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Clogg Lecture 2016 Given by Mark Handcock

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The 2016 Clifford C. Clogg lecture was given by Mark Handcock. The general talk was on March 21, 2016, with a statistics talk on March 22, 2016 and his third talk given on March 23, 2016.

Mark Hancock

 

The 2016 Clifford C. Clogg Memorial Lecture was given by Mark S. Handcock, Professor and Chair of Statistics at the University of California, Los Angeles. The lecture series includes a public lecture intended for a general audience, titled “Some Impacts of Social Research on Statistical Methodology”. Handcock will also give two specialized lectures in statistics and sociology: “Some New Models for Social Networks”, and “Statistical Methods to Survey Hidden Networked Populations”.


 

Mark S. Handcock is Professor and Chair of Statistics at the University of California, Los Angeles. He received his B.Sc. from the University of Western Australia and his Ph.D. from the University of Chicago.

Dr. Handcock’s research involves methodological development, and is based largely on motivation from questions in the social sciences, demography and epidemiology. His work focuses on the development of statistical models for the analysis of social network data, spatial processes and longitudinal data arising in labor economics. He also works in the fields of distributional comparisons, environmental statistics, spatial statistics and inference for stochastic processes.

He is a Fellow of the American Statistical Association and a Fellow of Royal Statistical Society. In addition, he is the winner of the 2012 International Network for Social Network Analysis (INSNA) citation award, and the recipient of the 2002 Richard A. Lester Prize for the Outstanding Book in Labor Economics and Industrial Relations. He was the Editor of Social Networks and Journal of Statistical Software.

  • About Clifford C. Clogg Memorial Lecture

Dr. Clogg was nationally and internationally known for his work in quantitative methods and demography, particularly on the analysis of rates, standardization methods, and latent structure analysis. Contributions from friends and colleagues led to the creation of the Clifford C. Clogg Memorial Lectureship fund. The fund was endowed in 1996. Leo Goodman gave the inaugural lecture on September 27, 1996.

  •  Public Lecture on March 21

Title: "Some Impacts of Social Research on Statistical Methodology"

To view the lecture:

"Some Impacts of Social Research on Statistical Methodology" - Part I
"Some Impacts of Social Research on Statistical Methodology" - Part II 
"Some Impacts of Social Research on Statistical Methodology" - Part III
 

Abstract: In 1992, Clifford C. Clogg published a paper on "the influence of sociological methodology, or methodology for social research more generally, on modern statistics." In it he emphasized the contributions of the social sciences to the development of statistical methodology. Since that time the social sciences has continued to produce, motivate and demand new statistical methodology. This presentation will review some of these recent contributions, including causality, networks, and the reproducibility of science itself.

  • Statistics Talk on March 22

Title: "Some New Models for Social Networks"

To view the lecture:

"Some New Models for Social Networks" - PART I
"Some New Models for Social Networks" - PART II
"Some New Models for Social Networks" - PART III

Abstract: 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 complex social phenomena. In this talk we will consider some new classes of models that generalize ERGM in different ways. First, we model the attributes of the social actors as random variates, thus creating a random model of both the relational and individual data, which we call Exponential-family Random Network Models (ERNM). This provides a framework for expanded analysis of network processes, including a new formulation for network regression where the outcomes, covariates and relations are socially endogenous. We illustrate this with a new class of latent cluster models and network regression. Next we introduce a class of models we call Tapered Exponential-family Random Network Models (TERNM). These models remove the degeneracy properties that hamper ERGM and ERNM while retaining their advantages. We show how these models can provide good fits to large networks. Finally we introduce spatial temporal exponential-family of point processes (STEPP) models to jointly represent the co-evolution of social relations and individual behavior in discrete time. This is joint work with Ian E. Fellows and Joshua D. Embree.  

 

  • Sociology Talk on March 23

Title: "Statistical Methods To Survey Hidden Networked Populations"

To view lecture:

"Statistical Methods To Survey Hidden Networked Populations" - Part I
"Statistical Methods To Survey Hidden Networked Populations" - Part II 
"Statistical Methods To Survey Hidden Networked Populations" - Part III 

Abstract: In many situations, standard survey sampling strategies fail because the target populations cannot be accessed through well-defined sampling frames. Typically, a sampling frame for the target population is not available, and its members are rare or stigmatized in the larger population so that it is prohibitively expensive to contact them through the available frames. We discuss statistical issues in studying hard-to-reach or otherwise "hidden" populations. These populations are characterized by the difficulty in survey sampling from them using standard probability methods. Examples in a demographic setting include unregulated workers and migrants. Examples of such populations in a behavioral and social settings include injection drug users, men who have sex with men, and female sex workers. Hard-to-reach populations are under-served by current sampling methodologies mainly due to the lack of practical alternatives to address these methodological difficulties. We will focus on populations where some form of social network information can be used to assist the data collection. In such situations sophisticated statistical methods are needed to allow the characteristics of the population to be inferred from the collected data.

This is joint work with Krista J. Gile and Katherine R. McLaughlin.

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