Average SAT Scores Research Articles

Analyzing Public School Education Inequalities in the Dallas-Fort Worth Metroplex

Purpose: This paper investigates the influences of racial and socioeconomic factors on public schools' performance in the Dallas-Fort Worth (DFW) Metroplex. Research Methods: I obtained data from the Texas Education Agency (TEA) and the American Community Survey (ACS), representing 366,568 students attending 239 public high schools in the DFW Metroplex, which included public schools located in Colin County, Dallas County, Tarrant County, and Denton County. I measured public school performance based on graduation rate metrics and average SAT scores from graduating students. Findings: I found a significant correlation between racial and socioeconomic factors on public school performance. I found that the percent composition of socioeconomically disadvantaged students was the highest correlator to decreased academic performance. Further, I found a positive correlation between the percentage of socioeconomically disadvantaged students attending a given high school in relation to the percentage of African-American and Hispanic students attending a given high school. Notably, I found that public schools in the DFW Metroplex were highly segregated by racial and socioeconomic factors. Implications: This study highlights the necessity for policy pushes to diversify public school districts. Many public schools in the DFW Metroplex remain extensively segregated by racial and socioeconomic factors, and our findings underscore the importance of ensuring equitable resource distribution amongst public schools, specifically in school systems with large percentages of African-American, Hispanic, and socioeconomically disadvantaged students.

Applicants to Historically Black Colleges and Universities

Historically Black colleges and universities (HBCUs) play an important role in the postsecondary education of Black students in the United States. Using data on where 1.2 million Black SAT takers send their SAT scores, we examine which students consider attending HBCUs and the common alternatives to HBCUs. Students with average SAT scores are the most likely to consider HBCUs, and students with the highest SAT scores are the least likely. Students who are relatively interested in HBCUs tend to also consider less selective, in-state, public options. However, that relationship is counteracted when the HBCUs under consideration are relatively more selective.

Individual and School Correlates of DIT-2 Scores Using a Multilevel Modeling and Data Mining Analysis

Moral reasoning was investigated with respect to individual characteristics (i.e., education level, political orientation and sex) and school-related (i.e., university/college) factors using multilevel modeling and data mining analysis. We used the multilevel modeling to detect school effects on moral reasoning as well as individual effects for 16,334 students representing 79 different higher education institutions across the U.S. The school-related factors, such as the racial composition, student–faculty ratio, average SAT score, institution type, institutions’ geographical region, frequencies of morally relevant words in college course catalog, college mission and value statements were collected through website searches. Data mining analysis was utilized to extract and calculate the frequencies of morally relevant words from the website content. There were significant effects for the individual characteristic of political orientation. Additionally, all school-related factors were significant. Only main effects were observed for some school-related factors (i.e., average SAT score, institution type, frequency of morally relevant words in mission statements, value statements and course catalogs). For other school-related factors (i.e., the region, student–faculty ratio and racial composition), main effects were also observed; however, these effects were particularly illuminating given their interactions with political orientation. Implications for educational communities are discussed.

Measurement Error Without the Proxy Exclusion Restriction

–This article studies the identification of the coefficients in a linear equation when data on the outcome, covariates, and an error-laden proxy for a latent variable are available. We maintain that the measurement error in the proxy is classical and relax the assumption that the proxy is excluded from the outcome equation. This enables the proxy to directly affect the outcome and allows for differential measurement error. Without the proxy exclusion restriction, we first show that the effects of the latent variable, the proxy, and the covariates are not identified. We then derive the sharp identification regions for these effects under any configuration of three auxiliary assumptions. The first weakens the assumption of no measurement error by imposing an upper bound on the noise-to-signal ratio. The second imposes an upper bound on the outcome equation coefficient of determination that would obtain had there been no measurement error. The third weakens the proxy exclusion restriction by specifying whether the latent variable and its proxy affect the outcome in the same or the opposite direction, if at all. Using the College Scorecard aggregate data, we illustrate our framework by studying the financial returns to college selectivity and characteristics and student characteristics when the average SAT score at an institution may directly affect earnings and serves as a proxy for the average ability of the student cohort.

College Rankings: Creating an Equitable Model of Transformation and Institutional Effectiveness

Colleges that serve working-class students show up poorly in traditional rankings of US colleges. Without appropriate outcome measures, measures of ‘quality’ of inputs drive most current ranking systems. The trouble is that quality is often just a measure of pre-existing privilege (e.g., selectivity, average SAT scores). In this article, I demonstrate the viability of a model that uses economic returns data while factoring in the relative lack of privilege of students attending any particular institution as a way of ranking that institution’s transformative efficacy and institutional effectiveness. The model was then tested on a diverse sample of 655 US colleges and universities for whom reliable economic returns and institutional effectiveness data are available. Unlike widely used rankings models, this proposed alternative model can distinguish between reproducing privilege (high economic returns as expected, low defaults, timely year to degree and fewer incompleters) and facilitating social mobility (higher returns and persistence than would be expected given the incoming characteristics of students). The article concludes with a discussion of the uses to which such a model could be best put.

A comprehensive merit aid allocation model

This paper highlights the development of a merit-based financial aid allocation model for a large private university incorporating both yield rate prediction and optimal fund distributions. The objective used in the optimal allocation is the average SAT score of the incoming class. In the application, the allocation decision is bound only by the financial aid budget and the number of accepted applicants in homogeneous SAT score groupings. Required yield rates are estimated utilising logistic regression with SAT score and merit aid award levels as the exogenous variables. The parameter estimates are based upon data from the previous year. Comparing the actual result with the model result shows a 17.3 point increase in the mean SAT score, which is shown as equivalent to a 20% increase in the merit aid budget.

The Effect of Selective Public Research University Enrollment: Evidence from California

I study the efficacy of test-based meritocracy in college admissions by evaluating the impact of a grade-based "top percent" policy implemented by the University of California. Eligibility in the Local Context (ELC) provided large admission advantages to the top four percent of 2001-2011 graduates from each California high school. I construct a novel longitudinal dataset linking the ELC era’s 1.8 million UC applicants to educational and labor market outcomes. I first employ a regression discontinuity design to show that ELC led over 10 percent of barely-eligible applicants from low-opportunity high schools to enroll at selective UC campuses instead of less-selective public colleges and universities. Half of those participants were from underrepresented minority groups, and their average SAT scores were at the 12th percentile of their UC peers. Instrumental variable estimates show that ELC participants' more-selective university enrollment caused increases in five-year degree attainment by 30 percentage points and annual early-career wages by up to $25,000. I then analyze ELC's general equilibrium effects by estimating a structural model of university application, admission, and enrollment with an embedded top percent policy. I find that ELC and counterfactual expansions of ELC substantively increase disadvantaged students’ net enrollment at selective public universities. Reduced-form and structural estimates show that ELC participants derived similar or greater value from more-selective university enrollment than their higher-testing peers. These findings suggest that access-oriented admission policies at selective universities can promote economic mobility without efficiency losses.

Show Who the Money? Teacher Sorting Patterns and Performance Pay across U.S. School Districts

AbstractPay for performance (PFP) remains one of the most controversial policy debates in the New Public Management reform era. Skepticism about PFP in the public sector is often grounded in theories of public service motivation that suggest a misalignment between PFP's focus on extrinsic market‐based pay incentives and intrinsically motivated government workers. Frequently missing from this analysis, however, is any consideration for whether PFP leads to positive “sorting” effects on the composition of a government agency's workforce through attraction, selection, and attrition processes. Using data from two waves of the Schools and Staffing Survey, the authors examine whether PFP influences the sorting patterns of K–12 public schoolteachers across U.S. school districts. Findings show that, on average, school districts that adopted PFP secured new teacher hires who had graduated from colleges and universities with average SAT scores that were about 30 points higher than the new teacher cohorts hired by districts that did not adopt PFP.

