Understanding the Psychology of Guilt – The Role of Self-discrepancies in Distinguishing Regret from Guilt – Chapter 2

DOI

Regret and guilt are self-conscious emotions. They stem from negative events for which people feel responsible. Both emotions reflect discrepancies between how people are (their “actual” self) and how they would like to be (their “ideal” or “ought” self). We examined whether regret and guilt were related to different self-discrepancies (i.e., “ideal” and “ought” self-discrepancies). Two studies (total N = 1998) with Chinese and US participants found that people feel more regret over ideal self-discrepancies than over ought self-discrepancies, whereas for guilt this is more complex. We also found a main effect for culture such that ideal self-discrepancies were associated more with both emotions in the USA compared to China. Implications for the differences between regret and guilt are discussed.

Additional documentation and metadata can be found in the files Data Report Chapter 2.pdf, Documentation of all author responsibilities.pdf, and the metadata files in the rawdata folders.

Preregistration: This research has preregistered all materials, hypothesis and sample size through: https://aspredicted.org/DPV_WOC.

Method: American sample was collected on Mturk website; Chinese sample 1 was collected from a Chinese university (Zhejiang Univeristy) sample pool; Chinese sample 2 was collected from Sojump website (Chinese equivalent Mturk)

Universe: Study 1 includes samples: American sample (N total = 227, 98 females, Mage = 35.20, SD = 10.17); Chinese sample1 (N total = 209, 118 females, Mage = 20.72, SD = 2.49); Chinese sample2 (N total = 223, 128 females, Mage = 34.06, SD = 7.42). Study 2 shares similar sample pattern as in Study 1.

Identifier
DOI https://doi.org/10.34894/EXMAMD
Related Identifier https://doi.org/10.1080/15298868.2020.1721316
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/EXMAMD
Provenance
Creator Zhang, Xiaolu ORCID logo
Publisher DataverseNL
Contributor Zhang, Xiaolu; DataverseNL
Publication Year 2021
Rights CC0 Waiver; info:eu-repo/semantics/openAccess; https://creativecommons.org/publicdomain/zero/1.0/
OpenAccess true
Contact Zhang, Xiaolu (Tilburg University, Tilburg School of Social and Behavioral Sciences, Department of Social Psychology)
Representation
Resource Type Experimental data; Dataset
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Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences