Partial least square structural equation modeling in the final phase of product and instrument development using universal design and agile development model

The partial least squares path modeling or PLS-PM, is best known as the partial least squares structural equation modeling or PLS-SEM. It is a method of structural equation modeling. This method allow the estimation of complex cause and effect relationship models with latent variables. This paper ex...

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Bibliographic Details
Main Author: Rosseni Din (Author)
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia, 2020.
Online Access:Get fulltext
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100 1 0 |a Rosseni Din,   |e author 
245 0 0 |a Partial least square structural equation modeling in the final phase of product and instrument development using universal design and agile development model 
260 |b Penerbit Universiti Kebangsaan Malaysia,   |c 2020. 
856 |z Get fulltext  |u http://journalarticle.ukm.my/17368/1/16.pdf 
520 |a The partial least squares path modeling or PLS-PM, is best known as the partial least squares structural equation modeling or PLS-SEM. It is a method of structural equation modeling. This method allow the estimation of complex cause and effect relationship models with latent variables. This paper explain how this method can be use in the final stage of a product design and development study. In the study, learning outcome becomes the center for universal design, development and implementation processes. There are two stages of major processes. First is the instructional design processes while the second is the development processes. The development processes ends with usability test. The next phase is the evaluation phase. Last phase is the modeling processes. The paper will first explain about the localized model of product design and development procedure. Subsequently it will elaborate the final phase of the second stage processes, which is important in impact study of product design and development. While at this, the partial least square structural equation modeling will be explain. It is a powerful statistical technique yet misconceptions happen a lot. Proper techniques is essential for methodological assumptions in order to attain robust results. Using latest software alone is not enough. 
546 |a en