requirements. This includes educational background, certifications, technical skills, and industry-specific expertise. Consider whether technical skills, past accomplishments, and roles have been applied in a way that demonstrates the ability to excel in the current position.
  
  
• Cultural fit: The extent to which the candidate's values, work style, and interpersonal skills align with the company's culture and values. A good cultural fit contributes to better teamwork and job satisfaction. Include candidates’ ethics and integrity, qualities that are vital for maintaining a positive work environment and upholding the company's reputation.
  
  
• Motivation and interest: An individual’s interest in the position and the company. Dig into both the extent of and the reasons for a candidate’s stated passion for the role, which can contribute to long-term success and commitment. Get a sense of their true enthusiasm for the company’s mission, business, and longer-term growth and career prospects to better project them as employees into the future.
  
  
• Problem-solving and critical thinking: The candidates' ability to analyze situations, think critically, and come up with effective solutions. This is particularly important for roles that require decision-making and innovation. Consider their past experiences, projects, and achieved outcomes as those relate to the challenges they will encounter in the new role and organization.
  
  
• Personality or style: The range of inherent traits that can serve as indicators of job and/or culture fit.  Consider the company culture and the attributes of high-performing, tenured employees who have progressed and grown with the organization over time. A great way to measure personality in this context is to leverage the widely used and accepted “Big Five” psychological traits including agreeableness, conscientiousness, extraversion, openness, and stress tolerance.
  
  
• Communication skills: Verbal and written communication skills, which are crucial for effective collaboration, client interaction, and conveying ideas within the organization. Consider the non-verbal skills of a candidate, to understand how well (and how appropriately) they communicate listening, understanding, agreement or disagreement, excitement or boredom.
  
  
• Adaptability: How well candidates can adjust to new situations, technologies, and challenges based on their past experiences and claimed motivations or strengths. A candidate's willingness to learn and grow is essential, especially in rapidly changing industries.
  
  
• Leadership potential: The presence of leadership qualities, including abilities to motivate, mentor, and guide others, particularly for roles that will immediately involve leadership or management responsibilities. Consider past experiences in formal (role-based) or informal (project, extracurricular) activities.
  
  
• Teamwork and collaboration:  The ability to work well in teams, as most roles require collaboration with colleagues, clients, and stakeholders. Consider conflict resolution abilities as experience handling conflicts and disagreements professionally and constructively contribute to a harmonious work environment.
  
  
• Innovation and creativity: The ability to think innovatively, act resourcefully, and contribute to the organization's growth. As some roles require candidates to bring fresh ideas and creative solutions to the table, it will be critical to gain an understanding of their past contributions and accomplishments.
  
  
• Stress management: Jobs often come with pressure and stress. Assess how candidates handle stress, tight deadlines, and challenging situations to ensure they can perform well under such conditions.
  
  
• Career goals alignment: Determine if the candidate's career goals align with (and are reasonable) the potential growth opportunities within the organization. Assess candidates’ motivation and past behavior related to self-driven skills development and growth. This helps enable long-term employee retention and development.
  
  
• Diversity: Consider candidates from diverse backgrounds to promote a diverse and inclusive workplace, which can lead to broader perspectives and improved problem-solving. Consider those with transferable experiences from different industries or applications of the same or similar skills, circumstances, and/or technologies.
  
  
• Customer orientation: For customer-facing roles, evaluate candidates' ability to understand and meet customer needs, and respond appropriately and constructively in a manner that supports positive interactions and relationships.
  
  
• Long-term potential: Look for candidates who not only meet the current job requirements but also have the potential to grow and take on additional responsibilities in the future. Consider skills that might be present but underdeveloped and useful for future roles.

What are the various ways to assess job candidates?

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:

1. Resume Review

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.

2. Application Forms

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.

3. Video Assessments

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.

4. Interviews

• Structured: These interviews follow a predetermined set of questions, ensuring consistency across interviewers and candidates, and allowing for fair comparisons between multiple candidates for the same role.
• Behavioral: Candidates are asked about previous experiences to gauge how they have handled specific situations in the past, as past behavior is often indicative of future behavior. These are especially useful when getting detailed insights into specified skills, situations, work methods used, and outcomes achieved.
• Panel: Multiple interviewers meet with each candidate simultaneously and take turns asking questions as a way to evaluate their ability to focus on the question at hand, relate to a number of interviewers’ perspectives and points of interest, and organize their thoughts and replies. These enable decision-makers to all hear the same responses to their questions at the same time, and then (often) deliberate on what they heard right after the interview.
  
