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Almario, C. V., Chey, W. D., & Spiegel, B. M. R. (n.d.). Increased Risk of COVID-19 Among Users of Proton Pump Inhibitors. 17.

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Darren L Dahly, PhD on Twitter: “Share this with whoever needs to hear it: ‘risk factors’, even strong ones that are robustly identified, are almost never useful for making actionable predictions, especially when considered in isolation. TLDR: [Big OR] does not equal [Good, Useful, or Easy Decision]” / Twitter. (n.d.). Twitter. Retrieved July 11, 2020, from https://twitter.com/statsepi/status/1249680569463721984

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”Blasphemy” and “irony” are mentioned--and continued through Kafer--to connote less math and masculinity and more--frankly--code, spoken from reclaimed empowerment. I couldn’t digest Haraway’s prose--within the coming Information Age she concentrates a lot of terms like machine parts herself--”Star Wars,” “Magic Mountain,” even “goddess” (Haraway 57). I wished for her own Dictionary.com to identify what these references meant to her.

Did she mean to confound us for shock-factor’s sake? Because within her text that I could decipher I felt a postgendered, post-species, colorblind coalition that I couldn’t fully find affinity within, as much as the vague image of the Cyborg tantalizes me. For unity did she mean to catalyze divisiveness through language?

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We branch with these powers because the prime factorization has \(2^3\). If it were instead \(2^5\), we would branch with \(2^0\), \(2^1\), \(2^2\), \(2^3\), \(2^4\), and \(2^5\), and we would have a branching factor of 6.

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The EFA vs. CFA question was a stickler for one peer reviewer, and I can understand why. When measurement is based on strong theory, then I believe that CFA is preferable to EFA. But that was rarely the case in these datasets.

I suspect that this factor was so strong because it consisted of only four observed variables, and three of them were written measures of verbal content. All of the verbal cariables correlated r = .72 to .89. Even the "non-verbal" variable (numerical ability) correlates r = .72 to .81 with the other three variables (Rehna & Hanif, 2017, p. 25). Given these strong correlations, a very strong first factor is almost inevitable.

This factor may be weak because the sample consists of Sudanese gifted children, which may have restricted the range of correlations in the dataset.

My colleague, Roberto Colom, stated in his blog (link below) that he would have discarded samples with fewer than 100 individuals. This is a legitimate analysis decision. See his other commentary (in Spanish) at https://robertocolom.wordpress.com/2018/06/01/la-universalidad-del-factor-general-de-inteligencia-g/

Except that the effect sizes are too small for them to be credible in the absence of pre-registration of this hypothesis.

Testimonial

Testimonial

On how increased pressure to publish diminishes creativity.

On the rise of sexy terms like "paradigm shift" in abstracts.

Matthew effect

Importance of "top" journals

On the incentives for journal editors to publish papers they think might improve IF... and how citations are gamed.

JIF is discredited in many reviews. See for example http://dx.doi.org/10.3389/fnhum.2013.00291. A recent independent review of metrics for the Higher Education Funding Council for England also strongly recommended against the use of measures like JIF: http://www.hefce.ac.uk/pubs/rereports/Year/2015/metrictide/

This seems to be at least one reproducible result!