Translation and Validation of the Thai Version of the Quality Nursing Care Questionnaire (T-QNCQ)

Vachira Posai, Armarapas Atthachaiwat, Teeraporn Sathira-Angkura, Kanogporn Jamsomboon, Uraiporn Janta-um-mou, Patcharee Kladjomphong, Supisara Phonkrut, Niphon Watada, Supitcha Udomchai

Abstract

Objective: The Quality Nursing Care Questionnaire (QNCQ) has been widely used to assess nurses’ perceptions of care quality. However, there was a lack of studies validating the QNCQ in Thai healthcare settings, highlighting the need for its adaptation. This study aimed to translate the QNCQ into Thai and evaluate its internal consistency and psychometric properties.
Materials and Methods: A cross-sectional study design was used in this research. The Thai version (T-QNCQ) was a self-reported questionnaire consisting of 38 items across 5 response categories, covering 6 dimensions: physical environment, staff characteristics, preconditions, task-oriented activities, human-oriented activities, and patient outcomes. A sample of 380 registered nurses from tertiary government hospitals in Thailand was recruited using purposive and convenience sampling. To assess the validity of the translated scale, confirmatory factor analysis (CFA), descriptive statistics, and reliability testing were conducted.
Results: The T-QNCQ demonstrated strong reliability and validity. The Cronbach’s alpha coefficient for the total scale was 0.98, with subscale values ranging from 0.86 to 0.94. Item-total correlations for the overall scale were positive, ranging from 0.61 to 0.84. Construct validity was supported by CFA, which yielded the following fit indices: comparative fit index=0.95, Tucker-Lewis index=0.94, root mean square error of approximation=0.05, and standardized root mean square residual=0.04.
Conclusion: The T-QNCQ was found to be a reliable and valid tool for assessing nurses’ perceptions of care quality in Thai healthcare settings. These results provide valuable insights for nurse administrators and policymakers, aiding improvements in care delivery.

 Keywords

nursing; psychometric properties; quality of care; questionnaires and surveys; validity and reliability

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References

Karaca A, Durna Z. Patient satisfaction with the quality of nursing care. Nurs Open 2019;6:535-45.

Stavropoulou A, Rovithis M, Kelesi M, Vasilopoulos G, Sigala E, Papageorgiou D, et al. What quality of care means? Exploring clinical nurses’ perceptions on the concept of quality care: a qualitative study. Clin Pract 2022;12:468-81.

Javaid M, Haleem A, Singh RP. Health informatics to enhance the healthcare industry’s culture: an extensive analysis of its features, contributions, applications and limitations. Inf Health 2024;1:123-48.

Ryan C, Powlesland J, Phillips C, Raszewski R, Johnson A, Banks-Enorense K, et al. Nurses’ perceptions of quality care. J Nurs Care Qual 2017;32:180-5.

Tsogbadrakh B, Kunaviktikul W, Akkadechanunt T, Wichaikhum O-A, Gaalan K, Badamdorj O, et al. Development and psychometric testing of quality nursing care scale in Mongolia. BMC Nurs 2021;20:68.

Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol 1993;46:1417-32.

Kunaviktikul W, Anders R, Srisuphan W, Chontawan R, Nuntasupawat R, Pumarporn O. Development of quality nursing care in Thailand. J Adv Nurs 2002;36:776-84.

Ngernthaisong C, Aungsuroch Y, Oumtanee A. Nurses’ Perceptions of outcomes of quality of care in Thai nursing homes: a qualitative study. Pac Rim Int J Nurs Res 2024;28: 509-24.

Pimsen A, Lin C-Y, Wirojratana V, Shu B-C. Psychometric properties of the Thai version of the nurses’ intention to participate in advance care planning instrument. Pac Rim Int J Nurs Res 2023;27:781-97.

Liu Y, Aungsuroch Y, Gunawan J, Sha L, Shi T. Development and psychometric evaluation of a quality nursing care scale from nurses’ perspective. Nurs Open 2021;8:1741-54.

Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol 1993;46:1417-32.

Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine 2000;25:3186-91.

White M. Sample size in quantitative instrument validation studies: a systematic review of articles published in Scopus, 2021. Heliyon 2022;8:e12223.

Althubaiti A. Sample size determination: a practical guide for health researchers. J Gen Fam Med 2023;24:72-8.

Kyriazos T. Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in General. Psychology 2018;9:2207-30.

Polit DF, Beck CT. The content validity index: are you sure you know what’s being reported? critique and recommendations. Res Nurs Health 2006;29:489-97.

Polit DF, Beck CT, Owen SV. Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health 2007;30:459-67.

Taber KS. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Res Sci Educ 2018;48:1273-96.

Robertson O, Evans MS. Just how reliable is your internal reliability? an overview of Cronbach’s alpha (α). PsyPag Quarterly 2020;1:23-7.

Knekta E, Runyon C, Eddy S. One size doesn’t fit all: using factor analysis to gather validity evidence when using surveys in your research. CBE Life Sci Educ 2019;18:rm1.

Mukaka MM. Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 2012; 24:69-71.

Shrestha N. Factor analysis as a tool for survey analysis. Am J Appl Math Stat 2021;9:4-11.

Ahmad S, Zulkurnain NNA, Khairushalimi FI. Assessing the fitness of a measurement model using confirmatory factor analysis (CFA). Int J Innov Appl Stud 2016;17:159.

West SG, Taylor AB, Wu W. Model fit and model selection in structural equation modeling. Handbook of structural equation modeling. 2012;1:209-31.

Goretzko D, Siemund K, Sterner P. Evaluating model fit of measurement models in confirmatory factor analysis. Educ Psychol Meas 2024;84:123-44.

Alavi M, Visentin DC, Thapa DK, Hunt GE, Watson R, Cleary M. Chi-square for model fit in confirmatory factor analysis. J Adv Nurs 2020;76:2209-11.

Groskurth K, Bluemke M, Lechner CM. Why we need to abandon fixed cutoffs for goodness-of-fit indices: an extensive simulation and possible solutions. Behav Res Methods 2024; 56:3891-914.

Brislin RW. Back-translation for cross-cultural research. J Cross-Cult Psychol 1970;1:185-216.

Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976) 2000;25:3186-91.

Kline RB. Principles and practice of structural equation modeling, 4th ed. New York: Guilford Press; 2016.

Hair JF, Black WC, Babin BJ. Multivariate data analysis: a global perspective. Upper Saddle River: Pearson Education; 2010.

Hair JF, Babin BJ, Black WC, Anderson RE. Multivariate data analysis. Boston: Cengage; 2019.

Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 1981;18:39-50.

Marsh HW, Wen Z, Hau KT. Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction. Psychol Methods 2004;9:275-300.

DOI: http://dx.doi.org/10.31584/jhsmr.20251228

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