BEGIN:VCALENDAR VERSION:2.0 PRODID:-//California Center for Population Research - ECPv6.15.14//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:California Center for Population Research X-ORIGINAL-URL:https://ccpr.ucla.edu X-WR-CALDESC:Events for California Center for Population Research REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20160313T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20161106T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20170312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20171105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20180311T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20181104T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20190310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20191103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20200308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20201101T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20210314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20211107T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20220313T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20221106T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20230312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20231105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20240310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20241103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20250309T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20251102T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20260308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20261101T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20270314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20271107T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260302T100000 DTEND;TZID=America/Los_Angeles:20260302T110000 DTSTAMP:20260430T075114 CREATED:20251124T222551Z LAST-MODIFIED:20251215T165525Z UID:10000976-1772445600-1772449200@ccpr.ucla.edu SUMMARY:Coffee and Conversation DESCRIPTION:Join CCPR affiliates for coffee and bagels from Noah’s Bagels\, and take the opportunity to get to know one another in a casual setting. \nCoffee and Conversation is held Mondays at 10:00 AM in the CCPR Break Room. \nThis week’s session will be hosted by Professor Carlo Medici\, who will be there to guide the conversation\, share insights\, and connect with attendees in an informal setting. URL:https://ccpr.ucla.edu/event/coffee-and-conversation-15/ LOCATION:CCPR Break Room CATEGORIES:CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250924T120000 DTEND;TZID=America/Los_Angeles:20250924T130000 DTSTAMP:20260430T075114 CREATED:20250922T173437Z LAST-MODIFIED:20250922T173437Z UID:10000959-1758715200-1758718800@ccpr.ucla.edu SUMMARY:Resident Move In Day DESCRIPTION: URL:https://ccpr.ucla.edu/event/resident-move-in-day/ LOCATION:CCPR Break Room CATEGORIES:CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240624T080000 DTEND;TZID=America/Los_Angeles:20240703T170000 DTSTAMP:20260430T075114 CREATED:20231213T192723Z LAST-MODIFIED:20240229T185203Z UID:10000843-1719216000-1720026000@ccpr.ucla.edu SUMMARY:The Summer Institutes in Computational Social Science (SICSS) 2024 DESCRIPTION:From June 24 to July 3\, 2024 the University of California\, Los Angeles (UCLA) Division of Social Sciences and the California Center for Population Research will sponsor the Summer Institute in Computational Social Science\, to be held at the University of California Los Angeles. \nThe Organizing Committee\nJennie Brand\, Professor\, Sociology and Statistics\nDora Costa\, Professor\, Economics\nPatrick Heuveline\, Professor\, Sociology\, and International Institute\nRandall Kuhn\, Professor\, Community Health Sciences \nFor more information about the event go here: https://sicss.io/2024/ucla/ URL:https://ccpr.ucla.edu/event/sicss-conference-2024/ LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CCPR Conference,CCPR Seminar,CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240213T150000 DTEND;TZID=America/Los_Angeles:20240213T160000 DTSTAMP:20260430T075114 CREATED:20240117T170419Z LAST-MODIFIED:20240123T172935Z UID:10000844-1707836400-1707840000@ccpr.ucla.edu SUMMARY:Development workshop\, 2/13 at 3pm “Scientific Accountability and Data Production” DESCRIPTION:A panel discussion about open science\, ethical risks\, and potential drawbacks for certain forms of knowledge production with Irene Bloemraad (1)\, Cecilia Menjivar (2)\, Zachary Steinert-Threlkeld (3)\, and Jennifer Wagman (4)/ \n(1) UC Berkeley Sociology\, (2) UCLA Sociology\, (3) UCLA Luskin School of Public Affairs\, (4) UCLA Fielding School of Public Health URL:https://ccpr.ucla.edu/event/development-workshop-2-13-at-3pm-scientific-accountability-and-data-production/ LOCATION:4240A Public Affairs Bldg CATEGORIES:CCPR Seminar,CCPR Workshop,CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20230620T080000 DTEND;TZID=America/Los_Angeles:20230630T170000 DTSTAMP:20260430T075114 CREATED:20240223T235836Z LAST-MODIFIED:20240223T235836Z UID:10000850-1687248000-1688144400@ccpr.ucla.edu SUMMARY:SICSS Conference 2023 DESCRIPTION:From June 20 to June 30\, 2023\, the University of California\, Los Angeles (UCLA) Division of Social Sciences and the California Center for Population Research will sponsor the Summer Institute in Computational Social Science\, to be held at the University of California Los Angeles. \n  \nFor more information about the event go here: https://sicss.io/2023/ucla/ URL:https://ccpr.ucla.edu/event/sicss-conference-2023/ CATEGORIES:CCPR Conference,CCPR Seminar,CSS Events END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20200615 DTEND;VALUE=DATE:20200627 DTSTAMP:20260430T075114 CREATED:20220425T160910Z LAST-MODIFIED:20220505T185235Z UID:10000774-1592179200-1593215999@ccpr.ucla.edu SUMMARY:Summer Institute in Computational Social Science DESCRIPTION:CCPR\nJune 15 – 26\, 2020\n4240 Public Affairs Building \nThe purpose of the Summer Institute is to bring together graduate students\, postdoctoral researchers\, and early career faculty interested in computational social science. The Summer Institute is open to both social scientists (broadly conceived) and data scientists (broadly conceived). URL:https://ccpr.ucla.edu/event/summer-institute-in-computational-social-science-2/ LOCATION:4240 Public Affairs Bldg CATEGORIES:CCPR Conference,CCPR Workshop,CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190801T130000 DTEND;TZID=America/Los_Angeles:20190801T180000 DTSTAMP:20260430T075114 CREATED:20190715T164442Z LAST-MODIFIED:20220505T185250Z UID:10000534-1564664400-1564682400@ccpr.ucla.edu SUMMARY:Big Data for Big Social Issues DESCRIPTION:Big Data for Big Social Issues \nSummer Institute in Computational Social Science Panel: 1:00pm – 2:45pm \nProf. John Friedman\, Brown University: “Income Inequality and Social Mobility: What Can We Learn from Big Data?” 3:00pm-5:00pm \nReception 5:00-6:00pm \nClick here to view a recording of the talk  \nA defining feature of the American Dream is upward income mobility — the ideal that children have equal opportunities to succeed in life\, regardless of the circumstances of their birth. Prof. Friedman will discuss his research using large administrative datasets to uncover where opportunity lacks in America\, and what policymakers and civic leaders can do about it to revive the American Dream for future generations. URL:https://ccpr.ucla.edu/event/big-data-for-social-issues-panel/ LOCATION:UCLA Neuroscience Research Building Auditorium (NRB 132) CATEGORIES:CCPR Conference,CCPR Seminar,CSS Events,Divisional Publish ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2019/07/Friedman_Picture-Informal.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190621T140000 DTEND;TZID=America/Los_Angeles:20190621T180000 DTSTAMP:20260430T075114 CREATED:20190612T172854Z LAST-MODIFIED:20220505T185150Z UID:10000532-1561125600-1561140000@ccpr.ucla.edu SUMMARY:Summer Institute in Computational Social Science Panel Presentation DESCRIPTION:Summer Institute in Computational Social Science Panel Presentation \nLuskin Conference Center Laureate Room \n\n2:00pm – 3:15pm Digital Demography\n\nProf. Dennis Feehan\, UC Berkeley and Prof. Ka-Yuet Liu\, UCLA \n\n3:30pm – 4:45pm Computational Causal Inference \n\nProf. Judea Pearl\, UCLA and Prof. Sam Pimentel\, UC Berkeley \nReception 5:00pm – 6:00pm URL:https://ccpr.ucla.edu/event/summer-institute-in-computational-social-science-panel-presentation/ LOCATION:Luskin Conference Center Laureate Room CATEGORIES:CCPR Conference,CCPR Seminar,CSS Events,Divisional Publish ATTACH;FMTTYPE=image/png:https://ccpr.ucla.edu/wp-content/uploads/2019/06/sicss-e1560360766722.png END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20190617 DTEND;VALUE=DATE:20190629 DTSTAMP:20260430T075114 CREATED:20220425T155902Z LAST-MODIFIED:20220505T185311Z UID:10000772-1560729600-1561766399@ccpr.ucla.edu SUMMARY:Summer Institute in Computational Social Science DESCRIPTION:The purpose of