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Re: Sample size calculation using G*power Analysis

Posted: Sat Oct 06, 2018 5:00 pm
by jedisummary
Hosam wrote: Thu Nov 26, 2015 7:48 pm However, if you want to use Gpower you could get sample size of 138 respondent as the following (please correct me if I'm mistaken)

F tests - Linear multiple regression: Fixed model, R² increase
Analysis: A priori: Compute required sample size
Input: Effect size f² = 0.15
α err prob = 0.05
Power (1-β err prob) = 0.95
Number of tested predictors = 5
Total number of predictors = 5
Output: Noncentrality parameter λ = 20.7000000
Critical F = 2.2828562
Numerator df = 5
Denominator df = 132
Total sample size = 138
Actual power = 0.9507643
Thanks for the link.

Re: Sample size calculation using G*power Analysis

Posted: Mon Feb 10, 2020 3:01 pm
by danurahardja
can anyone show me a tutorial on how to use the program? i a currently doing my thesis, with 2 independent variable, 1 mediator and 1 dependent

Re: Sample size calculation using G*power Analysis

Posted: Fri Feb 21, 2020 7:15 am
by astratom
thanks for the help guys

Re: Sample size calculation using G*power Analysis

Posted: Wed Mar 03, 2021 12:46 pm
by chenchenchen
Hello all,

I have a reflective-formative type model and there are two first order latent variables. I am interested in testing the weights of the two first order latent variables to second order latent variable. I will use the disjointed two stage approach to model analysis. Should I calculate the sample size in the second stage by using n=2 for the number of predictors and the t-test method? Or should I calculate the sample size in the first stage by using n=20 for the number of predictors and the t-test method? (one of the first order latent variables have 20 indicators and another have 9 indicators.)

Thank you.

Re: Sample size calculation using G*power Analysis

Posted: Wed Sep 22, 2021 3:59 am
by navya66
Hi everyone,
In continuation to the discussion here, I had a query.
When calculating minimum sample size in GPower for a study that has one tailed hypotheses, I have used the following settings (based on 2nd order, 2 latent variables predicting another 2nd order latent variable, all reflective):

t-test
Linear multiple regression: Fixed model , single regression coefficient
A priori analysis
One tailed
Effect size 0.05
Alpha error prob. 0.05
Power 0.80
No. of predictors = 2
This gives me a minimum sample size of 126

As part of my analysis, I have done a group specific analysis (2 groups with greater than 126 sample size) as well as analysis of the total dataset (greater than 300 sample size).
Am I correct to assume that I have followed the correct settings for GPower and I can say that the sample size of the study is sufficient based on the above GPower A priori analysis?

Re: Sample size calculation using G*power Analysis

Posted: Fri Jan 14, 2022 8:37 am
by jmbecker
Your sample size is sufficient to detect effects with an effect size of 0.05 with 5% error probability and 80% power in each group. However, that does not mean that you are able to detect differences between the groups. That would be another different power analysis.

Re: Sample size calculation using G*power Analysis

Posted: Fri Jan 14, 2022 9:58 am
by navya66
Yes, I have understood your point. As I mentioned, I have done separate analysis for each group as well as an overall analysis, I have not done any comparison of the two groups.
Thank you Dr. Becker and wish you a very happy new year :)

Re: Sample size calculation using G*power Analysis

Posted: Sun Jan 30, 2022 11:11 pm
by rathor1072@gmail.com
There is another method to calculate power
Aguirre-Urreta, M., & Rönkkö, M. (2015). Sample size determination and statistical power analysis in PLS using R: an annotated tutorial. Communications of the Association for Information Systems, 36(1), 3.

Has anyone tried this?

Re: Sample size calculation using G*power Analysis

Posted: Mon Aug 07, 2023 3:11 am
by michaelaskew
To perform a sample size calculation using G*Power analysis, follow these steps:

Choose a Statistical Test: Determine the specific statistical test you plan to use for your study, such as t-test, ANOVA, regression, etc.

Select Effect Size: Decide on the effect size you want to detect. This is often based on previous research or your expectations.

Choose Significance Level (α): Set the significance level, typically denoted as α (alpha), which represents the probability of making a Type I error (false positive). Common values are 0.05 or 0.01.

Select Power (1 - β): Decide on the desired statistical power, often denoted as 1 - β (beta). This is the probability of correctly rejecting a false null hypothesis (i.e., avoiding a Type II error). Common values are 0.80 or 0.90.

Indicate Number of Groups (if applicable): If your study involves multiple groups, indicate the number of groups you will be comparing.

Specify Other Parameters: Depending on the test, you might need to input additional parameters, such as the correlation between variables, degrees of freedom, etc.

Run G*Power: Input all the above information into the G*Power software. It will calculate the required sample size for your study.

Interpret Results: G*Power will provide you with the recommended sample size based on your inputs. https://coinflipper.net/