2,267 research outputs found
Controlling for individual heterogeneity in longitudinal models, with applications to student achievement
Longitudinal data tracking repeated measurements on individuals are highly
valued for research because they offer controls for unmeasured individual
heterogeneity that might otherwise bias results. Random effects or mixed models
approaches, which treat individual heterogeneity as part of the model error
term and use generalized least squares to estimate model parameters, are often
criticized because correlation between unobserved individual effects and other
model variables can lead to biased and inconsistent parameter estimates.
Starting with an examination of the relationship between random effects and
fixed effects estimators in the standard unobserved effects model, this article
demonstrates through analysis and simulation that the mixed model approach has
a ``bias compression'' property under a general model for individual
heterogeneity that can mitigate bias due to uncontrolled differences among
individuals. The general model is motivated by the complexities of longitudinal
student achievement measures, but the results have broad applicability to
longitudinal modeling.Comment: Published at http://dx.doi.org/10.1214/07-EJS057 in the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The Relationship Between Prior Experiences in Mathematics and Pharmacy School Success
Objective. To assess studentsā pre-pharmacy math experiences, confidence in math ability, and relationship between experiences, confidence, and grades in math-based pharmacy courses.
Methods. A cross-sectional survey of first year to third year pharmacy students was conducted. Students reported type of pre-pharmacy math courses taken, when they were taken [high school (HS) vs. college] and year of HS and college graduation. Students rated their confidence in math ability using the previously validated 11-item Fogerty Math Confidence Scale (Cronbach alpha=0.92). Math grade point average (GPA), Pharmacy College Admission Test quantitative (PCAT quant) scores, and grades (calculations and kinetics) were obtained from transcripts and school records. Spearman correlation and multivariate linear regression were used to compare math experiences, confidence, and grades.
Results. There were 198 students who reported taking math courses 7.1 years since HS graduation and 2.9 years since their last schooling prior to pharmacy school. Students who took math courses with more time since HS/last schooling had lower calculations and kinetics grades. Students reporting having taken more HS math courses had better calculations grades. Students with higher math GPA, and PCAT quant scores also had higher calculations and kinetics grades. Greater confidence in math ability was associated with higher calculations grades. In multivariate regressions, PCAT quant scores and years since HS independently predicted calculations grades, and PCAT quant scores independently predicted kinetics grades.
Conclusion. The number of pre-pharmacy math courses and time elapsed since they were taken are important factors to consider when predicting a pharmacy studentās success in math-based pharmacy school courses
Specific SPS construction studies: Construction tasks, construction base
A concept for building the 5000 MW reference solar power satellite in Earth orbit is discussed. The system uses silicon solar cells. The GEO base has contiguous facilities for concurrent assembly and subsequent mating of the satellite energy conversion system and its power transmission antenna. The end builder construction system uses 10 synchronized beam machines to automatically fabricate continuous longitudinal beams for the energy conversion system
Cooperation after War: International Development in Bosnia, 1995 to 1999
This paper discusses how predispositions, incentives, the number and heterogeneity of participants, and leadership (Faerman et al. 2001) jointly influenced the international effort to develop Bosnia and Herzegovina. International coalitions, task forces, and advisory groups are increasingly charged with implementing reforms following civil conflict. This requires a complex web of interorganizational relationships among NGOS, donors and host nations at both global and āgroundā levels. To better understand development assistance, attention must be paid to the relationships between these varied players. We find that four factors influenced relationships between policy, donor, and implementing organizations; and those strained relationships, in turn, affected development success. The paper draws on interviews, conducted in Bosnia, with 43 development professionals, observation of development meetings in Tuzla and Sarajevo, and review of related documents from international development programs.international development, interorganizational relationships and cooperation
Missing data in value-added modeling of teacher effects
The increasing availability of longitudinal student achievement data has
heightened interest among researchers, educators and policy makers in using
these data to evaluate educational inputs, as well as for school and possibly
teacher accountability. Researchers have developed elaborate "value-added
models" of these longitudinal data to estimate the effects of educational
inputs (e.g., teachers or schools) on student achievement while using prior
achievement to adjust for nonrandom assignment of students to schools and
classes. A challenge to such modeling efforts is the extensive numbers of
students with incomplete records and the tendency for those students to be
lower achieving. These conditions create the potential for results to be
sensitive to violations of the assumption that data are missing at random,
which is commonly used when estimating model parameters. The current study
extends recent value-added modeling approaches for longitudinal student
achievement data Lockwood et al. [J. Educ. Behav. Statist. 32 (2007) 125--150]
to allow data to be missing not at random via random effects selection and
pattern mixture models, and applies those methods to data from a large urban
school district to estimate effects of elementary school mathematics teachers.
We find that allowing the data to be missing not at random has little impact on
estimated teacher effects. The robustness of estimated teacher effects to the
missing data assumptions appears to result from both the relatively small
impact of model specification on estimated student effects compared with the
large variability in teacher effects and the downweighting of scores from
students with incomplete data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS405 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Cooperation after war
This paper discusses how predispositions, incentives, the number and heterogeneity of participants, and leadership (Faerman et al. 2001) jointly influenced the international effort to develop Bosnia and Herzegovina. International coalitions, task forces, and advisory groups are increasingly charged with implementing reforms following civil conflict. This requires a complex web of interorganizational relationships among NGOS, donors and host nations at both global and "ground" levels. To better understand development assistance, attention must be paid to the relationships between these varied players. We find that four factors influenced relationships between policy, donor, and implementing organizationsand those strained relationships, in turn, affected development success. The paper draws on interviews, conducted in Bosnia, with 43 development professionals, observation of development meetings in Tuzla and Sarajevo, and review of related documents from international development programs
Where you Come From or Where You Go? Distinguishing Between School Quality and the Eļ¬ectiveness of Teacher Preparation Program Graduates
Where you Come From or Where You Go? Distinguishing Between School Quality and the Eļ¬ectiveness of Teacher Preparation Program Graduates
An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse treatment programs
Participants in longitudinal studies on the effects of drug treatment and
criminal justice system interventions are at high risk for institutionalization
(e.g., spending time in an environment where their freedom to use drugs, commit
crimes, or engage in risky behavior may be circumscribed). Methods used for
estimating treatment effects in the presence of institutionalization during
follow-up can be highly sensitive to assumptions that are unlikely to be met in
applications and thus likely to yield misleading inferences. In this paper we
consider the use of principal stratification to control for
institutionalization at follow-up. Principal stratification has been suggested
for similar problems where outcomes are unobservable for samples of study
participants because of dropout, death or other forms of censoring. The method
identifies principal strata within which causal effects are well defined and
potentially estimable. We extend the method of principal stratification to
model institutionalization at follow-up and estimate the effect of residential
substance abuse treatment versus outpatient services in a large scale study of
adolescent substance abuse treatment programs. Additionally, we discuss
practical issues in applying the principal stratification model to data. We
show via simulation studies that the model can only recover true effects
provided the data meet strenuous demands and that there must be caution taken
when implementing principal stratification as a technique to control for
post-treatment confounders such as institutionalization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS179 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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