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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181114T120000
DTEND;TZID=America/Los_Angeles:20181114T133000
DTSTAMP:20260430T090132
CREATED:20180828T003331Z
LAST-MODIFIED:20210424T023141Z
UID:10000630-1542196800-1542202200@ccpr.ucla.edu
SUMMARY:Adrian Raftery\, University of Washington
DESCRIPTION:Title: Bayesian Population Projections with Migration Uncertainty \nAbstract: The United Nations recently issued official probabilistic population projections for all countries for the first time\, using a Bayesian hierarchical modeling framework developed by our group at the University of Washington. These take account of uncertainty about future fertility and mortality\, but not international migration. We propose a Bayesian hierarchical autoregressive model for obtaining joint probabilistic projections of migration rates for all countries\, broken down by age and sex. Joint trajectories for all countries are constrained to satisfy the requirement of zero global net migration. We evaluate our model using out-of-sample validation and compare point projections to the projected migration rates from a persistence model similar to the UN’s current method for projecting migration\, and also to a state of the art gravity model. We also resolve an apparently paradoxical discrepancy between growth trends in the proportion of the world population migrating and the average absolute migration rate across countries. This is joint work with Jonathan Azose and Hana Ševčíková. \nCo-sponsored with the Center for Social Statistics \nMore on Prof. Raftery
URL:https://ccpr.ucla.edu/event/adrian-raftery-university-of-washington/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2018/08/Raftery_Adrian.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181017T120000
DTEND;TZID=America/Los_Angeles:20181017T133000
DTSTAMP:20260430T090132
CREATED:20181001T192236Z
LAST-MODIFIED:20181008T201210Z
UID:10000642-1539777600-1539783000@ccpr.ucla.edu
SUMMARY:Erin Hartman\, University of California Los Angeles
DESCRIPTION:Title: Covariate Selection for Generalizing Experimental Results \nAbstract: Researchers are often interested in generalizing the average treatment effect (ATE) estimated in a randomized experiment to non-experimental target populations. Researchers can estimate the population ATE without bias if they adjust for a set of variables affecting both selection into the experiment and treatment heterogeneity. Although this separating set has simple mathematical representation\, it is often unclear how to select this set in applied contexts. In this paper\, we propose a data-driven method to estimate a separating set. Our approach has two advantages. First\, our algorithm relies only on the experimental data. As long as researchers can collect a rich set of covariates on experimental samples\, the proposed method can inform which variables they should adjust for. Second\, we can incorporate researcher-specific data constraints. When researchers know certain variables are unmeasurable in the target population\, our method can select a separating set subject to such constraints\, if one is feasible. We validate our proposed method using simulations\, including naturalistic simulations based on real-world data. \nCo-Sponsored with The Center for Social Statistics \nMore on Prof. Hartman
URL:https://ccpr.ucla.edu/event/erin-hartman-university-of-california-los-angeles/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2018/08/Hartman_Erin-1.jpg
ORGANIZER;CN="CCPR Seminars":MAILTO:seminars@ccpr.ucla.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180313T140000
DTEND;TZID=America/Los_Angeles:20180313T151500
DTSTAMP:20260430T090132
CREATED:20180312T172714Z
LAST-MODIFIED:20180312T172714Z
UID:10000597-1520949600-1520954100@ccpr.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
URL:https://ccpr.ucla.edu/event/jake-bowers-university-of-illinois-at-urbana-champaign/
LOCATION:Franz Hall 2258A
CATEGORIES:CSS Events,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180221T120000
DTEND;TZID=America/Los_Angeles:20180221T133000
