Tyler H. McCormick

Segregation in social networks based on acquaintanceship and trust.

Using 2006 General Social Survey data, the authors compare levels of segregation by race and along other dimensions of potential social cleavage in the contemporary United States. Americans are not as isolated as the most extreme recent estimates suggest. However, hopes that “bridging” social capital is more common in broader acquaintanceship networks than in core networks are not supported. Instead, the entire acquaintanceship network is perceived by Americans to be about as segregated as the much smaller network of close ties. People do not always know the religiosity, political ideology, family behaviors, or socioeconomic status of their acquaintances, but perceived social divisions on these dimensions are high, sometimes rivaling racial segregation in acquaintanceship networks. The major challenge to social integration today comes from the tendency of many Americans to isolate themselves from others who differ on race, political ideology, level of religiosity, and other salient aspects of social identity.

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS

How many people do you know?: Efficiently estimating personal network size

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RACIAL INEQUALITIES IN CONNECTEDNESS TO IMPRISONED INDIVIDUALS IN THE UNITED STATES

score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial fibrillation. Our model is as interpretable as CHADS

Clustering South African Households Based on their Asset Status Using Latent Variable Models.

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Using Twitter for Demographic and Social Science Research Tools for Data Collection and Processing

, but more accurate.

Probabilistic Cause-of-Death Assignment Using Verbal Autopsies

In this article we develop a method to estimate both individual social network size (i.e., degree) and the distribution of network sizes in a population by asking respondents how many people they know in specific subpopulations (e.g., people named Michael). Building on the scale-up method of Killworth et al. (1998b) and other previous attempts to estimate individual network size, we propose a latent non-random mixing model which resolves three known problems with previous approaches. As a byproduct, our method also provides estimates of the rate of social mixing between population groups. We demonstrate the model using a sample of 1,370 adults originally collected by McCarty et al. (2001). Based on insights developed during the statistical modeling, we conclude by offering practical guidelines for the design of future surveys to estimate social network size. Most importantly, we show that if the first names asked about are chosen properly, the estimates from the simple scale-up model enjoy the same bias-reduction as the estimates from our more complex latent nonrandom mixing model.

Reactive point processes: A new approach to predicting power failures in underground electrical systems

In just the last forty years, imprisonment has been transformed from an event experienced by only the most marginalized to a common stage in the life course of American men—especially Black men with low levels of educational attainment. Although much research considers the causes of the prison boom and how the massive uptick in imprisonment has shaped crime rates and the life course of the men who experience imprisonment, in recent years, researchers have gained a keen interest in the spillover effects of mass imprisonment on families, children, and neighborhoods. Unfortunately, although this new wave of research documents the generally harmful effects of having a family member or loved one incarcerated, it remains unclear how much the prison boom shapes social inequality through these spillover effects because we lack precise estimates of the racial inequality in connectedness—through friends, family, and neighbors—to prisoners. Using the 2006 General Social Survey, we fill this pressing research gap by providing national estimates of connectedness to prisoners—defined in this article as knowing someone who is currently imprisoned, having a family member who is currently imprisoned, having someone you trust who is currently imprisoned, or having someone you know from your neighborhood who is currently imprisoned—for Black and White men and women. Most provocatively, we show that 44% of Black women (and 32% of Black men) but only 12% of White women (and 6% of White men) have a family member imprisoned. This means that about one in four women in the United States currently has a family member in prison. Given these high rates of connectedness to prisoners and the vast racial inequality in them, it is likely that mass imprisonment has fundamentally reshaped inequality not only for the adult men for whom imprisonment has become common, but also for their friends and families.

A Practical Guide to Measuring Social Structure Using Indirectly Observed Network Data

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure-this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region.

Estimating uncertainty in respondent-driven sampling using a tree bootstrap method

Despite recent and growing interest in using Twitter to examine human behavior and attitudes, there is still significant room for growth regarding the ability to leverage Twitter data for social science research. In particular, gleaning demographic information about Twitter users—a key component of much social science research—remains a challenge. This article develops an accurate and reliable data processing approach for social science researchers interested in using Twitter data to examine behaviors and attitudes, as well as the demographic characteristics of the populations expressing or engaging in them. Using information gathered from Twitter users who state an intention to not vote in the 2012 presidential election, we describe and evaluate a method for processing data to retrieve demographic information reported by users that is not encoded as text (e.g., details of images) and evaluate the reliability of these techniques. We end by assessing the challenges of this data collection strategy and discussing how large-scale social media data may benefit demographic researchers.