DOI: https://doi.org/10.14710/medstat.18.1.49-60

RANDOM EFFECTS META-REGRESSION USING WEIGHTED LEAST SQUARES (CASE STUDY: EFFECTIVENESS OF ACCEPTANCE AND COMMITMENT THERAPY IN REDUCING DEPRESSION)

*Felinda Arumningtyas  -  Department of Statistic, Faculty of Mathematics and Natural Sciences, Jenderal Soedirman University, Jl. DR. Soeparno No 61, Karangwangkal, Banyumas, Indonesia, 53122, Indonesia
Bambang Widjanarko Otok  -  Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia
Santi Wulan Purnami  -  Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia
Open Access Copyright (c) 2025 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Meta-analysis is a statistical method for synthesizing quantitative data from multiple related studies, yet heterogeneity among studies often complicates interpretation. Meta-regression extends this approach by incorporating study-level covariates to explain variations in outcomes. With the global increase in depression, Acceptance and Commitment Therapy(ACT) has attracted attention as an effective psychological intervention. Therefore, a deeper understanding of the factors that influence its effectiveness across studies is needed. However, to date, only a few meta-analyses have quantitatively examined moderator variables that influence ACT outcomes using a random effects meta-regression approach. This study aims to fill this gap. This study estimated the model parameters using the Weighted Least Squares (WLS) method. Thirty-three published studies testing the effectiveness of ACT in reducing depression were collected from PubMed, Google Scholar, and Science Direct. The homogeneity test results showed significant heterogeneity, supporting the use of a random effects model. The combined effect size of -0.321 indicates that ACT significantly reduces depression levels compared to the control group. Meta-regression analysis revealed that variation in effect size was significantly influenced by differences in the average age of patients and duration of therapy. These findings provide new insights into the conditions and characteristics that make ACT therapy more effective.

Fulltext View|Download
Keywords: ACT; Depression; Meta Analysis; Meta Regression; Random Effect; Weighted Least Square.

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
COMPARISON OF RANDOM FOREST AND SUPPORT VECTOR MACHINE CLASSIFICATION METHODS FOR PREDICTING THE ACCURACY LEVEL OF MADRASAH DATA RANDOM EFFECTS META-REGRESSION USING WEIGHTED LEAST SQUARES (CASE STUDY: EFFECTIVENESS OF ACCEPTANCE AND COMMITMENT THERAPY IN REDUCING DEPRESSION) ANALYSIS MULTILEVEL SURVIVAL DATA USING COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL More recent articles
Most cited articles
Front-Matter ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL Penerapan Regresi Logistik Ordinal Proportional Odds Model pada Analisis Faktor-Faktor yang Mempengaruhi Kelengkapan Imunisasi Dasar Anak Balita di Provinsi Aceh Tahun 2015 DISTRIBUSI RAYLEIGH UNTUK KLAIM AGREGASI Front-Matter More cited articles
  1. Ahmadsaraei, N., Doost, H., Manshaee, G., & Nadi, M. (2017). The Effectiveness of Acceptance and Commitment Therapy on Depression among Patients with Type II Diabetes. Iranian Journal of Diabetes and Obesity, 9(1–2), 6–13
  2. Anggraeni, J., & Budiarto, S. (2020). Menurunkan Tingkat Depresi Melalui Acceptance and Commitment Therapy (ACT) pada ODHA. Jurnal Psikologi Terapan dan Pendidikan, 2(2), 111–117
  3. Aravind, A, Agarwal, M, Malhotra, S, & Ayyub, S. (2024). Effectiveness of Acceptance and Commitment Therapy on Mental Health Issues: A Systematic Review. Annals of Neurosciences, 32(4):321–327. doi: 10.1177/09727531241300741
  4. Blazquez, D., Sánchez, D., Botella, J., & Suero, M. (2023). Heterogeneity Estimation in Meta-Analysis of Standardized Mean Differences. BMC Medical Research Methodology, 23(19), 1–22
  5. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021). Introduction to Meta-Analysis. Second Edition, Hoboken: Wiley
  6. Collins, N., & Corna, L. (2018). General Practitioner Referral of Older Patients to Improving Access to Psychological Therapies (IAPT): An Exploratory Qualitative Study. BJPsych Bull, 42(3), 115–118
  7. Esmaeili, E., Azizi, H., Sarbazi, E., & Khomadoradi, F. (2023). The Global Case Fatality Rate Due To COVID-19 In Hospitalized Elderly Patients by Sex, Year, Gross Domestic Product, and Continent: A Systematic Review, Meta-Analysis, and Meta-Regression. New Microbes and New Infections, 51(C), 1–9
  8. Han, A. & Kim, T. H. (2022). The Effects of Internet-Based Acceptance and Commitment Therapy on Process Measures: Systematic Review and Meta-analysis. Journal of Internet Medical Research, 24(8), 1–18. https://doi.org/10.2196/39182
  9. Lin, L., & Chu, H. (2018). Quantifying publication bias in meta‐analysis. Biometrics, 74(3), 785–794
  10. Mathur, M. B. & VanderWeele, T. J. (2022). Meta-regression Methods to Characterize Evidence Strength Using Meaningful-Effect Percentages Conditional on Study Characteristics. Research Synthesis Methods, 12(6), 731–749
  11. Nakagawa, S., Yang, Y., Macartney, E. L., Spake, R., & Lagisz, M. (2023). Quantitative Evidence Synthesis: A Practical Guide on Meta-Analysis, Meta-Regression, and Publication Bias Tests for Environmental Sciences. Environmental Evidence, 12(8)
  12. Parling, T., Cernvall, M., Ramklint, M., Holmgren, S., & Graderi, A. (2016). A Randomised Trial of Acceptance and Commitment Therapy for Anorexia Nervosa after Daycare Treatment, Including Five-Year Follow-up. BMC Psychiatry, 16(1), 272–284
  13. Retnawati, H., Apino, E., Kartianom, Djidu, H., & Anazifa, R. D. (2018). Pengantar Analisis Meta. Yogyakarta: Parama Publishing
  14. Stanley TD, Doucouliagos H. (2017). Neither Fixed nor Random: Weighted Least Squares Meta-Regression. Research Synthesis Methods. 8(1),19–42
  15. Sebayang, J.S., Yuniarto, B. (2017). Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda. Media Statistika, 10(1), 13–23
  16. Ye, F. Lee, J. L., & Xue, D. (2023). Acceptance and Commitment Therapy for depression: A meta-analysis. JAMA Network Open¸6(12)
  17. Yun, X., Zhao, B., Yin, T., Qu, H., Zhang, J., Cheng, X., Chen, X. (2025). Effect of Acceptance and Commitment Therapy for Adolescent Depression: A Meta-Analysis. Front. Psychiatry, 16, 1–18
  18. WHO, W. (2019). The Report on Depression. Global Health Estimates
  19. Williams, A. J., Botanov, Y., Giovanetti, A. K., Perko, V. L., Sutherland, C., Youngren, W., & Sakaluk, J. (2023). A Metascientific Review of the Evidential Value of Acceptance and Commitment Therapy for Depression, Behavior Therapy, 54(6):989–1005

Last update:

No citation recorded.

Last update: 2025-10-17 01:43:59

No citation recorded.