BEGIN:VCALENDAR
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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=America/New_York:20231012T130000
DTEND;TZID=America/New_York:20231012T180000
DTSTAMP:20231003T173955
CREATED:20221031T162504Z
LAST-MODIFIED:20230417T003455Z
UID:1772-1697115600-1697133600@www.percontor.org
SUMMARY:Exploring the power of predictive analytics: A step-by-step introduction to building a student-at-risk prediction model
DESCRIPTION:A 5-hour workshop taught by Serge Herzog\, Ph.D. \nPredictive analytics workshop series discount! \nRegister for this along with Student success prediction at your fingertips: Developing online dashboards with Microsoft Power BI© 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. \nRegister\nOverview\nThe purpose of this workshop is to teach institutional research\, assessment\, and evaluation professionals how to effectively build and implement a predictive model to identify students at risk of dropping out using standard regression methods with SPSS. Instruction will be delivered in a hands-on format\, offering an interactive step-by-step model-building process that allows participants to develop their own prediction model\, using preloaded data that mimics information available with the typical college enrollment matriculation system. \nExpected outcomes\nBy the end of the workshop participants will be able to: \n\nDevelop a conceptual understanding of how predictive models developed can improve institutional effectiveness with a focus on student retention;\nLearn how to set up a matriculation system (or census warehouse) data file in IBM-SPSS that can be used to develop a predictive statistical model to identify students at risk;\nLearn how to use historical data to ‘automatically’ develop predictor coefficients to estimate (score) the dropout risk for students in future cohorts; and\nLearn how to translate the student dropout risk into a relative percentile risk score to assist student support services with ‘actionable’ information in a timely fashion.\n\nPricing and schedule\nTime: Thursday\, October 12\, 1PM to 6PM (EDT)\nCost: $225\nLocation: Online \nSeries discount – Register for this along with Student success prediction at your fingertips: Developing online dashboards with Microsoft Power BI© 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. \nTime permitting\, Dr. Herzog will also answer questions about participants’ specific 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 educational researchers who are familiar with logistic regression\, and wish to use it to develop prediction models to estimate student dropout risk or other student or educational outcomes that are categorical in nature. This is an applied course\, so no advanced math skills are required beyond an understanding of logistic regression and its associated statistical output and model fit indicators (which will be explained in the workshop). \nAttendees should be proficient in the basic use of and have access to at least version 20 of IBM-SPSS\, with the regression module\, in order to participate in hands-on exercises to develop a prediction model with furnished data and syntax files. You may access a 14-day free trial of SPSS here. \nIf you are unfamiliar with logistic regression\, we encourage you to take our logistic regression workshop. \nAgenda\n\nIntroduce the power of predictive analytics (including examples of forecasting data used in improving student success and college operations).\nExamine elements needed and available at start/middle of semester to predict student dropout at end of semester (including pre-college academic preparation\, student socio-demographic data\, income/financial aid profile\, semester course data\, on-campus housing and campus engagement data).\nConduct exploratory data analysis: Discussion of variable selection\, variable coding\, missing data imputation\, composite variable construction to achieve maximum model parsimony.\nDiscuss regression model: Prediction versus variance explanation in logit analysis.\nDevelop training dataset using historical data to generate predictor coefficients for future data (outcome estimation for future cases)\nEstimate outcome probability for future data (cohorts) Identify statistical outliers and develop ROC curve to maximize correct classification rate.\nChose model with optimal balance in correct classification.\nTransform outcome probability for each case (student) into ‘actionable information’.\nDiscuss how predictive analytics improves organizational productivity and outcomes\n\nAbout the instructor\nSerge Herzog\, Ph.D.\, is the Vice President of Institutional Effectiveness at Rocky Mountain University (RMU). Prior to joining RMU\, Dr. Herzog was the Director of Institutional Analysis at the University of Nevada\, Reno since 2001. His research has been covered in the Chronicle of Higher Education\, the University Business Magazine\, and Campus Technology Magazine among others. Most recently\, he co-edited (with Nicolas Bowman) Methodological Advances and Issues in Studying College Impact (New Directions for Institutional Research) San Francisco: Jossey-Bass\, 2014. \nRegister\n
URL:https://www.percontor.org/upcoming-workshop/exploring-power-predictive-analytics-3-2/
