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## January 2019

### Introduction to matching and propensity score analysis

A 6-hour workshop taught by Stephen R. Porter, Ph.D. You may also be interested in our introduction to binary logistic regression class. Register Overview Propensity 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. This workshop provides a concise introduction to matching for the…

Find out more »### Increasing web survey response rates: What works?

A 4-hour workshop taught by Paul D. Umbach, Ph.D. Register Overview Are 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…

Find out more »## February 2019

### Exploring the power of predictive analytics: A step-by-step introduction to building a student-at-risk prediction model

A 5-hour workshop taught by Serge Herzog, Ph.D. Register Overview The 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…

Find out more »### Refresher on multiple regression for the applied researcher

A 6-hour workshop taught by Stephen R. Porter, Ph.D. Register Overview Many 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…

Find out more »### Student success prediction at your fingertips: Developing online dashboards with Microsoft Power BI©

A 5-hour workshop taught by Serge Herzog, Ph.D. Participants of this workshop will benefit from also taking our Predictive Analytics workshop. Register Overview The 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…

Find out more »### Basics of multilevel modeling: handling grouped data in research and evaluation

A 6-hour workshop taught by Paul D. Umbach, Ph.D. Register Overview This 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…

Find out more »### Regression discontinuity designs for evaluating programs and policies

A 4-hour workshop taught by Brad Curs, Ph.D. You may also be interested in our propensity score analysis workshop. Register Overview This 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…

Find out more »## March 2019

### Writing and evaluating good survey questions

A 4-hour workshop taught by Paul D. Umbach, Ph.D. Register Overview Writing 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…

Find out more »### Logistic regression: Analyzing binary outcomes

A 4-hour workshop taught by Stephen R. Porter, Ph.D. Register Overview Many 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…

Find out more »## April 2019

### Exploring the power of predictive analytics: A step-by-step introduction to building a student-at-risk prediction model

A 5-hour workshop taught by Serge Herzog, Ph.D. Register Overview The 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…

Find out more »### Introduction to matching and propensity score analysis

A 6-hour workshop taught by Stephen R. Porter, Ph.D. You may also be interested in our introduction to binary logistic regression class. Register Overview Propensity 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. This workshop provides a concise introduction to matching for the…

Find out more »### Student success prediction at your fingertips: Developing online dashboards with Microsoft Power BI©

A 5-hour workshop taught by Serge Herzog, Ph.D. Participants of this workshop will benefit from also taking our Predictive Analytics workshop. Register Overview The 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…

Find out more »## May 2019

### Handling missing data: Multiple imputation for the applied researcher

A 4-hour workshop taught by Ryan S. Wells, Ph.D. You may also be interested in our increasing web survey response rates workshop. Register Overview Item-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…

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