Measurement Error Without Exclusion: The Returns to College Selectivity and Characteristics

This paper studies the identification of the coefficients in a linear equation when data on the outcome, covariates, and an error-laden proxy for a latent variable are available. We maintain that the measurement error in the proxy is classical and relax the assumption that the proxy is excluded from the outcome equation. This enables the proxy to directly affect the outcome and allows for differential measurement error. Without the proxy exclusion restriction, we first show that the effects of the latent variable, the proxy, and the covariates are not identified. We then derive the sharp identification regions for these effects under any configuration of three auxiliary assumptions. The first weakens the assumption of no measurement error by imposing an upper bound on the noise to signal ratio. The second imposes an upper bound on the outcome equation coefficient of determination that would obtain had there been no measurement error. The third weakens the proxy exclusion restriction by specifying whether the latent variable and its proxy affect the outcome in the same or the opposite direction, if at all. Using the College Scorecard aggregate data, we illustrate our framework by studying the financial returns to college selectivity and characteristics and student characteristics when the average SAT score at an institution may directly affect earnings and serves as a proxy for the average ability of the student cohort.

Measuring inflation in grades: An application of price indexing to undergraduate grades

Rising average grades at American universities have prompted fears of “grade inflation.” This paper applies the methods used to estimate price inflation to examine the causes of rising grades. We use rich data from a large public university to decompose the increase in average grades into those components explained by changes in student characteristics and course choices, and the unexplained component, which we refer to as “inflation.” About one-quarter of the increase in grades from 1982 to 2001 was driven by changes in the courses selected by students; enrollment shifted toward historically ‘easier-grading’ departments over time, mechanically increasing average grades. An additional one-quarter of the increase is attributable to increases in the observable quality of students, such as average SAT scores. Less than half of the increase in average grades from 1982 to 2001 appears to arise from the unexplained factors, or “inflation.” These results add to the evidence suggesting that differences in relative grades across departments discourage students from studying in low-grading departments, like math, physics, or engineering.

The costs and benefits of enrolling in an academically matched college

In response to increased efforts to raise college completion rates through improved academic match between students and their colleges, we examine the costs and benefits to students of following such advice as well as the impact on postsecondary institutions. We analyze data from the National Postsecondary Student Aid Study, the College Board, the National Student Clearinghouse, and the Integrated Postsecondary Education Data System to estimate the impact of improved academic match on students’ predicted net price and bachelor's completion probabilities. The results indicate that undermatching low-income students across the distribution of academic ability would experience a substantial boost in bachelor's degree completion probability – 13.5% points, on average – if they attended a college that better matched their academic credentials. Given this average effect and the number of undermatched low-income students who are minimally “treated” under our simulation, we predict that an additional 3500 low-income students per cohort would complete a bachelor's degree. We find that moving all undermatched low-income students into “safety” colleges would not overly burden this set of institutions, which, on average, would only need to increase the size of first-year cohorts by less than 1%. Moreover, such colleges would experience no change in average SAT scores and overall graduation rates. One estimate of the financial impact on colleges is substantial (i.e., on average, $6.5 M–7.5 M annually per cohort per institution that has a simulated net gain in enrollment) if institutions cover full tuition and fees for these additional low-income students.

Are College Education and Job Experience Complements or Substitutes? Evidence from Hedge Fund Portfolio Performance

We examine, for various educational characteristics of hedge fund managers, the performance profile of hedge fund portfolios along their managers’ professional experience path. We find that during the initial years following their graduation, hedge fund managers who majored in business or economics outperform other managers. However, in subsequent years, hedge fund managers who studied science or engineering improve their performance and eventually outperform all other managers. These results are consistent with the view that business education is a substitute for, while science education is a complement to, job experience in acquiring the skills that help hedge fund managers generate excess returns. Consistent with Chevalier and Ellison’s (1999b) findings for mutual fund managers, we find a positive and significant relation between the academic ability of hedge fund managers (as measured by the average SAT scores of the students in the school that the managers attended) and their fund performance. However, a significant positive relation does not exist between fund performance and other school characteristics such as tuition, school ranking, and student-faculty ratio.

