Database is provided by ASL Marketing and covers the United States of America. With ASL Marketing Reaching GenZ has never been easier. Current high school student data customized by: Class year Date of Birth Gender GPA Geo Household Income Ethnicity Hobbies College-bound Interests College Intent Email

Dataset

This dataset was created by Mohammad Al-Azawi

Released under Database: Open Database, Contents: Database Contents

Contents

Verbal and Quantitative Reasoning GRE scores and percentiles were collected by querying the student database for the appropriate information. Any student records that were missing data such as GRE scores or grade point average were removed from the study before the data were analyzed. The GRE Scores of entering doctoral students from 2007-2012 were collected and analyzed. A total of 528 student records were reviewed. Ninety-six records were removed from the data because of a lack of GRE scores. Thirty-nine of these records belonged to MD/PhD applicants who were not required to take the GRE to be reviewed for admission. Fifty-seven more records were removed because they did not have an admissions committee score in the database. After 2011, the GRE’s scoring system was changed from a scale of 200-800 points per section to 130-170 points per section. As a result, 12 more records were removed because their scores were representative of the new scoring system and therefore were not able to be compared to the older scores based on raw score. After removal of these 96 records from our analyses, a total of 420 student records remained which included students that were currently enrolled, left the doctoral program without a degree, or left the doctoral program with an MS degree. To maintain consistency in the participants, we removed 100 additional records so that our analyses only considered students that had graduated with a doctoral degree. In addition, thirty-nine admissions scores were identified as outliers by statistical analysis software and removed for a final data set of 286 (see Outliers below). Outliers We used the automated ROUT method included in the PRISM software to test the data for the presence of outliers which could skew our data. The false discovery rate for outlier detection (Q) was set to 1%. After removing the 96 students without a GRE score, 432 students were reviewed for the presence of outliers. ROUT detected 39 outliers that were removed before statistical analysis was performed. Sample See detailed description in the Participants section. Linear regression analysis was used to examine potential trends between GRE scores, GRE percentiles, normalized admissions scores or GPA and outcomes between selected student groups. The D’Agostino & Pearson omnibus and Shapiro-Wilk normality tests were used to test for normality regarding outcomes in the sample. The Pearson correlation coefficient was calculated to determine the relationship between GRE scores, GRE percentiles, admissions scores or GPA (undergraduate and graduate) and time to degree. Candidacy exam results were divided into students who either passed or failed the exam. A Mann-Whitney test was then used to test for statistically significant differences between mean GRE scores, percentiles, and undergraduate GPA and candidacy exam results. Other variables were also observed such as gender, race, ethnicity, and citizenship status within the samples. Predictive Metrics. The input variables used in this study were GPA and scores and percentiles of applicants on both the Quantitative and Verbal Reasoning GRE sections. GRE scores and percentiles were examined to normalize variances that could occur between tests. Performance Metrics. The output variables used in the statistical analyses of each data set were either the amount of time it took for each student to earn their doctoral degree, or the student’s candidacy examination result.

A little paragraph from one real dataset, with a few little changes to protect students' private information. Permissions are given.

Goals

You are going to help teachers with only the data: 1. Prediction: To tell what makes a brilliant student who can apply for a graduate school, whether abroad or not. 2. Application: To help those who fails to apply for a graduate school with advice in job searching.

Tips

1. Educational data may have subtle structures, hierarchies and heterogeneity are probably involved. Simple regressions can hardly make any difference. Also, you should keep an eye on the collinearity in some indicators collected by teachers who have already forgot statistics.
2. Not all students are free to choose to apply for a graduate school, but some were born with privileges.
3. Some of the students are trying (or planning to try) to apply for a graduate school for years, you should be responsible to give advice accurately under their circumstances

About the Data

Some of the original structure are deleted or censored. For those are left: Basic data like: - ID - class: categorical, initially students were divided into 2 classes, yet teachers suspect that of different classes students may performance significant differently. - gender - race: categorical and censored - GPA: real numbers, float

Some teachers assume that scores of math curriculums can represent one's likelihood perfectly: - Algebra: real numbers, Advanced Algebra - ......

Some assume that background of students can affect their choices and likelihood significantly, which are all censored as: - from1: students' home locations - from2: a probably bad indicator for preference on mathematics - from 3: how did students apply for this university (undergraduate) - from4: a probably bad indicator for family background. 0 with more wealth, 4 with more poverty

The final indicator y: - 0, one fails to apply for the graduate school, who may apply again or search jobs in the future - 1, success, inland - 2, success, abroad

Objectives: To investigate the relation between personality traits and academic performance of medical students and evaluate if correlations between personality traits and examination scores are affected by gender or year of study.

