Network diversity and educational attainment: a case study in China

Fung, Ka Yi

doi:10.1186/s40711-015-0014-x

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…First, only one American university was used to survey participants, limiting responses to one region of the country. Previous research reports differences in students' social networks between rural and urban communities (Fung, 2015), supporting the claim that our scope of inference is limited.…”

Section: Discussionsupporting

confidence: 84%

Decision Tree-Based Predictive Models for Academic Achievement Using College Students’ Support Networks

Frazier¹,

Silva²,

Meilak³

et al. 2021

Journal of Data Science

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In this study, we examine a set of primary data collected from 484 students enrolled in a large public university in the Mid-Atlantic United States region during the early stages of the COVID-19 pandemic. The data, called Ties data, included students' demographic and support network information. The support network data comprised of information that highlighted the type of support, (i.e. emotional or educational; routine or intense). Using this data set, models for predicting students' academic achievement, quantified by their self-reported GPA, were created using Chi-Square Automatic Interaction Detection (CHAID), a decision tree algorithm, and cforest, a random forest algorithm that uses conditional inference trees. We compare the methods' accuracy and variation in the set of important variables suggested by each algorithm. Each algorithm found different variables important for different student demographics with some overlap. For White students, different types of educational support were important in predicting academic achievement, while for non-White students, different types of emotional support were important in predicting academic achievement. The presence of differing types of routine support were important in predicting academic achievement for cisgender women, while differing types of intense support were important in predicting academic achievement for cisgender men.

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Section: Discussionsupporting

confidence: 84%

Decision Tree-Based Predictive Models for Academic Achievement Using College Students’ Support Networks

Frazier¹,

Silva²,

Meilak³

et al. 2021

Journal of Data Science

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“…In order for commercial and non-profit organizations to succeed, it is important for those organizations to recruit a workforce that is not only skilled, but also diverse, as diversity has been positively associated with performance [15]. However, diversity cannot be measured only in terms of numbers: it is known that negative effects may happen when a network is structured in a way that resources are not accessible through the social capital accessible to members of a minority group [9]. Social capital consists of bridging resources from outside of an individual's group (inter-group connections) and bonding resources from internal group connections (intra-group connections) [13].…”

Section: Introductionmentioning

confidence: 99%

On Measuring the Diversity of Organizational Networks

Jalali¹,

Kenthapadi²,

Soundarajan³

2021

Preprint

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The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole. However, in many fields there is a severe under-representation of minority groups; moreover, minority individuals may be segregated from the rest of the network or isolated from one another. While the problem of increasing the representation of minority groups in various fields has been well-studied, diversification in terms of numbers alone may not be sufficient: social relationships should also be considered. In this work, we consider the problem of assigning a set of employment candidates to positions in a social network so that diversity and overall fitness are maximized, and propose Fair Employee Assignment (FairEA), a novel algorithm for finding such a matching. The output from FairEA can be used as a benchmark by organizations wishing to evaluate their hiring and assignment practices. On real and synthetic networks, we demonstrate that FairEA does well at finding high-fitness, high-diversity matchings.

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