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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180412T120000
DTEND;TZID=America/Los_Angeles:20180412T130000
DTSTAMP:20260502T050625
CREATED:20180403T170740Z
LAST-MODIFIED:20180403T170740Z
UID:1493-1523534400-1523538000@css.stat.ucla.edu
SUMMARY:Workshop: Bayesian Concepts for Data Analysis
DESCRIPTION:Instructor: Michael Tzen \nContent:\nThis 1 hour workshop will provide a sampling of introductory concepts for bayesian analysis. We will use Bayes Rule (and its implications) to think about data analysis. When used as a framework to model phenomenon\, the analyst gets to work with 4 useful distributions: the prior\, posterior\, prior predictive\, & posterior predictive. We will predict what clothing size 2Chainz wears. We’ll also look at the Gompertz Rule from demography. In both examples\, the bayesian framework allows us to clearly express the estimand\, information from data\, information from prior knowledge\, and the estimator. \nThis workshop is the first of a two part series. The first workshop is conceptual while the second workshop will focus on software. The date for the second workshop is TBD. \nPlease RSVP Here: \nhttps://goo.gl/forms/CF4wuaobfqpug9Js1 \n 
URL:https://css.stat.ucla.edu/event/workshop-bayesian-concepts-for-data-analysis/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180313T140000
DTEND;TZID=America/Los_Angeles:20180313T151500
DTSTAMP:20260502T050625
CREATED:20180312T165958Z
LAST-MODIFIED:20180312T165958Z
UID:1486-1520949600-1520954100@css.stat.ucla.edu
SUMMARY:Jake Bowers\, University of Illinois at Urbana-Champaign
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRules of Engagement in Evidence-Informed Policy: Practices and Norms of Statistical Science in Government\n\nCollaboration between statistical scientists (data scientists\, behavioral and social scientists\, statisticians) and policy makers promises to improve government and the lives of the public. And the data and design challenges arising from governments offer academics new chances to improve our understanding of both extant methods and behavioral and social science theory. However\, the practices that ensure the integrity of statistical work in the academy — such as transparent sharing of data and code — do not translate neatly or directly into work with governmental data and for policy ends. This paper proposes a set of practices and norms that academics and practitioners can agree on before launching a partnership so that science can advance and the public can be protected while policy can be improved. This work is at an early stage. The aim is a checklist or statement of principles or memo of understanding that can be a template for the wide variety of ways that statistical scientists collaborate with governmental actors. \n\nSpeaker:\nJake Bowers\, Associate Professor at University of Illinois and Fellow of the Office of Evaluation Sciences\nsite
URL:https://css.stat.ucla.edu/event/jake-bowers-university-of-illinois-at-urbana-champaign/
LOCATION:Franz Hall 2258A\, Franz Hall 2258A
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180221T120000
DTEND;TZID=America/Los_Angeles:20180221T133000
DTSTAMP:20260502T050625
CREATED:20180209T002136Z
LAST-MODIFIED:20180209T002136Z
UID:1413-1519214400-1519219800@css.stat.ucla.edu
SUMMARY:Yu Xie\, Princeton
DESCRIPTION:The California Center for Population Research and the Center for Social Statistics Presents:\nHeterogeneous Causal Effects: A Propensity Score Approach\nHeterogeneity is ubiquitous in social science.  Individuals differ not only in background characteristics\, but also in how they respond to a particular treatment. In this presentation\, Yu Xie argues that a useful approach to studying heterogeneous causal effects is through the use of the propensity score. He demonstrates the use of the propensity score approach in three scenarios: when ignorability is true\, when treatment is randomly assigned\, and when ignorability is not true but there are valid instrumental variables. \nSpeaker:\nYu Xie\, Professor\, Princeton\nsite
URL:https://css.stat.ucla.edu/event/yu-xie-princeton/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180206T140000
DTEND;TZID=America/Los_Angeles:20180206T153000
DTSTAMP:20260502T050625
CREATED:20180129T183306Z
LAST-MODIFIED:20180129T183306Z
UID:1368-1517925600-1517931000@css.stat.ucla.edu
SUMMARY:Per Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich)