DTSTAMP:20260430T090132
CREATED:20170802T173822Z
LAST-MODIFIED:20180209T002255Z
UID:10000592-1519214400-1519219800@ccpr.ucla.edu
SUMMARY:Yu Xie\, Princeton
DESCRIPTION:“Heterogeneous Causal Effects: A Propensity Score Approach “ \nAbstract: Heterogeneity 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. \n  \nMore on Prof. Xie
URL:https://ccpr.ucla.edu/event/yu-xie-princeton/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2017/08/Yu-Xie.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180206T140000
DTEND;TZID=America/Los_Angeles:20210423T170000
DTSTAMP:20260430T090132
CREATED:20210424T025431Z
LAST-MODIFIED:20210424T025431Z
UID:10000739-1517925600-1619197200@ccpr.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://ccpr.ucla.edu/event/per-block-eth-zurich-swiss-federal-institute-of-technology-in-zurich/
CATEGORIES:CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180124T120000
DTEND;TZID=America/Los_Angeles:20180124T133000
DTSTAMP:20260430T090132
CREATED:20170724T202504Z
LAST-MODIFIED:20210424T023053Z
UID:10000583-1516795200-1516800600@ccpr.ucla.edu
SUMMARY:Rob Warren\, University of Minnesota
DESCRIPTION:“When Should Researchers Use Inferential Statistics When Analyzing Data on Full Populations?“ \nAbstract: Many 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. \n*Co-sponsored with the Center for Social Statistics \nMore on Prof. Warren \nAccess Podcast here
URL:https://ccpr.ucla.edu/event/rob-warren-university-minnesota/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2017/07/Warren_1_24_18.jpg
ORGANIZER;CN="CCPR Seminars":MAILTO:seminars@ccpr.ucla.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171213T120000
DTEND;TZID=America/Los_Angeles:20171213T133000
DTSTAMP:20260430T090132
CREATED:20171201T201239Z
LAST-MODIFIED:20220505T184317Z
UID:10000487-1513166400-1513171800@ccpr.ucla.edu
SUMMARY:Nathaniel Osgood\, University of Saskatchewan\, "Using Smartphones and Wearables for Public Health Insight: A Hands-On Introduction"
DESCRIPTION:Abstract: Acquisition of evidence-based understanding of human health behavior and exposure to environments forms a central focus of health research\, and a critical prerequisite for effective health policy. The use of mobile devices to study health behavior via cross-linked sensor data and on-device self-reporting and crowdsourcing offer compelling advantages to complement traditional techniques. Data collected on such devices can be particularly powerful in supporting understanding of health behaviors in areas where accurate self-reporting is difficult\, including nutritional intake\, physical activity and sedentary behaviour\, and exposures to physical and social environments. Through structured surveys and crowdsourcing mechanisms\, such devices can further provide potent means of gaining insight into knowledge\, attitudes\, beliefs\, and perceptions in health areas. Finally\, while little explored\, some of the most powerful uses of such day lie in terms of understanding the particular causal pathways impacted by interventions. This hands-on talk will provide public health researchers and practitioners with a high-level introduction to the motivation\, state-of-the-art in and tools for use of mobile data collection in public health.  Topics touched on include elements of motivation\, study design\, behavioral ethics concerns and needs\, data collection systems requiring low technical involvement\, and analysis.  Participants will be invited to experience a state-of-the-art and widely used mobile data collection system during the talk that illustrates many of the principles discussed. \nSponsored by The Department of Community Health Sciences along with the Center for Social Statistics and the California Center for Population Research
URL:https://ccpr.ucla.edu/event/using-smartphones-wearables-public-health-insight-hands-introduction/
LOCATION:CHS 61-269
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171212T140000
DTEND;TZID=America/Los_Angeles:20171212T153000
DTSTAMP:20260430T090132
CREATED:20171201T200948Z
LAST-MODIFIED:20220505T184243Z
UID:10000485-1513087200-1513092600@ccpr.ucla.edu
