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RESEARCH PRODUCT

A Comparison of Dyadic and Social Network Assessments of Peer Influence.

Dawn DelayBrett LaursenNoona KiuruAdam A. RogersThomas A. KindermannJari-erik Nurmi

subject

Social Psychologysocial network analysisLogical reasoningmedia_common.quotation_subjectkoululaisetsosiaalinen vuorovaikutusArticleEducationDevelopmental Neurosciencesosiaaliset verkostot0502 economics and businesssocial contextDevelopmental and Educational PsychologyPeer influencedyadic data analysismatemaattiset taidotvertaisoppiminen0501 psychology and cognitive sciencesLife-span and Life-course StudiesDyadic data analysismedia_commonpeer influenceSocial networkbusiness.industry4. Educationlongitudinal methods05 social sciencesSocial network analysis (criminology)Social environmentalakoululaisetsosiaaliset suhteetFriendshippeer relationshipsbusinessPsychologySocial psychology050203 business & managementSocial Sciences (miscellaneous)vertaissuhteet050104 developmental & child psychologyNetwork analysis

description

The present study compares two methods for assessing peer influence: the longitudinal actor–partner interdependence model (L-APIM) and the longitudinal social network analysis (L-SNA) Model. The data were drawn from 1,995 (49% girls and 51% boys) third grade students ( Mage= 9.68 years). From this sample, L-APIM ( n = 206 indistinguishable dyads and n = 187 distinguishable dyads) and L-SNA ( n = 1,024 total network members) subsamples were created. Students completed peer nominations and objective assessments of mathematical reasoning in the spring of the third and fourth grades. Patterns of statistical significance differed across analyses. Stable distinguishable and indistinguishable L-APIM dyadic analyses identified reciprocated friend influence such that friends with similar levels of mathematical reasoning influenced one another and friends with higher math reasoning influenced friends with lower math reasoning. L-SNA models with an influence parameter (i.e., average reciprocated alter) comparable to that assessed in L-APIM analyses failed to detect influence effects. Influence effects did emerge, however, with the addition of another, different social network influence parameter (i.e., average alter influence effect). The diverging results may be attributed to differences in the sensitivity of the analyses, their ability to account for structural confounds with selection and influence, the samples included in the analyses, and the relative strength of influence in reciprocated best as opposed to other friendships.

10.1177/0165025421992866https://pubmed.ncbi.nlm.nih.gov/33927465