Nicolas Sommet ๐Ÿ‡บ๐Ÿ‡ฆ Profile picture
SNSF Ambizione Lecturer in Social Psychology @Centre_LIVES/@unil. Tweet once a week. Interests: #IncomeInequality #SocialClass #Motivation #OpenScience #Stats
Dec 15, 2022 โ€ข 12 tweets โ€ข 6 min read
At the top U.S. universities in psychology, there are 17 Democrats for every 1 Republican.

Some argue that this kind of political imbalance is not a cause for concern.

Others believe that it poses an existential threat to the field.

๐Ÿงต A dialectical thread ๐Ÿงต ๐—ง๐—›๐—˜๐—ฆ๐—œ๐—ฆ

Two studies suggest that the political orientation of researchers or their studies has little effect on research outcomes.
Sep 12, 2022 โ€ข 5 tweets โ€ข 8 min read
@dom_muller @mjbsp @FGabarrot @cedricbatailler I get your point & that of @AntalHaans/@seriousstats. FYI, we intend to adjust the wording of the piece to be more precise & add a subsection on interaction ES. That being said, I don't think there are any mathematical flaws in the preprint or any confound in the sims. 1/5 @dom_muller @mjbsp @FGabarrot @cedricbatailler @AntalHaans @seriousstats First, I agree that:
1) power calculation is the same for main effects & interactions (eq. 1) and
2) ES calculation is essentially the same for main effects & interactions (eqs. 2 & 3, respectively).
(note that the overall interaction ES is displayed by the web app) 2/5 Image
Sep 8, 2022 โ€ข 15 tweets โ€ข 7 min read
Power analysis for #interactions can be tough!

๐Ÿ“ข Our new preprint features:
๐Ÿญ An intuitive taxonomy of 12 types of interaction
...with the ๐˜•s to reach power = .80/.90
๐Ÿฎ A ๐Ÿ˜ญ meta-study
๐Ÿฏ Simulations testing 3 ways to โ†—๏ธ power
๐Ÿฐ A cool web app!

๐Ÿงต

osf.io/xhe3u/ ๐Ÿญ๐—ฎ As we know from popular blogs/papers, power analyses differ b/w main effects & interactions because:

๐Ÿ‘‰a main effect corresponds to a difference b/w means

๐Ÿ‘‰a two-way interaction corresponds to a difference b/w mean subdifferences

(using simple b/w-Ss designs as examples) Image
Sep 27, 2018 โ€ข 5 tweets โ€ข 2 min read
The Spirit Level has been cited โ‰ˆ10K times (โ‰ˆ700 times in 2018).

The book is straightforward: It uses cross-sectional data to show negative effects of #IncomeInequality on health.

The problem: It does NOT hold up to scrutiny.

๐Ÿ’ Thread ๐Ÿ’ #1 ๐Ÿ’-picking.

In the Spirit Level, some countries are excluded from the analysis without justification. When including these countries and using the latest estimates available, the core findings of the book disappear. [2/5]