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Wang, WC, Pina, MF, Pestana, M.H. & Liu, Yu-Tso (2026). Integrating ANOVA and Logistic Regression for Treatment Comparison: A Methodological Illustration with Simulated Data. International Journal of Management studies and Social Science Research. 08 (04), 371-382
WanChen-Wang et al., "Integrating ANOVA and Logistic Regression for Treatment Comparison: A Methodological Illustration with Simulated Data", in Int. Journal of Management studies and Social Science Research, vol. 08, no. 04, pp. 371-382, 2026
@article{wanchen-wang2026_1788311624325,
author = "Wang, WC and Pina, MF and Pestana, M.H. and Liu, Yu-Tso",
title = "Integrating ANOVA and Logistic Regression for Treatment Comparison: A Methodological Illustration with Simulated Data",
journal = "International Journal of Management studies and Social Science Research",
year = "2026",
volume = "08",
number = "04",
doi = "10.56293/IJMSSSR.2",
pages = "371-382",
url = "http://www.ijmsssr.org"
}
TY - JOUR TI - Integrating ANOVA and Logistic Regression for Treatment Comparison: A Methodological Illustration with Simulated Data T2 - International Journal of Management studies and Social Science Research VL - 08 IS - 04 AU - Wang, WC AU - Pina, MF AU - Pestana, M.H. AU - Liu, Yu-Tso PY - 2026 SP - 371-382 SN - 2582-0265 DO - 10.56293/IJMSSSR.2 UR - http://www.ijmsssr.org AB - Anxiety disorders impair daily functioning and well-being. CBT and MBCT are established treatments whose comparative effectiveness is often examined through a single analytic lens. This didactic study uses simulated data (N = 60; CBT n = 30; MBCT n = 30) to illustrate complementary statistical approaches to between-group differences in post-treatment anxiety, assessed by the Beck Anxiety Inventory (BAI). One-way ANOVA indicated a strong group effect, F(1, 58) = 24.8, p = 6.07×10⁻⁶, Cohen’s d = 1.29, with MBCT showing lower anxiety (M = 18.5, SD = 4.9) than CBT (M = 25.0, SD = 5.2). Binary logistic regression showed each one-point BAI increase was associated with 23% lower odds of MBCT membership (OR = 0.77), with good discrimination (AUC = 0.819) and 76.7% accuracy. These analyses demonstrate that significance testing, effect-size estimation, and classification performance answer complementary questions. Data are fully simulated; findings are not intended for clinical generalization. ER -
English