Advanced Multidimensional Item Response Theory Modeling for High-Stakes, Cross-Disciplinary Competency Assessments in Sub-Saharan Africa: A Psychometric Approach to Equity, Adaptivity, and Policy Integration

High-stakes assessments play a critical role in determining academic progression, university admissions, and employment eligibility in Sub-Saharan Africa. However, traditional unidimensional Item Response Theory (IRT) models may fail to capture the complex, cross-disciplinary nature of student competencies, potentially leading to misclassification, test bias, and reduced predictive validity. This study applied Advanced Multidimensional Item Response Theory (MIRT) modeling to evaluate the reliability, predictive validity, and fairness of competency-based assessments in secondary and tertiary education across five Sub-Saharan African countries (Ghana, Nigeria, Kenya, South Africa, and Uganda). A total of 1,200 students were selected using multistage stratified random sampling, comprising senior secondary school students (grades 10–12) and first-year university students. Data collection involved a structured competency-based test covering STEM, language proficiency, and cognitive problem-solving skills, complemented by a survey questionnaire on demographic factors and perceptions of test fairness. The study employed normality tests, descriptive and inferential statistics, psychometric modeling using MIRT and Rasch analysis, Differential Item Functioning (DIF) analysis, and Structural Equation Modeling (SEM) to evaluate the effectiveness of MIRT-based assessments. Results demonstrated that MIRT models (2D and 3D) significantly outperformed traditional IRT models in terms of marginal reliability (MIRT-3D: 0.92 vs. 1PL-IRT: 0.72), test-retest correlation (MIRT-3D: 0.88 vs. 1PL-IRT: 0.68), and predictive validity (MIRT-3D: β = 0.79 vs. 1PL-IRT: β = 0.52). Adaptive testing using MIRT models improved measurement precision, reducing test length by 35% while maintaining high measurement accuracy. DIF analysis revealed that 12.4% of test items exhibited statistically significant bias across socioeconomic and linguistic subgroups, underscoring the need for culturally responsive assessment designs. The study concluded that MIRT-based assessments provide a more reliable, valid, and equitable framework for competency evaluation in Sub-Saharan Africa. The findings emphasize the need for education policymakers to transition from traditional IRT models to advanced psychometric approaches, ensuring greater accuracy, fairness, and predictive utility in high-stakes testing.