Be Wary of Those Who Ask: A Randomized Experiment on the Size and Determinants of the Enumerator Effect

Abstract During survey data collection, respondents’ answers may be influenced by the behavior and characteristics of the enumerator, the so-called enumerator effect. Using a large-scale experiment in Uganda in which the study randomly pairs enumerators and respondents, the study explores for which types of questions the enumerator effect may exist. It is found that the enumerator effect is minimal in many questions, but is large for political preference questions, for which it can account for over 30 percent of the variation in responses. The study then explores which enumerator characteristics, and which of their combination with respondent characteristics, could account for this effect. Finally, the conclusion provides some practical suggestions on how to minimize enumerator effects, and potential bias, in various types of data collections.

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Comparing respondent characteristics based on different travel survey data collection and respondent recruitment methods

Case Studies on Transport Policy ◽

10.1016/j.cstp.2020.05.015 ◽

2020 ◽

Vol 8 (3) ◽

pp. 870-877

Author(s):

Ary P. Silvano ◽

Jenny Eriksson ◽

Per Henriksson

Keyword(s):

Data Collection ◽

Survey Data ◽

Recruitment Methods ◽

Travel Survey ◽

Respondent Characteristics

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Correcting for experiment-specific variability in expression compendia can remove underlying signals

GigaScience ◽

10.1093/gigascience/giaa117 ◽

2020 ◽

Vol 9 (11) ◽

Author(s):

Alexandra J Lee ◽

YoSon Park ◽

Georgia Doing ◽

Deborah A Hogan ◽

Casey S Greene

Keyword(s):

Gene Expression ◽

Large Scale ◽

Original Signal ◽

Batch Effects ◽

Technical Variability ◽

The Past ◽

Statistical Correction ◽

Before And After ◽

Data Collections ◽

Biological Patterns

Abstract Motivation In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, or "batch effects," may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to extract true underlying biological patterns. As more integrative analysis methods arise and data collections get bigger, we must determine how technical variability affects our ability to detect desired patterns when many experiments are combined. Objective We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprising data aggregated across multiple experiments. Method We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability. Results The signal from a baseline compendium was obscured when the number of added sources of variability was small. Applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased, it became easier to detect the original signal even without correction. In fact, statistical correction reduced our power to detect the underlying signal. Conclusion When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.

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Enhancing Survey Data Collection Among Youth and Adults

CIN Computers Informatics Nursing ◽

10.1097/00024665-200409000-00004 ◽

2004 ◽

Vol 22 (5) ◽

pp. 255-265 ◽

Cited By ~ 20

Author(s):

JAMES A. BOBULA ◽

LORI S. ANDERSON ◽

SUSAN K. RIESCH ◽

JANIE CANTY-MITCHELL ◽

ANGELA DUNCAN ◽

...

Keyword(s):

Data Collection ◽

Survey Data

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Marsh rabbit mortalities tie pythons to the precipitous decline of mammals in the Everglades

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2015.0120 ◽

2015 ◽

Vol 282 (1805) ◽

pp. 20150120 ◽

Cited By ~ 39

Author(s):

Robert A. McCleery ◽

Adia Sovie ◽

Robert N. Reed ◽

Mark W. Cunningham ◽

Margaret E. Hunter ◽

...

Keyword(s):

National Park ◽

Large Scale ◽

Ongoing Debate ◽

Faunal Communities ◽

Python Molurus ◽

Scale Experiment ◽

Precipitous Decline ◽

Python Molurus Bivittatus ◽

The Impact ◽

Ecological Functioning

To address the ongoing debate over the impact of invasive species on native terrestrial wildlife, we conducted a large-scale experiment to test the hypothesis that invasive Burmese pythons ( Python molurus bivittatus ) were a cause of the precipitous decline of mammals in Everglades National Park (ENP). Evidence linking pythons to mammal declines has been indirect and there are reasons to question whether pythons, or any predator, could have caused the precipitous declines seen across a range of mammalian functional groups. Experimentally manipulating marsh rabbits, we found that pythons accounted for 77% of rabbit mortalities within 11 months of their translocation to ENP and that python predation appeared to preclude the persistence of rabbit populations in ENP. On control sites, outside of the park, no rabbits were killed by pythons and 71% of attributable marsh rabbit mortalities were classified as mammal predations. Burmese pythons pose a serious threat to the faunal communities and ecological functioning of the Greater Everglades Ecosystem, which will probably spread as python populations expand their range.

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