Measuring the measurement error: A method to qualitatively validate survey data

Empirical social science relies heavily on self-reported data, but subjects may misreport behaviors, especially sensitive ones such as crime or drug abuse. If a treatment influences survey misreporting, it biases causal estimates. We develop a validation technique that uses intensive qualitative wor...

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Vydáno v:	Journal of development economics Ročník 120; s. 99 - 112
Hlavní autoři:	Blattman, Christopher, Jamison, Julian, Koroknay-Palicz, Tricia, Rodrigues, Katherine, Sheridan, Margaret
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.05.2016 Elsevier Elsevier Sequoia S.A
Témata:	Behavior Crime Crime prevention Criminology Data Drug abuse Drugs Expenditures Field experiments Internet Liberia Measurement Measurement error Measurement errors Polls & surveys Researcher subject relations Responses risky behavior Risky behaviors Social sciences Substance abuse Substance abuse treatment Survey data Treatment methods Underreporting Validation Validation studies Validity Validation Drugs Liberia Risky behaviors Crime Survey data Field experiments Measurement error
ISSN:	0304-3878, 1872-6089
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Abstract	Empirical social science relies heavily on self-reported data, but subjects may misreport behaviors, especially sensitive ones such as crime or drug abuse. If a treatment influences survey misreporting, it biases causal estimates. We develop a validation technique that uses intensive qualitative work to assess survey misreporting and pilot it in a field experiment where subjects were assigned to receive cash, therapy, both, or neither. According to survey responses, both treatments reduced crime and other sensitive behaviors. Local researchers spent several days with a random subsample of subjects after surveys, building trust and obtaining verbal confirmation of four sensitive behaviors and two expenditures. In this instance, validation showed survey underreporting of most sensitive behaviors was low and uncorrelated with treatment, while expenditures were under reported in the survey across all arms, but especially in the control group. We use these data to develop measurement error bounds on treatment effects estimated from surveys. •We develop and test a new technique for validating self-reported data.•We use intensive qualitative study in a random subsample to validate key outcomes.•Results suggest the control group underreported sensitive behaviors and expenditures.•Hence the impacts of an anti-crime experiment may be larger than implied by surveys.•Meanwhile the impact on expenditures may be lower than implied by surveys.
AbstractList	Empirical social science relies heavily on self-reported data, but subjects may misreport behaviors, especially sensitive ones such as crime or drug abuse. If a treatment influences survey misreporting, it biases causal estimates. We develop a validation technique that uses intensive qualitative work to assess survey misreporting and pilot it in a field experiment where subjects were assigned to receive cash, therapy, both, or neither. According to survey responses, both treatments reduced crime and other sensitive behaviors. Local researchers spent several days with a random subsample of subjects after surveys, building trust and obtaining verbal confirmation of four sensitive behaviors and two expenditures. In this instance, validation showed survey underreporting of most sensitive behaviors was low and uncorrelated with treatment, while expenditures were under reported in the survey across all arms, but especially in the control group. We use these data to develop measurement error bounds on treatment effects estimated from surveys. Empirical social science relies heavily on self-reported data, but subjects may misreport behaviors, especially sensitive ones such as crime or drug abuse. If a treatment influences survey misreporting, it biases causal estimates. We develop a validation technique that uses intensive qualitative work to assess survey misreporting and pilot it in a field experiment where subjects were assigned to receive cash, therapy, both, or neither. According to survey responses, both treatments reduced crime and other sensitive behaviors. Local researchers spent several days with a random subsample of subjects after surveys, building trust and obtaining verbal confirmation of four sensitive behaviors and two expenditures. In this instance, validation showed survey underreporting of most sensitive behaviors was low and uncorrelated with treatment, while expenditures were under reported in the survey across all arms, but especially in the control group. We use these data to develop measurement error bounds on treatment effects estimated from surveys. •We develop and test a new technique for validating self-reported data.•We use intensive qualitative study in a random subsample to validate key outcomes.•Results suggest the control group underreported sensitive behaviors and expenditures.•Hence the impacts of an anti-crime experiment may be larger than implied by surveys.•Meanwhile the impact on expenditures may be lower than implied by surveys. Empirical social science relies heavily on self-reported data, but subjects may misreport behaviors, especially sensitive ones such as crime or drug abuse. If a treatment influences survey misreporting, it biases causal estimates. We develop a validation technique that uses intensive qualitative work to assess survey misreporting and pilot it in a field experiment where subjects were assigned to receive cash, therapy, both, or neither. According to survey responses, both treatments reduced crime and other sensitive behaviors. Local researchers spent several days with a random subsample of subjects after surveys, building trust and obtaining verbal confirmation of four sensitive behaviors and two expenditures. In this instance, validation showed survey underreporting of most sensitive behaviors was low and uncorrelated with treatment, while expenditures were under reported in the survey across all arms, but especially in the control group. We use these data to develop measurement error bounds on treatment effects estimated from surveys. [web URL: http://www.sciencedirect.com/science/article/pii/S0304387816000122]
Author	Rodrigues, Katherine Blattman, Christopher Jamison, Julian Sheridan, Margaret Koroknay-Palicz, Tricia
Author_xml	– sequence: 1 givenname: Christopher orcidid: 0000-0001-7748-4507 surname: Blattman fullname: Blattman, Christopher email: chrisblattman@columbia.edu organization: Columbia University SIPA, 420 W 118th St., New York, NY, United States – sequence: 2 givenname: Julian surname: Jamison fullname: Jamison, Julian email: julison@gmail.com organization: Global Insights Initiative, The World Bank, 1818 H St NW, Washington, DC, United States – sequence: 3 givenname: Tricia surname: Koroknay-Palicz fullname: Koroknay-Palicz, Tricia email: t.gonwa@gmail.com organization: The World Bank, 1818 H St NW, Washington, DC, United States – sequence: 4 givenname: Katherine surname: Rodrigues fullname: Rodrigues, Katherine email: katherine.rodrigues@rescue.org organization: International Rescue Committee, Research Department, 122 East 42nd St., New York, NY, United States – sequence: 5 givenname: Margaret surname: Sheridan fullname: Sheridan, Margaret email: sheridan.margaret@unc.edu organization: University of North Carolina at Chapel Hill, Clinical Psychology, United States
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SubjectTerms	Behavior Crime Crime prevention Criminology Data Drug abuse Drugs Expenditures Field experiments Internet Liberia Measurement Measurement error Measurement errors Polls & surveys Researcher subject relations Responses risky behavior Risky behaviors Social sciences Substance abuse Substance abuse treatment Survey data Treatment methods Underreporting Validation Validation studies Validity
Title	Measuring the measurement error: A method to qualitatively validate survey data
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