Even if you end up diverging quite far from these steps, it can still be useful to the modeller and eventual reader to know what knowledge you started with and what you discovered along the way.
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Indeed. Before coming to psychology I was in biology. When someone was a "modeler" this almost NEVER meant they used *statistical* models. Rather, they were mathematical models to, for example, study animal response and survival from environmental change. Basically, to understand
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the logic of a system and to provide some interesting ways to think of things for empirical researchers. On the other hand, now that I am in psychology, the "modeling" I do is all based on statistical models and, IMO, can almost never be used to draw strong conclusions. Rather,
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I literally "trail blaze" a path from the data to the finished model, all in an effort to first get a model that converges and to also highlight what the MODEL can be used for in general. There are so (so) many data dependent decisions along the way, that I would conclude nothing
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from them, which is to EJs point. However, the intention is never meant to be inferential but to find some data to show case a MODEL that the hope is others can use in their own research.
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So for this kind of modeling the data is often intentionally selected (and combed over) with the goal of highlighting what the MODEL can be used for. However, I do think pre-reg would be helpful here as applied researchers often like to interpret the findings.
End of conversation
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