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nModelling Mobility Tables as Weighted Networks\nContemporary research on occupational mobility\, i.e. how people move between jobs\, tends to view mobility as being mostly determined by individual and occupational characteristics. These studies focus on people’s sex\, ethnicity\, age\, education or class origin and how they get access to jobs of different wages\, working conditions\, desirability\, skill profiles and job security. Consequently\, observations in occupational mobility tables are understood as independent of one another\, which allows the use of a variety of well-developed statistical models. As opposed to these “classical” approaches focussed on individual and occupational characteristics\, I am interested in modelling and understanding endogenously emerging patterns in occupational mobility tables. These emergent patterns arise from the social embedding of occupational choices\, when occupational transitions of different individuals influence each other. To analyse these emergent patterns\, I conceptualise a disaggregated mobility table as a network in which occupations are the nodes and connections are made of individuals transitioning between occupations.\n\n\nIn this paper\, I present a statistical model to analyse these weighted mobility networks. The approach to modelling mobility as an interdependent system is inspired by the exponential random graph model (ERGM); however\, some differences arise from ties being weighted as well as from specific constraints of mobility tables. The model is applied to data on intra-generational mobility to analyse the interdependent transitions of men and women through the labour market\, as well as to understanding the extent to which clustering in mobility can be modelled by exogenously defined social classes or through endogenous structures.\n  \nPer Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich)\nsite
URL:https://css.stat.ucla.edu/event/per-block-eth-zurich/
LOCATION:Franz Hall 2258A\, Franz Hall 2258A
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180124T120000
DTEND;TZID=America/Los_Angeles:20180124T133000
DTSTAMP:20260502T050625
CREATED:20180119T224032Z
LAST-MODIFIED:20180119T225154Z
UID:1361-1516795200-1516800600@css.stat.ucla.edu
SUMMARY:Rob Warren\, University of Minnesota
DESCRIPTION:The California Center for Population Research and the Center for Social Statistics presents:\nWhen Should Researchers Use Inferential Statistics When Analyzing Data on Full Populations?\nMany researchers uncritically use inferential statistical procedures (e.g.\, hypothesis tests) when analyzing complete population data—a situation in which inference may seem unnecessary. We begin by reviewing and analyzing the most common rationales for employing inferential procedures when analyzing full population data. Two common rationales—having to do with handling missing data and generalizing results to other times and/or places—either lack merit or amount to analyzing sample (not population) data.  Whether it is appropriate to use inferential procedures depends on whether researchers are analyzing sample or population data and on whether they seek to make causal or descriptive claims. When doing descriptive research\, the distinction between sample and population data is paramount: Inferential statistics should only be used to analyze sample data (to account for sampling variability) and never to analyze population data. When doing causal research\, the distinction between sample data and population data is unimportant: Inferential procedures can and should always be used to distinguish (for example) robust associations from those that may have come about by chance alone. Crucially\, using inferential procedures to analyze population data to make descriptive claims can lead to incorrect substantive conclusions—especially when population sizes and/or effect sizes are small. \nSpeaker:\nRob Warren\, Professor of Sociology and Director of the Minnesota Population Center\nsite
URL:https://css.stat.ucla.edu/event/rob-warren-university-minnesota/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171212T140000
DTEND;TZID=America/Los_Angeles:20171212T153000
DTSTAMP:20260502T050625
CREATED:20171211T190142Z
LAST-MODIFIED:20171211T190142Z
UID:1354-1513087200-1513092600@css.stat.ucla.edu
SUMMARY:Nathaniel Osgood\, University of Saskatchewan
DESCRIPTION:The UCLA Department of Community Health Sciences and the Center for Social Statistics presents:\nDynamic Modeling for Health in the Age of Big Data\nTraditional approaches to public health concerns have conferred great advances in the duration and quality of life. Public health interventions – from improved sanitation efforts\, to vaccination campaigns\, to contact tracing and environmental regulations – have helped reduce common risks to health throughout many areas of the world. Unfortunately\, while traditional methods from the health sciences have proven admirably suited for addressing traditional challenges\, a troubling crop of complex health challenges confront the nation and the world\, and threaten to stop – and even reverse the – rise in length and quality of life that many have taken for granted. Examples include multi-factorial problems