116 research outputs found
Exponential-family Random Network Models
Random graphs, where the connections between nodes are considered random
variables, have wide applicability in the social sciences. Exponential-family
Random Graph Models (ERGM) have shown themselves to be a useful class of models
for representing com- plex social phenomena. We generalize ERGM by also
modeling nodal attributes as random variates, thus creating a random model of
the full network, which we call Exponential-family Random Network Models
(ERNM). We demonstrate how this framework allows a new formu- lation for
logistic regression in network data. We develop likelihood-based inference for
the model and an MCMC algorithm to implement it. This new model formulation is
used to analyze a peer social network from the National Lon- gitudinal Study of
Adolescent Health. We model the relationship between substance use and
friendship relations, and show how the results differ from the standard use of
logistic regression on network data
Analysis of Partially Observed Networks via Exponential-family Random Network Models
Exponential-family random network (ERN) models specify a joint representation
of both the dyads of a network and nodal characteristics. This class of models
allow the nodal characteristics to be modelled as stochastic processes,
expanding the range and realism of exponential-family approaches to network
modelling. In this paper we develop a theory of inference for ERN models when
only part of the network is observed, as well as specific methodology for
missing data, including non-ignorable mechanisms for network-based sampling
designs and for latent class models. In particular, we consider data collected
via contact tracing, of considerable importance to infectious disease
epidemiology and public health
Deducer: A Data Analysis GUI for R
While R has proven itself to be a powerful and flexible tool for data exploration and analysis, it lacks the ease of use present in other software such as SPSS and Minitab. An easy to use graphical user interface (GUI) can help new users accomplish tasks that would otherwise be out of their reach, and improves the efficiency of expert users by replacing fifty key strokes with five mouse clicks. With this in mind, Deducer presents dialogs that are understandable for the beginner, and yet contain all (or most) of the options that an experienced statistician, performing the same task, would want. An Excel-like spreadsheet is included for easy data viewing and editing. Deducer is based on Java's Swing GUI library and can be used on any common operating system. The GUI is independent of the specific R console and can easily be used by calling a text-based menu system. Graphical menus are provided for the JGR console and the Windows R GUI
Supporting Widening Participation Students Without Creating Dependency or Leaving Them Unprepared for Work in the Neoliberal Era: A Discussion Paper
In a rapidly changing employment context, research holds that the desirable attributes of grit, resilience and context adaptation will become fundamental to the ongoing employability of current students. The employment landscape has become atomised and volatile under neoliberalism, exemplified by the broken psychological contract between employer and employee. Graduates from widening participation backgrounds entering the labour market will see their existing barriers to career success exacerbated by these changes in the world of work. For students from widening participation backgrounds, the neoliberal policies that underpin these changes are congruent with other policies that exacerbate their socioeconomic and educational disadvantages. Consequently, universities must play an increasingly important role in supporting students within and outside of the taught curriculum. This paper explores ways universities can continue to provide students with the necessary support without building dependencies or expectations of support that would inhibit the development of grit, resilience, and context adaptation. As a starting point for further discussion, this paper proposes guiding principles for universities to inform institutional Teaching and Learning strategies
One-step Estimation of Networked Population Size: Respondent-Driven Capture-Recapture with Anonymity
Population size estimates for hidden and hard-to-reach populations are
particularly important when members are known to suffer from disproportion
health issues or to pose health risks to the larger ambient population in which
they are embedded. Efforts to derive size estimates are often frustrated by a
range of factors that preclude conventional survey strategies, including social
stigma associated with group membership or members' involvement in illegal
activities.
This paper extends prior research on the problem of network population size
estimation, building on established survey/sampling methodologies commonly used
with hard-to-reach groups. Three novel one-step, network-based population size
estimators are presented, to be used in the context of uniform random sampling,
respondent-driven sampling, and when networks exhibit significant clustering
effects. Provably sufficient conditions for the consistency of these estimators
(in large configuration networks) are given. Simulation experiments across a
wide range of synthetic network topologies validate the performance of the
estimators, which are seen to perform well on a real-world location-based
social networking data set with significant clustering. Finally, the proposed
schemes are extended to allow them to be used in settings where participant
anonymity is required. Systematic experiments show favorable tradeoffs between
anonymity guarantees and estimator performance.
Taken together, we demonstrate that reasonable population estimates can be
derived from anonymous respondent driven samples of 250-750 individuals, within
ambient populations of 5,000-40,000. The method thus represents a novel and
cost-effective means for health planners and those agencies concerned with
health and disease surveillance to estimate the size of hidden populations.
Limitations and future work are discussed in the concluding section
One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity
Size estimation is particularly important for populations whose members experience disproportionate health issues or pose elevated health risks to the ambient social structures in which they are embedded. Efforts to derive size estimates are often frustrated when the population is hidden or hard-to-reach in ways that preclude conventional survey strategies, as is the case when social stigma is associated with group membership or when group members are involved in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, for use in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. We give provably sufficient conditions for the consistency of these estimators in large configuration networks. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which also perform well on a real-world location based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable trade-offs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population size estimates are derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. We discuss limitations and future work in the concluding section
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