for a repeated measures design

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marj_aslan
PLS Junior User
Posts: 1
Joined: Wed Sep 15, 2021 3:06 am
Real name and title: Marjan Aslan

for a repeated measures design

Post by marj_aslan »

Dear All,
For a research with a repeated measure design, I was wondering whether it is possible to calculate paired-samples t-tests manually using the Smartpls output from the MGA? Please see below for more details:
In this research, as a first step (before being exposed to the conditions/scenarios) participants' psychological disposition was measured. Then the same group of participants were exposed to multiple scenarios/conditions (a factorial design). After each scenario, participants completed a questionnaire to give their ratings in relation to a particular dependent variable (i.e., the study used a repeated measure design). The goal is to explore in which of these conditions/scenarios the influence of a psychological disposition (as an independent variable) on the dependent variable is significantly stronger (or weaker). In other words, the goal is to explore how the impact of a particular psychological disposition changes in multiple scenarios. Any advice is appreciated.
Best Regards,
M.
cwang
PLS Junior User
Posts: 1
Joined: Sat Jun 18, 2022 12:52 am
Real name and title: C. V.

Re: for a repeated measures design

Post by cwang »

Hi marj_asian,

I was wondering if you found an answer to your question?
I have a similar design in my study and I would also like to know how to handle repeated measures (in my case: longitudinal) data in smart PLS and how to test for differences. As far as I know, you use MGA for independent groups like gender, but if all the people saw the same scenarios, an MGA should not be conducted?

Any help from the smartPLS community is appreciated.

Best regards,
C.
rayouby
PLS Junior User
Posts: 9
Joined: Thu Jul 26, 2007 2:07 am
Real name and title: Reem Ayouby, Ph.D.
Contact:

Re: for a repeated measures design

Post by rayouby »

Hello everyone,

I think you may find these references useful:

Roemer, E. (2016). A tutorial on the use of PLS path modeling in longitudinal studies. Industrial Management & Data Systems, 116(9), 1901–1921. https://doi.org/10.1108/IMDS-07-2015-0317

Streukens, S., & Leroi-Werelds, S. (2016). PLS FAC-SEM: An illustrated step-by-step guideline to obtain a unique insight in factorial data. Industrial Management & Data Systems, 116(9), 1922–1945. https://doi.org/10.1108/IMDS-07-2015-0318

Would love to hear about what you have done for your research.
Reem Ayouby, Ph.D.
reemayouby.com
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