Lynne Steuerle Schofield

Effect of a home intervention program on pediatric asthma in an environmental justice community.

Asthma prevalence rates are at an all-time high in the United States with over 25 million persons diagnosed with asthma. African Americans and other minorities have higher asthma prevalence and higher exposure to environmental factors that worsen asthma as compared to Caucasians. This article describes the evaluation of an inner-city home-based asthma education and environmental remediation program that addressed both indoor and outdoor triggers through collaboration between a health system and local environmental justice organization. The program enrolled 132 children older than 2.5 years and centers on a 4- to 6-week intervention with peer counselors using the U.S. Environmental Protection Agency Asthma Home Environment Checklist and the You Can Control Asthma curriculum. Families receive asthma-friendly environmental home kits. Peer counselors reinforce key asthma management messages and facilitate the completion of Asthma Action Plans. The environmental justice community partner organized block cleanups to reduce outdoor triggers. The evaluation used a pretest–posttest design to assess changes in client behavior and asthma symptoms. Data were collected at baseline and during a 6-month postintervention period. Participants saw enhanced conditions on asthma severity and control. The improvement was greatest for children whose asthma was considered “severe” based on the validated Asthma Control Test. Other positive results include the following: greater completion of Asthma Action Plans, significant reduction in the number of emergency room visits (p = .006), and substantial decreases in school absenteeism (p = .008) and use of rescue medications (p = .049). The evaluation suggests that the program was effective in improving asthma self-management in a high-risk population living within an environmental justice community.

The use of cognitive ability measures as explanatory variables in regression analysis

AbstractCognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual’s wage, or a decision such as an individual’s education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score, constructed via standard psychometric practice from individuals’ responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a “mixed effects structural equations” (MESE) model, may be more appropriate in many circumstances.JEL CodeJ01

Predictive Inference Using Latent Variables with Covariates.

Plausible values (PVs) are a standard multiple imputation tool for analysis of large education survey data, which measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations model of Schofield (Modeling measurement error when using cognitive test scores in social science research. Doctoral dissertation. Department of Statistics and Heinz College of Public Policy. Pittsburgh, PA: Carnegie Mellon University, 2008).

Correcting For Measurement Error In Latent Variables Used As Predictors

This paper represents a methodological-substantive synergy. A new model, the Mixed Effects Structural Equations (MESE) model which combines structural equations modeling and item response theory is introduced to attend to measurement error bias when using several latent variables as predictors in generalized linear models. The paper investigates racial and gender disparities in STEM retention in higher education. Using the MESE model with 1997 National Longitudinal Survey of Youth data, I find prior mathematics proficiency and personality have been previously underestimated in the STEM retention literature. Pre-college mathematics proficiency and personality explain large portions of the racial and gender gaps. The findings have implications for those who design interventions aimed at increasing the rates of STEM persistence among women and under-represented minorities.

Community-Based Learning Versus Traditional Courses in Statistics: Who Takes Them and Why

I compare students in two different data analysis courses: one course included a community-based learning (CBL) component and one did not. I find that students who chose to take the CBL course were more likely to have less mathematical and statistical background (as measured by the number of college-level mathematics and statistics courses and self-reporting), were more interested in community service, and wanted a statistics course that aligned closely with their career goals and aspirations. In comparison, students in the more traditional course had stronger mathematical and statistical skills, were more interested in a traditional course where they learned the methods from a variety of “case-studies,” and had less time for and interest in a community project. The results of the comparison suggest that there is a place for both CBL and traditional courses and that students tend to appropriately self-select into the course that meets their needs. CBL courses may also be a good place to draw in students who might otherwise not choose to take a data analysis course in college.

The Methuselah Effect: The Pernicious Impact of Unreported Deaths on Old-Age Mortality Estimates

We examine inferences about old-age mortality that arise when researchers use survey data matched to death records. We show that even small rates of failure to match respondents can lead to substantial bias in the measurement of mortality rates at older ages. This type of measurement error is consequential for three strands in the demographic literature: (1) the deceleration in mortality rates at old ages; (2) the black-white mortality crossover; and (3) the relatively low rate of old-age mortality among Hispanics, often called the “Hispanic paradox.” Using the National Longitudinal Survey of Older Men matched to death records in both the U.S. Vital Statistics system and the Social Security Death Index, we demonstrate that even small rates of missing mortality matching plausibly lead to an appearance of mortality deceleration when none exists and can generate a spurious black-white mortality crossover. We confirm these findings using data from the National Health Interview Survey matched to the U.S. Vital Statistics system, a data set known as the “gold standard” (Cowper et al. 2002) for estimating age-specific mortality. Moreover, with these data, we show that the Hispanic paradox is also plausibly explained by a similar undercount.