Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.
Faculty of Business and Management, Universiti Sultan Zainal Abidin, Terengganu 21300, Malaysia.
Int J Environ Res Public Health. 2023 Feb 6;20(4):2860. doi: 10.3390/ijerph20042860.
A long-established approach, Confirmatory Factor Analysis (CFA) is used to validate measurement models of latent constructs. Employing CFA can be useful for assessing the validity and reliability of such models. The study adapted previous instruments and modified them to suit the current setting. The new measurement model is termed NENA-q. Exploratory factor analysis (EFA) revealed the instruments of the NENA-q model formed a construct of the second order with four dimensions, namely organizational contribution (OC), academic institution contribution (AIC), personality traits (PT), and newly employed nurses' adaptation (NENA). Researchers administered the questionnaires to a sample of 496 newly employed nurses working in hospitals under the Ministry of Health (MOH) for the confirmation of the extracted dimensions. The study performed a two-step CFA procedure to validate NENA-q since the model involves higher-order constructs. The first step was individual CFA, while the second step was pooled CFA. The validation procedure through confirmatory factor analysis (CFA) found the model achieved the threshold of construct validity through fitness index assessment. The model also achieved convergent validity when all average variance extracted (AVE) exceeded the threshold value of greater than 0.5. The assessment of the composite reliability (CR) value indicates all CR values exceeded the threshold value of 0.6, which indicates the construct achieved composite reliability. Overall, the NENA-q model consisting of the OC construct, AIC construct, PT construct, and NENA construct for CFA has met the fitness indexes and passed the measurements of the AVE, CR, and normality test. Once the measurement models have been validated through CFA procedure, the researcher can assemble these constructs into structural model and estimate the required parameter through structural equation modelling (SEM) procedure.
一种由来已久的方法，验证性因子分析（CFA）用于验证潜在结构的测量模型。采用 CFA 可以帮助评估此类模型的有效性和可靠性。该研究采用了先前的工具，并对其进行了修改，以适应当前的环境。新的测量模型称为 NENA-q。探索性因子分析（EFA）显示，NENA-q 模型的工具形成了二阶结构，包含四个维度，即组织贡献（OC）、学术机构贡献（AIC）、人格特质（PT）和新雇用护士的适应（NENA）。研究人员向在卫生部（MOH）下属医院工作的 496 名新雇用护士发放了问卷，以确认提取的维度。由于该模型涉及高阶结构，因此研究采用两步 CFA 程序来验证 NENA-q。第一步是个体 CFA，第二步是汇总 CFA。通过验证性因子分析（CFA）的验证程序发现，该模型通过拟合指数评估达到了结构有效性的阈值。当所有平均方差提取（AVE）超过大于 0.5 的阈值时，该模型也达到了收敛有效性。综合可靠性（CR）值的评估表明，所有 CR 值均超过了 0.6 的阈值，这表明结构达到了综合可靠性。总体而言，OC 结构、AIC 结构、PT 结构和 NENA 结构的 NENA-q 模型用于 CFA 已经满足了拟合指数，并通过了 AVE、CR 和正态性测试的测量。一旦通过 CFA 程序验证了测量模型，研究人员就可以将这些结构组装到结构模型中，并通过结构方程建模（SEM）程序估计所需的参数。
