Taking a look at multilevel modeling from an economics perspective, and then from a psychology perspective

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I’ve posted a preprint on endogeneity and entrepreneurship research at https://osf.io/75tn8/. I am hoping that entrepreneurship researchers find it useful!

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It’s time for a new default visualization for continuous-by-continuous interaction effects

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I’m banning the phrase theoretical contribution from my reviews and decision letters, and trying to evaluate a paper on the basis of its usefulness to an identifiable audience of scholars

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When you get a rejection, avoid the temptation to make changes before sending it out for review again. Reject the rejection.

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Data wrangling is an essential skill for all applied researchers. Along with taking messy data and making it useful, being an effective wrangler provides critical insights to your data and how to model it effectively. It’s a skill worth investing in!

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Increasing precision in our hypotheses leads to better science, better transparency, and a reduction in the tendency to infer a causal relationship from associational results.

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In a first round review, focus on a few really big issues rather than a lot of smaller issues.

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Tidy Data is friendly data, and friendly data makes data science possible. So remember, always be Tidy!

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Being a jerk reviewer is embarrassing for the editor, rarely provides actionable insights, and creates more work.

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