such as obesity and obesity-related chronic disease\, the spread of drug-resistant and rapidly mutating pathogens that evade control efforts\, and “syndemics” of mutually reinforcing health conditions (such as Diabetes and TB; substance abuse\, violence and HIV/AIDS; obesity & stress). Such challenges have proven troublingly policy resistant\, with interventions being thwarted by “blowback” from the complex feedbacks involved\, and attendant costs threaten to overwhelm health care systems. In the face of such challenges public health decision makers are increasingly supplementing their toolbox using “system science” techniques. Such methods – also widely known as “complex systems approaches” – provide a way to understand a system’s behavior as a whole and as more than the sum of its parts\, and a means of anticipating and managing the behavior of a system in more judicious and proactive fashion. However\, such approaches offer substantially greater insight and power when combined with rich data sources. Within this talk\, we will highlight the great promise afforded by combining of Systems Science techniques and rich data sources\, particularly emphasizing the role of cross-linking models with “big data” offering high volume\, velocity\, variety and veracity. Examples of such data include fine-grained temporal and spatial information collected by smartphone-based and wearable as well as building and municipal sensors\, data from social media posts and search behavior\, helpline calls\, website accesses and rich cross-linked databases. Decision-oriented models grounded by such novel data sources can allow for articulated theory building regarding difficult-to-observe aspects of human behavior. Such models can also aid in informing evaluation of and judicious selection between sophisticated interventions to lessen the health burden of a wide variety of health conditions. Such models are particularly powerful when complemented by machine learning and computational statistics techniques that permit recurrent model regrounding in the newest evidence\, and which allow a model to knit together holistic portrait of the system as a whole\, and which support grounded investigation of between intervention strategies tradeoffs. \nSponsored by The Department of Community Health Sciences along with the Center for Social Statistics and the California Center for Population Research \nSpeaker:\nNathaniel Osgood\, Professor\, Department of Computer Science\, Associate Faculty at Department of Community Health & Epidemiology and Bioengineering Division at the University of Saskatchewan
URL:https://css.stat.ucla.edu/event/nathaniel-osgood-saskatchewan/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171108T153000
DTEND;TZID=America/Los_Angeles:20171108T163000
DTSTAMP:20260502T050625
CREATED:20171103T183129Z
LAST-MODIFIED:20171103T202044Z
UID:1334-1510155000-1510158600@css.stat.ucla.edu
SUMMARY:Hadley Wickham\, RStudio
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nProgramming data science with R & the tidyverse\nTidy evaluation is a new framework for non-standard evaluation that\nwill be used throughout tidyverse. In this talk\, I’ll introduce you to\nthe problem that tidy eval solves\, illustrated with examples of the\nvarious approaches used in R. I’ll then explain the most important\ncomponents so that you can start writing your own functions instead of\ncopying and pasting tidyr and dplyr code. I’ll finish with a small\nshiny app that shows how tidy eval is a natural fit for handling user\ninput. \nHadley Wickham\, RStudio\nhttp://hadley.nz/
URL:https://css.stat.ucla.edu/event/hadley-wickham-rstudio/
LOCATION:1200 Rolfe Hall\, 1200 Rolfe Hall
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171024T140000
DTEND;TZID=America/Los_Angeles:20171024T150000
DTSTAMP:20260502T050625
CREATED:20171016T182654Z
LAST-MODIFIED:20171016T182837Z
UID:1324-1508853600-1508857200@css.stat.ucla.edu
SUMMARY:Sander Greenland\, UCLA Department of Epidemiology
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nStatistical Significance and Discussion of the Challenges of Avoiding the Abuse of Statistical Methodology\nSander Greenland will offer his perspective on the paper\, “Redefine Statistical Significance”\, which was the topic of the previous week’s seminar. Also he will discuss the challenges of avoiding the abuse of statistical methodology. \nSpeaker:\nSander Greenland\, Professor Emeritus\, UCLA Department of Epidemiology
URL:https://css.stat.ucla.edu/event/sander-greenland-ucla/
LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171017T140000
DTEND;TZID=America/Los_Angeles:20171017T150000
DTSTAMP:20260502T050625
CREATED:20210424T023825Z
LAST-MODIFIED:20210424T023825Z
UID:2081-1508248800-1508252400@css.stat.ucla.edu
SUMMARY:Daniel Benjamin\, USC Dornsife Center for Economic and Social Research
