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PRODID:-//Percontor - ECPv4.7.1//NONSGML v1.0//EN
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X-WR-CALNAME:Percontor
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200610T120000
DTEND;TZID=UTC+0:20200610T160000
DTSTAMP:20240223T140714
CREATED:20200416T152332Z
LAST-MODIFIED:20200711T192130Z
UID:1173-1591790400-1591804800@www.percontor.org
SUMMARY:Logistic regression: Analyzing binary outcomes
DESCRIPTION:A 4-hour workshop taught by Steve Porter\, Ph.D. \nRegister\nOverview\nMany outcomes in education are binary in nature: accept or decline an offer of admission\, pass or fail a course\, persist to another year or stop out. Logistic regression\, rather than multiple regression\, is the standard approach to analyzing discrete outcomes. This workshop will train participants in applying logistic regression to their research\, focusing on 1) the parallels with multiple regression\, and 2) how to interpret model results for a wide audience. \nExpected outcomes\nBy the end of the workshop\, participants should understand logistic regression well enough to begin using it in their research. They will understand when to use logistic regression\, how to interpret logistic regression coefficients\, and how to calculate and discuss model fit. \nPricing and schedule\nTime: Wednesday\, June 10\, 12PM to 4PM (EST)\nCost: $200\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. Find out about our discounts here. \nTime permitting\, Dr. Porter 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 is researchers who are familiar with multiple regression but are not familiar with logistic regression\, and wish to begin using it in their research (or those researchers looking for a quick refresher). This is an applied course\, so no advanced math skills are required; however\, you should understand how to interpret a multiple regression coefficient. Software demonstrations will use Stata\, but output from SAS and SPSS will be included and reviewed so that participants can understand and interpret logistic regression models estimated using these software. \nIf you are interested in propensity score analysis\, this is an excellent workshop to attend prior to our matching workshop. \nAgenda\n\nWhy logistic regression is preferred over multiple regression (the linear probability model)\nHow logistic regression estimates coefficients (maximum likelihood)\, and the problem this poses for interpretation\nSimilarities with multiple regression: most of what you know can be applied directly to logistic regression\nPredicted probabilities versus Y-hat from multiple regression\nInterpreting results using odds ratios: what they are and why you don’t want to use them\nInterpreting results using discrete changes in probability (delta-p statistic)\nDifferent ways of measuring model fit (pseudo R-squared\, percent correctly predicted)\n\nRegister\n
URL:https://www.percontor.org/upcoming-workshop/logistic-regression-analyzing-binary-outcomes-2/
CATEGORIES:Introduction to Binary Logistic Regression,Logistic regression: Analyzing binary outcomes,Research Methods
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