DeLay Dawn, Laursen Brett, Kiuru Noona, Rogers Adam, Kindermann Thomas, Nurmi Jari-Erik
Arizona State University.
Florida Atlantic University.
Int J Behav Dev. 2021 May;45(3):275-288. doi: 10.1177/0165025421992866. Epub 2021 Mar 2.
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) 3 grade students (M=9.68 years). From this sample, L-APIM ( = 206 indistinguishable dyads; = 187 distinguishable dyads) and L-SNA ( = 1,024 total network members) subsamples were created. Students completed peer nominations and objective assessments of mathematical reasoning in the spring of the 3 and 4 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.
纵向的行为者-伙伴-相互依存模型(L-APIM)和纵向的社会网络分析模型(L-SNA)。数据取自1995名三年级学生(49%为女生;51%为男生),平均年龄9.68岁。从这个样本中,创建了L-APIM(206个不可区分的二元组;187个可区分的二元组)和L-SNA(共1024个网络成员)子样本。学生们在三年级和四年级春季完成了同伴提名以及数学推理的客观评估。不同分析的统计显著性模式有所不同。稳定的可区分和不可区分的L-APIM二元组分析确定了相互的朋友影响,即具有相似数学推理水平的朋友相互影响,数学推理能力较高的朋友影响数学推理能力较低的朋友。与L-APIM分析中评估的影响参数(即平均相互改变)相当的L-SNA模型未能检测到影响效应。然而,在添加另一个不同的社会网络影响参数(即平均改变影响效应)后,影响效应确实出现了。结果的差异可能归因于分析的敏感性差异、它们解释选择和影响中的结构混杂因素的能力、分析中包含的样本,以及相互最好的友谊与其他友谊相比的相对影响强度。