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRedefine Statistical Significance\nDaniel Benjamin will discuss his paper (written by him and 71 other authors)\, “Redefine Statistical Significance”. The paper proposes that the default p-value threshold should be changed from 0.05 to 0.005. \nThe paper is available at this link. \nSpeaker:\nDaniel Benjamin\, Associate Professor\, USC Dornsife Center for Economic and Social Research \n 
URL:https://css.stat.ucla.edu/event/daniel-benjamin-usc-dornsife-center-for-economic-and-social-research/
LOCATION:CA
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171017T140000
DTEND;TZID=America/Los_Angeles:20171017T150000
DTSTAMP:20260502T050625
CREATED:20171016T181816Z
LAST-MODIFIED:20171016T182927Z
UID:1315-1508248800-1508252400@css.stat.ucla.edu
SUMMARY:Daniel Benjamin\, USC Dornsife Center for Economic and Social Research
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRedefine Statistical Significance\nDaniel Benjamin will discuss his paper (written by him and 71 other authors)\, “Redefine Statistical Significance”. The paper proposes that the default p-value threshold should be changed from 0.05 to 0.005. \nThe paper is available at this link. \nSpeaker:\nDaniel Benjamin\, Associate Professor\, USC Dornsife Center for Economic and Social Research \n 
URL:https://css.stat.ucla.edu/event/daniel-benjamin-usc/
LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171013T120000
DTEND;TZID=America/Los_Angeles:20171013T130000
DTSTAMP:20260502T050625
CREATED:20170928T001141Z
LAST-MODIFIED:20170928T001327Z
UID:1307-1507896000-1507899600@css.stat.ucla.edu
SUMMARY:Workshop: Useful R 4 Stata Users Brown Bag
DESCRIPTION:Instructor: \nMichael Tzen\, CCPR UCLA \n  \nAbstract: \n\n\n\nThis workshop is a brown bag forum. Participants are encouraged to bring in tangible questions they wish to explore using R. To serve as a background road map\, the instructor will provide an abbreviated sample of what he thinks are the most useful features of R. However\, the goal is to have participants ask questions that the collective group can figure out using R. Any R question is fair game\, for example: questions about fundamental R concepts or even questions about how to run Stata-equivalent R commands. Participants will be provided access to Rstudio\, so please bring a laptop. \nThis CCPR brown-bag is intended to be an open forum that complements the 3 great resources below. Please see the resources\, especially the first one. \n1) 10 minute demo: interactive call–response slideshow of R basics\nhttp://tryr.codeschool.com/ \n2) Worked out examples from a UCLA IDRE workshop on R concepts\nhttps://stats.idre.ucla.edu/r/seminars/intro/ \n3) R 4 Data Science e-book\nhttp://r4ds.had.co.nz/ \n\n\n\n\n  \n  \nRSVP Signup: \n\nplease fill out the form found here
URL:https://css.stat.ucla.edu/event/workshop-useful-r-4-stata-users-brown-bag/
LOCATION:CA
CATEGORIES:ccpr workshop,css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170609T120000
DTEND;TZID=America/Los_Angeles:20170609T130000
DTSTAMP:20260502T050625
CREATED:20170530T165922Z
LAST-MODIFIED:20170530T180714Z
UID:1262-1497009600-1497013200@css.stat.ucla.edu
SUMMARY:James Robins\, Harvard University
DESCRIPTION:The UCLA Departments of Epidemiology\, Biostatistics\, Statistics and the Center for Social Statistics presents:\nCausal Methods in Epidemiology: Where has it got us and what can we expect in the future?\nThe principal focus of Dr. Robins’ research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments. The new methods are to a large extent based on the estimation of the parameters of a new class of causal models – the structural nested models – using a new class of estimators – the G estimators.\n\nPlease RSVP: https://goo.gl/wScewQ \n\nSpeaker:\nJames Robins\, Mitchell L. and Robin LaFoley Dong Professor of Epidemiology\, Harvard University\nhttps://www.hsph.harvard.edu/james-robins/
URL:https://css.stat.ucla.edu/event/james-robins-harvard-university/
LOCATION:Room 33-105 CHS Building\, 650 Charles E Young Drive South\, Los Angeles\, CA\, 90095 \, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170602T120000
DTEND;TZID=America/Los_Angeles:20170602T160000
DTSTAMP:20260502T050625
CREATED:20170512T203737Z
LAST-MODIFIED:20170512T204238Z
UID:1235-1496404800-1496419200@css.stat.ucla.edu
SUMMARY:Fragile Families Challenge: Getting Started Workshop
DESCRIPTION:Instructor: \nIan Lundberg\, Ph.D. Student\, Sociology and Social Policy\, Princeton University \n  \nAbstract: \nThe Fragile Families Challenge is a scientific mass collaboration that combines predictive modeling\, causal inference\, and in-depth interviews in order to learn more about the lives of disadvantaged children. Fragile Families Challenge builds on the Fragile Families and Child Wellbeing Study that has been running for about 20 years. The Fragile Families research team has been following about 5\,000 families—collecting information about them and their environment at regular intervals—in order to understand how to improve the lives of disadvantaged children in the US. \n*Co-Sponsored with the California Center for Population Research\, UCLA \nRegister now @ fragilefamilieschallenge.org\, please specify you plan to attend the UCLA workshop
