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Improving Numerical Measures of Human Feelings: The Case of Pain

Research output: Working paper

Published

Standard

Improving Numerical Measures of Human Feelings: The Case of Pain. / Garagnani, Michele ; Schweinhardt, Petra; Tobler, Philippe N. et al.
Lancaster: Lancaster University, Department of Economics, 2025. (Economics Working Papers Series).

Research output: Working paper

Harvard

Garagnani, M, Schweinhardt, P, Tobler, PN & Alos Ferrer, C 2025 'Improving Numerical Measures of Human Feelings: The Case of Pain' Economics Working Papers Series, Lancaster University, Department of Economics, Lancaster.

APA

Garagnani, M., Schweinhardt, P., Tobler, P. N., & Alos Ferrer, C. (2025). Improving Numerical Measures of Human Feelings: The Case of Pain. (Economics Working Papers Series). Lancaster University, Department of Economics.

Vancouver

Garagnani M, Schweinhardt P, Tobler PN, Alos Ferrer C. Improving Numerical Measures of Human Feelings: The Case of Pain. Lancaster: Lancaster University, Department of Economics. 2025 Mar 24. (Economics Working Papers Series).

Author

Garagnani, Michele ; Schweinhardt, Petra ; Tobler, Philippe N. et al. / Improving Numerical Measures of Human Feelings: The Case of Pain. Lancaster : Lancaster University, Department of Economics, 2025. (Economics Working Papers Series).

Bibtex

@techreport{f1c28405403d4b07b1ac184ba8ba5143,
title = "Improving Numerical Measures of Human Feelings: The Case of Pain",
abstract = "Numerical self-report scales are extensively used in economics, psychology, and even medicine to quantify subjective feelings, ranging from life satisfaction to the experience of pain. These scales are often criticized for lacking an objective foundation, and defended on the grounds of empirical performance.We focus on the case of pain measurement, where existing self-reported measures are the workhorse but known to be inaccurate and difficult to compare across individuals. We provide a new measure, inspired by standard economic elicitation methods, that quantifies the negative value of acute pain in monetaryterms, making it comparable across individuals. In three preregistered studies, 330 healthy participants were randomly allocated to receive either only a high- or only a low-pain stimulus or a high-pain stimulus after having double-blindly received a topical analgesic or a placebo. In all three studies, the new measuregreatly outperformed the existing self-report scales at distinguishing whether participants were in the more or the less painful condition, as confirmed by effect sizes, Bayesian factor analysis, and regression-based predictions.",
keywords = "Self-Reported Scales, Preference Elicitation, Pain",
author = "Michele Garagnani and Petra Schweinhardt and Tobler, {Philippe N.} and {Alos Ferrer}, Carlos",
year = "2025",
month = mar,
day = "24",
language = "English",
series = "Economics Working Papers Series",
publisher = "Lancaster University, Department of Economics",
type = "WorkingPaper",
institution = "Lancaster University, Department of Economics",

}

RIS

TY - UNPB

T1 - Improving Numerical Measures of Human Feelings: The Case of Pain

AU - Garagnani, Michele

AU - Schweinhardt, Petra

AU - Tobler, Philippe N.

AU - Alos Ferrer, Carlos

PY - 2025/3/24

Y1 - 2025/3/24

N2 - Numerical self-report scales are extensively used in economics, psychology, and even medicine to quantify subjective feelings, ranging from life satisfaction to the experience of pain. These scales are often criticized for lacking an objective foundation, and defended on the grounds of empirical performance.We focus on the case of pain measurement, where existing self-reported measures are the workhorse but known to be inaccurate and difficult to compare across individuals. We provide a new measure, inspired by standard economic elicitation methods, that quantifies the negative value of acute pain in monetaryterms, making it comparable across individuals. In three preregistered studies, 330 healthy participants were randomly allocated to receive either only a high- or only a low-pain stimulus or a high-pain stimulus after having double-blindly received a topical analgesic or a placebo. In all three studies, the new measuregreatly outperformed the existing self-report scales at distinguishing whether participants were in the more or the less painful condition, as confirmed by effect sizes, Bayesian factor analysis, and regression-based predictions.

AB - Numerical self-report scales are extensively used in economics, psychology, and even medicine to quantify subjective feelings, ranging from life satisfaction to the experience of pain. These scales are often criticized for lacking an objective foundation, and defended on the grounds of empirical performance.We focus on the case of pain measurement, where existing self-reported measures are the workhorse but known to be inaccurate and difficult to compare across individuals. We provide a new measure, inspired by standard economic elicitation methods, that quantifies the negative value of acute pain in monetaryterms, making it comparable across individuals. In three preregistered studies, 330 healthy participants were randomly allocated to receive either only a high- or only a low-pain stimulus or a high-pain stimulus after having double-blindly received a topical analgesic or a placebo. In all three studies, the new measuregreatly outperformed the existing self-report scales at distinguishing whether participants were in the more or the less painful condition, as confirmed by effect sizes, Bayesian factor analysis, and regression-based predictions.

KW - Self-Reported Scales

KW - Preference Elicitation

KW - Pain

M3 - Working paper

T3 - Economics Working Papers Series

BT - Improving Numerical Measures of Human Feelings: The Case of Pain

PB - Lancaster University, Department of Economics

CY - Lancaster

ER -