Determinants of academic attainment in the United States: A quantile regression analysis of test scores

Haile, Getinet Astatike; Nguyen, Anh Ngoc

doi:10.1080/09645290701523218

Cited by 23 publications

(17 citation statements)

References 31 publications

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“…In addition, quantile regression possesses other specific advantages, i.e., robustness to the distribution assumption, monotone equivariance characteristics, and insensitivity to the outliers without the necessity of subjectively selecting data [16,[20][21][22]. Due to the flexible characteristic of quantile regression, it has been widely used in various research areas such as ecology [23,24], education policy [25], medicine [26], economic research [27,28], and forest resource management [29]. Zhang et al [22] used quantile regression to model the self-thinning line for a Pinus strobus L. plantation and the results showed that quantile regression performed well in modeling the maximum density line.…”

Section: Introductionmentioning

confidence: 99%

Using Linear Mixed-Effects Models with Quantile Regression to Simulate the Crown Profile of Planted Pinus sylvestris var. Mongolica Trees

Sun¹,

Gao²,

Li³

2017

Forests

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Abstract:Crown profile is mostly related to the competition of individual trees in the stands, light interception, growth, and yield of trees. A total of 76 sample trees with a total number of 889 whorls and 3658 live branches were used to develop the outer crown profile model of the planted Pinus sylvestris var. mongolica trees in Heilongjiang Province, China. The power-exponential equation, modified Kozak equation, and simple polynomial equation were used and the model which showed the best performance was used as the basic model. The dummy variable approach was used to analyze the effect of stand age and stand density on the crown profile. Quantile regression for linear mixed-effects model, where the correlations between the series measurements on the same subject were considered, was used to model the outer crown profile. The results indicated that the power-exponential equation had the smallest error and was used as the basic model. Based on the dummy variable approach, stand age and stand density showed significant effects on the crown profile on the whole. Thus, they were directly included into the linear form of the power-exponential equation by a natural logarithm transformation to develop the quantile regression for the linear mixed-effects model. The 0.95th quantile regression model performed best in modeling the outer crown profile when compared to other quantiles. The prediction accuracy of the 0.95th quantile regression model by adding the random effects increased when compared to the quantile regression without random effect. The quantile regression for the linear mixed-effects model also showed an excellent performance in the largest crown radius prediction when compared to the quantile regression model. From suppressed trees to dominant trees, the crown radius increased, with tree size increasing for the same stand age and stand density increases. The crown radius of the suppressed trees from 21 to 40 year stands was the largest and the smallest was from older (>40 years) stands. The crown radius for both the intermediate and dominant trees from 21 to 40 year stands were similar and were larger than the younger (10-20 years) stands. The crown radius increased with tree size when the stand variables were constant. Furthermore, the crown radius increased with the increase of stand age, decreased with increasing stand density, and decreased with increased ratio of tree height to diameter at the breast height (HD) for trees with the same tree variables. Stand density had a weaker effect on the crown profile when compared to the HD. The growth rate of the crown radius of planted Pinus sylvestris var. mongolica trees increased with increasing stand age, and decreased with decreasing stand density. The growth rate of the crown radius decreased with increasing HD.

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Section: Introductionmentioning

confidence: 99%

Using Linear Mixed-Effects Models with Quantile Regression to Simulate the Crown Profile of Planted Pinus sylvestris var. Mongolica Trees

Sun¹,

Gao²,

Li³

2017

Forests

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“…Socioeconomic factors such as poverty rate have fairly consistently been found to be the primary determinants of high school student achievement (Haile & Nguyen, 2008;Mishel & Roy, 2007). This study confirmed that this finding is true for Indiana.…”

Section: Socioeconomic Variablesmentioning

confidence: 99%

College Bound or Bound to Fail? Determinants of Indiana’s High School Graduation and College-Going Rates

Correll¹

2016

JPUR

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His research and extension work includes state and local government budgets, local taxing options, and property tax issues, especially farmland assessment. He teaches an introductory macroeconomics class with an enrollment of more than 300 each semester. DeBoer was awarded Purdue's Hovde Award for service to the rural people of Indiana in 2009. In 2015 he received the College of Agriculture's Kohls Award for undergraduate teaching, and the Murphy Award, Purdue University's highest award for undergraduate teaching.

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“…For example, the development of networks and access to science socialization, early educational endowments, scholarships, and grants are directly related to students’ social identities, such as economic status. 91 Alternatively, social identities such as ethnicity 92 or gender 93 influence students’ educational plans and may be directly related to the development of their scientific human capital bundles. Curiel 94 (p90) describes the interaction among sets of variables pertaining to family, culture, language, and schools, noting that “the limited parental involvement of Hispanic parents in education [is] in part due to language barriers, lack of familiarity with structural arrangements of schools, and negative attitudes that are shared by both school authorities and parents.”…”

Section: The Asset Bundle Model: a Conceptual Framework For Decreasinmentioning

confidence: 99%

Perspective

Johnson

Bozeman²

2012

Academic Medicine

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The authors contend that increasing diversity in the scientific pipeline (e.g., academic medicine, science, technology, engineering and mathematics) requires a systematic approach to retain minority high school and college students. Such an approach should focus on the interrelated and multilayered challenges that these students face. The authors fuse an alternative conceptualization of the scientific and technical human capital theoretical framework and the theory of social identity contingencies to offer a conceptual model for targeting the critical areas in which minority students may need additional support in order to continue toward a career in science. Their proposed asset bundles model is grounded in the central premise that making greater progress in recruiting and retaining minorities likely requires institutions to respond simultaneously to various social cues that signal devaluation of certain identities (e.g., gender, race, or socioeconomic status). The authors define “asset bundles” as the specific sets of abilities and resources individuals develop that help them succeed in educational and professional tasks, including but not limited to science and research. The model consists of five asset bundles, each of which is supported in the research literature as a factor relevant to educational achievement and, the authors contend, may lead to improved and sustained diversity: educational endowments, science socialization, network development, family expectations, and material resources. Using this framework, they suggest possible ways of thinking about the task of achieving diversity as well as guideposts for next steps. Finally, they discuss the feasibility of implementing such an approach.

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Determinants of academic attainment in the United States: A quantile regression analysis of test scores

Cited by 23 publications

References 31 publications

Using Linear Mixed-Effects Models with Quantile Regression to Simulate the Crown Profile of Planted Pinus sylvestris var. Mongolica Trees

Using Linear Mixed-Effects Models with Quantile Regression to Simulate the Crown Profile of Planted Pinus sylvestris var. Mongolica Trees

College Bound or Bound to Fail? Determinants of Indiana’s High School Graduation and College-Going Rates

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