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X-WR-CALNAME:Percontor
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X-WR-CALDESC:Events for Percontor
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230330T110000
DTEND;TZID=America/New_York:20230330T170000
DTSTAMP:20230326T185313
CREATED:20200307T203038Z
LAST-MODIFIED:20230301T145752Z
UID:1152-1680174000-1680195600@www.percontor.org
SUMMARY:Basics of multilevel modeling: Handling grouped data in research and evaluation
DESCRIPTION:A 6-hour workshop taught by Paul D. Umbach\, Ph.D. \nRegression workshop series discount! \nRegister for this along with our workshops on Refresher on multiple regression for the applied researcher and Logistic Regression: Analyzing binary outcomes for only $550 (would normally cost $700 for all three). If you are interested in the workshop series\, email us at mail@percontor.org for discount codes. \nRegister\nOverview\nThis workshop provides the basics of multilevel modeling\, focusing on practical applications rather than statistical theory. Researchers and evaluators from a range of disciplines often collect data that have a hierarchical structure. Students are nested within schools (and/or classrooms)\, employees are nested within firms\, and multiple test scores are nested within students. One consequence of this nested structure is that observations are not statistically independent\, thus violating a basic assumption of standard analytical techniques. Ignoring the nesting effect and proceeding with conventional\, single-level methods of analysis (like linear regression) can yield misleading results. This one-day seminar provides an introduction to multilevel models (sometimes called hierarchical linear models or general linear models)\, a statistical approach that accounts for the nesting effect and avoids these problems\, as well as those associated with aggregation and disaggregation. \nExpected outcomes\nBy the end of this workshop\, participants should know the following \n\nThe conceptual foundations of multilevel models.\nAn appreciation of the advantages and disadvantages of multilevel modeling as compared with other approaches to nested data.\nModeling slopes and intercepts as outcomes.\nApproach to building growth models in a multilevel context.\nHow to interpret and explain the output from multilevel modeling software.\nPractical tools and strategies for developing and testing multilevel models.\nA clear understanding of the differences between fixed and random effects.\n\nPricing and schedule\nTime: Thursday\, March 30\, 11:00AM to 5:00PM (EDT)\nCost: $250\nLocation: Online \nSeries discount – Register for this along with our workshops on Refresher on multiple regression for the applied researcher and Logistic Regression: Analyzing binary outcomes for only $550 (would normally cost $700 for all three). If you are interested in the workshop series\, email us at mail@percontor.org for discount codes. \nWe offer $50 discounts for graduate students and $25 discounts for multiple workshop enrollments. Find out about our discounts here. \nTime permitting\, at the end of the session Dr. Umbach will also 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. \nWho should attend?\nThe target audience for this workshop is a range of educational researchers\, including institutional researchers\, evaluators\, policy analysts\, student affairs professionals\, assessment professionals\, graduate students\, and faculty. Participants must have a strong working knowledge of ordinary least squares regression. If you do not feel comfortable with regression\, consider enrolling in our regression refresher workshop. \nAgenda\n\nWhy we need multilevel modeling and why other approaches are inadequate\nBrief discussion of conceptual underpinnings of multilevel modeling\nExtending regression with random intercepts and slopes as outcomes\nCentering the variables we include in our models\nBuilding the two level model: Null model\, random intercept model\, and full model\nInterpreting two-level model output\nUsing multilevel model to study growth\nInterpreting growth model output\nIntroduction to extensions of the two-level model: multilevel models with categorical outcomes\, three level models\, and cross classified models.\n\nSoftware\nSoftware demonstrations will use Stata\, but syntax and output from SAS and SPSS will be included for participants who use those software packages in their work. \nRegister\n
URL:https://www.percontor.org/upcoming-workshop/basics-multilevel-modeling-handling-grouped-data-education-settings-2/
CATEGORIES:Basics multilevel modeling: handling grouped data in education settings,Research Methods
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230420T110000
DTEND;TZID=America/New_York:20230420T170000
DTSTAMP:20230326T185313
CREATED:20221031T165247Z
LAST-MODIFIED:20230301T145833Z
UID:1776-1681988400-1682010000@www.percontor.org
SUMMARY:Introduction to matching and propensity score analysis
DESCRIPTION:A 6-hour workshop taught by Steve Porter\, Ph.D. \nCausal inference workshop series discount! \nRegister for this along with our workshop on Regression discontinuity designs for evaluating programs and policies for only $350 (would normally cost $450 for both). If you are interested in the workshop series\, email us at mail@percontor.org for discount codes. \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. \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: Thursday\, April 20\, 11AM to 5PM (EDT)\nCost: $250\nLocation: Online \nSeries discount – Register for this along with our workshop on Regression discontinuity designs for evaluating programs and policies for only $350 (would normally cost $450 for both). If you are interested in the workshop series\, email us at mail@percontor.org for discount codes. \nWe offer $50 discounts for graduate students and $25 discounts for multiple workshop enrollments. 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-4-3/
CATEGORIES:Introduction to matching and propensity score analysis,Research Methods
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