Table · dataset · 2017
How to make loss aversion disappear and reverse : tests of the decision by sampling origin of loss aversion
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In 4 experiments, we tested this proposition by manipulating the range of gains and losses that individuals saw during the process of eliciting their loss aversion.
Description
We were able to find loss aversion, loss neutrality, and even the reverse of loss aversion. One of the most robust empirical findings in the behavioral sciences is loss aversion—the finding that losses loom larger than gains.
We offer a new psychological explanation of the origins of loss aversion in which loss aversion emerges from differences in the distribution of gains and losses people experience.<br><br>Data record consists of five data files in .txt and .csv format, a documentation file describing the experiments undertaken and a readme file. n Experiment 1a, participants were randomly allocated to one of four conditions. Both gains and losses ranged up to $20 or up to $40 generating a 2 2 design.
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Experiment 1b is an exact replication of Experiment 1a with new participants. In Experiment 2, we used two distributions for gains and losses, one ranging from $6 to $20 (in $2 increments) and one three times larger, ranging from $18 to $60 (in $6 increments). We only tested the two asymmetric cases.
Unlike in Experiments 1a and 1b, the possible gains and losses were randomly drawn and paired from the distributions to produce 64 pairs. Two ranges of gains and losses were used in Experiment 3. Monetary values could range from $5 to $20 (in $1 increments) or from $10 to $40 (in $2 increments).
We chose to include both asymmetric treatment conditions, but only one of the symmetric cases (both gains and losses ranging from $10 to $40) to maximize power.
Links
Where it is published
- DOI doi.org/10.82444/warw.33802927.v1 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
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- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Mathematics & Statistics · Medicine & Health · Ocean & Atmospheric Science · Psychology & Behavioral Science · Social Science
- Inferred from text
- Applied economics 72%
Provenance · 1 source records, 19 field assertions
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|---|---|---|---|
| figshare | oai:figshare.com:article/33802927 | 7 d ago | JSON v1 |
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