Bipolar vs. unipolar scaling in dynamic network analyses of Ecological Momentary Assessment data - Arwin Nemani; Bettina Hufschmidt; Viktoria Kohl; Lucie Sendig; Mareike Ebert; Desiree Bonarius; Stefan G. Hofmann; Ulrich Stangier (PLoS ONE, 2025)
This study examined the statistical and clinical benefits of using bipolar versus unipolar scaling in dynamic network analysis of Ecological Momentary Assessment (EMA) data. Methods: Forty-seven students completed EMA reports three times daily for five weeks via either unipolar (n = 24) or bipolar (n = 23) scales. The data were analyzed to construct idiographic network models. Results: The bipolar scaling group presented significantly lower zero inflation (2.37% vs. 10.31%, U = 2407756, r = 0.75, p < .05) and greater response variability. Network analysis revealed more participants with significant network edges in the bipolar group (69.57% vs. 41.67%, χ²(1) = 12.06, p = .0007). Additionally, the bipolar group had lower odds of zero responses than the unipolar group did (p = .038). Conclusion: Bipolar scaling enhances EMA data quality by reducing zero inflation and increasing variability, resulting in richer dynamic network models. Further research is needed to confirm these findings in clinical populations.