Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process

Disability, Method of Simulated Movements, Multivariate Probit, Social Security.

Meeting Highlights: Genome Sequencing and Biology 2001

We bring you a report from the CSHL Genome Sequencing and Biology Meeting, which has a long and prestigious history. This year there were sessions on large-scale sequencing and analysis, polymorphisms (covering discovery and technologies and mapping and analysis), comparative genomics of mammalian and model organism genomes, functional genomics and bioinformatics

Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process

We have estimated a 4-step sequential probit model with and without sample separation information to characterize SSA's disability determination process. Under the program provisions, different criteria dictate the outcomes at different steps of the process. We used data on health, activity limitations, demographic traits, and work from 1990 SIPP exact matched to SSA administrative records on disability determinations. Using GHK Monte Carlo simulation technique, our estimation results suggest that the correlations in errors across equations that may arise due to unobserved individual heterogeneity are not statistically significant. In addition, we examined the value of administrative data on the basis for allow/deny determinations at each stage of the process. Following the marginal likelihood approach adopted by Benitez-Silva, Buchinsky, Chan, Rust, and Sheidvasser (1999), we also estimated the above sequential probit model without the sample separation information for the purpose of direct comparison. We found that without the detailed administrative information on outcomes at each stage of the screening process, we could not properly evaluate the importance of a large number of program-relevant survey-based explanatory variables. In terms of both in-sample and jackknife-type out-of-sample predictive analysis, the value of modeling the sequential structure of the determination process in generating correct eligibility probabilities is confirmed.Disability, Method of Simulated Movements, Multivariate Probit, Social Security

Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process

We have estimated a 4-step sequential probit model with and without sample separation information to characterize SSA's disability determination process. Under the program provisions, different criteria dictate the outcomes at different steps o f the process. We used data on health, activity limitations, demographic traits, and work from 1990 SIPP exact matched to SSA administrative records on disability determinations. Using GHK Monte Carlo simulation technique, our estimation results suggest that the correlations in errors across equations that may arise due to unobserved individual heterogeneity are not statistically significant. In addition, we examined the value of administrative data on the basis for allow/deny determinations at each sta ge of the process. Following the marginal likelihood approach adopted by Benitez-Silva, Buchinsky, Chan, Rust, and Sheidvasser (1999), we also estimated the above sequential probit model without the sample separation information for the purpose of direct comparison. We found that without the detailed administrative information on outcomes at each stage of the screening process, we could not properly evaluate the importance of a large number of program-relevant survey-based explanatory v ariables. In terms of both in-sample and jackknife-type out-of-sample predictive analysis, the value of modeling the sequential structure of the determination process in generating correct eligibility probabilities is confirmed.

An Inductive Method of Measuring Students’ Cognitive and Affective Processes via Self-Reports in Digital Learning Environments

Student affect can play a profoundly important role in students\u27 post-school lives. Understanding students\u27 affective states within online learning environments in particular has become an important matter of research, as digital tutoring systems have the potential to intervene at the moment that students are struggling and becoming frustrated, bored or disengaged. However, despite the importance of assessing students\u27 affective states, there is no clear consensus about what emotions are most important to assess, nor how these emotions can be best measured. This dissertation investigates students’ self-reports of their emotions and causal attributions of those emotions collected while they are solving math problems within a mathematics tutoring system. These self-reports are collected in two conditions: through limited choice Likert response and through open response text boxes. The conditions are combined with students’ cognitive attributions to describe epistemic (neither purely affective nor purely cognitive) emotions in order to explain the relationship between observable student behaviors in the MathSpring.org tutoring system and student affect. These factors include beliefs, expectations, motivations, and perceptions of ability and control. A special emphasis of this dissertation is on analyzing the role of causal attributions for the events and appraisals of the learning environment, as possible causes of student behaviors, performance, and affect