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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=UTC+0:20191206T110000
DTEND;TZID=UTC+0:20191206T170000
DTSTAMP:20191121T162150
CREATED:20190406T113348Z
LAST-MODIFIED:20190802T221721Z
UID:1096-1575630000-1575651600@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: Friday\, December 6\, 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-3-2-2/
CATEGORIES:Introduction to matching and propensity score analysis,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200130T110000
DTEND;TZID=UTC+0:20200130T170000
DTSTAMP:20191121T162150
CREATED:20191030T224128Z
LAST-MODIFIED:20191030T224351Z
UID:1112-1580382000-1580403600@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: Thursday\, January 30\, 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-3-2-2-2/
CATEGORIES:Introduction to matching and propensity score analysis,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200205T120000
DTEND;TZID=UTC+0:20200205T160000
DTSTAMP:20191121T162150
CREATED:20190406T114021Z
LAST-MODIFIED:20191031T134155Z
UID:1098-1580904000-1580918400@www.percontor.org
SUMMARY:Increasing web survey response rates: What works?
DESCRIPTION:A 4-hour workshop taught by Paul D. Umbach\, Ph.D. \nRegister\nOverview\nAre you getting low response rates when you conduct web surveys? This workshop provides valuable information about how to increase web survey response rates. The purpose of this class is to arm you with the knowledge and range of techniques researchers suggest increase web survey response rates. We examine the heuristics people use when deciding to participate in a survey and answer survey questions and explore ways to increase the likelihood that they will respond. We look at simple ways to increase response rates\, such as how to structure your invitation and reminder emails\, how many reminders to send and when to send them\, whether to use an incentive or lottery\, and how to tweak your web survey design to encourage participation. You will leave this workshop with new strategies for getting higher response rates and more complete survey data. \nExpected outcomes\nBy the end of this workshop\, participants should understand the causes and consequences of nonresponse in web surveys\, have a framework for knowing how to reduce nonresponse\, and know how to calculate response rates. Most importantly\, participants will be armed with simple tools and techniques that will increase web survey response rates in their own work. \nPricing and Schedule\nTime: Wednesday\, February 5\, 12PM to 4PM (EST)\nCost: $175\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. Find out about our discounts here. \nTime permitting\, at the end of the class 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?\nThis course is aimed at anyone who wishes to increase response rates of their web surveys. We expect that you have some experience and knowledge of survey design\, but this course is taught at an introductory level. The target audience for this workshop is a range of educational researchers\, including institutional researchers\, policy analysts\, student affairs professionals\, assessment professionals\, graduate students\, and faculty\, who use surveys in their work. We also expect those implementing web surveys outside of educational settings would also benefit from this course. \nAgenda\n\nDefine nonresponse\nExamine causes and consequences of nonresponse: How much of a problem is it?\nExplain a framework to guide your decisions about how to reduce nonresponse\nExplore practical ways to reduce nonresponse in web surveys (we’ll spend most of our time on this)\n\nAdministration techniques to boost participation (e.g\, structure of invitation and reminder emails\, timing/number of emails)\nDesign elements that increase likelihood of response (e.g.\, question order\, progress updates)\nQuestion wording and nonresponse (e.g.\, questions that get interest)\nIncentives\, lotteries\, and other inducements\n\n\nExamine ways to reduce item nonresponse (e.g.\, skipping questions) and breakoff (stopping before completion) and ways to reduce them\n\nQuestion wording and item nonresponse\nQuestion order and breakoff (e.g.\, where to place open-ended questions)\nSensitive questions and item nonresponse\n\n\nDiscuss various ways to calculate and communicate response rates\nBriefly discuss about what to do after you’ve collected the data\n\nPostsurvey adjustments for unit nonresponse (e.g.\, weighting\, response propensity)\nWays to handle item nonresponse\nConducting nonresponse studies\n\n\n\nRegister \n
URL:https://www.percontor.org/upcoming-workshop/increasing-web-survey-response-rates-what-works-3-2-2/
CATEGORIES:Increasing web survey response rates: what works?,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200211T130000
DTEND;TZID=UTC+0:20200211T180000
DTSTAMP:20191121T162150
CREATED:20190328T231351Z
LAST-MODIFIED:20191031T134527Z
