Student-level demographic data

Student-level demographic information for approximately 98,000 degree-seeking undergraduate students, keyed by student ID. Data at the "student-level" refers to information collected by undergraduate institutions about individual students, for example, age, sex, and race/ethnicity at matriculation.

Usage

data(student)

Format

A data.frame and data.table with 13 variables and 97,555 observations of unique students occupying 18 MB of memory:

mcid

Character, anonymized student identifier, e.g., MCID3111142225.

institution

Character, anonymized institution name, e.g., Institution B.

transfer

Character, transfer status, possible values are First-Time in College, First-Time Transfer.

hours_transfer

Numeric, number of credit hours transferred (or NA).

race

Character, race/ethnicity, e.g., Asian, Black, International, Latine, etc.

sex

Character, sex, possible values are Female, Male, Unknown.

age_desc

Character, age group, possible values are 25 and Older, Under 25.

us_citizen

Character, US citizenship, possible values are No, Yes.

home_zip

Character, home ZIP code (or NA), e.g., 02056, 20170, 51301, 80129, etc.

high_school

Character, code for the last high school attended before admission (or NA), e.g., 060075, 210512, 431800, 502195, etc.

sat_math

Numeric, SAT mathematics test score (or NA).

sat_verbal

Numeric, SAT reading test score (or NA).

act_comp

Numeric, ACT composite test score (or NA).

Source

2022 MIDFIELD database

Details

Student data are structured in row-record form, that is, information associated with a particular ID occupies a single row---one record per student.

The data in midfielddata are a proportionate stratified sample of the MIDFIELD database, but are not suitable for drawing inferences about program attributes or student experiences---midfielddata provides practice data, not research data.

See also

Package midfieldr for tools and methods for working with MIDFIELD data in R.

Other datasets: course, degree, term

Examples

if (FALSE) {

# Load data
data(student)

# Select specific rows and columns
rows_we_want <- student$mcid == MCID3112192438
cols_we_want <- c(mcid, institution, transfer, race, sex, age_desc)

# View observations for this ID 
student[rows_we_want, cols_we_want]

}