This article provides an overview of different computational options for inference following normal theory maximum likelihood (ML) estimation in structural equation modeling (SEM) with incomplete normal and nonnormal data. Complete data are covered as a special case. These computational options include whether the information matrix is observed or…

This appendix documents the methodology used by the Georgetown University Center on Education and the Workforce to project educational demand within the US economy. The methodology produces forecasts using data from two private analytics companies. The authors use occupational forecasts provided by Lightcast that are calibrated to total employment…

The staggering highs and lows of the recent US economy and their effect on the labor force has been deeply unsettling. The US has come through the COVID-19 recession, the deepest economic downturn since the Great Depression, followed by the quickest recovery ever. One trend in the workforce has remained unaltered throughout this historic change:…

Predictive studies have been widely undertaken in the field of education to provide strategic information about the extensive set of processes related to teaching and learning, as well as about what variables predict certain educational outcomes, such as academic achievement or dropout. As in any other area, there is a set of standard techniques…

Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models…

Most enrollment management systems today use historical data to build rough forecasts of what percentage of students will likely accept an offer of enrollment based on historical acceptance rates. While this aggregate forecast method has its uses, we propose that building an enrollment model based on predicting an individual's likelihood of…

Many low-income young people are not reaching important milestones for success (for example, completing a program or graduating from school on time). But the social-service organizations and schools that serve them often struggle to identify who is at more or less risk. These institutions often either over- or underestimate risk, missing…

This post is one in a series highlighting MDRC's methodological work. Contributors discuss the refinement and practical use of research methods being employed across the organization. Across policy domains, practitioners and researchers are benefiting from a trend of greater access to both more detailed and frequent data and the increased…

Since 2012, the Wisconsin Department of Public Instruction (DPI) has maintained a statewide predictive analytics system providing schools with an early warning in middle grades of students at risk for not completing high school. DPI is considering extending and enhancing this system, known as the Dropout Early Warning System (DEWS). The proposed…

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…

Business schools seek students who can evaluate, synthesize and extract the important information and sort out the noise from very large volumes of data. With the launch of the Integrated Reasoning section in June, the GMAT exam started measuring these skills, which are essential for learning in today's programs, are expected of those who intend…

As teachers we are encouraged to contextualize the mathematics that we teach. In this article, Belinda Beaman explains how she used the weather as a context for developing decimal understanding. We particularly enjoyed reading how the students were involved in estimating.

Federal education legislation continues to emphasize the role of assessment in elementary and secondary schools. Perhaps most prominently, the Elementary and Secondary Education Act (ESEA), as amended by the Every Student Succeeds Act (ESSA; P.L. 114-95), requires the use of test-based educational accountability systems in states and specifies the…

Conventional pundits, pollsters, and forecasters are focused on whether the economy will improve sufficiently in 2012 for President Barack Obama to gain reelection. The Keys to the White House, a prediction system that the author developed in collaboration with Vladimir Keilis-Borok, founder of the International Institute of Earthquake Prediction…

The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…