A Monte Carlo Simulation Study of the Psychometric Validation Strategy for the Residual Abandonment Index (RAI)

The Residual Abandonment Index (RAI) is a new multidimensional index proposed by the authors, who are high school students at Le Quy Don High School for the Gifted, Da Nang, Vietnam. RAI was developed by the authors to operationalize persistent abandonment-related experiences through five proposed dimensions. As a newly proposed index rather than an adaptation of an established scale, this study examines whether its proposed measurement structure can be statistically recovered under controlled conditions. A Monte Carlo simulation used a known five-factor population model with 25 items across 12 conditions crossing sample size (N = 300, 600, 1,200), primary loading (.50, .70), and interfactor correlation (.30, .60), with 200 replications per condition. Exploratory recovery used common-factor principal-axis factoring followed by an oblique Promax-type rotation, with confirmatory recovery additionally conducted on 20 replications per condition. EFA congruence, loading RMSE, internal consistency, Monte Carlo uncertainty, and CFA fit indices were evaluated. Mean congruence ranged from .866 to .997, with 11 of 12 conditions exceeding .90 and recovery rates ranging from .745 to 1.000. Confirmatory recovery produced mean CFI values above .997 with low RMSEA and SRMR across conditions. These findings indicate that the proposed five-factor measurement structure can be statistically recovered under the simulated conditions. However, the results do not constitute empirical validation of RAI in Vietnamese adolescents.