forum.smartpls.com

hello everyone,
I just use smart PLS, so I dont know clearly about it.
Can I ask you some questions.

1. how about the sample size to use PLS?
Because my sample size is small (about 50 observations), so i am afraid I cant use PLS or the it can affect to the significant of the model

2. as far as I know I can take t-statistic through bootstrap and then, compute P-value by using excel function TDIST.


TDIST= (T-statistic, DF, Tailed)

I dont know what is DF and how can i take it.

Sorry, I can't use English very well
Thanks for your help.

Hi,

1) Sample size:
1.1)A priori = How big should my sample be?
- Look for the latent variable (LV) with the bigger number of arrows that arrive (could be from structural model ou from the formative measurement model). Use this number as "number of predictors"
- Download G*Power3 from (http://www.psycho.uni-duesseldorf.de/ab ... d-register)
- Install and open G*Power3
- Tests/Correlation../Linear Multipl... R² deviation from zero.
- A priori
-f² = 0.15 (medium)
- alpha = 0,05
- Power = 0.8
- Predictors = max number of arrows arriving in a LV
- Calculate --> n

1.2) Post-hoc
I already colected my data, Were they enough?
- If t > 1.96 = the relations are significant and Power ok (you have not type II error)
- if t < 1.96 = Doubt! [relations = zero] or [sample size too small]?
test the Power using Post hoc test in G*Power 3, if Power > 0.8, you could conclude that the sample was enough and relations = 0 (in the population).

2) If you ru bootstrap with 1000 resamples, the DF = 999.
DF = resamples - 1

Best regards,

Bido

Dear Prof. Bido,

do you have any paper or other source I can cite in my dissertation? I am looking especially for a source of your recommendation to use the "max number of arrows arriving in a LV" for the "number of predictors".

Thank you very much in advance for your answer.

Danny

Hi,
Here is some references:

CHIN, W. W. The partial least squares approach for structural equation modeling. In: G. A. Marcoulides (Ed.); Modern methods for business research. p.295–336, 1998. New Jersey: Lawrence Erlbaum Associates, Inc.

CHIN, W. W.; NEWSTED, P. R. Structural equation modeling analysis with small samples using partial least squares. In: R. H. Hoyle (Ed.); Statistical strategies for small sample research. p.307–341, 1999. Thousand Oaks, CA: Sage Publications. p.326-327

HAIR JR., J. F.; HULT, G. T. M.; RINGLE, C. M.; SARSTEDT, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications, Inc., 2014. p.20-21

Best regards,
Bido