I found this table (as appears in the attached image)
in this book
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
I need to understand how to use this table
Basically, the table outlines that the sample size depends on three factor.
First, the number of variables or constructs that are intended to explain the dependent variable. Given you have a lot of variables; you need a higher sample size. This is why, if you look down the columns, the necessary number of participants increases, the more “arrows” point at the dependent variable.
Second, the estimated effect size. R square can be used to estimate the effect size (have a look at the effect size f). If you guess or prior research let you conclude that your independent variables will have substantial impact, in other words, a large effect size on the dependent construct, you need less participants. If you guess that the effect size is small, you need more participants to “find” the effect.
Third, the significance level. If you define the relationship as significant at a 10% alpha error, you need less participants compared to a defined alpha error of 1%. That is, the more you accept being susceptible to make an alpha error, the less participants you need.
Hope that helps.
Rafael
Basically, the table outlines that the sample size depends on three factor.
First, the number of variables or constructs that are intended to explain the dependent variable. Given you have a lot of variables; you need a higher sample size. This is why, if you look down the columns, the necessary number of participants increases, the more “arrows” point at the dependent variable.
Second, the estimated effect size. R square can be used to estimate the effect size (have a look at the effect size f). If you guess or prior research let you conclude that your independent variables will have substantial impact, in other words, a large effect size on the dependent construct, you need less participants. If you guess that the effect size is small, you need more participants to “find” the effect.
Third, the significance level. If you define the relationship as significant at a 10% alpha error, you need less participants compared to a defined alpha error of 1%. That is, the more you accept being susceptible to make an alpha error, the less participants you need.
Hope that helps.
Rafael
You can use the Software G*Power (FREE) to obtain the Statiscal Power of your data (a priori, when you are design the research or a posteriori, when you have colected your data, so use the Cohen values). The software indicates the size of sample, with explained above.
http://www.gpower.hhu.de/en.html
Some authors (ex., Barclay, Higgins, & Thompson, 1995; Chin, 1998) recommend PLS users follow a similar "rule of 10" guideline as SEM users: at least 10 cases per measured variable for the larger of (1) the number of indicators in the largest latent factor block, or (2) the largest number of incoming causal arrows for any latent variable in the model. Note, however, that appropriate sample size choice is more complex than any rule of thumb. The appropriate size depends in part both on the degree that factor structure is well defined (ex., are weights > .70) and how small are the path coefficients the researcher seeks to establish (prove different from 0; ex., a much larger sample is needed to establish path coefficients of .1 than .7). The “rule of 10” may yield sample size with inadequate statistical power. The larger the sample, the more reliable the PLS estimates.
Check this out:
https://www.smartpls.com/resources/ebook_on_pls-sem.pdf
Hey,
the 10 times rule can be seen as outdated (Rigdon, 2016). If you want to determine the power of your statistical test, I recommend to conduct a Monte Carlo simulation, see Aguirre-Urreta & Rönkkö (2015) for a tutorial. However, currently, their guideline only works for models where the constructs are operationalized by common factors. Moreover, you should ensure that you use PLSc to estimate your model if it contains common factors (Dijkstra & Henseler, 2015).
Best regards,
Florian
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