Confirmatory of Composite Analysis
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Abstract
The advantage of the variance-based structural equation model is that simple convergence, limited sample size and non-normal distribution data are especially in comparison to the covariance-based structural equation model, such as the confirmatory factor analysis. In addition, the variance-based approach was developed in 2015 using factor variable influence less bias and the scholar proposed a confirmatory composite analysis. As a result, variance-based can operate both confirmatories, while covariance-based only performs a confirmatory factor analysis. This study is investigating the results of confirmatory composite analysis to use a variance-based or partial least square model of structural equation using Jhantasana data (2020). The outcome demonstrates flexible composite confirmation analysis. However, the significance of this method is multicollinearity, including model-fit criteria, which by solving it can increase the quality of a model. Confirmatory composite analysis can focus more on many fields as many organizations create a composite index for many indicators than a latent variable used only for behavioral studies.
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บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของวารสารการบัญชีและการจัดการ
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับมหาวิทยาลัยมหาสารคาม และคณาจารย์ท่านอื่นๆในมหาวิทยาลัยฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว
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