A systematic review of social network methods in high impact developmental psychology journals

Abstract

Although researchers have collected social network data to characterize childhood and adolescent relationships since the 1930s, an expansion of social network analytic techniques has led to the rapid growth of social network data collection in the field of developmental psychology. Despite this growth, reflection on social network methods in developmental psychology has not kept pace. This systematic review aims to understand how articles published in the most visible journals in developmental psychology have collected and analyzed social network data with the goal of critically evaluating these methods to move the field forward. Specifically, after screening 787 articles drawn from a systematic search of the 30 journals with the highest impact factors in developmental psychology, 201 empirical articles employing social network analysis were reviewed. Findings focus on where social network data are collected, who social network data are collected on, what types of social network relationships are collected, and how social network data are collected and analyzed. Review results are used to highlight potential gaps in social network methods as well as the implications of these gaps for understanding developmental processes and to identify recommendations for future social network studies in developmental psychology.

Sleepy and Secluded: Sleep Disturbances are Associated with Connectedness in Early Adolescent Social Networks

“…For the purpose of this study, friendship was defined as liking the time spent with a nominated peer at least somewhat. This definition captures both the personal contact and relational affinity of friendships without relying on semantic evaluations (e.g., I like and spend time with person but don't consider them a friend) and is consistent with best-practice recommendations for social network methodology (Neal, 2020).…”

“…Participants' eigenvector represents connection with highly connected peers. These metrics represent commonly used indicators of social connectedness by developmental scientists who study social networks (Neal, 2020). (Forrest et al, 2018).…”

False positives using social cognitive mapping to identify children’s peer groups

“…Backbone extraction methods differ in how they determine the number of group co-occurrences that would be expected at random (i.e. the null model), but here we focus on the Stochastic Degree Sequence Model (SDSM) because it is fast, welldocumented, and easy to compute using the R backbone package (Domagalski, Neal, & Sagan, 2019, 2020 Impact of classroom characteristics on the number of false positives from SCM.…”

“…To determine the specific variant of SCM that is most commonly used, we started with a dataset of 201 papers from a recent review of social network data collection methods in developmental psychology (J. Neal, 2020). These papers were initially identified using Google Scholar and reflect papers published or online (a) prior to February 2019 (b) in the 30 top-ranked journals classified by Web of Science as "Psychology, developmental" in 2016 (c) that contained the phrase "social network" and one or more network-relevant keywords (e.g.…”

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

“…To determine the specific variant of SCM that is most commonly used, we started with a dataset of 201 papers from a recent review of social network data collection methods in developmental psychology (J. W. Neal, 2020a). These papers were initially identified using Google Scholar and reflect papers published or online (a) prior to February 2019 (b) in the 30 top-ranked journals classified by Web of Science as "Psychology, developmental" in 2016 (c) that contained the phrase "social network" and one or more network-relevant keywords (e.g.…”