An R data package containing anonymized student-level records for 98,000 undergraduates—a practice-data sample of the MIDFIELD database.
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.
| Practice data table | Each row is | No. of rows | No. of columns | Memory |
|---|---|---|---|---|
| course | one student per course per term | 3,289,532 | 12 | 324 Mb |
| term | one student per term | 639,915 | 13 | 73 Mb |
| student | one degree-seeking student | 97,555 | 13 | 18 Mb |
| degree | one student per degree earned | 49,543 | 5 | 5 Mb |
These data 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.
Usage
Data tables can be loaded individually or in groups as needed.
# Load multiple tables at once
data(student, course, term, degree, package = "midfielddata")To illustrate the data structure, we examine the student-level data for one specific student.
# One student ID
mcid_we_want <- "MCID3112192438"student
The student data table contains one observation per student. Here we select a subset of columns for a less cluttered printout.
# Select specific rows and columns
rows_we_want <- student$mcid == mcid_we_want
cols_we_want <- c("mcid", "institution", "transfer", "race", "sex", "age_desc")
# All observations for this ID
df <- student[rows_we_want, cols_we_want]
# Cleanup and display
row.names(df) <- NULL
df
#> mcid institution transfer race sex age_desc
#> 1 MCID3112192438 Institution C First-Time in College White Female Under 25course
Course data are structured in block-record form, that is, records associated with a particular ID can span multiple rows—one record per student per course per term.
For the example case, the course records span 47 rows.
# Select specific rows and columns
rows_we_want <- course$mcid == mcid_we_want
cols_we_want <- c("mcid", "term_course", "course", "grade")
# All observations for this ID
df <- course[rows_we_want, cols_we_want]
# Cleanup and display
row.names(df) <- NULL
df
#> mcid term_course course grade
#> 1 MCID3112192438 20051 Key Academic Community Seminar A
#> 2 MCID3112192438 20051 Humans and Other Animals B
#> 3 MCID3112192438 20051 Health and Wellness A
#> 4 MCID3112192438 20051 College Composition A
#> 5 MCID3112192438 20051 Moral and Social Problems A
#> 6 MCID3112192438 20053 Africn-Americn Hist Since 1865 B+
#> 7 MCID3112192438 20053 Individual&Family Development B
#> 8 MCID3112192438 20053 First-Year Spanish I A-
#> 9 MCID3112192438 20061 Chicana/o History and Culture C+
#> 10 MCID3112192438 20061 Basic Concepts of Plant Life B
#> 11 MCID3112192438 20061 Basic Concepts-Plant Life Lab A
#> 12 MCID3112192438 20061 Advertising B-
#> 13 MCID3112192438 20061 Ldrshp in Higher Ed Environ A
#> 14 MCID3112192438 20061 Introductn to Criminal Justice A
#> 15 MCID3112192438 20063 First-Year Spanish II A-
#> 16 MCID3112192438 20063 Contemporary Sociolgicl Theory A
#> 17 MCID3112192438 20071 Introduction to Social Work B+
#> 18 MCID3112192438 20071 Human Behavior Social Environ A
#> 19 MCID3112192438 20071 Practicum-Communication Skills A
#> 20 MCID3112192438 20071 Methds of Sociological Inquiry A-
#> 21 MCID3112192438 20073 Psychology of Differences B+
#> 22 MCID3112192438 20073 Research Methds in Psychology A
#> 23 MCID3112192438 20073 Social Psychology A
#> 24 MCID3112192438 20073 Introductn-Statistical Methods C
#> 25 MCID3112192438 20081 Writing Arguments A
#> 26 MCID3112192438 20081 Organizational Psychology B
#> 27 MCID3112192438 20081 Organizational Psychology Lab A
#> 28 MCID3112192438 20081 History&Systems of Psychology A
#> 29 MCID3112192438 20081 Computer Methods in Sociology A
#> 30 MCID3112192438 20081 Sociology of Disaster A
#> 31 MCID3112192438 20083 Concepts-Human Anat&Physiology A
#> 32 MCID3112192438 20083 Principles of Human Physiology B
#> 33 MCID3112192438 20083 Mind, Brain, and Behavior A
#> 34 MCID3112192438 20083 Sensation and Perception A
#> 35 MCID3112192438 20083 Sensation and Perception Lab A
#> 36 MCID3112192438 20083 Symbolic Interaction A
#> 37 MCID3112192438 20091 Psychology of Human Sexuality A
#> 38 MCID3112192438 20091 Abnormal Psychology A
#> 39 MCID3112192438 20091 Social Stratification A+
#> 40 MCID3112192438 20091 Internship A
#> 41 MCID3112192438 20091 Seminar A
#> 42 MCID3112192438 20093 Introduction to Ethnic Studies A+
#> 43 MCID3112192438 20093 Independent Study A
#> 44 MCID3112192438 20093 Leadership for Greeks A
#> 45 MCID3112192438 20093 Health and the Mind A+
#> 46 MCID3112192438 20093 Social Psychology Laboratory A
#> 47 MCID3112192438 20093 Group Study A+term
Term data are also structured in block-record form—one record per student per term.
