Hi,

Your question:

What's the minimum sample size to conduct the SEM analysis utilizing AMOS software?

For conduct the Structural equation model analysis using AMOS software, mininum 100 samples were needed.  Generally, SEM undergoes five steps of model specification, identification, estimation, evaluation, and modifications (possibly).  Using the above mentioned 5 steps you should do the SEM analysis by AMOS.

thans.

I am afraid there is no way to answer this question without further information. Technically you will need more cases than variables. But anything beyond that will depend on the number of variables, their standard deviations/errors, the aim of the analysis (degree of generalisation) ...

As noted, more information is needed. For SEM designs, then the number of variables in total and number of indicators is needed. For SEM designs (e.g. using AMOS), I tell my students that 100 is the absolute minimum, though 200+ is preferred. If using PLS (e.g. SmartPLS), then it is 10 observations per arrow to a construct is the minimum, and whilst PLS can work with small samples, larger samples allow a greater ability to detect smaller path coefficients as being significant. As they say, "the more the merrier"!

Hi,

one of the most central paper in this regard is

Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and meta-analysis. Sociological Methods & Research, 26(3), 329–367.

A few comments:

a) rules of thumb (as so often) are useless. Consequences of low sample size depend on the context (see the paper)

b) the "N-question" depends on the consequences: your first goal should be the test of the model (otherwise all issues like unbiasedness of parameters and efficiency are of less importance). With regard to the chi-square test, low sample size leads on one hand to low power but on the other hand to an overrejection of correct models. However, there are correction methods that can be applied with a little R function (the SWAIN correction) - see 

Herzog, W., & Boomsma, A. (2009). Small-sample robust estimators of noncentrality-based and incremental model fit. Structural Equation Modeling, 16, 1–27.

c) If your model fits, then low sample size biases your parameters. This is a concern (but as you see, its the last in the sequence). But there is no magical border of N.

I personnally would refrain from using PLS. What does it help that this method can estimate *something* with less bias and higher efficiency when it is unclear what this 'somethin'g is (and whether it reflects something reasonable (=no test of causal assumptions). 

Overall, the best bet is to conduct a simple Monte-Carlo-simulation (for instance using the simsem-package in R) in which you specify your target model as the population model an test if - given your sample size - this model could be recovered. See

Muthén, L. K., & Muthén, B. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599–620.

HTH

Holger

Hi,

Your question:

What's the minimum sample size to conduct the SEM analysis utilizing AMOS software?

For conduct the Structural equation model analysis using AMOS software, mininum 100 samples were needed.  Generally, SEM undergoes five steps of model specification, identification, estimation, evaluation, and modifications (possibly).  Using the above mentioned 5 steps you should do the SEM analysis by AMOS.

thans.

Hi all, 

You can check Hoyle [R.H. (ED.) (2012). Handbook of structural equation modeling,  Guilford Press] for a discussion on how many indicators should be included for each latent variable (p.65). Some ideas included:

̵   For a single latent variable with reflective indicators, 3

̵   When a model includes more than one latent variable and the latent variables are related, allowing for latent variables with even fewer indicators is

-  When sample size is small, estimation failures are less likely as the number of indicators per latent variables increases

I also recommend Hair et al. [Hair, Black, Babin, Anderson & Tatham. (2014). Multivariate Data Analysis, 7th Edition]. See discussion on sample size in pages 573-574. They also include some guidelines for using cutoff values for GOD indices depending on the model complexity (sample size and number of observed variables) (pp. 583-584)

Hope this helps,

Emilio

This could be of help as no direct or right answer:

http://www.quantpsy.org/rmsea/rmsea.htm