SUMMARY:Nathaniel Osgood\, University of Saskatchewan\, "Dynamic modeling for health in the age of big data"
DESCRIPTION:Abstract: Traditional 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
URL:https://ccpr.ucla.edu/event/dynamic-modeling-health-age-big-data/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171108T153000
DTEND;TZID=America/Los_Angeles:20210423T170000
DTSTAMP:20260430T090132
CREATED:20210424T024124Z
LAST-MODIFIED:20210424T024124Z
UID:10000738-1510155000-1619197200@ccpr.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://ccpr.ucla.edu/event/hadley-wickham-rstudio/
CATEGORIES:CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171024T140000
DTEND;TZID=America/Los_Angeles:20210423T170000
DTSTAMP:20260430T090132
CREATED:20210424T024039Z
LAST-MODIFIED:20210424T024039Z
UID:10000737-1508853600-1619197200@ccpr.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://ccpr.ucla.edu/event/sander-greenland-ucla-department-of-epidemiology/
CATEGORIES:CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171017T130000
DTEND;TZID=America/Los_Angeles:20171017T140000
DTSTAMP:20260430T090132
CREATED:20210424T023721Z
LAST-MODIFIED:20220415T204502Z
UID:10000736-1508245200-1508248800@ccpr.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://ccpr.ucla.edu/event/james-robins-harvard-university-2/
CATEGORIES:CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170609T120000
DTEND;TZID=America/Los_Angeles:20170609T130000
DTSTAMP:20260430T090132
CREATED:20170530T220912Z
LAST-MODIFIED:20170530T220912Z
UID:10000563-1497009600-1497013200@ccpr.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.\nPlease RSVP: https://goo.gl/wScewQ \nSpeaker:\nJames Robins\, Mitchell L. and Robin LaFoley Dong Professor of Epidemiology\, Harvard University\nhttps://www.hsph.harvard.edu/james-robins/
URL:https://ccpr.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 Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170602T120000
DTEND;TZID=America/Los_Angeles:20170602T160000
DTSTAMP:20260430T090132
CREATED:20170504T213904Z
LAST-MODIFIED:20210424T024800Z
UID:10000562-1496404800-1496419200@ccpr.ucla.edu
SUMMARY:Fragile Families Challenge: Getting Started Workshop
DESCRIPTION:“Fragile Families Challenge: Getting Started Workshop” \nIan Lundberg  \nPh.D. Student\, Sociology and Social Policy\,  Princeton University \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 Center for Social Statistics\, UCLA  \nRegister now @ fragilefamilieschallenge.org\, please specify you plan to attend the UCLA workshop
URL:https://ccpr.ucla.edu/event/fragile-families-challenge-getting-started-workshop/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Workshop,CSS Events
ORGANIZER;CN="CCPR Seminars":MAILTO:seminars@ccpr.ucla.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170524T120000
DTEND;TZID=America/Los_Angeles:20170524T133000
DTSTAMP:20260430T090132
CREATED:20170410T210610Z
LAST-MODIFIED:20170501T205311Z
UID:10000560-1495627200-1495632600@ccpr.ucla.edu
SUMMARY:Shahryar Minhas\, Duke University
DESCRIPTION:The Center for Social Statistics Presents:\nPredicting the Evolution of Intrastate Conflict: Evidence from Nigeria\nurl: http://css.stat.ucla.edu/event/shahryar-minhas/\n\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://ccpr.ucla.edu/event/shahryar-minhas-duke-university/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170423T080000
DTEND;TZID=America/Los_Angeles:20170425T170000
DTSTAMP:20260430T090132
CREATED:20170403T155701Z
LAST-MODIFIED:20220505T185059Z
UID:10000559-1492934400-1493139600@ccpr.ucla.edu
SUMMARY:West Coast Experiments Conference\, UCLA 2017
DESCRIPTION:The 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. \nThe conference registration webpage is wce2017ucla.eventbrite.com. \nThe 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.  The deadline to register is April 18\, 2017 at 8:00 AM PDT. \nWe 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\, UCR School of Public Policy\, California Center for Population Research and the Center for Social Statistics. \nThe organizing committee this year is Chad Hazlett\, Judea Pearl\, Rodrigo Pinto\, and Manisha Shah.
