There is no clear cut answer how many the minimum sample size requirement is. Different experts have proposed different methods and numbers. Below are among the most commonly used in social science research. 

With respect to PLS, literature frequently uses the “10 times” rule of thumb as the guide for estimating the minimum sample size requirement. It suggests that PLS only requires a sample size of 10 times the most complex relationship within the research model. 

The most complex relationship is the larger value between

• The construct with the largest number of formative indicators if there are formative constructs in the research model (i.e., largest measurement equation (LME), and

• The latent variable (LV) with the largest number of independent LVs influencing it (i.e., the largest structural equation (LSE)). 

Condition

This rule is only applies when certain conditions (e.g., strong effect sizes & high reliability of measurement items) are met.

The more complex the model, the more the sample size is required. A “typical” sample size is about 200 cases.

PLS-SEM is advantageous when used with small sample sizes (e.g., in terms of the robustness of estimations and statistical power; Reinartz et al., 2009). However, some researchers abuse this advantage by relying on extremely small samples relative to the underlying population. 

All else being equal, the more heterogeneous the population in a structure, the more observations are needed to reach an acceptable sampling error level. 

Sample size should be determined based on the number of parameter estimates (N:q rule). 5:1 is acceptable. 

Use G*Power software to determine sample size requirement.

Please Click Here to download the software and to get further information about G*Power. 

How many predictors do we have in the models?

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior research methods, 41(4), 1149-1160. Click here.

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods, 39(2), 175-191. Click here.

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: The Guilford Press. Click here.

Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112(1), 155-159. Click here.

Manual of G*Power 3.1. Click here.