Measuring Gunas and Gunatita: A Proof-of-Concept Study of the Bhagavad Gita-based Mindset Composition Assessment (MCA)

This record contains the complete research package for a proof-of-concept study (n = 137) of the Mindset Composition Assessment (MCA), a 20-item self-report instrument that operationalizes the Bhagavad Gita's guna framework into four measurable mindsets (Defender, Negotiator, Giver, Yogi), adaptability (ease of shifting between mindsets), and three "locks" (fear-, hurt-, and attachment-based barriers). Key findings: 90.8% of 65 raters judged their personalized AI-generated report "very" or "extremely" accurate. Five of six transitions between mindsets showed the predicted negative association with the lock guarding their target mindset (r = −.33 to −.47), supporting the hypothesized transition-lock structure; the sixth showed a theoretically interpretable cross-lock association. A Giver × Yogi interaction showed that higher Giver (sattva) was associated with a stronger Spiritual Lock at lower Yogi levels, whereas high Giver joined with high Yogi (gunatita) was associated with a weaker Spiritual Lock, a pattern consistent with the Bhagavad Gita's teaching that sattva, too, can bind (B.G. 14.6). An exploratory two-cluster solution yielded profiles broadly corresponding to the theorized Karmic Net and Karmic Flow configurations. Cross-sectional mediation analysis was consistent with an indirect pathway in which the Yogi's association with lower locks was carried almost entirely through adaptability (Yogi → Adaptability → Locks; indirect effect −0.22, 95% CI [−0.34, −0.13]). We therefore propose the Yogi–adaptability–locks triad as, to our knowledge, a first empirical operationalization of gunatita, prescribed but not measured in prior guna research. Files in this record MCA_Pilot_Manuscript.pdf is full manuscript Data.xlsx is complete de-identified dataset, including all 137 assessments (20 item responses, scale scores, recruitment stream, cluster assignment, and matched-rater ratings), 65 accuracy ratings with free-text feedback, and the instrument's 20 items reproduce_analyses.py is a Python script that regenerates the tables and statistics reported in the manuscript directly from the data file. Run: python reproduce_analyses.py "Data.xlsx" Ethics and provenance The study was reviewed by Sterling IRB and determined exempt under 45 C.F.R. §46.104(d), Category 2 (IRB ID 17532-MReddy; determination dated August 19, 2026). The dataset is de-identified: no names, contact details, or timestamps beyond year-month are included. A version of this manuscript is under submission to a peer-reviewed journal. Contact: Madhukar Reddy, PhD [email protected] · madhukarreddyauthor.com