Evidence map›Paper›PMID 37530103›Full record

ArticleThe British journal of social psychology2024

Connecting groups and behaviours: A network analysis of identity-infused behaviours.

Emily A Hughes, Samuel Ellis, Joanne R Smith

Open access · hybridAbstract read
In one paragraph

Article in The British journal of social psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Emily A HughesUniversity of Exeter, Exeter, UK.ORCID https://orcid.org/0000-0002-6172-3988
Samuel EllisUniversity of Exeter, Exeter, UK.ORCID https://orcid.org/0000-0001-9019-6040
Joanne R SmithUniversity of Exeter, Exeter, UK.ORCID https://orcid.org/0000-0002-3710-4941
University of Exeter · GB

Funding

Economic and Social Research Council
6 · The paper itself

Abstract

Research in the social identity tradition acknowledges the multiplicity of our identities and the implications that identity compatibility has for our health and well-being. However, current measures of multiple group membership have not yet captured the richness and complexity of our social identity networks at the wider sample level, and data regarding the different behaviours typically associated with different group memberships are scarce. Adopting a network approach, we explore the co-occurrence of different group memberships within an individual (identity-by-identity network), the behaviours that are shared among identities (behaviour-by-identity network), and whether identities that are shared also share common behaviours (identity-by-behaviour network). An online survey asked participants (N = 286) to list the groups they are part of, as well as the behaviours viewed to be typical of group members. The networks identified several identities and behaviours to significantly co-occur at a rate both higher and lower than chance. Networks were found to be low in modularity; there was no evidence of clustering within the data. Permutation analyses demonstrated the overall structure of the networks to be significantly different than expected by chance. The co-occurrences identified serve as a meaningful resource for those conducting research into identities, group norms and their associated behaviours.

Indexed as

Social IdentificationHumansSurveys and Questionnairesgroup membershipnetwork analysissocial identity

Identifiers

PMID37530103
PMCPMC10952172
OpenAlexW4385476653

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.