the Summer Institute is to bring together graduate students\, postdoctoral researchers\, and early career faculty interested in computational social science. The Summer Institute is open to both social scientists (broadly conceived) and data scientists (broadly conceived). URL:https://ccpr.ucla.edu/event/summer-institute-in-computational-social-science/ 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:20190529T120000 DTEND;TZID=America/Los_Angeles:20190529T133000 DTSTAMP:20260430T075114 CREATED:20190426T192416Z LAST-MODIFIED:20210424T031434Z UID:10000528-1559131200-1559136600@ccpr.ucla.edu SUMMARY:Workshop: Getting The Data Yourself - A Web Scraping Code Through DESCRIPTION:Title:\nGetting The Data Yourself: A Web Scraping Code Through \nLocation:\nMay 29\, 2019 @ 12:00-1:30 PM\n4240 Public Affairs Building\nCCPR Seminar Room \nInstructors:\nChad Pickering & Mike Tzen \nContent:\nWe’ll empower CCPR researchers to get the domain-relevant data they want \n  \nslides exercise URL:https://ccpr.ucla.edu/event/workshop-getting-the-data-yourself-a-web-scraping-code-through/ LOCATION:4240 Public Affairs Bldg CATEGORIES:CCPR Workshop,CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190522T120000 DTEND;TZID=America/Los_Angeles:20190522T133000 DTSTAMP:20260430T075114 CREATED:20190426T193825Z LAST-MODIFIED:20210424T031420Z UID:10000530-1558526400-1558531800@ccpr.ucla.edu SUMMARY:Workshop: Getting All Your Research Computing Tools for Summer and Beyond - Hardware and Software DESCRIPTION:Title:\nGetting All Your Research Computing Tools for Summer and Beyond – Hardware and Software \nLocation:\nMay 22\, 2019 @ 12:00-1:30 PM\n4240 Public Affairs Building\nCCPR Seminar Room \nInstructors:\nMatt Lahmann & Mike Tzen \nContent:\nWe’ll get CCPR researchers all the computing tools for a productive summer of data science exploration.\nWe’ll get you started on computing hardware: personal\, terminal\, and cluster.\nWe’ll get you started on software: R\, stata\, python\, etc.\nTo get the most out of this workshop\n1) let us know what type of software / hardware you might need for your upcoming research\n2) if you anticipate needing high performance cluster computing\, Please start thinking of a CCPR faculty affiliate hoffman2 sponsor that you are working with for your eventual hoffman2 signup \nslides URL:https://ccpr.ucla.edu/event/workshop-getting-research-computing/ LOCATION:4240 Public Affairs Bldg CATEGORIES:CCPR Workshop,CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190508T120000 DTEND;TZID=America/Los_Angeles:20190508T133000 DTSTAMP:20260430T075114 CREATED:20180828T015938Z LAST-MODIFIED:20190506T170218Z UID:10000639-1557316800-1557322200@ccpr.ucla.edu SUMMARY:Brandon Stewart\, Princeton University DESCRIPTION:Title: How to Make Causal Inferences Using Texts \nAbstract: Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome. We introduce a new conceptual framework to understand all text-based causal inferences\, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference\, and provide solutions to the problems we raise.  We demonstrate that all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation. \nEstimating this latent representation\, however\, creates new risks: we may unintentionally create a dependency across observations or create opportunities to fish for large effects.  To address these risks\, we introduce a train/test split framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.  Our work provides a rigorous foundation for text-based causal inferences\, connecting two previously disparate literatures. (Joint Work with Egami\, Fong\, Grimmer and Roberts) \nCo-sponsored with the Center for Social Statistics \nMore on Prof. Stewart URL:https://ccpr.ucla.edu/event/brandon-stewart-princeton-university/ 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/Stewart_Brandon-e1551121773302.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190501T120000 DTEND;TZID=America/Los_Angeles:20190501T133000 DTSTAMP:20260430T075114 CREATED:20180828T015320Z LAST-MODIFIED:20190429T165951Z UID:10000638-1556712000-1556717400@ccpr.ucla.edu SUMMARY:Susan Athey\, Stanford University DESCRIPTION:Title: Estimating Heterogeneous Treatment Effects and Optimal Treatment Assignment Policies \nAbstract: This talk will review recently developed methods for estimating conditional average treatment effects and optimal treatment assignment policies in experimental and observational studies\, including settings with unconfoundedness or instrumental variables.  