Evidence map›Paper›PMID 33487786›Full record

ArticlePopulation and environment2019

Assessing recall bias and measurement error in high-frequency social data collection for human-environment research.

Andrew Bell, Patrick Ward, Md Ehsanul Haque Tamal, Mary Killilea

Open access · hybridAbstract read
In one paragraph

Article in Population and environment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
13.7field-weighted citation impact, top 1% of its field
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

27 citing papers in PubMed, 103 citations in OpenAlex.

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  5. Leveraging the American Housing Survey to Quantify Risk and Resilience.Risk analysis : an official publication of the Society for Risk Analysis · 2025
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  15. The Role of Social Media in Building Pandemic Resilience in an Urban Community: A Qualitative Case Study.International journal of environmental research and public health · 2023
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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

4 authors at 2 institutions in 4 countries.

Andrew BellDepartment of Environmental Studies, New York University, 285 Mercer St., New York, NY 10003, USA.ORCID 0000-0002-1164-312X
Patrick WardMaster of Environmental Policy Program, Duke Kunshan University, No. 8 Duke Avenue, Kunshan, Jiangsu 215316, China.
Md Ehsanul Haque Tamalicddr,b, 68, Shaheed Tajuddin Ahmed Sarani, Mohakhali, Dhaka 1212, Bangladesh.
Mary KillileaDepartment of Environmental Studies, New York University, 285 Mercer St., New York, NY 10003, USA.
International Food Policy Research Institute · USMercer (Czechia) · CZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural systems data. While many environmental variables are collected with high frequency, gridded in time and space, social data is typically conducted irregularly, in waves that are far apart in time. These efforts typically engage respondents for hours at a time, and suffer from decay in participants' ability to recall their experiences over long periods of time. Systematic use of mobile and smartphones has the potential to transcend these challenges, with a critical first step being an evaluation of where survey respondents experience the greatest recall decay. We present results from, to our knowledge, the first systematic evaluation of recall bias in components of a household survey, using the Open Data Kit (ODK) platform on Android smartphones. We tasked approximately 500 farmers in rural Bangladesh with responding regularly to components of a large household survey, randomizing the frequency of each task to be received weekly, monthly, or seasonally. We find respondents' recall of consumption and experience (such as sick days) to suffer much more greatly than their recall of the use of their households' time for labor and farm activities. Further, we demonstrate a feasible and cost-effective means of engaging respondents in rural areas to create and maintain a true socio-economic "baseline" to mirror similar efforts in the natural sciences.

Indexed as

Android smartphoneBangladeshHigh-frequency data collectionMicrotasks for micropayments

Identifiers

PMID33487786
PMCPMC7745111
OpenAlexW2914780939

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.