For the example case, the term records span 10 rows.
# Select specific rows and columns
rows_we_want <- term$mcid == mcid_we_want
cols_we_want <- c("mcid", "term", "cip6", "level", "standing", "gpa_cumul")
# All observations for this ID
df <- term[rows_we_want, cols_we_want]
# Cleanup and display
row.names(df) <- NULL
df
#> mcid term cip6 level standing gpa_cumul
#> 1 MCID3112192438 20051 451101 01 First-year Good Standing 3.80
#> 2 MCID3112192438 20053 190701 01 First-year Good Standing 3.63
#> 3 MCID3112192438 20061 451101 02 Second-year Good Standing 3.49
#> 4 MCID3112192438 20063 451101 02 Second-year Good Standing 3.54
#> 5 MCID3112192438 20071 451101 03 Third-year Good Standing 3.58
#> 6 MCID3112192438 20073 451101 03 Third-year Good Standing 3.54
#> 7 MCID3112192438 20081 451101 03 Third-year Good Standing 3.58
#> 8 MCID3112192438 20083 451101 04 Fourth-year Good Standing 3.61
#> 9 MCID3112192438 20091 451101 04 Fourth-year Good Standing 3.65
#> 10 MCID3112192438 20093 451101 05 Fifth-year Plus Good Standing 3.68degree
Degree data are also structured in block-record form—one record per student per degree. Multiple degrees can occur in the same term or in different terms.
For the example case, the degree records indicate a double degree in Psychology and Sociology earned in the same term.
# Select specific rows and columns
rows_we_want <- degree$mcid == mcid_we_want
cols_we_want <- c("mcid", "term_degree", "cip6", "degree")
# All observations for this ID
df <- degree[rows_we_want, cols_we_want]
# Cleanup and display
row.names(df) <- NULL
df
#> mcid term_degree cip6 degree
#> 1 MCID3112192438 20093 420101 Bachelor of Science in Psychology
#> 2 MCID3112192438 20093 451101 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 Biological Sciences in 1996 and a second baccalaureate degree in Animal Biology in 1999. In most analyses, only the first baccalaureate degree would be used.
# Select specific rows and columns
rows_we_want <- degree$mcid == "MCID3111315508"
cols_we_want <- c("mcid", "term_degree", "cip6", "degree")
# All observations for this ID
df <- degree[rows_we_want, cols_we_want]
# Cleanup and display
row.names(df) <- NULL
df
#> mcid term_degree cip6 degree
#> 1 MCID3111315508 19961 260101 Bachelor of Science in Biological Sciences
#> 2 MCID3111315508 19994 260701 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, which has two consequences. First, the installation takes some time. Second, we host the package on the MIDFIELD ‘drat’ repository instead of CRAN because 24 Mb exceeds the CRAN package limit (5 Mb).
More information
midfieldr
A companion R package that provides tools and detailed procedures for working with MIDFIELD data.
MIDFIELD
The MIDFIELD database contains, as of October 2022, individual student-level data for 1.7M undergraduates at 19 US institutions from 1987 through 2018. Access to the MIDFIELD research database is currently limited to MIDFIELD partner institutions. A sample of these data is supplied by midfielddata.