  

5. Technical/Functional Assessments

• Skill tests: Candidates are tested on specific skills relevant to the job, such as coding tests for developers, writing tests for content creators, equipment operation for manual workers, or assembly/issue detection/related actions for skilled tradespeople.
• Work samples: Candidates are given tasks directly drawn from actual work duties and responsibilities, designed to mimic the manner and circumstances in which the task is typically presented to an employee working for that particular employer.
• Case studies: Candidates are given real or hypothetical problems to solve, allowing employers to assess their problem-solving abilities and analytical skills. Most typically used with knowledge workers who exercise discretion, creativity, and resourcefulness in their roles.
• Presentations: Candidates may be asked to give a presentation on a relevant topic to demonstrate their expertise or to showcase their previous work or project. Used to evaluate their capabilities, subject knowledge, and ability to convey information effectively. These are especially valuable for sales, customer support/service, marketing, or leadership roles, as well as those in creative and professional roles, such as marketers, writers, researchers, architects, etc.
  
  

6. Psychometric Assessments

• Personality: These tests assess candidates' personality traits, work style, and cultural fit within the organization. These should be tested for applicability and predictive accuracy before full implementation, as they tend to be expensive, often require certification to interpret the results, and can be time-consuming to administer.
• General mental abilities (GMA): These test core cognitive capabilities related to reasoning, processing, comprehension, memory, problem-solving abilities, etc. They can include assessments of verbal, numeric, logical, and spatial reasoning.  These have been shown to be excellent candidate evaluation methods that predict the most desirable job outcomes across a wide variety of job roles.
  
  

7. Group Exercises

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.

8. Reference Checks

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.

9. Background Checks

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.

What are the pros and cons of using AI when screening and selecting candidates for jobs?

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:

Proposed Advantages:

• Efficiency and scalability: AI-powered screening can quickly process a large volume of applications, making it suitable for large-scale recruitment efforts or industries with frequent hiring needs, while saving significant time for recruiters and allowing them to focus on more strategic tasks.
• Consistency: AI is designed to apply a consistent set of criteria to all applicants, potentially reducing bias and ensuring fair evaluations.
• Objective selection: AI algorithms can be designed to focus solely on candidate qualifications, skills, and job fit, minimizing the impact of personal biases during the initial screening process.
• Data-driven insights: AI can analyze historical hiring data to identify patterns of successful candidates, helping to refine the screening criteria and improve the quality of hires over time.
• Diversity and inclusion: When properly designed and calibrated, AI systems can help mitigate unconscious bias in the hiring process, leading to more diverse and inclusive candidate pools.
• Time savings: Automated screening allows recruiters to focus on higher-value tasks such as interviewing, relationship-building, and strategic planning.
  
  

Potential Issues:

• Algorithm bias: AI systems can inherit biases from historical data that they “learn from”, leading to unintentional discrimination against certain groups. It's essential to carefully design and regularly audit these systems to ensure fairness, as this is the most commonly cited issue with its use in employment decisions. Additionally, its use in automated assessments (e.g., video) can penalize candidates with accents or speech impediments.
• Lack of context: AI may struggle to grasp nuanced information present in resumes, such as career gaps or unconventional experience, leading to potentially qualified candidates being overlooked. These systems may also struggle with parsing unconventional resume formats or non-standard information, leading to errors in evaluation.
• Loss of human interaction: Automated processes can make candidates feel devalued or impersonalized, missing the opportunity to provide a positive candidate experience. However, using it for resume screening lowers the likelihood of creating such an impression, as human interaction is least expected there.
• Complexity and costs: Developing and maintaining AI systems requires expertise, resources, and ongoing efforts to ensure accuracy and effectiveness. Tool validation and bias assessments require specialized skills and training.
• Limited emotional intelligence: AI can't fully assess qualities like emotional intelligence, cultural fit, or how well a candidate might mesh with the team. Assessing those is currently best left to other forms of assessment such as human observation or validated psychometric assessments.
• Privacy concerns: The use and analysis of personal data may raise privacy concerns and require review/mitigation to ensure compliance with data protection laws and regulations.
• Unpredictable outcomes: AI systems can occasionally yield surprising or counterintuitive results due to the complexity of the algorithms used. Human oversight and intervention are and will continue to be required for the foreseeable future.
• High initial investment: Implementing AI-driven hiring solutions can require a substantial upfront investment in technology and training. Purchasing commercially available applicant tracking systems (ATS) or other recruitment-focused products with embedded AI capabilities can mitigate development costs by providing a multi-capability technology.
  
  

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.

How to overcome or manage the issues faced with using candidate assessments?

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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