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1. Estimating the Influence of Financial Aid on Student Retention: A Discrete-Choice Propensity Score-Matching Model

Author

University of Arkansas, Education Working Paper Archive and Herzog, Serge

Abstract

The greatest limitation in establishing causality in observational studies on the effect of financial aid is the presence of endogeneity or selection bias associated with aid status. To control for this statistical confoundedness that besets the research corpus to date, this study estimates the effect of financial aid on freshmen retention at a moderately selective, public university using propensity score-matching in multi-stage regression analyses. The correlational pattern that emerged from twenty-four logit models suggests higher-income students accrue a retention benefit from financial aid, unlike low-income students, net of first-year academic experience and type and amount of aid received. Conversely, retention of low-income freshmen is more likely due to academic performance compared to those from high-income background. Findings on the effect of aid are consistent with the economics of moral hazard and unobservable behavior. (Contains 6 footnotes, 8 tables, and 2 figures.)

Published
2008

2. Diversity and Educational Benefits: Moving Beyond Self-Reported Questionnaire Data

Author

University of Arkansas, Education Working Paper Archive and Herzog, Serge

Abstract

Effects of ethnic/racial diversity among students and faculty on cognitive growth of undergraduate students are estimated via a series of hierarchical linear and multinomial logistic regression models. Using objective measures of compositional, curricular, and interactional diversity based on actuarial course enrollment records of over 6,000 students at a public research university, the study finds no patterns of positive correlation with objective measures of cumulative academic achievement (i.e., final graduating GPA, GRE/GMAT test scores, graduate school enrollment) net of academic preparation at college entry and socio-demographic background, and with or without accounting for academic major, college curricular experience, and financial aid. Results are consistent with student self-assessed level of critical thinking skills after graduation, but not with self-assessed level of understanding of racial and cultural issues, both affective outcomes showing a positive correlation with curricular diversity. As the findings contradict most of the higher education literature on survey-based cognitive benefits of ethnic/racial diversity, the study calls for use of objective measures to advance the research in this area. (Contains 19 tables, 4 graphs and 10 footnotes.)

Published
2007

3. Do Students Care about School Quality? Determinants of Dropout Behavior in Developing Countries

Author

University of Arkansas, Education Working Paper Archive, Hanushek, Eric A., Lavy, Victor, and Hitomi, Kohtaro

Abstract

School quality and grade completion by students are shown to be directly linked, leading to very different perspectives on educational policy in developing countries. Unique panel data on primary school age children in Egypt permit estimation of behavioral models of school leaving. Students perceive differences in school quality, measured as expected achievement improvements in a given school, and act on it. Specifically, holding constant the student's own ability and achievement, a student is much less likely to remain in school if attending a low quality school rather than a high quality school. This individually rationale behavior suggests that common arguments about a trade-off between quality and access to schools may misstate the real issue and lead to public investment in too little quality. Further, because of this behavioral linkage, there is an achievement bias such that common estimates of rates of return to years of school will be overstated. The paper demonstrates the analytical importance of employing output-based measures of school quality. (Contains 29 footnotes.)

Published
2007

4. Laser-diffraction and pipette-method grain size of Dutch sediments: correlations for fine fractions of marine, fluvial, and loess samples

Author

Buurman, P., Paper, T., Reijneveld, J.A., de Jong, F., and van Gelder, E.

Subjects
WIMEK, Laboratorium voor Bodemkunde en geologie, bodemtextuur, soil texture, Soil Biology, Laboratory of Soil Science and Geology, bodemstructuur, analytische methoden, analytical methods, correlation, grondanalyse, Alterra - Centre for Water and Climate, deeltjesgrootteverdeling, particle size distribution, soil analysis, soil structure, Bodembiologie, lasers, Alterra - Centrum Water en Klimaat, correlatie
Abstract

To evaluate correlations between silt and clay fractions determined by pipette method and laser diffraction, samples from Dutch fine marine, fluvial, and loess deposits were analysed by both methods. For fluvial deposits, correlations for fractions 50 µm were excellent (R2 > 0.95), those for 2-4, 4-8, 16-32 and 32-50 µm were satisfactory (R2 = 0.80 - 0.95), while that for the fraction 8-16 µm had an R2 of only 0.68. For marine deposits, correlations for 50 µm were in the same range, but those of all other fractions except 8-16 µm were lower. In the loess samples, correlations for all but the 8-16 µm fraction were unsatisfactory. Laser diffraction gave 42% of pipette clay in marine samples, and 62% in fluvial and loess samples if regressions are forced through 0. Sand fractions detected by laser diffraction were 107% of the sieve fraction in marine samples, and 99% in the fluvial samples. Correlations for fractions smaller than reference size are generally better than those for individual size fractions. Both the 2 µm and the 50 µm boundary cause problems in the comparison. The first because of platy shape of clay minerals, and the second due to both a change in method in the pipette/sieving procedure, and to nonsphericity of particles. Apparently, correlations for clay- and silt-size fractions obtained by pipette method and laser diffraction will be different for each type of sediment.

Published
2001

5. A test for gene-environment interaction in the presence of measurement error in the environmental variable

Author

Hugues Aschard, Donna Spiegelman, Molin Wang, Vincent Laville, Pete Kraft, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Harvard T.H. Chan School of Public Health, The authors thank the reviewers for their helpful comments that have improved the paper. This research is supported by grant R01CA050597 from the National Cancer Institute (NCI) and R21HG007687 from the National Human Genome Research Institute (NHGRI). The authors have no conflict of interest to declare., and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Epidemiology, Genome-wide association study, Breast Neoplasms, MESH: Logistic Models, Interaction, Logistic regression, 01 natural sciences, Article, Correlation, 010104 statistics & probability, 03 medical and health sciences, Bias, Statistics, Linear regression, MESH: Bias, Humans, normal discriminant analysis, MESH: Genetic Variation, MESH: Models, Genetic, 0101 mathematics, Gene–environment interaction, gene-environment interaction test, Genetics (clinical), Mathematics, [STAT.AP]Statistics [stat]/Applications [stat.AP], Observational error, MESH: Humans, Models, Genetic, Genetic Variation, Reproducibility of Results, MESH: Gene-Environment Interaction, Linear discriminant analysis, 3. Good health, MESH: Reproducibility of Results, 030104 developmental biology, Logistic Models, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, genome-wide association studies, MESH: Genome-Wide Association Study, Gene-Environment Interaction, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [STAT.ME]Statistics [stat]/Methodology [stat.ME], measurement error, MESH: Breast Neoplasms, Genome-Wide Association Study
Abstract

International audience; The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.

Published
2018
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