Methods: A sample of 307 medical students, at the Hashemite University, Jordan completed an online questionnaire to identify Big Five Model personality traits. Grade Point Average (GPA) scores were retrieved from the University database and data analysed using SPSS 16.0.

Results: Correlation between personality traits and GPA score was investigated using Pearson coefficient. Only Conscientiousness had a significant positive correlation (r = .231, p < .001). Two-way ANOVA testing investigated the effect of gender, personality trait, and the interaction between them on GPA. Only Conscientiousness had a statistically significant effect on GPA (P = .001) and there was no significant effect of gender or its interaction with personality traits on GPA. Investigating the interaction between year of study and personality traits, there was only a statistically significant interaction effect between year of study and Openness (F (1, n=307) = 10.297, P =.001) on GPA.

Since 2002, AmeriList has been the nation’s premier provider of student-marketing data, offering a broad suite of ethically compiled, highly accurate, and deliverable mailing, email, and telemarketing lists targeting families, high-school students, college-bound freshmen, enrolled college students, and adult learners for continuing education

Comprehensive Dataset Overviews • Parents of Students / Households with Children – Reach parents alongside teens and pre-teens, ideal for programs like prom services, tutoring, summer camps, and private school admissions • High-School Students – Access ~5 million U.S. students and their parents, with robust selects including GPA, class rank, SAT/GED scores, arts/athletic interests, intended college, school year, and more • College-Bound Students Database – Tap into over 3–4 million incoming freshmen making major purchases (electronics, school supplies, dorm essentials, apparel), with segmentation by college attending, GPA, sports interest, geography, income, credit usage, and more • College Students Mailing List – Access ~24.4 million enrolled college students, segmented by class year, gender, field of study, hobbies, buying habits, and more for highly targeted outreach • Adult Learners / Continuing Education – Reach over 30 million individuals who have completed some college or are interested in continuing education, vocational or trade programs

How the Data Is Compiled & Maintained AmeriList uses a rigorous, ethical data-collection methodology, aggregating information from direct responses, internet and telephone surveys, public records, club memberships, purchase history, self-reported data, and proprietary sources.

All lists undergo monthly updates and data hygiene processes, including: - CASS-certification for address standardization - DPV (Delivery Point Validation) removal of unverifiable addresses - NCOALink, LACSLink, and Address Change processing for forwarding accuracy - Do-Not-Call, DMA suppression, in-house suppression for compliance - Deceased-record scrubbing via internal and third-party checks

Recommended Uses • Parents & High-School Campaigns – Promote private schooling, test prep, student loans, scholarships, events like prom or summer camps, trade schools, teen retail, or electronics • College-Bound Freshmen – Ideal for marketing student loans, scholarships, credit cards, dorm suppliers, school supplies, electronics, study aids, and apparel • Enrolled College Students – Excellent for textbook vendors, academic supplies, coupons, food delivery, financial aid, campus services, tech products, and lifestyle brands • Adult Learners / Continuing Ed – Perfect for vocational schools, certificate programs, online learning, re-enrollment, or career enhancement marketing

With data that is fresh, accurate, and ethically sourced, AmeriList gives you the tools to launch smarter, more impactful campaigns across mail, email, and telemarketing channels. Backed by two decades of expertise, proven results, and unmatched audience coverage, AmeriList is the trusted partner for organizations that want to connect with the student market and drive measurable growth.

In 1999, the Bill and Melinda Gates foundation started the Gates Millennium Scholars Program (GMS), a 20-year initiative which intends to expand access to higher education for high achieving, low-income minority students. In addition to its academic objectives, GMS also has the goal of creating future leaders in minority groups. The program is administered by the United Negro College Fund (UNCF). Awardees can receive the scholarship for up to 5 years as an undergraduate and 4 years as a graduate student. The scholarship is renewable through graduate school in math, science, engineering, library science, and education. To be eligible for GMS, students had to meet several qualifications. They must: (1) be of African American, American Indian/Alaska Native, Asian American, Hispanic/Latino, or Pacific Islander background; (2) be full-time students entering college or university; (3) have a GPA of at least 3.3 on a 4.0 scale; (4) be eligible for Pell Grants; and (5) be leaders in community service, extracurricular, or other activities. These data include selected variables from administrative data as collected by UNCF. There are 5 major data sources from which these data were compiled, which include:

United Negro College Fund (UNCF) Administrative Databases;; National Student Clearing House (NSC);; Higher Education Directory (HED);; Free Application for Federal Student Aid (FAFSA); and; Institutional Student Information Records (ISIRs).;

These data were collected to represent the aggregation of administrative data utilized throughout the GMS cohort-level data. The data was structured in a way to allow analysis and utility beyond the administrative data's original purpose. The initial release includes only records for GMS scholarship recipients and one higher education institution per person. Subsequent releases will include information for non-recipient finalists and the full spectrum of institutions attended.