False Positives Using Social Cognitive Mapping to Identify Children’s Peer Groups

Neal, Zachary P.; Neal, Jennifer Watling; Domagalski, Rachel

doi:10.1525/collabra.17969

Cited by 4 publications

(12 citation statements)

References 38 publications

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“…While two legislators may be viewed as having an alliance when they are observed to sponsor many of the same bills (i.e., the edge weight in a co-sponsorship network), backbone models offer a way to identify pairs of legislators that have sponsored significantly more bills together than expected. In developmental science, the package has been used to measure childrens’ social networks from their peer-reported or observed group memberships [ 46 , 47 ]. This is useful because data on childrens’ social relations are difficult to collect directly, but they can often be inferred from bipartite projections.…”

Section: Discussionmentioning

confidence: 99%

backbone: An R package to extract network backbones

Neal

2022

PLoS ONE

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Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.

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Section: Discussionmentioning

confidence: 99%

backbone: An R package to extract network backbones

Neal

2022

PLoS ONE

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“…J. W. Neal et al (2014), Z. Neal et al (2021), and J. Neal and Neal (2013) have recently raised several issues with using Cairns' and Cairns' Social Cognitive Mapping (SCM) method, particularly the SCM 4.0 program (Leung & Alston, 1998) to aggregate and analyze the data for the purpose of assigning children to an "SCM group" (i.e., a discrete, affiliation-based cluster of children within the larger network).…”

Section: Discussionmentioning

confidence: 99%

Individual difference in perceptions of social structure: Social standing predicts accuracy in social network perception

et al. 2022

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This study examined the extent to which social standing based on reputation (i.e., popularity), affective regard (i.e., peer acceptance and peer rejection), and affiliation‐network centrality (i.e., degree, betweenness, and closeness centrality), in addition to gender and grade in school, predicted children's accuracy in detecting affiliation patterns within peer networks. Third through fifth‐grade students (n = 400) were from three semi‐rural elementary schools and clustered in eight grade‐level units. The relationship between a child's perception of the affiliation (i.e., “hanging‐out”) network and the collective perception of the affiliation network served as the indicator of network perception accuracy. In general, girls, older children, and more popular and well‐connected children had more accurate perceptions of the social environment. In particular, girls, popular children, children with high degrees centrality, and fifth graders, were more likely to report ties that existed in the collectively perceived network (made fewer false‐negative reports). Fifth graders’ specific patterns of accuracy/inaccuracy suggested a more developed cognitive model of social network structures. Children experiencing peer rejection made more false negative errors (failed to report existing ties). Interestingly, net of the other network variables in the model, closeness centrality was related to a decrease in falsely reporting ties (improving accuracy) but an increase in failing to report ties that existed (worsening accuracy). These relationships between children's social standing and grade level on their accuracy of network perception has conceptual and methodological implications for studies of children's social functioning within their school‐based peer networks.

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“…On the surface, this seems to be a devastating critique. On closer inspection, however, most of these points in the paper 9 do not hold up to scrutiny. To give an example, SCM is depicted as the "dominant" method for identifying cohesive subgroups in naturalistic settings from data in which multiple peer observers report on groups (p. 1).…”

Section: Capturing Peer Group Contexts: In Defense Of Socio-cognitive...mentioning

confidence: 92%

“…The evaluation of the critiques of Socio-Cognitive Mapping strategies is based on the latest attack, a paper by Neal, Neal and Domagalski 9 entitled "False Positives Using Social Cognitive Mapping to Identify Children's Peer Groups." The critique rests on four points.…”

Section: Capturing Peer Group Contexts: In Defense Of Socio-cognitive...mentioning

confidence: 99%

Capturing Peer Group Contexts: In Defense of Socio-Cognitive Mapping Strategies to Identify Children’s Peer Network Affiliations

Kindermann

2022

MRAJ

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Socio-Cognitive Mapping is an observational method to collect information about people’s social networks in settings in which participant observers know each other well, for example in school settings. Compared to traditional self-report data, observation reports make it possible to include (anonymized) network information about people who do not participate. In a series of papers, Neal and colleagues have criticized the methodology of Socio-Cognitive Mapping studies. However, the criticisms do not pertain to the data but only to a specific analysis program, SCM4, that was used in about 80% of the reviewed studies. To document their critiques, the authors introduce a new analysis strategy intended to correct some of the problems identified, and combine this with a promising new Community Detection method. They compare their results to SCM4 results and find in random simulations that, when using criteria that are more restrictive, fewer groups and fewer group members are identified. I highlight the extent to which the critique of the program is only justified under restrictive conditions, explain that the backbone of the proposed method has been used before, list problems of analyses that their method does not overcome, and outline avenues for their solution.

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False Positives Using Social Cognitive Mapping to Identify Children’s Peer Groups

Cited by 4 publications

References 38 publications

backbone: An R package to extract network backbones

backbone: An R package to extract network backbones

Individual difference in perceptions of social structure: Social standing predicts accuracy in social network perception

Capturing Peer Group Contexts: In Defense of Socio-Cognitive Mapping Strategies to Identify Children’s Peer Network Affiliations

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