midfielddata is an R data package that supplies anonymized student-level records for 98,000 undergraduates from the MIDFIELD database. Provides practice data for the tools and methods of midfieldr.
Introduction
Data at the “student-level” refers to information collected by undergraduate institutions on individual students, including:
- course information, e.g., course name & number, credit hours, and student grades
- term information, e.g., program, academic standing, and grade point average
- student demographic information, e.g., age, sex, and race/ethnicity
- degree information, e.g., institution, program, term, and baccalaureate degree
midfielddata provides anonymized student-level records for 98,000 undergraduates at three US institutions from 1988 through 2018, collected in four data tables keyed by student ID.
| Dataset | Each row is | Students | Rows | Columns | Memory |
|---|---|---|---|---|---|
| course | one student per course | 97,555 | 3,289,532 | 12 | 324.3 MB |
| term | one student per term | 97,555 | 639,915 | 13 | 72.8 MB |
| student | one student | 97,555 | 97,555 | 13 | 17.3 MB |
| degree | one student per degree | 49,543 | 49,665 | 5 | 5.2 MB |
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 are for practice, not research.
Notes on syntax. We use data.table for data manipulation. Some users may prefer base R or dplyr. Each system has its strengths—users are welcome to translate our examples to their preferred syntax.
format(Sys.Date(), "%Y-%m-%d") # Today's date
#> [1] "2024-05-16"
packageVersion("midfielddata") # Student-level records practice data
#> [1] '0.2.1'
packageVersion("data.table") # For data manipulation
#> [1] '1.15.4'Usage
Start. If you are writing your own script to follow along, we use these packages in this vignette:
Load data tables. Data tables can be loaded individually or collectively as needed.
We display the records for one specific student, using their ID to subset each dataset.
# One student ID
id_we_want <- "MCID3112192438"Student. As expected, student yields one row per student.
# Observations for a selected ID
student[mcid == id_we_want]
#> mcid institution transfer hours_transfer race
#> 1: MCID3112192438 Institution C First-Time in College NA White
#> sex age_desc us_citizen home_zip high_school sat_math sat_verbal act_comp
#> 1: Female Under 25 Yes 80521 <NA> 580 390 27Course. For this student, the records span 47 rows, one row per course.
# Observations for a selected ID
course[mcid == id_we_want]
#> mcid institution term_course course
#> 1: MCID3112192438 Institution C 20051 Key Academic Community Seminar
#> 2: MCID3112192438 Institution C 20051 Humans and Other Animals
#> 3: MCID3112192438 Institution C 20051 Health and Wellness
#> ---
#> 45: MCID3112192438 Institution C 20093 Health and the Mind
#> 46: MCID3112192438 Institution C 20093 Social Psychology Laboratory
#> 47: MCID3112192438 Institution C 20093 Group Study
#> abbrev number section type faculty_rank hours_course
#> 1: KA 192 009 <NA> Instructor 3
#> 2: BZCC 101 002 <NA> Assistant Professor 3
#> 3: EXCC 145 004 <NA> Non-Academic Professional 3
#> ---
#> 45: PSY 121 001 Face-to-Face Non-Academic Professional 1
#> 46: PSY 317 L02 Face-to-Face Graduate Assistant 2
#> 47: PSY 496 004 Face-to-Face Instructor 3
#> grade discipline_midfield
#> 1: A Academic Support
#> 2: B Biological and Biomedical Sciences: Botany
#> 3: A Education: Physical and Coaching
#> ---
#> 45: A+ Psychology
#> 46: A Psychology
#> 47: A+ PsychologyTerm. Here, the records span 10 rows, one row per term.
# Observations for a selected ID
term[mcid == id_we_want]
#> mcid institution term cip6 level standing
#> 1: MCID3112192438 Institution C 20051 451101 01 First-year Good Standing
#> 2: MCID3112192438 Institution C 20053 190701 01 First-year Good Standing
#> 3: MCID3112192438 Institution C 20061 451101 02 Second-year Good Standing
#> 4: MCID3112192438 Institution C 20063 451101 02 Second-year Good Standing
#> 5: MCID3112192438 Institution C 20071 451101 03 Third-year Good Standing
#> 6: MCID3112192438 Institution C 20073 451101 03 Third-year Good Standing
#> 7: MCID3112192438 Institution C 20081 451101 03 Third-year Good Standing
#> 8: MCID3112192438 Institution C 20083 451101 04 Fourth-year Good Standing
#> 9: MCID3112192438 Institution C 20091 451101 04 Fourth-year Good Standing
#> 10: MCID3112192438 Institution C 20093 451101 05 Fifth-year Plus Good Standing
#> coop hours_term hours_term_attempt hours_cumul hours_cumul_attempt gpa_term
#> 1: No 15 15 15 15 3.80
#> 2: No 11 11 26 26 3.40
#> 3: No 16 16 42 42 3.25
#> 4: No 8 8 50 50 3.81
#> 5: No 12 12 62 62 3.75
#> 6: No 13 13 75 75 3.38
#> 7: Yes 14 14 89 89 3.79
#> 8: No 16 16 105 105 3.75
#> 9: No 13 13 118 118 4.00
#> 10: No 12 12 130 130 4.00
#> gpa_cumul
#> 1: 3.80
#> 2: 3.63
#> 3: 3.49
#> 4: 3.54
#> 5: 3.58
#> 6: 3.54
#> 7: 3.58
#> 8: 3.61
#> 9: 3.65
#> 10: 3.68Degree. In this example, the records span 2 rows, one row per degree. The degrees were earned in the same term, Spring 2009.
# Observations for a selected ID
degree[mcid == id_we_want]
#> mcid institution term_degree cip6
#> 1: MCID3112192438 Institution C 20093 420101
#> 2: MCID3112192438 Institution C 20093 451101
#> degree
#> 1: Bachelor of Science in Psychology
#> 2: Bachelor of Arts in SociologyNot all students with more than one degree earn them in the same term. For example, the next student earned a degree in 1996 and a second degree in 1999. In most analyses, only the first baccalaureate degree would be used.
# Observations for a different ID
degree[mcid == "MCID3111315508"]
#> mcid institution term_degree cip6
#> 1: MCID3111315508 Institution C 19961 260101
#> 2: MCID3111315508 Institution C 19994 260701
#> degree
#> 1: Bachelor of Science in Biological Sciences
#> 2: Bachelor of Science in Animal BiologyInstallation
Install with:
install.packages("midfielddata",
repos = "https://MIDFIELDR.github.io/drat/",
type = "source"
)The installed size of midfielddata is about 24 Mb, so installation will take longer than that of a conventional CRAN package. Also because of its size, the package is not hosted on CRAN (with its 5 MB size limit)—instead, we host it on the MIDFIELDR drat repository as indicated above.
Link to installation instructions for midfieldr below.
More information
midfieldr
A companion R package that provides tools and methods for studying undergraduate student-level records from the MIDFIELD database.
MIDFIELD
A database of anonymized student-level records for approximately 2.4M undergraduates at 21 US institutions from 1987-2022. Access to this database requires a confidentiality agreement and Institutional Review Board (IRB) approval for human subjects research. For a detailed description of the database, see (Ohland & Long, 2016).
Acknowledgments
This work was supported by the US National Science Foundation through grant numbers 1545667 and 2142087.