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1銆佽绋嬪悕绉帮細Event History Analysis 銆婁簨浠跺彶鍒嗘瀽鏂规硶銆�/strong>
鏃堕棿锛�/strong>2016骞�/strong>7鏈�/strong>18鏃ヨ嚦29鏃�/strong>
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涓昏鏁欏笀锛�/strong>Daniel A. Powers鏁欐巿
Dan Powers is a professor of sociology in the Department of Sociology at the University of Texas at Austin with interests in social demography and statistics. He is a research associate with the Population Research Center at the University of Texas at Austin where he has participated in funded research over the past 21 years. He has substantive research interests in fertility, mortality, social inequality, and health disparities. His research examines issues relating to the Hispanic epidemiological paradox in infant mortality, temporal change in infant mortality, multivariate decomposition methodology for hazard rates, and statistical methods for adjusting life tables and survivor functions. Dan plays a key role in statistics and methods training in the Department of Sociology and at the Population Research Center by teaching graduate courses in categorical data analysis and longitudinal data analysis. He has taught courses in Event History Analysis in the Department of Sociology for the past 20 years and at the Summer Statistics Institute at the University of Texas at Austin for the past 4 years. He has served on over 90 MS and Ph.D. committees, and is former graduate advisor and GSC chair of the masters in statistics program in the Division of Statistics and Scientific Computation at UT.
缃戝潃锛�/strong>http://www.utexas.edu/cola/depts/sociology/faculty/dpowers
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The purpose of this course is to introduce and apply methods and models for event history analysis. Event history analysis deals with methods for events occurring in time. This topic is also known as Survival Analysis, and includes the study of methods and models for the analysis of transition rates. This is an applied course that will draw on data from sociology, demography and health fields. The course will provide in-depth treatment of the most widely-used methods for event-history analysis. This course should be useful for graduate students and faculty in the social, behavioral, biological, and health sciences as well as applied researchers in a variety of fields.
Students should have had a course in basic statistics and a course in linear regression and some familiarity with a computer package. The intended audience is graduate students, researchers, and faculty members interested in an overview of event history modeling with the goal of understanding the theory, models, and methods for the analysis of event histories. This course will prepare participants to analyze event history data, and to understand the empirical application of these methodologies in their particular field of study.
2銆佽绋嬪悕绉帮細Causal Inference 銆婂洜鏋滄帹璁哄垎鏋愭柟娉曘�
鏃堕棿锛�/strong>2016骞�/strong>8鏈�/strong>1鏃ヨ嚦12鏃�/strong>
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涓昏鏁欏笀锛�/strong>Kenneth Frank鏁欐巿
Kenneth Frank received his Ph.D. in measurement, evaluation and statistical analysis from the School of Education at the University of Chicago in 1993. He is currently a professor in Counseling, Educational Psychology and Special Education as well as in Fisheries and Wildlife at Michigan State University. His substantive interests include the study of schools as organizations, social structures of students and teachers and school decision-making, and social capital. His substantive areas are linked to several methodological interests: social network analysis, causal inference and multi-level models. His publications include quantitative methods for representing relations among actors in a social network, robustness indices for inferences, and the effects of social capital in schools and other social contexts. He teaches general introductory courses in research methods and quantitative methods as well as advanced courses in multivariate analysis and seminars in social network analysis and causal inference. Dr. Frank’s current projects include studies of how schools respond to increases in core curricular requirements, cognitive linkages among how aspects of knowledge, how adolescents respond to their social contexts in schools, the diffusion of knowledge about climate change, and how the decisions about natural resource use in small communities are embedded in social contexts.
缃戝潃锛�a href="http://www.msu.edu/~kenfrank/">http://www.msu.edu/~kenfrank/
璇剧▼浠嬬粛锛�/span>
There is currently great debate regarding the basis for causal inferences across the social sciences. Can we make causal inferences only from experiments? What about ethical or logistical limitations, or concerns that the experimental paradigm is artificial because of the necessity for extreme control over conditions? On the other hand, though observational studies are applied to natural conditions, can we rely on statistical control to make causal inferences? What about unmeasured, or unrecognized confounding factors? At what point does a statistical inference sustain a causal inference? Answers to these questions are more than merely academic and philosophical. For example they have immediate implications for policy-making regarding the implementation of innovations.
To address questions such as the above this course will explore causal inference from the perspectives of statistics and philosophy of science. We will begin with a comparison of causal inferences in the social sciences with those of the experimental sciences. Drawing on eclectic readings (Manski, Heckman, Rubin, Holland, Pearl, Shadish, Cook, Campbell Sobel, Dawid), we will use concepts such as the counterfactual, homogeneity of units and internal and external validity to describe causal inference. Furthermore, we will discuss statistical techniques such as propensity score matching and instrumental variables that might be used to improve the likelihood of valid inferences. Finally, we will use recent work to quantify how robust inferences are to potential threats the validity.
In the first half the course I will present methods including regression, propensity score matching, instrumental variables, regression discontinuity, random versus fixed effects, and sensitivity analysis. In the second half of the course we will turn to intensive projects or readings.
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