Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review (original) (raw)

Abstract

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limita...

Figures (1)

Jair et al., (2014) have discussed another alternative method instead of “10 times rule” for minimum sample size 2stimation and Kock & Hadaya, (2018) referred it as the “minimum R-squared method” since it uses minimum 22 in the model for estimating the minimum sample size. This method particularly has been built on Cohen, 1988) power table for least squares regression and three elements require for determining the sample size. The irst element of the minimum R-squared method is the maximum number of arrows pointing ata latent variable n a model, used significance level is the second and third is the minimum R? in the model. Table 01 illustrates he reduced version of the table presented by Hair et al., (2014) and it depends on the significance level of 0.05, which is the most commonly used significance level and assumes that the power is set at 0.8. This method appears to be an improvement over the 10-times rule method, as it takes as an input at least one additional alement beyond the network of links in the model.

Jair et al., (2014) have discussed another alternative method instead of “10 times rule” for minimum sample size 2stimation and Kock & Hadaya, (2018) referred it as the “minimum R-squared method” since it uses minimum 22 in the model for estimating the minimum sample size. This method particularly has been built on Cohen, 1988) power table for least squares regression and three elements require for determining the sample size. The irst element of the minimum R-squared method is the maximum number of arrows pointing ata latent variable n a model, used significance level is the second and third is the minimum R? in the model. Table 01 illustrates he reduced version of the table presented by Hair et al., (2014) and it depends on the significance level of 0.05, which is the most commonly used significance level and assumes that the power is set at 0.8. This method appears to be an improvement over the 10-times rule method, as it takes as an input at least one additional alement beyond the network of links in the model.

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