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

Dawn DelayBrett LaursenNoona KiuruAdam RogersThomas KindermannJari-Erik Nurmi
Published in: International journal of behavioral development (2021)
The present study compares two methods for assessing peer influence: the longitudinal Actor-Partner-Interdependence-Model (L-APIM) and the longitudinal Social Network Analysis Model (L-SNA). The data were drawn from 1,995 (49% girls; 51 % boys) 3rd grade students (Mage=9.68 years). From this sample, L-APIM (n = 206 indistinguishable dyads; 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 3rd and 4th 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.
Keyphrases
  • healthcare
  • network analysis
  • mental health
  • machine learning
  • big data
  • human immunodeficiency virus
  • artificial intelligence
  • antiretroviral therapy