Returns to MBA quality: Pecuniary and non-pecuniary returns to peers, faculty, and institution quality

A large literature has focused on estimating the returns to schooling and has typically done so by incorporating institutional heterogeneity in quality along merely one dimension (such as average SAT scores). Using longitudinal survey data of registrants for the GMAT exam and school level information from other sources, we create, in the context of graduate management education, multiple indices of school quality, and estimate the effect of these quality measures on multiple indicators of career success. In particular, we create quality measures of MBA programs based on: (1) institutional and curricular factors, (2) characteristics of the student body, and (3) characteristics of the faculty. We create aggregate quality indices by combining individual proxies using factor analysis. We also extend the literature by considering the effects of quality on both earnings and non-monetary outcomes, namely attainment of managerial goals relative to initial individual expectations, self-assessed skill gains, and various measures of job satisfaction. We include several unique individual control variables, and further control for unobserved heterogeneity through the use of instrumental variables and individual fixed effects. Results indicate that the quality of peers and schools may matter most for earnings. When individual fixed effects are included, estimates of quality premiums diminish somewhat, though the estimated premium associated with school quality increases, emphasizing the importance of controlling for selection into programs of varying quality. School quality is also an important predictor of several non-pecuniary outcomes.

Returns to MBA Quality: Pecuniary and Non-Pecuniary Returns to Peers, Faculty, and Institution Quality

A large literature has focused on estimating the returns to schooling and has typically done so by incorporating institutional heterogeneity in quality along merely one dimension (such as average SAT scores). Using longitudinal survey data of registrants for the GMAT exam and school level information from other sources, we create, in the context of graduate management education, multiple indices of school quality, and estimate the effect of these quality measures on multiple indicators of career success. In particular, we create quality measures of MBA programs based on: (1) institutional and curricular factors, (2) characteristics of the student body, and (3) characteristics of the faculty. We create aggregate quality indices by combining individual proxies using factor analysis. We also extend the literature by considering the effects of quality on both earnings and non-monetary outcomes: attainment of managerial goals relative to initial individual expectations, self-assessed skill gains, and various measures of job satisfaction. We include several unique individual control variables, and further control for unobserved heterogeneity through the use of instrumental variables and individual fixed effects. Results indicate that the quality of peers matters most for earnings, especially for OLS estimates. When individual fixed effects are included, estimates of quality premiums diminish somewhat, emphasizing the importance of controlling for selection into programs of varying quality. School quality is also an important predictor of several non-pecuniary outcomes.

Graduation Odds and Probabilities among Baccalaureate Colleges and Universities

Graduation outcomes are analyzed at public and private baccalaureate colleges and universities in the United States. The purpose is to determine the effect of institutional characteristics on a binary indicator of college graduation. The effect of the percentage of Pell grant recipients on graduation outcomes is of primary interest, controlling for other covariates. Institutional characteristics have a major effect on college graduation outcomes. Key institutional variables that appear to have the largest effect on graduation outcomes include percent Pell recipients, private college status, and institutional average SAT scores. College size and expenditures per FTE student have a direct but more moderate relationship to graduation outcomes. Implications for baccalaureate education are discussed.

