Abstract
There is no gain-saying that machines can learn from data to derive patterns and insights to aid
various applications, also known as artificial intelligence, and fast-gaining relevance today. This study
implemented feature data generation (FDG) as a novel technique for psychometrics improvements
using the Post-hoc Simulation approach. The descriptive design of the correlation type was adopted for
this study and deployed quantitatively. The instrument for the study was a test aligned to the behavioral
objectives of the Postgraduate Certificate Curriculum with the program enrollees as the study participants.
The test underwent a thorough validation procedure which yielded a reliability coefficient of 0.98. The item
parameters of the test were analyzed using XCalibre 4.2 to analyze the real data from 38 respondents, while
the WINGEN application through the post-hoc approach was used to generate the simulated data with 500
respondents. The findings of the study revealed that the 3 Parameter Logistic Model fit the generated data
determined using chi-square goodness of fit statistics. The FDG is a viable approach with a strong and
positive correlation between real data and simulated data, which enables the generalization of findings on
the basis on which conclusions were made. The developed FDG method for psychometric improvement has
wide applicability, a plus for the novel technique while strengthening transdisciplinary research.