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## Increasing web survey response rates: What works?

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

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

A 5-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…

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…

Find out more »## 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…

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. Register Participants of this workshop will benefit from also taking our Predictive Analytics workshop. 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…

Find out more »## 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…

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…

Find out more »## Factor analysis using SPSS

Register A 4-hour workshop taught by Mary Ann Coughlin, D.P.E. Overview Factor analysis is a statistical technique commonly used to establish evidence of survey instrument validity. Exploratory factor analysis explores the relationships among variables to discover if those variables can be grouped into a smaller set of underlying factors. Often researchers and applied practitioners are faced with the difficult task of summarizing numerous variables from a survey and want to reduce the data into a smaller…

Find out more »## 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…

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…

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

A 6-hour workshop taught by Paul D. Umbach, Ph.D. Register Overview This workshop provides the basics multilevel modeling, focusing on practical applications rather than statistical theory. Researchers and evaluators from a range 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 not statistically independent, thus violating a basic assumption of standard…

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