Career Preparation and the Political Science Major: Evidence from Departments

AbstractWe know little about the amount of career preparation offered to students in political science departments. This lack of information is particularly troubling given the state of the current job market and the growth of applied degree programs on university campuses. To address this issue, this article presents the results of a December 2010 survey of 279 political science department chairs that asked questions about the level of career preparation in their respective departments. Based on our empirical findings, we believe that political science departments are not doing enough to address their students' career preparation. Our results demonstrate that most departments rely on voluntary internships and faculty advisers to address career-related issues for political science majors. Only a few departments use required internships, required resumes, specific classes related to career preparation, and social media sites such as Facebook and LinkedIn to support career preparation. We also found substantial differences in career preparation across department type (BA, MA, and PhD) and between public and private universities, urban and rural universities, and universities with different average SAT scores. Our findings should interest faculty and administrators who are concerned with different approaches to career preparation on college campuses.

Foreign Students, Immigrants, Domestic Minorities and Admission to Texas' Selective Flagship Universities Before and After the Ban on Affirmative Action

A growing body of research examines the effects of state affirmative action bans on domestic minority students’ application and admission rates. This study expands previous research, considering how Texas's implementation of a race-neutral percent plan influenced admission opportunities for two understudied groups: foreign students and in-state Black and Hispanic immigrants. Using a census of all applicants to Texas's flagship universities—University of Texas-Austin (UT) and Texas A&M (A&M)—between 1992 and 2002, descriptive analysis and logistic regressions help examine how the passage of the Top Ten Percent Plan influenced selectivity in terms of observed admission rates and counterfactual admission probabilities for students of varying SAT scores and science, technology, engineering, and mathematics (STEM) versus non-STEM-proposed majors for foreign students compared to a variety of in- and out-of-state Black and Hispanic immigrants and domestic minorities. Findings point to salient institutional differences in selectivity for foreign applicants. The surge in UT's foreign applicant numbers post–Top Ten led to significant decreases in admission rates and increases in average SAT score and STEM foreign admits. Post–Top Ten A&M, on the other hand, maintained numbers of foreign admits on par with UT by accepting a significantly larger percentage of less selective foreign applicants.

An Examination of the Impact of Selected Teacher Variables on Student Achievement

An Examination of the Impact of Selected Teacher Variables on Student Achievement

Are smarter groups more cooperative? Evidence from prisoner's dilemma experiments, 1959–2003

Are more intelligent groups better at cooperating? A meta-study of repeated prisoner's dilemma experiments run at numerous universities suggests that students cooperate 5–8% more often for every 100-point increase in the school's average SAT score. This result survives a variety of robustness tests. Axelrod [Axelrod, R., 1984. The Evolution of Cooperation. Basic Books, New York] recommends that the way to create cooperation is to encourage players to be patient and perceptive; experimental evidence suggests that more intelligent groups implicitly follow this advice.

PREDICTING GRADES IN DIFFERENT TYPES OF COLLEGE COURSES

ABSTRACTThe ability of high school grades (high school GPA) and SAT® scores to predict cumulative grades in different types of college courses was evaluated in a sample of 26 colleges. Each college contributed data from three cohorts of entering freshmen, and each cohort was followed for at least four years. Colleges were separated into four levels by average SAT scores. Grade point averages for four categories of courses (English; science, math, and engineering [S/M/E]; social science; and education) were computed, and analyses were run separately for gender within race/ethnicity classifications. Correlations of the combined predictors with course grades over four or more years, corrected for range restriction, ranged from .45 for education courses to 0.64 for S/M/E courses. The SAT increment, that is, the increase in the multiple correlations when SAT scores are added to high school grades, ranged from 0.03 in education courses to 0.08 in S/M/E courses. Because these seemingly small numbers are frequently misinterpreted, an additional analysis showed how the percentage of students succeeding at a high level (cumulative GPA of 3.5 or higher) increases as SAT scores increase for students with similar high school grades. For example, for students with a high school GPA of 3.7 or higher in colleges where the mean combined SAT score is below 1200, only 2 percent of the students at the lowest SAT level (800 or lower combined score) were highly successful in social science courses. At the highest SAT level (1410‐1600), 77 percent were highly successful.