URL:https://css.stat.ucla.edu/event/fragile-families-challenge-getting-started-workshop/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:ccpr workshop,css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170524T120000
DTEND;TZID=America/Los_Angeles:20170524T133000
DTSTAMP:20260502T050625
CREATED:20170323T201513Z
LAST-MODIFIED:20170501T205334Z
UID:784-1495627200-1495632600@css.stat.ucla.edu
SUMMARY:Shahryar Minhas\, Duke University
DESCRIPTION:The Center for Social Statistics Presents:\nPredicting the Evolution of Intrastate Conflict: Evidence from Nigeria\nThe endogenous nature of civil conflict has limited scholars’ abilities to draw clear inferences about the drivers of conflict evolution. We argue that three primary features characterize the complexity of intrastate conflict: (1) the interdependent relationships of conflict between actors; (2) the impact of armed groups on violence as they enter or exit the conflict network; and (3) the ability of civilians to influence the strategic interactions of armed groups. Using ACLED event data on Nigeria\, we apply a novel network-based approach to predict the evolution of intrastate conflict dynamics. Our network approach yields insights about the effects of civilian victimization and key actors entering the conflict. Attacks against civilians lead groups to both be more violent\, and to become the targets of attacks in subsequent periods. Boko Haram’s entrance into the civil war leads to an increase in violence even in unrelated dyads. Further\, our approach significantly outperforms more traditional dyad-group approaches at predicting the incidence of conflict.\n  \nSpeaker: \nShahryar Minha\, Postdoctoral Fellow\, Duke University\nAssistant Professor\, Michigan State University\nDepartment of Political Science and the Social Science Data Analytics Program (SSDA) \nhttp://s7minhas.com/
URL:https://css.stat.ucla.edu/event/shahryar-minhas/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170519T120000
DTEND;TZID=America/Los_Angeles:20170519T133000
DTSTAMP:20260502T050625
CREATED:20170502T232838Z
LAST-MODIFIED:20170502T233045Z
UID:1227-1495195200-1495200600@css.stat.ucla.edu
SUMMARY:Analysis of Complex Surveys using R and Stata
DESCRIPTION:Instructors: \nMichael Tzen\, CCPR UCLA \nAndy Lin\, IDRE UCLA \n  \nAbstract: \nIn this workshop\, attendees will learn how to analyze survey data while accounting for its complex survey design. Using both the R and Stata software packages\, we will demonstrate how to specify the survey design\, impute any missing data\, and analyze the survey outcomes of interest. We will discuss how our downstream “analysis” steps are related to initial operational “design” choices made by the survey data provider. \n  \nNote: \nPlease bring your own laptop.\nFor R\, we will use instantaneous Jupyter notebooks (no install required)\nFor Stata\, please have a TS1 account or your own copy of Stata\n(for software assistance\, contact emoss@ccpr.ucla.edu)\nRSVP Signup: \nplease fill out the form found here
URL:https://css.stat.ucla.edu/event/1227/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:ccpr workshop,css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20170423
DTEND;VALUE=DATE:20170426
DTSTAMP:20260502T050625
CREATED:20170327T184026Z
LAST-MODIFIED:20170501T204445Z
UID:928-1492905600-1493164799@css.stat.ucla.edu
SUMMARY:West Coast Experiments Conference\, UCLA 2017
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nWest Coast Experiments Conference\, UCLA 2017\nhttps://www.eventbrite.com/e/west-coast-experiments-2017-ucla-tickets-32968584925 \nThe tenth annual West Coast Experiments Conference will be held at UCLA on Monday\, April 24 and Tuesday\, April 25\, 2017\, preceded by in-depth methods training workshops on Sunday\, April 23. The WCE is an annual conference that brings together leading scholars and graduate students in economics\, political science and other social sciences who share an interest in causal identification broadly speaking. Now in its tenth year\, the WCE is a venue for methodological instruction and debate over design-based and observational methods for causal inference\, both theory and applications. \nThe speakers are Judea Pearl\, Rosa Matzkin\, Niall Cardin\, Angus Deaton\, Chris Auld\, Jeff Wooldridge\, Ed Leamer\, Karim Chalak\, Rodrigo Pinto\, Clark Glymour\, Elias Barenboim\, Adam Glynn\, and Karthika Mohan. \nRegistration is free\, but you must register at wce2017ucla.eventbrite.com