Mark S Handcock
Biographic Data
| ID | 163981 |
|---|---|
| NAME | Mark S Handcock |
| GIVEN NAMES | Mark S |
| FAMILY NAME | Handcock |
| SIGNATURE | HANDCOCK M S |
| AFFILIATIONS | University of Washington |
| ORCID | 0000-0002-9985-2785 |
| VERIFIED | Yes |
| TOTAL WORKS | 34 |
| TOTAL CITATIONS | 574 |
| AUTHOR COUNT | 34 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1994 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 7 |
Understanding networks with exponential-family random network models
Sequential Neighborhood Effects
Prior research has suggested that children living in a disadvantaged neighborhood have lower achievement test scores, but these studies typically have not estimated causal effects that account for neighborhood choice. Recent studies used propensity score methods to account for the endogeneity of neighborhood exposures, comparing disadvantaged and nondisadvantaged neighborhoods. We develop an alternative propensity function approach in which cumul…
If You Are Not Counted, You Don’t Count
A Separable Model for Dynamic Networks
Models of dynamic networks—networks that evolve over time—have manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and flexibility of the class of exponential family random-graph models. The model—a separable temporal exponential family random-graph model—facilitates separable modelling of the tie duration distributions and the structural dynamics of tie formation. We develop …
Identifying Sources of Health Care Underutilization Among California's Immigrants
Modeling social networks from sampled data
Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of a…
Respondent-Driven Sampling
Respondent-driven sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample. The current estimators of population averages make strong assumptions in order to treat the data as a probability sample. We e…
A description of within-family resource exchange networks in a Malawian village
In this paper we explore patterns of economic transfers between adults within household and family networks in a village in Malawi's Rumphi district, using data from the 2006 round of the Malawi Longitudinal Study of Families and Health. We fit Exponential-family Random Graph Models (ERGMs) to assess individual, relational, and higher-order network effects. The network effects of cyclic giving, reciprocity, and in-degree and out-degree distributi…
Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models
Bayesian estimation of hispanic fertility hazards from survey and population data
Previous studies have demonstrated both large gains in efficiency and reductions in bias by incorporating population information in regression estimation with sample survey data. These studies, however, assumed that the population values are exact. This assumption is relaxed here through a Bayesian extension of constrained maximum likelihood estimation applied to U.S. Hispanic fertility. The Bayesian approach allows for the use of both auxiliary …
A statnet Tutorial
The statnet suite of R packages contains a wide range of functionality for the statistical analysis of social networks, including the implementation of exponential-family random graph (ERG) models. In this paper we illustrate some of the functionality of statnet through a tutorial analysis of a friendship network of 1,461 adolescents.
Ergm
We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and interrelated, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets fr…
Goodness of Fit of Social Network Models
We present a systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks. These procedures compare structural statistics of the observed network with the corresponding statistics on networks simulated from the fitted model. We apply this approach to the study of friendship relations among high school st…
Specification of Exponential-Family Random Graph Models
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also …
A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models
Model-Based Clustering for Social Networks
Network models are widely used to represent relations between interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by attributes of the actors or dyads, and clustering. Interest often focuses on finding clusters of actors or ties, and the number of groups in the data is typically unknown. We propose a new model, t…
Recent developments in exponential random graph (p*) models for social networks
Inference in Curved Exponential Family Models for Networks
Network data arise in a wide variety of applications. Although descriptive statistics for networks abound in the literature, the science of fitting statistical models to complex network data is still in its infancy. The models considered in this article are based on exponential families; therefore, we refer to them as exponential random graph models (ERGMs). Although ERGMs are easy to postulate, maximum likelihood estimation of parameters in thes…
Prevalence of HIV Infection Among Young Adults in the United States
Objectives. We estimated HIV prevalence rates among young adults in the United States. Methods. We used survey data from the third wave of the National Longitudinal Study of Adolescent Health, a random sample of nearly 19000 young adults initiated in 1994–1995. Consenting respondents were screened for the presence of antibodies to HIV-1 in oral mucosal transudate specimens. We calculated prevalence rates, accounting for survey design, response ra…
New Specifications for Exponential Random Graph Models
