Contemporary testing standards emphasize that passing scores should be supported by a comprehensive validity argument rather than by any single standard-setting procedure. The proposed study examines latent class analysis (LCA) and Rasch mixture models as independent sources of validity evidence for SME-derived modified Angoff standards, rather than as replacements for expert judgment. The study evaluates whether credentialing data exhibit stable, interpretable latent proficiency classes; whether class boundaries align with the minimally competent region identified by SMEs; whether Rasch mixture models add interpretive value beyond LCA; and the extent to which model-based classifications converge with modified Angoff pass/fail decisions. The study advances a framework in which SME judgment, psychometric modeling, and empirical classification evidence collectively support a comprehensive validity argument for credentialing performance standards.