Category: Multilevel Modeling

Multilevel models with group-level predictors

Kari Lock Morgan writes: I’m writing now though with a multilevel modeling question that has been nagging me for quite some time now. In your book with Jennifer Hill, you include a group-level predictor (for example, 12.15 on page 266), but then end up fitting this as an individual-level predictor with lmer. How can this […]

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Perhaps you could try a big scatterplot with one dot per dataset?

Joe Nadeau writes: We are studying variation in both means and variances in metabolic conditions. We have access to nearly 200 datasets that involve a range of metabolic traits and vary in sample size, mean effects, and variance. Some traits differ in mean but not variance, others in variance but not mean, still others in […]

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Cool postdoc position in Arizona on forestry forecasting using tree ring models!

Margaret Evans sends in this cool job ad: Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity […]

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You’ve got data on 35 countries, but it’s really just N=3 groups.

Jon Baron points to a recent article, “Societal inequalities amplify gender gaps in math,” by Thomas Breda, Elyès Jouini, and Clotilde Napp (supplementary materials here), and writes: A particular issue bothers me whenever I read studies like this, which use nations as the unit of analysis and then make some inference from correlations across nations. […]

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Multilevel data collection and analysis for weight training (with R code)

[image of cat lifting weights] A graduate student who wishes to remain anonymous writes: I was wondering if you could answer an elementary question which came to mind after reading your article with Carlin on retrospective power analysis. Consider the field of exercise science, and in particular studies on people who lift weights. (I sometimes […]

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The hot hand—in darts!

Roland Langrock writes: Since on your blog you’ve regularly been discussing hot hand literature – which we closely followed – I’m writing to share with you a new working paper we wrote on a potential hot hand pattern in professional darts. We use state-space models in which a continuous-valued latent “hotness” variable, modeled as an […]

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“Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models”

Aki points us to this paper by Tore Selland Kleppe, which begins: Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterisation so that the re-parameterised target distribution has close to constant […]

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The competing narratives of scientific revolution

Back when we were reading Karl Popper’s Logic of Scientific Discovery and Thomas Kuhn’s Structure of Scientific Revolutions, who would’ve thought that we’d be living through a scientific revolution ourselves? Scientific revolutions occur on all scales, but here let’s talk about some of the biggies: 1850-1950: Darwinian revolution in biology, changed how we think about […]

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The competing narratives of scientific revolution

Back when we were reading Karl Popper’s Logic of Scientific Discovery and Thomas Kuhn’s Structure of Scientific Revolutions, who would’ve thought that we’d be living through a scientific revolution ourselves? Scientific revolutions occur on all scales, but here let’s talk about some of the biggies: 1850-1950: Darwinian revolution in biology, changed how we think about […]

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