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Showing posts from 2017

I think this technically violates Asimov's zeroth law...

First, AI was tasked with dealing with the pesky Reviewer #2 problem of the scientific peer review process (ok, the Evise feature is just a search & match function, not really AI).  Now, AI is here to handle the messy business of actually writing your scientific manuscript for you.

SciNote has their new magic AI plug-in (sarcasm intended) that will purportedly take the results of your analyses and links to relevant literature and "magically" turn it into a scientific manuscript.  From the product page:
This is where the magic happens
Once your data is nicely organized in sciNote, Manuscript Writer can do its job!
Based on the data you have in sciNote, it will generate a draft of your manuscript. oof.    Insert lateral plaintiff face type emoji here.  

This only perpetuates the issues with paper mills/publishers (that thankfully get exposed (using a fake manuscript generator no less)). At least they didn't launch this new product at 2:14 a.m. Eastern time,  on August…

Precision in Stata

In this post, I explore how to deal with precision issues with Stata.
First, Create Data for Example.
.clear . set obs 1000 obs was 0, now 1000 . g x = 1.1. list in 1/5, noobs +-----+ | x | |-----| | 1.1 | | 1.1 | | 1.1 | | 1.1 | | 1.1 | +-----+ . count if x ==1.1 // zero matches!! 0Precision of Stata storage formatsStata isnt wrong, it's just that you stored the variable x with too little precision (some decimal numbers have no exact finite-digit binary representation in computing). If we change the precision to float or store the variable as double format then it fixes the issue. Note below how  x is represented in Hexidecimal and Binary IEEE format vs. Stata general (16g) and fixed (f) format.
. . count if x == float(1.1) 1000. **formats . di %21x x //hex+1.19999a0000000X+000. di %16L x //IEEE precision000000a09999f13f. di %16.0g round(x, .1) 1.1. di %4.2f round(x, .1)1.10. di %23.18f round(x, .1) 1.100000000000000089Double formatsStoring the …

I did a thing....

In 2009, New Mexico adopted more rigorous high school graduation requirements. I (finally) completed the last of my remaining REL studies that examined the changes in New Mexico’s high school students’ advanced course completion rates under these new requirements. We're providing a webinar and you can join in and listen to the results of the study if this is a topic you're interested in.  See the webinar announcement (at the newly minted Gibson blog) for registration details.

The study that will be presented:
Booth, E., Shields, J., & Carle, J. (2017). Advanced course completion rates among New Mexico high school students following changes in graduation requirements (REL 2018–278). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southwest.
Accessible at https://ies.ed.gov/ncee/edlabs/projects/project.asp?projectID=4491.

Whitewashing your standard errors

Great quote from Gary King warning about the dangers of the all-to-common " , robust " (or I guess it's ( , vce(robust) now) solution for whitewashing the SEs in your model :

"[...] if robust and classical standard errors diverge—which means the author acknowledges that one part of his or her model is wrong—then why should readers believe that all the other parts of the model that have not been examined are correctly specified? We normally prefer theories that come with measures of many validated observable implications; when one is shown to be inconsistent with the evidence, the validity of the whole theory is normally given more scrutiny, if not rejected (King, Keohane, and Verba 1994). Statistical modeling works the same way: each of the standard diagnostic tests evaluates an observable implication of the statistical model. The more these observable implications are evaluated, the better, since each one makes the theory vulnerable to being proven wrong. This is ho…