The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), also known as p* models. The strong point of these models is that they can represent a variety of structural tendencies, such as transitivity, that define complicated dependence patterns not easily modeled by more basic probability models. Recently, Markov ch…
Comparative Geographic Concentrations of 4 Sexually Transmitted Infections
Objectives. We measured and compared the concentration of primary and secondary syphilis, gonorrhea, chlamydial infection, and genital herpes in a large county with urban, suburban, and rural settings. Methods. We geocoded sexually transmitted infections reported to King County, Washington health department in 2000–2001 to census tract of residence. We used a model-based approach to measure concentration with Lorenz curves and Gini coefficients. …
Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship
Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population-level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achi…
Improved Regression Estimation of A Multivariate Relationship with Population Data on the Bivariate Relationship
Regression coefficients specify the partial effect of a regressor on the dependent variable.Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data.We show here that such population-level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data.The method of constrained MLE is used to achieve…
Persistent Inequality? Answers From Hybrid Models for Longitudinal Data
Many questions in social research must be evaluated over time. For example, in studies of intragenerational mobility, measuring opportunity for economic advancement requires longitudinal data. The authors develop and use a class of hybrid functional models to demonstrate how different models can lead to extremely different substantive conclusions. They provide guidelines for longitudinal data analyses in which variance partitions are central to t…
Latent Space Approaches to Social Network Analysis
Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved…
New Specifications for Exponential Random Graph Models
The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), also known as p* models. The strong point of these models is that they can represent a variety of structural tendencies, such as transitivity, that define complicated dependence patterns not easily modeled by more basic probability models. Recently, Markov ch…
Respondent-Driven Sampling
Respondent-driven sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample. The current estimators of population averages make strong assumptions in order to treat the data as a probability sample. We e…
Women's Gains or Men's Losses? A Closer Look at the Shrinking Gender Gap in Earnings
The recent closing of the gender wage gap is often attributed to increases in women's human capital. This explanation neglects the effect of growing inequality in men's earnings. The authors develop a decomposition that allows them to test how distributional changes in men's and women's earnings combine to yield changes in women's economic status. Using Current Population Survey data from 1967 to 1987, the authors find that the striking polarizat…
Economic Inequality
Two positions dominate the debate over the recent increases in economic inequality in the United States. The job-skill mismatch thesis attributes rising inequality to growth in the number of high-skill, high-wage jobs that leaves less-skilled workers behind. The thesis, in contrast, argues that the service shift generates growth in the number of both high-wage and low-wage jobs, and declines in the middle. Standard summary measures of distributio…
Relative Distribution Methods
We present an outline of relative distribution methods, with an application to recent changes in the U.S. wage distribution. Relative distribution methods are a nonparametric statistical framework for analyzing data in a fully distributional context. The framework combines the graphical tools of exploratory data analysis with statistical summaries, decomposition, and inference. The relative distribution is similar to a density ratio. It is techni…
Sequential Neighborhood Effects
Prior research has suggested that children living in a disadvantaged neighborhood have lower achievement test scores, but these studies typically have not estimated causal effects that account for neighborhood choice. Recent studies used propensity score methods to account for the endogeneity of neighborhood exposures, comparing disadvantaged and nondisadvantaged neighborhoods. We develop an alternative propensity function approach in which cumul…
Combining registration-system and survey data to estimate birth probabilities
With the widespread availability of event-history data, demographers have increasingly eschewed registration-system data in favor of survey data. We propose instead using survey and registration-system data in combination, via a constrained maximum-likelihood framework for demographic hazard modeling. As an application, we combine panel survey data and birth registration data to estimate annual birth probabilities by parity. The general fertility…
Bayesian estimation of hispanic fertility hazards from survey and population data
Previous studies have demonstrated both large gains in efficiency and reductions in bias by incorporating population information in regression estimation with sample survey data. These studies, however, assumed that the population values are exact. This assumption is relaxed here through a Bayesian extension of constrained maximum likelihood estimation applied to U.S. Hispanic fertility. The Bayesian approach allows for the use of both auxiliary …
