Methodological considerations in the use of name generators and interpreters

Eagle, David E.; Proeschold‐Bell, Rae Jean

doi:10.1016/j.socnet.2014.07.005

Cited by 110 publications

(42 citation statements)

References 37 publications

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“…From the fully restricted sample, the most frequent network size is one acquaintance (34.6 percent). 4 Past research has discussed the vulnerability of name generators to "interviewer effect" where characteristics of interviewers could affect responses (e.g.Mardsen, 2003;Eagle and Proeschold-Bell, 2015). For this survey, in the case of the network size, the intraclass correlation (ICC) was 0.28, which means that 28 percent of the variance in the network size could be attributed to interviewers' characteristics.…”

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confidence: 84%

Asymmetric Social Comparison and Life Satisfaction in Social Networks

2020

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The aim of this study is to understand the effect of asymmetric social comparison on subjective well-being, and how it differs due to reciprocity. Our approach considers the social network of individuals as a local reference group. We tested competing hypotheses on negative and positive effects of comparison with worse-off (downward) and better-off (upward) targets with a representative sample of 1,596 Chileans over the age of 18.The findings support that life satisfaction is influenced by social comparison. By considering the social network as a reference group, the positive effect of downward comparison and the negative effect of upward comparison are confirmed. Upward comparison seems to be more substantial than downward comparison. Additionally, the positive effect of downward comparison decreases slightly with a reciprocal exchange of support between respondents and targets. The application of social network analysis opens a path to understanding the mechanisms underlying social comparison processes. Key wordsHappiness; reciprocity; social networks; asymmetric social comparison.3 This study has a threefold aim. First, to estimate the effect that comparison with relevant acquaintances, as identified by each subject, has on the own life satisfaction. Second, to understand how these comparisons work in relation to better or worse-off reference groups (upward and downward comparison, respectively). And third, to examine the influence that reciprocity with those relevant others has on this social comparison. By reciprocity, we understand the mutual exchange of support between the individual and their comparison targets, who are members of their social network. This threefold aim enables us to discuss the theoretical equivalence between the definitions of reference groups for social comparisons. Happiness research has used a wide variety of reference groups, such as classmates (Dijkstra, Kuyper, van der Werf, Buunk and van der Zee, 2008), colleagues (Gätcher, Nosenzo and Sefton, 2012; Gätcher and Thöni, 2010), and proximity of residence in the same city (Götz, Ehret, Jullien and Hall, 2006). However, research on the local dominance effect has shown the preeminence of closer targets in social comparison processes when multiple resources are available (Zell and Alicke, 2010). Despite this wide range of groups, social networks have not so far been considered. To our knowledge, this the first study considering social networks as the reference group of comparison. As we shall see later, the social network is the closest social surrounding of each individual and eventually, a more accessible, concrete, and suitable cognitive reference of comparison.Moreover, this study attempts to understand the cognitive basis of the social comparison process.Previous studies (Ateca-Amestoy et al., 2014;Senik, 2008;McBride, 2001) have been grounded in the analysis of the material life circumstances of a reference group, mainly income. Discussions on income reference groups are a growing field of research with a broad perspective. Senik...

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confidence: 84%

Asymmetric Social Comparison and Life Satisfaction in Social Networks

2020

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“…Recent work has challenged that assumption, showing that many network measures can be well-estimated with incomplete information (Borgatti et al, 2006; Smith and Moody, 2013). This does not mean that measurement concerns can be ignored (for example, see Marsden, 1993; Brewer and Webster, 2000; Marin and Hampton, 2007; Eagle and Proeschold-Bell, 2015), only that missing data itself does not necessarily invalidate a network study. Still, we are only beginning to understand the practical consequences of missing data for network studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Network sampling coverage II: The effect of non-random missing data on network measurement

2017

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Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.

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“…To be sure, ego-network data collected as a part of a survey are attractive because their results can be generalized to the wider population; however, the network data they collect are seldom, if ever, as robust as whole network data are in tapping into the nuances of network effects. Moreover, as several recent studies have documented (Eagle and Proeschold-Bell, 2015;Fischer, 2009;Paik and Sanchagrin, 2013), ego-network data can be particularly sensitive, and usually in negative ways, to interviewer effects. Of the studies listed in Table 7 that did not use whole network data, Lindsay's (2006Lindsay's ( , 2007Lindsay's ( , 2008 analysis of evangelical elites is something of an exception.…”

Section: Network and Religion: Looking Forwardmentioning

confidence: 99%

Networks and Religion

Everton

2018

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That social networks play a central role in religious life is well accepted by most social scientists. We are reasonably confident, for instance, that they are crucial for the recruitment and retention of members, the diffusion of religious ideas and practices, motivating individuals to volunteer and become politically active, the health and well-being of people of faith, and conflict, radicalization, and (sometimes) violence. However, in conference presentations, journal articles, and books social network analysts have shown little interest in exploring the interplay of networks and religion. In this paper, I review, and in some cases expand upon, what social scientists of religion have learned about networks and religion. I conclude with a call for social network analysts to focus the analytical tools of social network analysis on a phenomenon that has and continues to exert considerable influence in today's world.

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Methodological considerations in the use of name generators and interpreters

Cited by 110 publications

References 37 publications

Asymmetric Social Comparison and Life Satisfaction in Social Networks

Asymmetric Social Comparison and Life Satisfaction in Social Networks

Network sampling coverage II: The effect of non-random missing data on network measurement

Networks and Religion

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