to get a ticket for each day you plan to attend. Registration is first-come-first-served. We also will host free in-depth methods training workshops on the afternoon of Sunday\, April 23. We are currently planning these workshops so please watch this space for upcoming details. The topics will include causal graphs and big data. You can register for these workshops when you register for the conference. \nThis conference is funded by a generous grant from the Alfred P. Sloan Foundation and sponsored by the UCLA Department of Political Science\, the UCLA California Center for Population Research\, the UCLA Center for Social Statistics\, and the UCR School of Public Policy. The organizing committee this year is Chad Hazlett\, Judea Pearl\, Rodrigo Pinto\, and Manisha Shah. \nFor those unable to attend\, we will be creating a conference webpage to archive the papers and presentations. Please check back to the Eventbrite announcement page one week prior to the conference where we will post the URL for the paper and presentation archive. \nhttps://www.eventbrite.com/e/west-coast-experiments-2017-ucla-tickets-32968584925 \n 
URL:https://css.stat.ucla.edu/event/west-coast-experiments-conference-ucla-2017/
LOCATION:UCLA\, Los Angeles\, CA\, 90024\, United States
CATEGORIES:conference,css seminar,css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161007T120000
DTEND;TZID=America/Los_Angeles:20161007T140000
DTSTAMP:20260502T050625
CREATED:20170324T181324Z
LAST-MODIFIED:20170501T204250Z
UID:868-1475841600-1475848800@css.stat.ucla.edu
SUMMARY:So You Want to Be a Researcher? Principles and Practical Data Tools to Help You Fly Transparently
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nInstructor: Michael Tzen\, CCPR UCLA\nPLEASE BRING A PERSONAL LAPTOP \nAbstract: \nResearchers go through fundamental steps in a data analysis project. This workshop highlights key steps in a data analyst’s workflow and encourages transparency in each of the steps. Throughout this workshop\, we go through hands on exercises that integrate: a transparency engine\, obtaining federal API data\, producing useful intermediate data structures\, and sharing analysis results. We will use Jupyter notebook for literate coding and if time allows demonstrate the Rstudio environment for reproducible development. \nPlease RSVP Here: \nhttps://goo.gl/forms/mdR9cfcZN0SjjjHg2
URL:https://css.stat.ucla.edu/event/want-researcher-principles-practical-data-tools-help-fly-transparently/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:ccpr workshop,css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160527T120000
DTEND;TZID=America/Los_Angeles:20160527T140000
DTSTAMP:20260502T050625
CREATED:20170324T180812Z
LAST-MODIFIED:20170501T204235Z
UID:865-1464350400-1464357600@css.stat.ucla.edu
SUMMARY:Ilan H. Meyer & Mark S. Handcock\, UCLA
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nInnovative Sampling Approaches for Hard to Reach Populations: Design of a National Probability Study of Lesbians\, Gay Men\, Bisexuals\, and Transgender Peoples and  Network Sampling of Hard to Reach Populations\nSpeakers: \nIlan H. Meyer\, Williams Distinguished Senior Scholar for Public Policy at the Williams Institute \nMark S. Handcock\, Professor of Statistics at UCLA and Director of the Center for Social Statistics \nDescription: \nCome for the exciting seminar then stay for the free lunch and discussion. A seminar led by Ilan H. Meyer followed immediately by a Brown Bag Lunch led by Mark S. Handcock. \nDr. Meyer is Principal Investigator of the Generations and TransPop Surveys. Generations is a survey of a nationally representative sample of 3 generations of lesbians\, gay men\, and bisexuals. TransPop is the first national probability sample survey of transgender individuals in the United States. Both studies attempt to obtain large nationally representative samples of hard to reach populations. Dr. Meyer will review sampling issues with LGBT populations and speak on the importance of measuring population health of LGBTs and the underlying aspects in designing a national probability survey. \nFrom a contrasting perspective\, the field of Survey Methodology is facing many challenges. The general trend of declining response rates is making it harder for survey researchers to reach their intended population of interest using classical survey sampling methods. \nIn the followup Brown Bag Lunch\, led by Mark S. Handcock\, participants will discuss statistical challenges and approaches to sampling hard to reach populations. Transgenders\, for example\, are a rare and stigmatized population. If the transgender community exhibits networked social behavior\, then network sampling methods may be useful approaches that compliment classical survey methods.\nParticipants are encouraged to speak on ideas of statistical methods for surveys.
