The Journal of Survey Statistics and Methodology (JSSAM) is sponsored by AAPOR and the American Statistical Association. Its objective is to publish cutting-edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data.
#JournalOfSurveyStatisticsAndMethodology #Survey #Methods #Statistics #data
| JSSAM homepage | https://academic.oup.com/jssam |
| JSSAM Instructions to Authors | https://academic.oup.com/jssam/pages/General_Instructions |
This is the last day of posts from the Univ. of Nebraska-based social media team. We set up the JSSAM social media accounts 4 years ago to spread the word about the great work published in the journal. The account has been managed by editor Kristen Olson, Angelica Phillips, & Ryan Doud.
The new editorial team of Emily Berg and Brad Edwards will pick up managing the JSSAM social media in 2025! Social media from the journal will be on hiatus until then.
Call for papers! Special Issue: Advances in Survey Paradata with Guest Editors: Brady T. West & Lynne S. Schofield
Submissions to the special issue are welcomed between December 16, 2024 and May 16, 2025
Details are here:
https://academic.oup.com/jssam/pages/survey-of-paradata-cfp
New article alert! Trinh H K Duong, Olivier Bouriaud, & Guillaume Chauvet on "A New Sampling Framework for Spatial Surveys with Application to the French National Forest Inventory"
"This study presents a novel framework called two-stage two-phase sampling, in which the first phase consists of a two-stage sampling design, a concept that has not been previously explored in the existing literature for spatial surveys."
New Article Alert! "Model-Based Prediction for Small Domains Using Covariates: A Comparison of Four Methods" by Victoire Michal, Jon Wakefield, Alexandra M Schmidt, Alicia Cavanaugh, Brian E Robinson, Jill Baumgartner
In this Article "We propose a procedure to compute prediction intervals that is valid for a variety of modeling method (including random forests and the LASSO) and that relaxes the assumption of exchangeable data."
New article alert! "Inferring a Population Composition From Survey Data With Nonignorable Nonresponse: Borrowing Information From External Sources" by Veronica Ballerini and Brunero Liseo
"Our study shows that employed individuals are more likely to respond to the survey. By neglecting nonresponse bias, conventional estimation methods may overestimate the employment rate, leading to inaccurate policy decisions and resource allocations."