Development, Validation, and Application of Beck's Anxiety Inventory (BAI) In the Kenyan Context
Authors
Tangaza University, Karen, Nairobi (Kenya)
Tangaza University, Karen, Nairobi (Kenya)
Article Information
DOI: 10.47772/IJRISS.2025.91100189
Subject Category: Psychology
Volume/Issue: 9/11 | Page No: 2377-2383
Publication Timeline
Submitted: 2025-11-21
Accepted: 2025-11-28
Published: 2025-12-04
Abstract
Given the central role anxiety plays in psychological practice, shaping clients’ behavior, therapeutic engagement, and treatment decisions, the development, validation, and application of the Beck Anxiety Inventory within the Kenyan context is essential to ensure accurate assessment and culturally relevant care. This paper critically examined the Beck Anxiety Inventory (BAI) in Kenya, highlighting limited local use and a lack of cultural validation. Most Kenyan studies emphasize prevalence rather than evaluating the BAI’s accuracy across diverse languages and cultural contexts. The tool’s Western origins raise concerns about cultural relevance and symptom interpretation, especially given the overlap of somatic anxiety with common physical illnesses in Kenyan populations. This review underscores the urgent need for thorough local validation, careful translation, and development of indigenous anxiety measures rooted in Kenyan culture. Enhancing psychometric rigor and regulatory oversight will strengthen psychological assessment and counselling practices in Kenya, ensuring they are both reliable and culturally sensitive. This critique assesses the BAI’s development, psychometric validation, and contextual application in Kenya, emphasizing methodological constraints and ethical implications. The paper highlights the pressing necessity for local validation, methodical translation, and the prospective creation of indigenous anxiety assessments rooted in Kenyan culture and language. Improving psychometric research and regulatory frameworks will make psychological testing in Kenya's changing counselling psychology field more reliable and culturally sensitive.
Keywords
Beck Anxiety Inventory (BAI), Anxiety assessment, psychometric validation
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References
1. Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988). An Inventory for Measuring Clinical Anxiety: Psychometric Properties. Journal of consulting and clinical psychology, 56(6), 893. [Google Scholar] [Crossref]
2. do Nascimento, R. L. F., Fajardo-Bullon, F., Santos, E., Landeira-Fernandez, J., & Anunciação, L. (2023). Psychometric Properties and Cross-Cultural Invariance of the Beck Depression Inventory-II and Beck Anxiety Inventory among a Representative Sample of Spanish, Portuguese, and Brazilian Undergraduate Students. International journal of environmental research and public health, 20(11), 6009. https://doi.org/10.3390/ijerph20116009 [Google Scholar] [Crossref]
3. Gacau, K. K., Mugendi, G., Kiragu, G., Ngayo, M. O., & Omosa, G. (2024). Burden and predictors of anxiety disorder among HIV patients on ART in Nairobi Kenya. PLOS Mental Health, 1(2), e0000072. [Google Scholar] [Crossref]
4. Jones, R., Patel, S., & Kimani, J. (2023). Evidence-based assessment and intervention in global mental health. International Journal of Mental Health Systems, 17(2), 45-59. https://doi.org/10.1186/s13033-023-00567-8 [Google Scholar] [Crossref]
5. Kimani, J., & Patel, S. (2023). Challenges in the clinical application of Western-developed anxiety scales in East Africa. International Journal of Mental Health Systems, 17(2), 60-72. https://doi.org/10.1186/s13033-023-00568-9 [Google Scholar] [Crossref]
6. Mazhak, I., & Sudyn, D. (2025). Psychometric assessment of the Beck anxiety inventory and key anxiety determinants among Ukrainian female refugees in the Czech Republic. Frontiers in Psychology, 15, 1529718. [Google Scholar] [Crossref]
7. Mwangi, P., Otieno, M., & Wambui, L. (2022). Psychometric tool adaptation for Kenyan primary care: Challenges and opportunities. African Journal of Psychological Assessment, 4(1), 101-110. https://doi.org/10.4102/ajopa.v4i1.67 [Google Scholar] [Crossref]
8. Omondi, R., Otieno, M., & Wambui, L. (2022). Psychometric evaluation of the Beck Anxiety Inventory in Kenyan populations. African Journal of Psychological Assessment, 4(1), 111-120. https://doi.org/10.4102/ajopa.v4i1.68 [Google Scholar] [Crossref]
9. Patel, V., & Ochieng, B. (2023). Integrating mental health into primary care in LMICs: The role of psychometrics. Global Mental Health, 10, e15. https://doi.org/10.1017/gmh.2023.15 [Google Scholar] [Crossref]
10. Rubin, L. H., Cho, K., Bolzenius, J., Mannarino, J., Easter, R. E., Dastgheyb, R. M., ... & Paul, R. (2025). Mental health phenotypes of well-controlled HIV in Uganda. Frontiers in Public Health, 12, 1407413. [Google Scholar] [Crossref]
11. Saal, W. L., Kagee, A., & Bantjes, J. (2019). Evaluation of the Beck Anxiety Inventory in predicting generalised anxiety disorder among individuals seeking HIV testing in the Western Cape province, South Africa. South African Journal of Psychiatry, 25(1), 1-5. [Google Scholar] [Crossref]
12. Sisay, T., Mulate, M., Hailu, T., & Belete, T. M. (2024). The prevalence of depression and anxiety among cardiovascular patients at University of Gondar specialized hospital using Beck's depression inventory II and Beck Anxiety Inventory: A cross-sectional study. Heliyon, 10(2), e24079. https://doi.org/10.1016/j.heliyon.2024.e24079 [Google Scholar] [Crossref]
13. Smith, J., & Lee, A. (2022). Advances in psychometric assessment in counselling psychology. Journal of Counseling Psychology, 69(1), 12-25. https://doi.org/10.1037/cou0000567 [Google Scholar] [Crossref]
14. Systematic and recent reviews noting BAI usage in LMICs and gaps in East Africa (scoping reviews and mental health tool inventories) [Google Scholar] [Crossref]
15. Wambua, D., & Smith, J. (2023). Cultural adaptation of anxiety assessment tools: Implications for Kenyan mental health services. Global Mental Health, 10, e16. https://doi.org/10.1017/gmh.2023.16 [Google Scholar] [Crossref]
16. Zamri, N., Ismail, S., Ismail, A., Abu Bakar, N., Hassan, S. N., Tuan Hadi, T. S., ... & Abu Bakar, N. A. Machine Learning and Deep Learning to Predict Malaysian Workers' Response to Different Mental Health Therapies. Available at SSRN 4839588. [Google Scholar] [Crossref]
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