esixtus
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Thanks a lot for your response! I will try that out. It will, however, take quite a while, cause I'm in fact very new to Bayesian LMMs. I was hoping for a "quick and dirty" (but sound) JASP solution with some default priors and just some c…
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I can see that all is not easy, and maybe not unambiguous, from a philosophical point of view. However, I still wonder, whether in principle my application of EJ's instruction could work (my previous comment, from yesterday, 1.57pm) or whether I fun…
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From the combined data of the first and second experiment I have the 95% CI [-0.222, 0.488]. Can I directly derive the mean 0.133 (i.e., mean value of the CI)? Then, my first hunch was to simply use the 95% CI as the new normal distribution (somethi…
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Hi, thanks a lot for this detail. So I guess I shouldn't just multiply BF[01]s, because previous experiments might have already led me to expect data in favour of the H0. In the first part of this cool paper you cite, you write that the updated dist…
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This sounds pretty cool and straightforward. Am I correct to assume that it works just the same with BF[01]s and with more of them? That is, Study 1 yields BF[01] = 3.4, Study 2 yields BF[01] = 1.2, Study 3 yields BF[01] = 3.6, and Study 4 yields BF…
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Hi, thanks for your reply! I'm afraid I need to delve a bit more into understanding the details of Bayesian statistics to know exactly what you mean. I thought that I could change the default values given in JASP under "Prior" into somethi…