URL:https://ccpr.ucla.edu/event/west-coast-experiments-conference-ucla-2017/
LOCATION:Covel Commons UCLA
CATEGORIES:CCPR Conference,CCPR Workshop,CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160527T120000
DTEND;TZID=America/Los_Angeles:20160527T140000
DTSTAMP:20260430T090132
CREATED:20160309T013131Z
LAST-MODIFIED:20220906T223634Z
UID:10000527-1464350400-1464357600@ccpr.ucla.edu
SUMMARY:Ilan H. Meyer & Mark S. Handcock\, UCLA
DESCRIPTION:“Innovative 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://ccpr.ucla.edu/event/ilan-mark-css-ucla/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Conference,CCPR Seminar,CCPR Workshop,CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160331T143000
DTEND;TZID=America/Los_Angeles:20160331T160000
DTSTAMP:20260430T090132
CREATED:20160330T171954Z
LAST-MODIFIED:20170501T204950Z
UID:10000433-1459434600-1459440000@ccpr.ucla.edu
SUMMARY:Rick Dale\, University of California\, Merced
DESCRIPTION:“Quantifying the dynamics of multimodal communication with multimodal data.” \n*Presented by the Center for Social Statistics \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://ccpr.ucla.edu/event/rick-dale/
LOCATION:314 Royce Hall\, 340 Royce Dr\, los angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160329T143000
DTEND;TZID=America/Los_Angeles:20160329T160000
DTSTAMP:20260430T090132
CREATED:20160328T165434Z
LAST-MODIFIED:20160512T220411Z
UID:10000537-1459261800-1459267200@ccpr.ucla.edu
SUMMARY:Betsy Sinclair\, Washington University in St Louis
DESCRIPTION:“Electronic 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. \nUrl: http://statistics.ucla.edu/seminars/2016-03-29/2:30pm/314-royce-hall
URL:https://ccpr.ucla.edu/event/betsy-sinclair-washington-university-st-louis/
LOCATION:314 Royce Hall\, 340 Royce Dr\, los angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151113T120000
DTEND;TZID=America/Los_Angeles:20151113T133000
DTSTAMP:20260430T090132
CREATED:20160315T210703Z
LAST-MODIFIED:20220906T223445Z
UID:10000531-1447416000-1447421400@ccpr.ucla.edu
SUMMARY:Reproducibility of Statistical Results
DESCRIPTION:Presented By: \n\nMark S. Handcock \n(Professor\, Statistics) \nJeffrey B. Lewis \n(Professor\, Political Science) \nMarc A. Suchard \n(Professor\, Biomathematics\, Biostatistics and Human Genetics) \n  \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://ccpr.ucla.edu/event/reproducing-statistical-results-2/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CCPR Workshop,CSS Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151015T120000
DTEND;TZID=America/Los_Angeles:20151015T133000
DTSTAMP:20260430T090132
CREATED:20150923T194114Z
LAST-MODIFIED:20160512T222227Z
UID:10000496-1444910400-1444915800@ccpr.ucla.edu
SUMMARY:Aude Hofleitner\, Facebook
DESCRIPTION:“Inferring 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://ccpr.ucla.edu/event/aude-hofleitner-facebook/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:CCPR Seminar,CSS Events
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2015/09/Aude_Hofleitner_10_15_15.jpg
ORGANIZER;CN="CCPR Seminars":MAILTO:seminars@ccpr.ucla.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150623T100000
DTEND;TZID=America/Los_Angeles:20150623T120000
DTSTAMP:20260430T090132
CREATED:20210422T033129Z
LAST-MODIFIED:20220509T183948Z
UID:10000733-1435053600-1435060800@ccpr.ucla.edu
SUMMARY:Bayesian Statistical Modeling Using Stan
DESCRIPTION:Daniel Lee\nJune 23\, 2015\n10:00 AM-12:00 PM\n\n4240 Public Affairs Building\n\n\n\n\n\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.
URL:https://ccpr.ucla.edu/event/bayesian-statistical-modeling-using-stan/
LOCATION:4240 Public Affairs Bldg
CATEGORIES:CCPR Workshop,CSS Events
END:VEVENT
END:VCALENDAR