The tight labor market makes it tempting to make opportunistic decisions. But the wrong hire costs a lot of money, a lot of time, AND can lower the retention of your best employees. Poor hiring decisions cause an estimated 80% of employee turnover (Harvard Business Review).
Avoid costly mismatches. A wrong hire costs an average of €15,000 and can add up to €50,000. This is due to lost productivity, replacement costs, and impact on the team. With objective selection assessments, you significantly reduce this risk.
Discover hidden talent. Traditional resume selection often misses valuable candidates. Research shows that companies that choose skills-based hiring select up to 30% additional suitable candidates who would otherwise have been rejected. Also read Untapped talent.
Strengthen your employer brand. Candidates value transparency and objectivity. Organizations that apply this have a 38% higher chance of candidates accepting an offer.
Selection assessments improve the match between candidate and organization, thereby reducing the risk of early departure. As a result, organizations realize an average of 30-40% less turnover in the first year (Schmidt & Hunter, 1998; Corporate Leadership Council, 2003). This saves costs and strengthens stability and commitment.
The Dutch labor market is in flux: there is tightness, explosive technological development, and still a growing emphasis on diversity and inclusion. Selection assessments and skills-based hiring provide HR professionals with the data and insights to make choices that are both objective and predictive.
Our labor market demands visibility of potential, speed, and quality in selection. Selection assessments are the answer: they provide objective data, focused on future performance and job fit, even if experience or education do not fully match.
Since 2017, ABN AMRO Clearing has been using our assessments worldwide to select and develop young graduates, managers, and C-level candidates. Objective measurements make skills and potential visible, provide direction for development, and offer an international benchmark for talent profiles.
“For us, the assessment is the basis for selection as well as development.” – ABN AMRO Clearing
Result: predictable performance, faster visibility of potential, international comparability of talent profiles, and increased management confidence in selection decisions.
Bias in selection leads to wrong choices and reduced diversity. Reduce bias with our assessments.
By reducing bias, decisions are based more on job-relevant factors → higher predictive validity.
Multi-method selections achieve validities of up to .65-.70, while an unstructured interview often does not exceed .18-.38.
Bias reduction through multi-method selection provides organizations:
We use AI to make assessments more efficient, monitor psychometric quality and provide candidates with a better experience.
Our expert systems automatically compile the most appropriate test program per job profile. This optimizes the balance between predictive validity (predictive value for future functioning) and the time investment required. The psychologist or client can always supplement or modify this program.
Some report forms allow candidates to ask in-depth questions about their results. Our LLM (large language model) provides immediately understandable and personalized explanations in the process.
This algorithm, developed by Aon (formerly Cut-e), adaptively provides questionnaires. As a result, a candidate does not have to answer all the questions: the algorithm chooses which questions are still needed based on previous answers. This considerably shortens the administration time (for example, from 60 to 20 minutes) while maintaining reliability and validity.
Algorithms analyze response patterns and estimate the likelihood of a socially desirable interpretation. This increases the interpretive validity of the results.
With real-time item generation, test items are matched to the candidate’s level. This increases measurement precision and prevents test leaks, cheating, and memory effects during repeated measurements.
Result: assessments that are scientifically sound and faster, more personal and safer to use.
Strategic workforce planning is all about foresight: making sure the right people with the right skills are ready to meet tomorrow’s goals. At the same time, talent acquisition is growing into an essential part of that planning: who you attract today determines tomorrow’s opportunities. Yet many organizations end up in a replacement trap: they are under pressure to find a replacement only when someone leaves. That leads to high costs, lower quality, and a reactive HR approach.
Assessments break this cycle and link recruitment to planning. Based on psychological and data-driven insights, they provide well-founded insights into the capacities, development potential, and learning needs of employees and candidates. They form a solid bridge between talent acquisition and strategic personnel planning.
By harnessing the predictive power of assessments, organizations strengthen their strategic workforce planning and our assessments result in:
Assessments shift the focus from reactive replacement to proactive talent strategy; evidence-based, predictive and focused on sustainable growth.
Selection assessments are only truly valuable when they integrate effortlessly into existing HR cycles. Starcheck offers three flexible forms, tailored to the setup of the HR process:
The assessment process is handled from start to finish, from matching the job profile and competencies to the final report. Always with informed consent from the candidate. Reports can be prepared in the organization’s competency language, so they can be directly applied to existing processes.
With our tooling, organizations can independently perform high-quality assessments. Starcheck supports with implementation and training. Integrate with in-house competency language, or via API links with HR systems such as Greenhouse, Recruitee, SAP SuccessFactors, Teamtailor, Bullhorn ATS, Lever, Workday, Workable, Freshteam, and Sage HR. Make competency-based information broadly applicable across all HR processes.
Organizations take assessments themselves, while Starcheck psychologists are available as a second line. They provide support in preparing for or conducting interviews and in providing feedback. Thus, independence is combined with specialist expertise where it adds the most value.
Starcheck delivers objective, evidence-based selection that is flexible, scalable, and future-proof – fully integrated into clients’ HR processes.
Improve your talent acquisition approach. After all, a wrong choice costs more than waiting a while. Assessments and skills-based hiring also help uncover hidden talent that may not be a perfect match on paper.
Yes. Assessments and skills-based selection look beyond a resume and reveal potential that would otherwise be missed. That’s how you enlarge the pond and improve quality and diversity.
In volume roles before the 1st interview, we will test this in a pilot if desired and integrate it into your workflow. For professionals/seniors between the 1st and 2nd interview. Leadership and executive assessments are usually used in the latter stages.
Schedule a (consulting) call today and find out how selection assessments and skills-based hiring increase the quality of hires and retention.
This fact sheet provides an overview of the most commonly used (psychological) selection methods, both classical and modern. The figures are based on meta-analyses and dominant scientific literature.
| Method | Predictive validity (r) | Typical reliability |
|---|---|---|
| Cognitive ability (GMA test) | .51 | High (.85-.95) |
| Work test | .54 | High (inter-rater ≥.70) |
| Structured interview | .51 | Medium-high (.60-.75) |
| Unstructured interview | .18-.38 | Low-medium (.40-.55) |
| Integrity test | .41 | High (α ≥.80) |
| Conscientiousness (Big Five) | .31 | Medium-high (α ~.75-.85) |
| Job knowledge test | .48 | High (≥.80) |
| Years of service | .18 | Not applicable |
| Video/asynchronous interview (incl. AI) | .30-.40 | Good at structuring; algorithmically variable |
| Machine learning / algorithmic models | .20-.50 | Depends on dataset; generalizability limited |
| Serious games / game-based work samples | .35-.50 | High on objective metrics |
| Social media screening | .00-.20 | Low and variable |
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