Great tutorial! I was just a bit unsure in terms of specifying the matrices, and more specifically how you can know the values that should be included on beforehand? Thanks in advance!
If you’re a novice, I wouldn’t worry too much about specifying matrices. This is advanced and most of the time unnecessary, imo. Lots of times, convention and other research results provide the number to put in the power analysis. Most effect sizes are based on consensus, so what is small or large is suggested by a researcher and other follow suit (of course this is offered with reasoning)
how do you know which effect size you need? do you have to calculate it yourself or could you choose that you want a medium effect size and just put in .15?
Usually, you grab the effect size from previous literature/studies. You find the smallest effect that has been reported. Barring that, you can calculate it yourself in G*Power using the Determine button/effect size calculator tray. If that fails, then yes, put in a conventional effect size. I would overshoot sample size with a small effect rather than a medium one, but it’s up to your theoretical narrative!
Can you please interpret in simple language what it mean when we are getting 92 total sample size after selecting R2 deviation from zero.. When we apply this test? and how we can interpret it in simple language.
If the output is telling you 92 sample size, it means you should get 92 people to participate to reach the predicted power level of the test you did (R-square dev from zero). If you think that is too low, then you can evaluate if you put the information in correctly into G*Power OR if that specific test is the one you really want.
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Great tutorial! I was just a bit unsure in terms of specifying the matrices, and more specifically how you can know the values that should be included on beforehand? Thanks in advance!
If you’re a novice, I wouldn’t worry too much about specifying matrices. This is advanced and most of the time unnecessary, imo. Lots of times, convention and other research results provide the number to put in the power analysis. Most effect sizes are based on consensus, so what is small or large is suggested by a researcher and other follow suit (of course this is offered with reasoning)
@@AlexanderSwan Thank you so much! This was very clarifying!
how do you know which effect size you need? do you have to calculate it yourself or could you choose that you want a medium effect size and just put in .15?
Usually, you grab the effect size from previous literature/studies. You find the smallest effect that has been reported. Barring that, you can calculate it yourself in G*Power using the Determine button/effect size calculator tray. If that fails, then yes, put in a conventional effect size. I would overshoot sample size with a small effect rather than a medium one, but it’s up to your theoretical narrative!
Can you please interpret in simple language what it mean when we are getting 92 total sample size after selecting R2 deviation from zero.. When we apply this test? and how we can interpret it in simple language.
If the output is telling you 92 sample size, it means you should get 92 people to participate to reach the predicted power level of the test you did (R-square dev from zero). If you think that is too low, then you can evaluate if you put the information in correctly into G*Power OR if that specific test is the one you really want.
how to know number of predictor?
This is how many X variables you have in your analysis.
@@AlexanderSwan thanks, btw can u tell me reference for that?
@@user-gs5tw1yu3j any regression tutorial will have the same explanation