[R-meta] (Too) Many effect sizes for one single group
Dear Lukasz, Thank you for your response! I have also opted for aggregating the results to check if that study came out as an influential/outlier study and it did not, would this also be a good approach? I will also run the models in the way suggested. Thank you again. Best wishes, Catia On Fri, 7 Jan 2022 at 15:38, Lukasz Stasielowicz <
lukasz.stasielowicz at uni-osnabrueck.de> wrote:
Dear Catia, under certain circumstances it could be a valid concern. Fortunately, one can test it directly. One could conduct a sensitivity analysis to examine the impact in your specific case: Do the results (mean effect, standard error etc.) change much if you exclude certain effect sizes? Example: Scenario 1: All effects are considered Scenario 2: The study with "too many" effect sizes is excluded Scenario 3: Only one or several effect sizes from the problematic study are considered, e.g. by using the sample() function and choosing a certain number of effects randomly. One could also repeat this procedure to check the influence of the selection procedure. If the estimates differ only slightly across the analyses then you could proceed with the original idea (including all effects). You could mention in the report that this decision is based on some sensitivity analyses that you've conducted. Best wishes Lukasz -- Lukasz Stasielowicz Osnabr?ck University Institute for Psychology Research methods, psychological assessment, and evaluation Seminarstra?e 20 49074 Osnabr?ck (Germany) Am 06.01.2022 um 12:00 schrieb r-sig-meta-analysis-request at r-project.org:
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Content-Type: text/plain; charset="utf-8" Dear Wolfgang, I hope you had a lovely start to the year. I am sorry for starting the year with questions, but I just wanted to
check
whether there is any drawback from including a lot of effect sizes from a single paper when most labs contributed to the meta-analysis with just
one
or two effect sizes? This resulted in a dataset where half of the effect sizes come from multiple experiments run by the same group. The nested nature of the data and dependency of some effect sizes coming from the
same
participants is acknowledged in the model. Thank you! Catia
C?tia Margarida Ferreira de Oliveira Psychology PhD Student Department of Psychology, Room A105 University of York, YO10 5DD Twitter: @CatiaMOliveira pronouns: she, her [[alternative HTML version deleted]]