URL:https://css.stat.ucla.edu/event/ilan-h-meyer-mark-s-handcock-ucla/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160408T120000
DTEND;TZID=America/Los_Angeles:20160408T130000
DTSTAMP:20260502T050625
CREATED:20170323T224257Z
LAST-MODIFIED:20170501T204213Z
UID:813-1460116800-1460120400@css.stat.ucla.edu
SUMMARY:Practical GIS for Demography
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nInstructor: Michael Tzen\, CCPR UCLA\nVIEW PODCAST HERE! \nPLEASE BRING A PERSONAL LAPTOP \nWe will provide all the software tools (no installation needed) \nAbstract: \nGeographically referenced data sets are becoming increasingly common. \nIn spatial analysis of demographic data\, three common spatial indices are used: points\, lines\, and polygons. Through interspersed hands on exercises\, we will: obtain\, shape\, and visualize demographic data over space. We will briefly discuss the motivation for incorporating geographic association into downstream models. \nBelow is an example of what we will produce with [R] \n1) Obtain GPS locations of In-N-Out’s obtained from the Google Radar API \n2) Compute their generated Voronoi Tesselation to address spatial competition \n3) Aggregate Census 2010 tract-level population counts into these competing geographies \nNo background knowledge of [R] will be required. The exercises are introductory. We will also highlight alternative software tools to achieve similar goals\, such as GrassGIS and Stata \nSign Up Today! \nhttp://goo.gl/forms/oLrgyVz5Xu
URL:https://css.stat.ucla.edu/event/practical-gis-demography/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:ccpr workshop,css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160331T143000
DTEND;TZID=America/Los_Angeles:20160331T160000
DTSTAMP:20260502T050625
CREATED:20170501T204927Z
LAST-MODIFIED:20170501T204927Z
UID:1221-1459434600-1459440000@css.stat.ucla.edu
SUMMARY:Rick Dale\, University of California\, Merced
DESCRIPTION:The Center for Social Statistics Presents:\nQuantifying the dynamics of multimodal communication with multimodal data\nAbstract: Human communication is built upon an array of signals\, from body movement to word selection. The sciences of language and communication tend to study these signals individually. However\, natural human communication uses all these signals together simultaneously\, and in complex social systems of various sizes. It is an open puzzle to uncover how this multimodal communication is structured in time and organized at different scales. Such a puzzle includes analysis of two-person interactions. It also involves an understanding of much larger systems\, such as communication over social media at an unprecedentedly massive scale. \nCollaborators and I have explored communication across both of these scales\, and I will describe examples in the domain of conflict. For example\, we’ve studied conflict communication in two-person interactions using video analysis of body and voice dynamics. At the broader scale\, we have also used large-scale social media behavior (Twitter) during a massively shared experience of conflict\, the 2012 Presidential Debates. These projects reveal the importance of dynamics. In two-person conflict\, for example\, signal dynamics (e.g.\, body\, voice) during interaction can reveal the quality of that interaction. In addition\, collective behavior on Twitter can be predicted even by simple linear models using debate dynamics between Obama and Romney (e.g.\, one interrupting the other). \nThe collection\, quantification\, and modeling of multitemporal and multivariate datasets hold much promise for new kinds of interdisciplinary collaborations. I will end by discussing how they may guide new theoretical directions for pursuing the organization and temporal structure of multimodality in communication. \nUrl: http://statistics.ucla.edu/seminars/2016-03-31/2:30pm/314-royce-hall
URL:https://css.stat.ucla.edu/event/rick-dale-university-california-merced/
LOCATION:CA
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160329T143000
DTEND;TZID=America/Los_Angeles:20160329T153000
DTSTAMP:20260502T050625
CREATED:20170323T225738Z
LAST-MODIFIED:20170501T204151Z
UID:829-1459261800-1459265400@css.stat.ucla.edu
SUMMARY:Betsy Sinclair\, Washington University in St Louis
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nElectronic Homestyle: Tweeting Ideology\nAbstract: Ideal points are central to the study of political partisanship and an essential component to our understanding of legislative and electoral behavior. We employ automated text analysis on tweets from Members of Congress to estimate their ideal points using Naive Bayes classification and Support Vector Machine classification. We extend these tools to estimate the proportion of partisan speech used in each legislator’s tweets. We demonstrate an association between these measurements\, existing ideal point measurements\, and district ideology. \n 
URL:https://css.stat.ucla.edu/event/betsy-sinclair-washington-university-st-louis/
LOCATION:CA
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151113T120000
DTEND;TZID=America/Los_Angeles:20151113T133000
DTSTAMP:20260502T050625
CREATED:20170324T180355Z
LAST-MODIFIED:20170501T204132Z
UID:861-1447416000-1447421400@css.stat.ucla.edu
SUMMARY:Reproducibility of Statistical Results
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nMark S. Handcock \n(Professor\, Statistics) \nJeffrey B. Lewis \n(Professor\, Political Science) \nMarc A. Suchard \n(Professor\, Biomathematics\, Biostatistics and Human Genetics) \nAbstract: \nReproducibility is one of the main principles of the scientific method. This panel of scholars will discuss issues in the importance of replication of statistical results. Increasing attention is being paid to improve reporting and hence reproducibility in the social and medical sciences. This panel will discuss some key concerns in study replication\, initiatives for increasing replication\, and then open the floor to discussion of how we move forward as a scientific community.
