Developing a quality assessment model (QAM) using logical prediction: Binary validation

Abstract

This study focuses on evaluating the quality of competency transfer through various assessment methods and results, considering diverse stakeholder perspectives. The research aims to introduce an innovative approach for validating assessment outcomes, leveraging predicted sub-measurements, and transforming Boolean parameters' symbols into a binary coding system. This transformation simplifies the validation process by employing logical equations. The study's sample involves the adaptation of a competency transfer model, which combines internal parameters with the novel logical assessment method. The research findings indicate that the binary 2x system effectively simplifies quantitative and qualitative data representation within the validation process. This system facilitates the early detection of potentially ambiguous results, enabling the creation of validation procedures grounded in organizational cultural dimensions, outcomes, reports, and assessments. The proposed Quality Assessment Model (QAM) serves as a powerful tool for prediction, enhancing the quality of both quantitative and qualitative data outcomes. This approach generates distinct values, precise predictive measurements, and valuable result quality suitable for informed decision-making in various contexts. Ultimately, the study contributes to the advancement of assessment methodologies, enabling stakeholders to make more accurate and reliable judgments based on the quality of competency transfer.