Here is a practical code snippet for users working with the public-use NSFS 383 file in R.
# Load NSFS 383 public-use data library(haven) nsfs383 <- read_dta("nsfs383_puf.dta")The population for NSFS 383 includes all individuals who:
Any output (tables, regression results) must be vetted by NCSES disclosure analysts to ensure no individual respondent can be identified. This adds 2–4 weeks to the publication timeline.
NSFS 383 has been used in hundreds of peer-reviewed articles. Here are three typical research questions it can answer. nsfs 383
One of the most important aspects of NSF 383 is its ability to adapt quickly to new health concerns. As of recent updates, the standard has incorporated stricter limits on:
Manufacturers seeking certification now must often prove their materials are PFAS-free or below extremely low detection limits.
Given the cryptic nature of NSFS 383, several interpretations have emerged: Here is a practical code snippet for users
svyby(~salary, ~gender, academic, svyquantile, quantiles = 0.5, ci = TRUE)
Important: Always use the provided survey weights (wtsurvy). Unweighted analysis of NSFS 383 will produce biased estimates because the stratified sampling oversamples small demographic groups. NSFS 383 has been used in hundreds of peer-reviewed articles
Research question: Did computer science PhDs experience a larger shift to telework compared to chemistry PhDs? NSFS 383 variable used:
covid_telework_freq,phd_field_cs,phd_field_chem.
Citation format for NSFS 383:
National Center for Science and Engineering Statistics (NCSES). 2021. National Survey of Doctorate Recipients (NSFS 383), 2019–2021 Restricted-Use File. Alexandria, VA: NSF. https://www.nsf.gov/statistics/srvydoctoratework/