CATEGORIES:Predictive analytics,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231017T110000
DTEND;TZID=America/New_York:20231017T170000
DTSTAMP:20231003T173955
CREATED:20200307T203038Z
LAST-MODIFIED:20230417T002518Z
UID:1152-1697540400-1697562000@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: Tuesday\, October 17\, 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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231020T120000
DTEND;TZID=America/New_York:20231020T160000
DTSTAMP:20231003T173955
CREATED:20150512T134330Z
LAST-MODIFIED:20230417T003336Z
UID:583-1697803200-1697817600@www.percontor.org
SUMMARY:Logistic regression: Analyzing binary outcomes
DESCRIPTION:A 4-hour workshop taught by Steve Porter\, Ph.D. \nRegression workshop series discount! \nRegister for this along with our workshops on Refresher on multiple regression for the applied researcher and Basics of multilevel modeling: Handling grouped data in research and evaluation 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\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: Friday\, October 20\, 12PM to 4PM (EDT)\nCost: $200\nLocation: Online \nSeries discount – Register for this along with our workshops on Refresher on multiple regression for the applied researcher and Basics of multilevel modeling: Handling grouped data in research and evaluation 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\, 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/
CATEGORIES:Introduction to Binary Logistic Regression,Logistic regression: Analyzing binary outcomes,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231026T130000
DTEND;TZID=America/New_York:20231026T180000
DTSTAMP:20231003T173955
CREATED:20221031T163353Z
LAST-MODIFIED:20230417T005648Z
UID:1774-1698325200-1698343200@www.percontor.org
SUMMARY:Student success prediction at your fingertips: Developing online dashboards with Microsoft Power BI©
DESCRIPTION:A 5-hour workshop taught by Serge Herzog\, Ph.D. \nParticipants of this workshop will benefit from also taking our Predictive Analytics workshop. \nPredictive analytics workshop series discount! \nRegister for this along with Exploring the power of predictive analytics: A step-by-step introduction to building a student-at-risk prediction model 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. \nRegister\nOverview\nThe purpose of this workshop is to teach education professionals how to build online dashboards to predict student outcomes literally with ‘the push of a button’. Participants learn how to use common data files (Excel\, CSV etc) with Power BI©\, and how to create dashboards with interactive data visualization and drill-down tools that display predicted student outcomes (e.g. admission yield\, enrollment persistence\, course grades\, semester GPA) at the click of a button. Step-by-step demonstration and guidance allow participants to develop their own dashboards with furnished sample data files and dashboard designs that can be readily used as templates at their own institutions. The hands-on workshop teaches the entire process of dashboard development from data import to online dashboard activation for end-user access. \nNote: Participation requires computer access with MS Excel (2010/2013) and MS Power BI©\, which can be downloaded for free here. \nExpected outcomes\nBy the end of the workshop\, participants should know how to do the following: \n\nImport\, edit\, and store data files in Power BI\, and how to create relational data structures using multiple imported files;\nUse preloaded and imported Power BI© visualization functions for data editing\, coding\, disaggregation\, and interactive display reporting;\nDesign step-by-step a dashboard using a range of visualization tools for ‘input’ selection parameters and predicted outcome measures that are intuitive and easy to understand;\nUse multiple data files to create easy-to-use dashboards for interactive reporting of predicted key outcome metrics (e.g.\, student retention\, graduation\, course grades etc);\nand use the administrative portal to define user access\, data security\, account management\, and monitor dashboard user navigation and traffic.\n\nPricing and schedule\nTime: Thursday\, October 26\, 1PM to 6PM (EDT)\nCost: $225\nLocation: Online \nSeries discount – Register for this along with Exploring the power of predictive analytics: A step-by-step introduction to building a student-at-risk prediction model 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. \nTime permitting\, Dr. Herzog will also answer questions about participants’ specific 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?\nAnyone\, within or outside the education community\, who wishes to develop and provide interactive online access to forecasting data based on online selected sample parameters that define a population of interest. While the workshop uses student outcome data for demonstration\, the step-by-step guidance to develop a dashboard is equally applicable to other data and outcomes (e.g. forecasting budget revenues\, expenditures\, or other quantifiable outcomes). \nParticipants of this workshop will benefit from also taking our Predictive Analytics workshop. \nAbout the instructor\nSerge Herzog\, Ph.D.\, is the Vice President of Institutional Effectiveness at Rocky Mountain University (RMU). Prior to joining RMU\, Dr. Herzog was the Director of Institutional Analysis at the University of Nevada\, Reno since 2001. His research has been covered in the Chronicle of Higher Education\, the University Business Magazine\, and Campus Technology Magazine among others. Most recently\, he co-edited (with Nicolas Bowman) Methodological Advances and Issues in Studying College Impact (New Directions for Institutional Research) San Francisco: Jossey-Bass\, 2014. \nRegister\n
