BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Percontor - ECPv4.7.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Percontor
X-ORIGINAL-URL:https://www.percontor.org
X-WR-CALDESC:Events for Percontor
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20190128T110000
DTEND;TZID=UTC+0:20190128T170000
DTSTAMP:20230203T190921
CREATED:20160223T032517Z
LAST-MODIFIED:20181019T184153Z
UID:784-1548673200-1548694800@www.percontor.org
SUMMARY:Introduction to matching and propensity score analysis
DESCRIPTION:A 6-hour workshop taught by Stephen R. Porter\, Ph.D. \nYou may also be interested in our introduction to binary logistic regression class. \nRegister\nOverview\nPropensity score analysis (also known as “matching”) is a popular way to estimate the effects of programs and policies on outcomes. Yet researchers face a dizzying array of choices\, in terms of particular matching techniques to use\, as well as many different options for implementing a specific technique. \nThis workshop provides a concise introduction to matching for the applied researcher. Rather than cover every possible matching technique\, we will focus on nearest neighbor matching (one of the most popular approaches) and inverse propensity weighting\, a simple and powerful matching approach that can be used without any specialized software (some software packages\, like SAS and SPSS\, do not come with built-in matching commands\, requiring the use of often opaque and difficult to use macros). \nExpected outcomes\nBy the end of the workshop\, participants should understand why matching is preferred over regression\, the major concepts underlying the counterfactual theory of causality\, the major issues with implementing nearest neighbor matching\, and whether they should estimate the average treatment effect or treatment effect for the treated for their particular research application. Most importantly\, they should be able to immediately begin using inverse propensity weighting in their research\, using any statistical software program. \nPricing and schedule\nTime: Monday\, January 28\, 11AM to 5PM (EST)\nCost: $225\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. Find out about our discounts here. \nThe main workshop material is scheduled for six hours. Time permitting\, Dr. Porter will answer questions about participants’ specific research projects. Participants can ask questions via chat\, microphone\, or telephone. In order to allow sufficient time for questions\, the number of workshop participants is limited to 30. \nIn order to use psmatch2\, part of the presentation will use Stata. Stata is not required to participate\, and the technique of inverse propensity weighting can be used in any software package that uses survey weights (e.g.\, SAS\, SPSS and R). \nWho should attend?\nThe target audience is researchers who typically use multiple regression\, logistic regression and hierarchical linear modeling in their research and 1) wish to know why matching has become popular\, and 2) how to use matching in their research. Participants should have a good understanding of multiple regression. Familiarity with logistic regression is helpful but not required. If you want to learn about logistic regression\, consider our class on binary logistic regression. \nAgenda\n\nAdvantages of matching over regression and other linear models\nRubin’s Causal Model – the counterfactual approach to causality\nUnderstanding treatment effects for research projects\nImplementing nearest neighbor matching by hand and with psmatch2\nChoosing the right variables for the propensity model\nAdvantages of inverse propensity weighting over all other matching techniques\nImplementing inverse propensity weighting using survey weight commands\nCommon mistakes to avoid when matching\n\nRegister\n
URL:https://www.percontor.org/upcoming-workshop/introduction-matching-propensity-score-analysis/
CATEGORIES:Introduction to matching and propensity score analysis,Research Methods
END:VEVENT
END:VCALENDAR