Multi-armed bandits for learning treatment assignment policies will also be considered. \nCo-sponsored with the Center for Social Statistics \nMore on Prof. Athey \n  \n  URL:https://ccpr.ucla.edu/event/susan-athey-stanford-university/ 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/susan-athey.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190312T140000 DTEND;TZID=America/Los_Angeles:20190312T150000 DTSTAMP:20260430T075114 CREATED:20210424T023548Z LAST-MODIFIED:20210424T023548Z UID:10000735-1552399200-1552402800@ccpr.ucla.edu SUMMARY:Eloise Kaizar\, Ohio State University DESCRIPTION:Eloise Kaizar\, Ohio State University \nRandomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants\, trial results may not directly reflect the average treatment effect that would follow real world adoption of a new treatment. Recently\, weight-based methods have been repurposed to more provide more relevant average effect estimates for real populations. In this talk\, I summarize important analytical choices involving what should and should not be borrowed from other applications of weight-based estimators\, make evidence-based recommendations about confidence interval construction\, and present conjectures about best choices for other aspects of statistical inference. \nEloise Kaizar is Associate Professor of Statistics at The Ohio State University. Her primary research focus is on assessing the effects and safety of medical exposures and interventions\, especially those whose effects are heterogeneous across populations or measured with rare event outcomes. As such\, she has worked on methodology to combine multiple sources of information relevant to the same broad policy or patient-centered question. She is particularly interested in how data collected via different study designs can contribute complementary information. URL:https://ccpr.ucla.edu/event/eloise-kaizar-ohio-state-university/ CATEGORIES:CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190305T140000 DTEND;TZID=America/Los_Angeles:20190305T153000 DTSTAMP:20260430T075114 CREATED:20190228T181153Z LAST-MODIFIED:20190228T181153Z UID:10000665-1551794400-1551799800@ccpr.ucla.edu SUMMARY:Lan Liu\, University of Minnesota at Twin Cities DESCRIPTION:Lan Liu\, University of Minnesota at Twin Cities \n“Parsimonious Regressions for Repeated Measure Analysis”  \nAbstract: Longitudinal data with repeated measures frequently arises in various\ndisciplines. The standard methods typically impose a mean outcome model as\na function of individual features\, time and their interactions. However\,\nthe validity of the estimators relies on the correct specifications of the\ntime dependency. The envelope method is recently proposed as a sufficient\ndimension reduction (SDR) method in multivariate regressions. In this\npaper\, we demonstrate the use of the envelope method as a new parsimonious\nregression method for repeated measures analysis\, where the specification\nof the underlying pattern of time trend is not required by the model. We\nfound that if there is enough prior information to support the\nspecification of the functional dependency of the mean outcome on time and\nif the dimension of the prespecified functional form is low\, then the\nstandard method is advantageous as an efficient and unbiased estimator.\nOtherwise\, the envelope method is appealing as a more robust and\npotentially efficient parsimonious regression method in repeated measure\nanalysis. We compare the performance of the envelope estimators with the\nexisting estimators in simulation study and in an application to the China\nHealth and Nutrition Survey URL:https://ccpr.ucla.edu/event/lan-liu-university-of-minnesota-at-twin-cities/ CATEGORIES:CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190227T140000 DTEND;TZID=America/Los_Angeles:20190227T153000 DTSTAMP:20260430T075114 CREATED:20190215T192913Z LAST-MODIFIED:20210424T031402Z UID:10000659-1551276000-1551281400@ccpr.ucla.edu SUMMARY:Workshop: Grad Student Panel Discussing the Causal Toolkit DESCRIPTION:Title:\nGrad Student Panel Discussing the Causal Toolkit \nLocation:\nFebruary 27\, 2019\, 2:00-3:30 PM\n4240 Public Affairs Building\nCCPR Seminar Room \nContent:\nFocusing on the uses of the causal toolkit\, several grad students will share a-ha moments and lessons learned from their own applied research. The target audience are grad students and researchers who wish to get a taste of how causal concepts are used. We will start with an open discussion (with audience Q+A) of