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Why can’t we be more idiographic in our research? It is the individual organism that is the principle unit of analysis in the science of psychology (Barlow & Nock, 2009).

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As put by Gelman et al. (2013, page 148): ‘because a probability model can fail to reflect the process that generated the data in any number of ways, posterior predictive p-values can be computed for a variety of test quantities in order to evaluate more than one possible model failure’.

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What is the difference between the errors and the residuals ? What does it mean for a model to predict something ? What is a link function ? In the current post, we use four R functions (viz., the predict, fitted, residuals and simulate functions) to illustrate the mechanisms and assumptions of the generalised linear model.

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In the current post, we present and compare three methods of obtaning an estimation of the ICC in multilevel logistic regression models.

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I have strong doubts that someone reading a blog called barely significant in this troubled period might have escaped the saga of the summer. However, as this story relates closely to the name of this blog and to the motivations behind its creation, I could not help myself to write a summary of this saga (or of its beginning, at least), to inform any unfortunate people that might have missed it.

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Selected Publications

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.
Frontiers in Psychology, 2018.

Rumination is predominantly experienced in the form of repetitive verbal thoughts. Verbal rumination is a particular case of inner speech. According to the Motor Simulation view, inner speech is a kind of motor action, recruiting the speech motor system. In this framework, we predicted an increase in speech muscle activity during rumination as compared to rest. We also predicted increased forehead activity, associated with anxiety during rumination. We measured electromyographic activity over the orbicularis oris superior and inferior, frontalis and flexor carpi radialis muscles. Results showed increased lip and forehead activity after rumination induction compared to an initial relaxed state, together with increased self-reported levels of rumination. Moreover, our data suggest that orofacial relaxation is more effective in reducing rumination than non-orofacial relaxation. Altogether, these results support the hypothesis that verbal rumination involves the speech motor system, and provide a promising psychophysiological index to assess the presence of verbal rumination.
Biological Psychology, 2017.

Teaching

I teach the following courses at Univ. Grenoble Alpes:

  • BA Psychology: Data analysis
  • BA Psychology: Introduction to Cognitive Psychology
  • BA Psychology: Cognitive Psychology: perception, action and categorisation
  • PhD, all disciplines: Introduction to Bayesian statistical modelling

Online resources

Multilevel modelling

Courses on Bayesian data analysis

  • Justin Esarey’s enlightening lectures on Bayesian statistics (amongst other things), and the Learn Bayes website.

  • Statistical rethinking lectures by Richard McElreath on youtube and associated contents.

  • Bayesian data analysis and cognitive modeling by Michael Franke & Fabian Dablander (slides).

  • Bayesian Basics, wonderful introduction to Bayesian data analysis by Michael Clark here. See also his other tutorials here.

Blogs worth reading