Nemani Arwin, Hufschmidt Bettina, Kohl Viktoria, Sendig Lucie, Ebert Mareike, Bonarius Desiree, Hofmann Stefan G, Stangier Ulrich
Department of Clinical Psychology and Psychotherapy, Goethe University Frankfurt, Frankfurt am Main, Germany.
Department of Psychology, Philipps-University Marburg, Marburg, Germany.
PLoS One. 2025 Mar 18;20(3):e0314102. doi: 10.1371/journal.pone.0314102. eCollection 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.
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.
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).
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.
本研究检验了在生态瞬时评估（EMA）数据的动态网络分析中使用双极量表与单极量表的统计学和临床益处。
47名学生通过单极量表（n = 24）或双极量表（n = 23），连续五周每天三次完成EMA报告。对数据进行分析以构建个性化网络模型。
双极量表组的零膨胀率显著更低（2.37%对10.31%，U = 2407756，r = 0.75，p < 0.05），且反应变异性更大。网络分析显示双极组中有更多参与者具有显著的网络边（69.57%对41.67%，χ²(1) = 12.06，p = 0.0007）。此外，双极组零反应的几率低于单极组（p = 0.038）。
双极量表通过降低零膨胀率和增加变异性来提高EMA数据质量，从而产生更丰富的动态网络模型。需要进一步研究以在临床人群中证实这些发现。