Prevalence of HIV Infection Among Young Adults in the United States
Objectives. We estimated HIV prevalence rates among young adults in the United States. Methods. We used survey data from the third wave of the National Longitudinal Study of Adolescent Health, a random sample of nearly 19000 young adults initiated in 1994–1995. Consenting respondents were screened for the presence of antibodies to HIV-1 in oral mucosal transudate specimens. We calculated prevalence rates, accounting for survey design, response ra…
Identifying Sources of Health Care Underutilization Among California's Immigrants
Comparative Geographic Concentrations of 4 Sexually Transmitted Infections
Objectives. We measured and compared the concentration of primary and secondary syphilis, gonorrhea, chlamydial infection, and genital herpes in a large county with urban, suburban, and rural settings. Methods. We geocoded sexually transmitted infections reported to King County, Washington health department in 2000–2001 to census tract of residence. We used a model-based approach to measure concentration with Lorenz curves and Gini coefficients. …
If You Are Not Counted, You Don’t Count
A description of within-family resource exchange networks in a Malawian village
In this paper we explore patterns of economic transfers between adults within household and family networks in a village in Malawi's Rumphi district, using data from the 2006 round of the Malawi Longitudinal Study of Families and Health. We fit Exponential-family Random Graph Models (ERGMs) to assess individual, relational, and higher-order network effects. The network effects of cyclic giving, reciprocity, and in-degree and out-degree distributi…
Improved Regression Estimation of A Multivariate Relationship with Population Data on the Bivariate Relationship
Regression coefficients specify the partial effect of a regressor on the dependent variable.Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data.We show here that such population-level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data.The method of constrained MLE is used to achieve…
Statistical Inference for the Relative Density
Social scientists are increasingly interested in techniques for comparing changes in distributional shape in addition to mean levels. One such technique is based on the relative distribution, a nonparametric summary of the information required for scale-invariant comparisons between two distributions. The relative distribution is being used by social scientists to represent and analyze distributional differences, enabling researchers to move well…
Percentages, Odds, and the Meaning of Inequality
Economic Inequality
Two positions dominate the debate over the recent increases in economic inequality in the United States. The job-skill mismatch thesis attributes rising inequality to growth in the number of high-skill, high-wage jobs that leaves less-skilled workers behind. The thesis, in contrast, argues that the service shift generates growth in the number of both high-wage and low-wage jobs, and declines in the middle. Standard summary measures of distributio…
Women's Gains or Men's Losses? A Closer Look at the Shrinking Gender Gap in Earnings
The recent closing of the gender wage gap is often attributed to increases in women's human capital. This explanation neglects the effect of growing inequality in men's earnings. The authors develop a decomposition that allows them to test how distributional changes in men's and women's earnings combine to yield changes in women's economic status. Using Current Population Survey data from 1967 to 1987, the authors find that the striking polarizat…
Bridge populations in the spread of HIV/Aids in Thailand
OBJECTIVE: To determine the extent to which men provide a bridge population between commercial sex workers (CSW) and the general female population in Thailand. DESIGN: Sexual network and serological data were collected from a systematic quota sample of low income men and truckers during 1992 in three Thailand provinces. Completed sample size was 1075 men aged 17-45 years and 330 truckers. METHODS: Sexual network information was used to identify t…
Percentages, Odds, and the Meaning of Inequality
Relative Distribution Methods
We present an outline of relative distribution methods, with an application to recent changes in the U.S. wage distribution. Relative distribution methods are a nonparametric statistical framework for analyzing data in a fully distributional context. The framework combines the graphical tools of exploratory data analysis with statistical summaries, decomposition, and inference. The relative distribution is similar to a density ratio. It is techni…
Combining registration-system and survey data to estimate birth probabilities
With the widespread availability of event-history data, demographers have increasingly eschewed registration-system data in favor of survey data. We propose instead using survey and registration-system data in combination, via a constrained maximum-likelihood framework for demographic hazard modeling. As an application, we combine panel survey data and birth registration data to estimate annual birth probabilities by parity. The general fertility…
Divergent Paths
Covariance Models For Latent Structure In Longitudinal Data
We present several approaches to modeling latent structure in longitudinal studies when the covariance itself is the primary focus of the analysis. This is a departure from much of the work on longitudinal data analysis, in which attention is focused solely on the cross-sectional mean and the influence of covariates on the mean. Such analyses are particularly important in policy-related studies, in which the heterogeneity of the population is of …
