Evidence map›Paper›PMID 30276667›Full record

ArticleDemography2018

Promises and Pitfalls of Using Digital Traces for Demographic Research.

Nina Cesare, Hedwig Lee, Tyler McCormick, Emma Spiro, Emilio Zagheni

Abstract read
In one paragraph

Article in Demography, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Digital Well-being Through the Use of Technology-A Perspective.International journal of MCH and AIDS · 2023
    Article
  6. Article
  7. Probabilistic forecasts of international bilateral migration flows.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  8. Article
  9. Representativeness is crucial for inferring demographic processes from online genealogies: Evidence from lifespan dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  10. Demography: Fast and Slow.Population and development review · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. The Internetization of International Migration.Population and development review · 2021
    Article
  15. Article
  16. Guest Editorial: Spatial demography in regional science.Journal of geographical systems · 2021
    Article
  17. Article
  18. Computational Social Science and Sociology.Annual review of sociology · 2020
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nina CesareDepartment of Global Health, Boston University, Boston, MA, USA. ncesare@bu.edu.
Hedwig LeeDepartment of Sociology, Washington University, St. Louis, MO, USA.
Tyler McCormickDepartment of Sociology, University of Washington, Seattle, WA, USA.
Emma SpiroDepartment of Sociology, Washington University, St. Louis, MO, USA.
Emilio ZagheniMax Planck Institute for Demographic Research, Rostock, Germany.

Funding

Estimating vital rates in the developing world: A Bayesian process modeling approachK01HD078452 · NICHD · UNIVERSITY OF WASHINGTON · PI MCCORMICK, TYLER · 2015 to 2019
$611k
NICHD NIH HHS K01 HD078452
6 · The paper itself

Abstract

The digital traces that we leave online are increasingly fruitful sources of data for social scientists, including those interested in demographic research. The collection and use of digital data also presents numerous statistical, computational, and ethical challenges, motivating the development of new research approaches to address these burgeoning issues. In this article, we argue that researchers with formal training in demography-those who have a history of developing innovative approaches to using challenging data-are well positioned to contribute to this area of work. We discuss the benefits and challenges of using digital trace data for social and demographic research, and we review examples of current demographic literature that creatively use digital trace data to study processes related to fertility, mortality, and migration. Focusing on Facebook data for advertisers-a novel "digital census" that has largely been untapped by demographers-we provide illustrative and empirical examples of how demographic researchers can manage issues such as bias and representation when using digital trace data. We conclude by offering our perspective on the road ahead regarding demography and its role in the data revolution.

Indexed as

Big DataResearchBiasBirth RateData CollectionDemographyEthics, ResearchHumansMortalityPrivacyRacial GroupsSocial MediaBig dataDemographic methodsDigital dataSocial media

Identifiers

PMID30276667
PMCPMC6292527

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.