 Hi, my name is Sabina Puzzle and I'm a PhD student at the Technical University of Munich. And today I would like to talk to you about how we use multiverse and specification curve analysis as an assessment of generality of effects for our major analytic structural equation model, where we're looking at the relationship between creative potential and creative self-assessment measures. I would like to start off with a quick theoretical background. Basically, it's assumed that everyone has a certain amount of creative potential within them. Two indicators of creative potential are, for example, divergent thinking and intelligence, both of which correlate with creative achievement. However, creative potential does not necessarily lead to creative achievements, as the relationship might be partially influenced by factors relating to the creative self, such as, for example, creative self-beliefs. This is why we chose to look at the relationship between creative self-assessment measures and two indicators of creative potential being intelligence and divergent thinking. We use structural equation modeling, specifically TSSEM with an extension for random effects models, and the VPL approach as multiple studies reported more than one effect size. And we assume that to be dependencies among effect sizes. Two tests on our robust or structural equation model is we use subgroup analysis to test whether the parameters estimates are actually equal across CSA type, so creative self-assessment types, and age groups. Just a quick heads up. These are all preliminary data, so please don't interpret them. And we just wanted to give you an idea how our models look like. So, for example, we are looking at the relationship between divergent thinking and creative self-assessments and testing whether they are mediated, for example, by intelligence. We're also, as I already told you, doing subgroup analysis, so we look at the different models depending on whether children's were assessed, or, for example, adults were assessed. One downside of meta-analytical structural equation modeling is actually that it's kind of limited when it comes to how many different moderators you can include at the same time, which is why we chose to also apply or use another approach to test the robustness of our model, being models in specification curve analysis. As I already told you, we're looking at different age groups and different creative self-assessment types, but there are various other mediated variables. For example, what kind of divergent thinking outcome was used, the test modality of divergent thinking, whether there was some kind of time condition in the tests. And so on. And all these different moderators, you could say, could potentially influence our primary studies. And in that, they could also influence our moderator or our meta-analytical structural equation model. When doing a multiple-use analysis, these different factors, which I told are called moderators before, are actually called witch factors. As you're thinking about which data could possibly have been analyzed in the primary studies. And you're also looking at how factors, which basically tell you how the data is meta-analysed. And what you're doing is you're combining all these different kinds of witch factors and the subgroups. So for example, whether a female sample was used, children were investigated, fluency was reported as an outcome and so on. And you're combining these with the different how factors. And these lead to a lot of different specifications and for all of which we are computing meta-analytical summary effects. And at the end, we're basically comparing all these possible meta-analytical summary effects and we get a mean over these summary effects and it tells us how robust a correlation is depending on which data was analyzed and how the data was analyzed. And yeah, we're doing the meta, this multi-version specification curve analysis for all our bivariate relationships. So for CSA in Divergent Thinking, Divergent Thinking Intelligence as well as Intelligence and CSA. Just as a way to get an idea of how robust our meta-analytical structural equation model actually is. I would like to show you our specification curve from the relationship between the version of thinking and creative self-assessment measures to give you an idea how it could look like or it looks like at the moment. And yeah, so at the top part of the model you can see the dark line which are the summary effects from all our different specifications. And these colored areas are the respective confidence intervals. So for example, this summary effect over here with this quite large confidence interval, then we're going down to the bottom part of the plot was computed using a total sex sample with adults, verbal modality, and down here you have the how factors. So this gives us the information on which kind of which factors and how factors were used, how do we are combined, and what kind of summary effect they yielded. We also get information by the colors. So for example, warmer or hot colors represent combinations of which and how factors that only included a few effect sizes and cooler colors such as blue or green represent a combination with more effect sizes. And when looking at our graph, you can see that there are a lot of effect sizes in the middle of the plot, and that actually the magnitude doesn't increase a lot. So most of our effect sizes are actually in the area of point one to point two, and which basically speaks for the robustness of our effect of especially when you're considering all these different kinds of possible specifications. Next off, we looked at over look at the look at the total summary effect from all these different kinds of specifications, which is a correlation of point 17. And as you can see, 50% of all our different combinations of which and how factors yielded a summary effect between point 13 and 21. So our correlation seems to be quite robust. We can also use a parametric bootstrapping approach to do inferential testing the red curve represents our specification curve and the gray area is basically the curve under the null scenario of the possible zero effect, which clearly deviates from our specification curve. So at least for the relationship between the original thinking and creative self assessment measures are the included correlation seem to be quite robust. So you could say it doesn't matter that we can't include so many different moderated variables. And as the correlation seem to be quite robust. That's it for my talk. I hope it gave you an idea how you can multiple use multiple specification curve analysis to test the robustness of your method and the structure equation model. As I already told you, we would like to do this, the same analysis for the other two bivariate relationships as well to test how robust our model is. Yeah, if you're generally or if you're interested in our project, feel free to write an email. We also did a preregistration of the study. And that's it for today. Thank you.