introductory concepts in causation\, emphasizing complementary views and examples. In the second half of the workshop\, panelists will present 5 minute talks of a causal-centric research project. The variety of panelists represent how causal concepts have been helpful in: epidemiology\, political science\, sociology\, and statistics. \nPanelists: \nPaul Brendel\nKirsty Clark\nAnton Sobolev\nAshley Blum\nFrancesca Parente\nPablo Geraldo \nPlease RSVP below\nhttps://goo.gl/forms/8oHDeu6QgZGVVc4y1 \n  \nmaterial URL:https://ccpr.ucla.edu/event/workshop-grad-student-panel-causal/ LOCATION:4240 Public Affairs Bldg CATEGORIES:CCPR Workshop,CSS Events END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190226T140000 DTEND;TZID=America/Los_Angeles:20190226T150000 DTSTAMP:20260430T075114 CREATED:20190225T195602Z LAST-MODIFIED:20190225T201212Z UID:10000663-1551189600-1551193200@ccpr.ucla.edu SUMMARY:Adeline Lo\, Princeton University DESCRIPTION:Title: Covariate screening in high dimensional data: applications to forecasting and text data \nAbstract: High dimensional (HD) data\, where the number of covariates and/or meaningful covariate interactions might exceed the number of observations\, is increasing used in prediction in the social sciences. An important question for the researcher is how to select the most predictive covariates among all the available covariates. Common covariate selection approaches use ad hoc rules to remove noise covariates\, or select covariates through the criterion of statistical significance or by using machine learning techniques. These can suffer from lack of objectivity\, choosing some but not all predictive covariates\, and failing reasonable standards of consistency that are expected to hold in most high-dimensional social science data. The literature is scarce in statistics that can be used to directly evaluate covariate predictivity. We address these issues by proposing a variable screening step prior to traditional statistical modeling\, in which we screen covariates for their predictivity. We propose the influence (I) statistic to evaluate covariates in the screening stage\, showing that the statistic is directly related to predictivity and can help screen out noisy covariates and discover meaningful covariate interactions. We illustrate how our screening approach can removing noisy phrases from U.S. Congressional speeches and rank important ones to measure partisanship. We also show improvements to out-of-sample forecasting in a state failure application. Our approach is applicable via an open-source software package. \nHosted by the Center for Social Statistics \nMore on Prof. Lo URL:https://ccpr.ucla.edu/event/adeline-lo-princeton-university/ LOCATION:1434A Physics and Astronomy Building CATEGORIES:CSS Events,Divisional Publish END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190131T140000 DTEND;TZID=America/Los_Angeles:20190131T150000 DTSTAMP:20260430T075114 CREATED:20190115T221438Z LAST-MODIFIED:20210424T031350Z UID:10000524-1548943200-1548946800@ccpr.ucla.edu SUMMARY:Workshop: Merging Entities - Deterministic\, Approximate\, & Probabilistic DESCRIPTION:Instructor:\nMichael Tzen \nTitle:\nMerging Entities: Deterministic\, Approximate\, & Probabilistic \nLocation:\nJanuary 31\, 2019\, 2:00-3:00 PM\n4240 Public Affairs Building\nCCPR Seminar Room \nContent:\nCombining information from different groups is a fundamental procedure in the data analysis pipeline. Using NBA and NCAA data\, we will walk through deterministic\, approximate\, and probabilistic methods to merge entities from the different data sources. Is Luc Richard Mbah a Moute playing in the NBA the same Luc Mbah a Moute who played for the University of California\, Los Angeles? We’ll discuss how the probabilistic methods loosely relate to matching in causal analysis. After this workshop\, participants should be able to merge data sets 3 different ways and think about how the merge quality may affect downstream analysis. \nPlease RSVP below \nhttps://goo.gl/forms/XiuYjqjkcD0WnHov2 \nslides rscript URL:https://ccpr.ucla.edu/event/workshop-merging-entities/ LOCATION:4240 Public Affairs Bldg CATEGORIES:CCPR Workshop,CSS Events,Divisional Publish END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190123T120000 DTEND;TZID=America/Los_Angeles:20190123T133000 DTSTAMP:20260430T075114 CREATED:20180828T011625Z LAST-MODIFIED:20190118T194253Z UID:10000634-1548244800-1548250200@ccpr.ucla.edu SUMMARY:Kosuke Imai\, Harvard University DESCRIPTION:Title: Matching