A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario
By Mumtaz Ali Memon, Hiram Ting, Thurasamy Ramayah, Francis Chuah, Jun-Hwa (Jacky) Cheah. 2017
239 citations recorded by the source corpus.
Although structural equation modeling (SEM) is a powerful statistical technique, understanding its methodological assumptions before data analyses is essential to attaining more robust results. In this editorial, we outline four major methodological issues which are related to the application of SEM in Malaysia along with their respective guidelines. These issues include 1) probability and non-probability sampling, 2) pre-testing and pilot study, 3) CB-SEM and PLS-SEM, and 4) exploratory and confirmatory factor analysis. We also recommend the steps that the local research community, especially the postgraduate students, should consider taking to keep themselves up-to-date with methodological advances and to make informed decisions about the use of SEM. This humble effort will help to clarify the confusion and doubts many lecturers and postgraduate students in Malaysia might have, and provide directions to help them proceed in a practical manner.
Subfields: Accounting, Education
View Paper: https://www.semanticscholar.org/paper/5e1da16603d3cf279e4711a0283577f5e06db4dc
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Citations in the corpus
- MODERATION ANALYSIS: ISSUES AND GUIDELINES (2019, background): “Also, a brief explanation related to pre-analysis guidelines is detailed in Memon et al. (2017).”
- ASSESSING REFLECTIVE MODELS IN MARKETING RESEARCH: A COMPARISON BETWEEN PLS AND PLSc ESTIMATES (2018, background): “The purpose was to elicit comments from them about any potential miscomprehension and flaws related to the questionnaire’s format, design and wording (Hair et al., 2010; Memon et al. 2017).”
- Sampling weight adjustments in partial least squares structural equation modeling: guidelines and illustrations (2020, background): “Most notably, depending on whether the research aims are to test a theory or make predictive statements, researchers have to ensure that a sample is representative of the population (Memon et al., 2017).”