Latent Space Approaches to Social Network Analysis
Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved…
Statistical Inference for the Relative Density
Social scientists are increasingly interested in techniques for comparing changes in distributional shape in addition to mean levels. One such technique is based on the relative distribution, a nonparametric summary of the information required for scale-invariant comparisons between two distributions. The relative distribution is being used by social scientists to represent and analyze distributional differences, enabling researchers to move well…
Comparative Geographic Concentrations of 4 Sexually Transmitted Infections
Objectives. We measured and compared the concentration of primary and secondary syphilis, gonorrhea, chlamydial infection, and genital herpes in a large county with urban, suburban, and rural settings. Methods. We geocoded sexually transmitted infections reported to King County, Washington health department in 2000–2001 to census tract of residence. We used a model-based approach to measure concentration with Lorenz curves and Gini coefficients. …
Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship
Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population-level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achi…
Improved Regression Estimation of A Multivariate Relationship with Population Data on the Bivariate Relationship
Regression coefficients specify the partial effect of a regressor on the dependent variable.Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data.We show here that such population-level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data.The method of constrained MLE is used to achieve…
Persistent Inequality? Answers From Hybrid Models for Longitudinal Data
Many questions in social research must be evaluated over time. For example, in studies of intragenerational mobility, measuring opportunity for economic advancement requires longitudinal data. The authors develop and use a class of hybrid functional models to demonstrate how different models can lead to extremely different substantive conclusions. They provide guidelines for longitudinal data analyses in which variance partitions are central to t…
Inference in Curved Exponential Family Models for Networks
Network data arise in a wide variety of applications. Although descriptive statistics for networks abound in the literature, the science of fitting statistical models to complex network data is still in its infancy. The models considered in this article are based on exponential families; therefore, we refer to them as exponential random graph models (ERGMs). Although ERGMs are easy to postulate, maximum likelihood estimation of parameters in thes…
Prevalence of HIV Infection Among Young Adults in the United States
Objectives. We estimated HIV prevalence rates among young adults in the United States. Methods. We used survey data from the third wave of the National Longitudinal Study of Adolescent Health, a random sample of nearly 19000 young adults initiated in 1994–1995. Consenting respondents were screened for the presence of antibodies to HIV-1 in oral mucosal transudate specimens. We calculated prevalence rates, accounting for survey design, response ra…
New Specifications for Exponential Random Graph Models
The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), also known as p* models. The strong point of these models is that they can represent a variety of structural tendencies, such as transitivity, that define complicated dependence patterns not easily modeled by more basic probability models. Recently, Markov ch…
Model-Based Clustering for Social Networks
Network models are widely used to represent relations between interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by attributes of the actors or dyads, and clustering. Interest often focuses on finding clusters of actors or ties, and the number of groups in the data is typically unknown. We propose a new model, t…
Recent developments in exponential random graph (p*) models for social networks
A statnet Tutorial
The statnet suite of R packages contains a wide range of functionality for the statistical analysis of social networks, including the implementation of exponential-family random graph (ERG) models. In this paper we illustrate some of the functionality of statnet through a tutorial analysis of a friendship network of 1,461 adolescents.
Ergm
We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and interrelated, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets fr…
Goodness of Fit of Social Network Models
We present a systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks. These procedures compare structural statistics of the observed network with the corresponding statistics on networks simulated from the fitted model. We apply this approach to the study of friendship relations among high school st…
Specification of Exponential-Family Random Graph Models
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also …
A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models
Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models
Mathematics (25 works) · Computer Science (22 works) · Statistics (22 works) · Econometrics (15 works) · Complex Network Analysis Techniques (13 works) · Graph (12 works) · Random graph (12 works) · Demography (11 works) · Exponential random graph models (11 works) · Population (11 works)