UID:1083-1581426000-1581444000@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. \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: Tuesday\, February 11\, 1PM to 6PM (EST)\nCost: $200\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. 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.\, has been the Director\, Office of Institutional Analysis\, and Consultant\, Center for Research Design & Analysis\, 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-2-2/
CATEGORIES:Predictive analytics,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200225T130000
DTEND;TZID=UTC+0:20200225T180000
DTSTAMP:20191121T162150
CREATED:20190328T232629Z
LAST-MODIFIED:20191031T135909Z
UID:1085-1582635600-1582653600@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. \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: Tuesday\, February 25\, 1PM to 6PM (EST)\nCost: $200\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. 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.\, has been the Director\, Office of Institutional Analysis\, and Consultant\, Center for Research Design & Analysis\, 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-2-2/
CATEGORIES:Microsoft power bi,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200227T110000
DTEND;TZID=UTC+0:20200227T170000
DTSTAMP:20191121T162150
CREATED:20150728T153148Z
LAST-MODIFIED:20191031T140151Z
UID:604-1582801200-1582822800@www.percontor.org
SUMMARY:Refresher on multiple regression for the applied researcher
DESCRIPTION:A 6-hour workshop taught by Stephen R. Porter\, Ph.D. \nRegister\nOverview\nMany researchers have taken a course that covers multiple regression\, the statistical workhorse of the social sciences\, but have forgotten much of what they learned. The goal of this workshop is to review many of the main concepts of regression\, from the perspective of the applied researcher (in other words\, we won’t be reviewing any proofs!). The workshop focuses on 1) the underlying statistical assumptions\, what happens when they are violated\, and simple ways to address violations\, 2) interpreting a variety of regression coefficients correctly\, and 3) model fit. \nExpected outcomes\nBy the end of the workshop\, participants should understand the basic assumptions underlying multiple regression and what they mean for the applied researcher\, how to interpret regression coefficients\, and how to discuss model fit. \nPricing and schedule\nTime: Thursday\, February 27\, 11AM to 5PM (EST)\nCost: $225\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 have taken a statistics course that covered multiple regression at some point\, but who have forgotten some of the basics. Researchers who know univariate statistics and would like to learn more about multiple regression are welcome\, but should realize that this is not a complete course on multiple regression. Software 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. \nAgenda\n\nReview the assumptions of regression\n\nIndependence of errors and clustered standard errors\nHomoskedasticity and robust standard errors\nNo omitted relevant variables and causal inference\nCollinearity\nError term\n\nWhat the random error term really is (it’s not random)\nNormality assumption: it’s the errors\, not the dependent variable\n\n\n\n\nInterpretation of regression coefficients\n\nUnderstanding the null hypothesis and p values for coefficients\nWhat the intercept tells you\nUnstandardized regression coefficients\nStandardized regression coefficients and when to use them\nDummy variables for two and more groups\nInterpreting nonlinear relationships\n\nLogged dependent and independent variables\nSquared terms\n\n\nInteraction terms\n\nWhy you can’t use standard software output to understand the interaction\nCorrectly estimating the standard errors\nHow to interpret the interactions by plotting\n\n\n\n\nModel fit\n\nInterpreting R-squared and standard error of the estimate\nWhen measures of model fit matter\n\n\n\nRegister\n
URL:https://www.percontor.org/upcoming-workshop/refresher-on-multiple-regression-for-the-applied-researcher/
CATEGORIES:Refresher on multiple regression for the applied researcher,Research Methods,Understanding and interpreting multiple regression models
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200304T120000
DTEND;TZID=UTC+0:20200304T160000
DTSTAMP:20191121T162150
CREATED:20191031T140612Z
LAST-MODIFIED:20191031T140620Z
UID:1119-1583323200-1583337600@www.percontor.org
SUMMARY:Writing and evaluating good survey questions
