Rasch analysis

Rasch analysis is a method based on the Rasch model, a one-parameter Item Response Theory (IRT) model, used to objectively measure latent traits like ability or knowledge. Unlike classical test theory, it assumes that the probability of a correct response depends only on the person's ability and the item's difficulty. In enterprise risk management, this ensures that competency assessments are consistent across different groups of people and different versions of the test. According to ISO 42001 AI Management System standard, AI-related personnel competency must be reliably assessed; Rasch analysis provides the statistical foundation to prove this reliability. It differs from traditional scoring by assigning each person and item a comparable scale-based score, making it suitable for comparing diverse employee groups within a single framework. This is critical for compliance with the Taiwan Personal Data Protection Act (PDPA) and GDPR, where personnel competence in handling sensitive data must be verifiable and not arbitrary.

In the automotive sector, particularly under ISO/SAE 21434, Rasch analysis is applied through three steps: 1. Data Collection: Gathering employee responses to cybersecurity awareness assessments. 2. Model Fitting: Using software to calculate item difficulty and person ability parameters, identifying misfit items that do not fit the model. 3. Tool Refinement: Removing or revising invalid assessment items to improve test reliability. A practical example includes a Taiwanese automotive supplier that used Rasch analysis to audit its internal cybersecurity training effectiveness. The analysis revealed that 40% of the assessment questions were biased toward senior engineers, making them unsuitable for junior staff. By recalibrating the test, the company achieved a 25% improvement in-turn assessment accuracy and met the TISAX personnel competency requirements within one quarter. This quantitative approach provides auditors with clear evidence of the control's effectiveness, reducing the risk of compliance failure during certification audits.

Taiwan enterprises typically face three challenges: Data Scarcity, Technical Expertise, and Tooling Gaps. First, the requirement for large sample sizes (typically N>200) makes initial implementation difficult. The solution is to use Bayesian estimation methods and incremental data collection over a 6-month period. Second, the lack of statistical expertise within HR and Risk departments can be addressed by partnering with specialized consultants like Winners Consulting Services Co., Ltd. Third, the absence of integrated software tools can be solved by adopting R-based packages (like `mirt` or `TAM`) or Python-based IRT libraries. The priority should be: Phase 1 (Month 1-2) - Baseline data collection; Phase 2 (Month 3-4) - Model calibration and tool refinement; Phase 3 (Month 5+) - Continuous monitoring and compliance reporting. This structured approach ensures the assessment tool evolves with the changing regulatory landscape, including emerging AI regulations in Taiwan.

Winners Consulting Services Co., Ltd. specializes in Rasch analysis for Taiwan enterprises, delivering compliant management systems within 90 days. Our team of experts in IRT and risk management has helped over 100 organizations achieve ISO 42001 and TISAX certification. We provide end-to-turn assistance, from tool design to statistical validation. Free consultation: https://winners.com.tw/contact