Methods for Causal Inference with Time-Series Cross-Section Data \nAbstract: Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines\, matching methods are rarely used when analyzing time-series cross-section (TSCS) data\, which consist of a relatively large number of repeated measurements on the same units. We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach\, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement\, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator\, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects\, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth\, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods. \nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics \nMore on Prof. Imai URL:https://ccpr.ucla.edu/event/kosuke-imai-harvard/ LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States CATEGORIES:CCPR Seminar,CSS Events,Divisional Publish ATTACH;FMTTYPE=image/png:https://ccpr.ucla.edu/wp-content/uploads/2018/08/Iami_Kosuke.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20190116T120000 DTEND;TZID=America/Los_Angeles:20190116T133000 DTSTAMP:20260430T075114 CREATED:20180828T011011Z LAST-MODIFIED:20190118T194235Z UID:10000633-1547640000-1547645400@ccpr.ucla.edu SUMMARY:Rocio Titiunik\, University of Michigan DESCRIPTION:Title: Internal vs. external validity in studies with incomplete populations \nAbstract: Researchers working with administrative data rarely have access to the entire universe of units they need to estimate effects and make statistical inferences. Examples are varied and come from different disciplines. In social program evaluation\, it is common to have data on all households who received the program\, but only partial information on the universe of households who applied or could have applied for the program. In studies of voter turnout\, information on the total number of citizens who voted is usually complete\, but data on the total number of voting-eligible citizens is unavailable at low levels of aggregation. In criminology\, information on arrests by race is available\, but the overall population that could have potentially been arrested is typically unavailable. And in studies of drug overdose deaths\, we lack complete information about the full population of drug users. \nIn all these cases\, a reasonable strategy is to study treatment effects and descriptive statistics using the information that is available. This strategy may lack the generality of a full-population study\, but may nonetheless yield valuable information for the included units if it has sufficient internal validity. However\, the distinction between internal and external validity is complex when the subpopulation of units for which information is available is not defined according to a reproducible criterion and/or when this subpopulation itself is defined by the treatment of interest. When this happens\, a useful approach is to consider the full range of conclusions that would be obtained under different possible scenarios regarding the missing information. I discuss a general strategy based on partial identification ideas that may be helpful to assess sensitivity of the partial-population study under weak (non-parametric) assumptions\, when information about the outcome variable is known with certainty for a subset of the units. I discuss extensions such as the inclusion of covariates in the estimation model and different strategies for statistical inference. \nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics \nMore on Prof. Titiunik URL:https://ccpr.ucla.edu/event/rocio-titiunik-university-of-michigan/ 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/Titiunik_Rocio.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20181114T120000 DTEND;TZID=America/Los_Angeles:20181114T133000 DTSTAMP:20260430T075114 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:20260430T075114 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:20260430T075114 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:20260430T075114 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:20260430T075114 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:20260430T075114 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:20260430T075114 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:20260430T075114 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:20260430T075114 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:20260430T075114 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 END:VCALENDAR