Movement without mobility: Adolescent status hierarchies and the contextual limits of cumulative advantage

Smith, Jeffrey A.; Faris, Robert

doi:10.1016/j.socnet.2014.10.004

Cited by 12 publications

(8 citation statements)

References 59 publications

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“…McFarland et al, 2014; Patacchini and Zenou, 2016). We thus focus on measurement issues most likely to cause problems in such settings (for example, see Laumann et al, 1983; Strully, 2014 for a discussion of the boundary problem; see also Smith and Faris, 2015; Hipp et al, 2015 for a discussion related specifically to longitudinal network data). Node missingness may be less of a concern when dealing with automated data, such as sensor, cell phone or online data (Bliss et al, 2014; González-Bailón et al, 2014), though these also have peculiar issues of their own, such as distinguishing between ‘real’ and ‘fake’ nodes in an online network (see Wang et al, 2012).…”

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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Section: Introductionmentioning

confidence: 99%

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

2017

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“…capturing two underlying dimensions (racial/ Hispanic salience). Similar examples can be found in effectively every subfield in the discipline; recent examples include such diverse topics as network structure among adolescents and labor markets in the pre-civil war south (e.g., McFarland et al 2014;Ruef 2012;Smith and Faris 2015). The hope is that this approach will make it easier to characterize theoretical tables in continuous terms: where it becomes easier to specify (and test) complicated hypotheses, those representing blends, or mixtures, of the core hypotheses.…”

Section: Resultsmentioning

confidence: 94%

A Social Space Approach to Testing Complex Hypotheses: The Case of Hispanic Marriage Patterns in the United States

Smith

2017

Socius: Sociological Research for a Dynamic World

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Where do individuals identifying as Hispanic fit in the racial landscape of the United States? The answer offered by past work is complex: The empirical results do not lend themselves to simple interpretation as no single hypothesis fits the Hispanic case very well. Instead, Hispanic integration is described as mixtures of different archetypical hypotheses, like panethnic formation, white assimilation, and racialized assimilation. My goal is to develop a formal framework to help make sense of this complex picture. I extend past work by showing which combination of integration processes (panethnic formation, white assimilation, etc.) best characterizes Hispanic marriage patterns. I make two analytical contributions. First, I organize past Hispanic hypotheses, both archetypical and blended, into a single theoretical framework defined by the salience of race and Hispanic ethnicity. Second, I parametize this theoretical framework using latent social space models. In this way, I am able to specify a set of interconnected, complex hypotheses in a tractable manner. I follow past work and use marriage/cohabitation data to test the hypotheses. Using American Community Survey data (2010)(2011)(2012), I find that Hispanic marriage/cohabitation patterns suggest high salience on both race and Hispanic ethnicity. Thus, categories like black-Mexican or white-Cuban represent relationally distinct social categories-distinct from both non-Hispanic racial categories (e.g., black or white) and Hispanic categories of a different racial identity.

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“…We simulate guessing by taking a random draw from a binomial distribution, with probability set to .5. We set the alter-alter tie to 0 or 1 depending on the random draw for that tie (for a similar procedure, see Smith and Faris 2015). Thus, 15 percent of alter-alter ties are just guesses, with no actual relationship to the true data.…”

Section: Figure A1mentioning

confidence: 99%

Estimating Contextual Effects from Ego Network Data

Smith

Gauthier

2020

Sociological Methodology

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Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to use the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. In this article the authors ask whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, in which one uses sampled ego network data to infer the network features of each context and then uses the inferred network features as second-level predictors in a hierarchical linear model. The authors test the validity of this idea in the case of network cohesion. Using two complete data sets as a test, the authors find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, such as attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.

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Movement without mobility: Adolescent status hierarchies and the contextual limits of cumulative advantage

Cited by 12 publications

References 59 publications

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

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

A Social Space Approach to Testing Complex Hypotheses: The Case of Hispanic Marriage Patterns in the United States

Estimating Contextual Effects from Ego Network Data

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