DESCRIPTION:A 4-hour workshop taught by Paul D. Umbach\, Ph.D. \nRegister\nOverview\nWriting good survey questions can be very difficult. This workshop will give participants a solid practical foundation in how to write good survey questions. The purpose of this workshop is to arm you with the knowledge to develop valid and reliable questionnaires by looking at approaches for developing survey questions and methods for evaluating them. As survey researchers\, it is difficult to be certain that respondents interpret your questions exactly as you intend\, recall the information you need\, and provide the answers you want. This workshop will introduce you to what is known about the psychology of survey response and generally accepted best practices for question writing. The workshop will also review methods for testing questions to ensure their validity and reliability. \nExpected outcomes\nYou will leave this workshop with new strategies for developing\, evaluating\, and critiquing survey questions. By the end of this workshop\, you will know how to write well-constructed questions that attend to common concerns such as respondent memory\, comprehension\, and judgment. You will be introduced to techniques\, such as cognitive interviews and pilot tests\, that help you diagnose problems with survey questions and find ways to reduce these problems. In the end\, you will not only be a better consumer of survey research\, but you will be able to develop valid and reliable survey questions and questionnaires. \nPricing and schedule\nTime: Wednesday\, March 4\, 12PM to 4PM (EST)\nCost: $175\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. Find out about our discounts here. \nTime permitting\, 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?\nThis course is aimed at anyone who writes or reviews survey questions. This course gives practical guidance to those who have written survey questions but who are not familiar with research on question design\, those who are just beginning to design survey instruments\, and those who use survey data but do not themselves design survey instruments. The target audience for this workshop is a range of researchers\, including institutional researchers\, policy analysts\, student affairs professionals\, assessment professionals\, graduate students\, and faculty\, who use surveys in their work. We also expect individuals from government\, business\, and non-profit organizations will also benefit from this course. \nAgenda\n\nIntroduction to question writing\n\nTypes of questions\nParts of a question\nCognitive processes for responding to survey questions\n\n\nGeneral guidelines for writing questions\nDeveloping questions about facts\nWriting questions about attitudes\nMethods for evaluating and improving survey questions\n\nExpert review\nFocus groups\nCognitive interviews\nPre-testing\n\n\nEstablishing validity and reliability\n\nRegister\n
URL:https://www.percontor.org/upcoming-workshop/writing-and-evaluating-good-survey-questions-2-2-2-2/
CATEGORIES:Developing and evaluating good survey questions,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200313T120000
DTEND;TZID=UTC+0:20200313T160000
DTSTAMP:20191121T162150
CREATED:20150512T134330Z
LAST-MODIFIED:20191031T142821Z
UID:583-1584100800-1584115200@www.percontor.org
SUMMARY:Logistic regression: Analyzing binary outcomes
DESCRIPTION:A 4-hour workshop taught by Stephen R. 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: Friday\, March 13\, 12PM to 4PM (EST)\nCost: $175\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/
CATEGORIES:Introduction to Binary Logistic Regression,Logistic regression: Analyzing binary outcomes,Research Methods
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200318T120000
DTEND;TZID=UTC+0:20200318T160000
DTSTAMP:20191121T162150
CREATED:20150830T173339Z
LAST-MODIFIED:20191119T003208Z
UID:653-1584532800-1584547200@www.percontor.org
SUMMARY:Regression discontinuity designs for evaluating programs and policies
DESCRIPTION:A 4-hour workshop taught by Brad Curs\, Ph.D. \nYou may also be interested in our propensity score analysis workshop. \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: Wednesday\, March 18\, 12PM to 4PM (EST)\nCost: $175\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. 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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200325T100000
DTEND;TZID=UTC+0:20200325T160000
DTSTAMP:20191121T162150
CREATED:20160223T041546Z
LAST-MODIFIED:20191031T143621Z
UID:786-1585130400-1585152000@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. \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.\nHow to build two-level models using HLM 7.\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: Wednesday March 25\, 10:00AM to 4:00PM (EST)\nCost: $225\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. 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\nUsing real data and HLM 7 to build models\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\nWe will be using HLM 7 for class demonstrations. You can download the HLM 7 student version at the SSI Scientific Software website. Please note that HLM is only compatible with Windows. I will also provide output\, data\, and syntax for SPSS\, Stata\, and SAS. \nRegister\n
URL:https://www.percontor.org/upcoming-workshop/basics-multilevel-modeling-handling-grouped-data-education-settings/
CATEGORIES:Basics multilevel modeling: handling grouped data in education settings,Research Methods
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200409T130000