URL:https://css.stat.ucla.edu/event/reproducibility-statistical-results/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151015T120000
DTEND;TZID=America/Los_Angeles:20151015T133000
DTSTAMP:20260502T050625
CREATED:20170323T224804Z
LAST-MODIFIED:20170501T204012Z
UID:816-1444910400-1444915800@css.stat.ucla.edu
SUMMARY:Aude Hofleitner\, Facebook
DESCRIPTION:The Center for Social Statistics Presents:\nInferring and understanding travel and migration movements at a global scale\nAbstract: Despite extensive work on the dynamics and outcomes of large-scale migrations\, timely and accurate estimates of population movements do not exist. While censuses\, surveys\, and observational data have been used to measure migration\, estimates based on these data sources are constrained in their inability to detect unfolding migrations\, and lack temporal and demographic detail. In this study\, we present a novel approach for generating estimates of migration that can measure movements of particular demographic groups across country lines. \nSpecifically\, we model migration as a function of long-term moves across countries using aggregated Facebook data. We demonstrate that this methodological approach can be used to produce accurate measures of past and ongoing migrations – both short-term patterns and long-term changes in residence. Several case studies confirm the validity of our approach\, and highlight the tremendous potential of information obtained from online platforms to enable novel research on human migration events. \nIf you are interested in meeting with or joining the speaker for lunch\, please send email to Seminars@ccpr.ucla.edu
URL:https://css.stat.ucla.edu/event/aude-hofleitner-facebook/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150623T100000
DTEND;TZID=America/Los_Angeles:20150623T120000
DTSTAMP:20260502T050625
CREATED:20170324T180102Z
LAST-MODIFIED:20170501T204051Z
UID:854-1435053600-1435060800@css.stat.ucla.edu
SUMMARY:Bayesian Statistical Modeling Using Stan
DESCRIPTION:The Center for Social Statistics Presents:\n\nInstructor: Daniel Lee\, Columbia University\n\nAbstract:\nStan is an open-source\, Bayesian inference tool with interfaces in R\, Python\, Matlab\, Julia\, Stata\, and the command line. Users write statistical models in a high-level statistical language. The default Bayesian inference algorithm is the no-U-turn sampler (NUTS)\, an auto-tuned version of Hamiltonian Monte Carlo. Stan was developed to address the speed and scalability issues of existing Bayesian inference tools. The goal of the workshop is the practical application of Stan to different models starting with ordinary linear regression and ending with more complex models such as generalized linear mixed and hierarchical models. Experience in Bayesian statistical modeling is recommended\, but not required. Workshop participants should bring their own laptop installed with RStan (or some other Stan interface). See http://mc-stan.org/ for instructions on how to install it.\n\nThe workshop is open\, but UCLA community members are especially welcomed.  To enroll for the workshop\, please go to: http://goo.gl/forms/CYHSj8Vkcw\n\nThe workshop is co-sponsored by the UCLA Department of Statistics\, the UCLA Department of Political Science\, and the Department of Biostatistics at UCLA.
URL:https://css.stat.ucla.edu/event/bayesian-statistical-modeling-using-stan/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:ccpr workshop,css workshop
END:VEVENT
END:VCALENDAR