URL:https://www.percontor.org/upcoming-workshop/microsoft-power-bi-3-3/
CATEGORIES:Microsoft power bi,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231027T120000
DTEND;TZID=America/New_York:20231027T160000
DTSTAMP:20231003T173955
CREATED:20150830T173339Z
LAST-MODIFIED:20230417T010507Z
UID:653-1698408000-1698422400@www.percontor.org
SUMMARY:Regression discontinuity designs for evaluating programs and policies
DESCRIPTION:A 4-hour workshop taught by Brad Curs\, Ph.D. \nCausal inference workshop series discount! \nRegister for this along with our workshop on Introduction to matching and propensity score analysis 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. \nRegister\nOverview\nThis workshop provides an introduction to the practical application of regression discontinuity design in evaluating programs and policies. Regression discontinuity (RD) is an observational research design which can be used to make causal inference of program effects in non-experimental situations. Regression discontinuity is applied when program treatments are allocated based upon a pre-determined rule. For example\, if a remediation intervention is provided to all students who scored below a certain threshold on an academic aptitude exam\, or\, a financial subsidy is provided to all applicants with a household income falling below a particular value. A primary advantage of regression discontinuity designs is that causal effects can be estimated when program benefits are distributed based upon the subject’s need for the intervention\, rather than randomization in the case of an experiment. \nExpected outcomes\nBy the end of the workshop\, participants will understand the advantages of regression discontinuity design\, how to estimate regression discontinuity designs across a number of statistical packages\, and how to use data to check the validity of these regression discontinuity estimates to make causal inference. Participants will learn both visual and statistical techniques to estimate and evaluate regression discontinuity treatment effects. Participants will learn both sharp regression discontinuity techniques (used when subjects are compliant with treatment intent) and fuzzy regression discontinuity techniques (when subjects are not compliant with treatment intent). Most importantly\, participants will be ready to identify opportunities to evaluate programs and policies using regression discontinuity designs and will be prepared to estimate program effects using any statistical software package. \nPricing and schedule\nTime: Friday\, October 27\, 12PM to 4PM (EDT)\nCost: $200\nLocation: Online \nSeries discount – Register for this along with our workshop on Introduction to matching and propensity score analysis 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. \nAlthough the main workshop material is scheduled for three hours\, Dr. Curs will stay online for an additional hour or so\, to ensure that he answers all questions. Time permitting\, he 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 researchers\, including institutional researchers\, market researchers\, policy analysts\, student affairs professionals\, assessment professionals\, graduate students\, and faculty\, who evaluate programs in their work. It is important that participants have a working knowledge of ordinary least squares. \nIf you need a refresher on ordinary least squares\, consider enrolling in our refresher on multiple regression workshop. Software demonstrations will use Stata\, but R code will also be included for participants who are using R for their research projects. \nAgenda\n\nWhat is regression discontinuity design\, and when can it be applied?\nThe advantages of regression discontinuity design over alternative research designs\nEstimating the sharp regression discontinuity design model (when subjects are compliant with treatment intent)\nEstimating the fuzzy regression discontinuity design model (when subjects are not compliant with treatment intent)\nMaking functional form and bandwidth decisions\nChecking the assumptions of regression discontinuity designs\nAlternative (non-parametric) approaches to estimating regression discontinuity designs\n\nRegister\n
URL:https://www.percontor.org/upcoming-workshop/regression-discontinuity/
CATEGORIES:Regression discontinuity,Research Methods
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20231117T110000
DTEND;TZID=UTC+0:20231117T170000
DTSTAMP:20231003T173955
CREATED:20210608T201408Z
LAST-MODIFIED:20230417T140856Z
UID:1638-1700218800-1700240400@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: Friday\, November 17\, 11AM to 5PM (EST)\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-4-2/
CATEGORIES:Introduction to matching and propensity score analysis,Research Methods
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