DTEND;TZID=UTC+0:20200409T180000
DTSTAMP:20191121T162150
CREATED:20191031T151248Z
LAST-MODIFIED:20191031T151701Z
UID:1123-1586437200-1586455200@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. \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\, April 9\, 1PM to 6PM (EST)\nCost: $200\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. 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.\, has been the Director\, Office of Institutional Analysis\, and Consultant\, Center for Research Design & Analysis\, 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-2-2-2/
CATEGORIES:Predictive analytics,Research Methods
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200417T110000
DTEND;TZID=UTC+0:20200417T170000
DTSTAMP:20191121T162150
CREATED:20191031T152016Z
LAST-MODIFIED:20191031T152024Z
UID:1127-1587121200-1587142800@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: Friday\, April 17\, 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-3-2-2-2-2/
CATEGORIES:Introduction to matching and propensity score analysis,Research Methods
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200423T130000
DTEND;TZID=UTC+0:20200423T180000
DTSTAMP:20191121T162150
CREATED:20191031T153652Z
LAST-MODIFIED:20191031T153652Z
UID:1129-1587646800-1587664800@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. \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\, April 23\, 1PM to 6PM (EST)\nCost: $200\nLocation: Online \nWe offer $25 graduate student and multiple workshop discounts. 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.\, has been the Director\, Office of Institutional Analysis\, and Consultant\, Center for Research Design & Analysis\, 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-2-2-2/
CATEGORIES:Microsoft power bi,Research Methods
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20200529T120000
DTEND;TZID=UTC+0:20200529T160000
DTSTAMP:20191121T162150
CREATED:20190328T233703Z
LAST-MODIFIED:20191031T160508Z
UID:1087-1590753600-1590768000@www.percontor.org
SUMMARY:Handling missing data: Multiple imputation for the applied researcher
DESCRIPTION:A 4-hour workshop taught by Ryan S. Wells\, Ph.D. \nYou may also be interested in our increasing web survey response rates workshop. \nRegister\nOverview\nItem-missing data is a serious concern for any quantitative researcher. Survey participants regularly skip questions\, and administrative data is often incomplete. But what do to about this issue is not always straightforward. Researchers often analyze only complete cases (listwise deletion) or they impute missing information using means or regression analysis. These approaches can lead to biased estimates and artificially small standard errors. \nThis workshop will help the applied researcher to analyze the extent and patterns of missing data\, and to decide how to address this challenge. Most of the workshop will focus on one common but powerful solution to missing data: multiple imputation. This technique has advantages over many other approaches\, which will be discussed. At the end of this workshop\, participants will understand the decisions\, strategies\, and code needed to successfully use multiple imputation in their research projects. \nExpected outcomes\nBy the end of the workshop\, participants should understand the problems associated with missing data for quantitative research\, as well as options for how to handle such data. Participants will gain specific insight about multiple imputation as an approach to dealing with missing data\, including the assumptions and data requirements underpinning the method. By the end of the workshop\, participants will understand the decisions\, strategies\, and statistical methods needed to successfully apply multiple imputation to their projects. \nPricing and schedule\nTime: Friday\, May 29\, 12PM to 4PM (EST)\nCost: $175\nLocation: Online \nWho should attend?\nThe target audience is researchers who have taken more than one statistics course. Basic knowledge of regression is useful. Those who will benefit the most are those who conduct quantitative research and may encounter missing data in their projects. Software demonstrations will use Stata\, but SPSS code and output will also be included for participants who are using SPSS for their research projects. \nAgenda\n\nWhat is “missing data?”\nNature and structure of missing data\nDangers of missing data\nPossible ways to address to missing data\nMultiple imputation – the basics\nPooling – Rubin’s Rules\nNumber of imputations\nVariable selection / Auxiliary variables\nComparing observed and imputed results\nComparing multiple approaches to missing data\nImputing complex survey or weighted data\nCommon mistakes and confusion\nCommunicating MI to other audiences\n\n\nRegister\n
URL:https://www.percontor.org/upcoming-workshop/handling-missing-data-multiple-imputation-for-the-applied-researcher-2/
CATEGORIES:Multiple imputation,Research Methods
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