An undergraduate student who completes their program and earns their first degree is a completer. To be counted among their program’s graduates however usually depends on whether they satisfy the criterion for timely completion. We derive a completion status variable to filter student-level records to obtain a bloc of graduates.
This vignette in the MIDFIELD workflow.
- Planning
- Initial processing
-
Blocs
- Ever-enrolled
- FYE proxies
- Starters
-
Graduates
- Ever-enrolled
- Groupings
- Metrics
- Displays
Definitions
- bloc
-
A grouping of student-level data dealt with as a unit, for example, starters, students ever-enrolled, graduates, transfer students, traditional and non-traditional students, migrators, etc.
- degree-seeking
-
Describes students advancing toward a bachelor’s degree, accumulating credit hours in their program with the goal of graduating from their institution.
- completers
-
Bloc of degree-seeking students who complete their baccalaureate programs, earning their first degrees.
- timely completion criterion
-
Completing a program in no more than a specified span of years, in many cases, within 6 years after admission (150% of the “normal” 4-year span), or possibly less for some transfer students.
- completion status
-
A derived midfieldr variable indicating whether a student completes a degree, and if so, whether their completion was timely. Possible values are “timely”, “late”, and “NA”. Late completers are often excluded from a count of “graduates.”
- graduates
-
Bloc of all graduates (timely completers) from a program, without regard to their starting programs.
Method
A bloc of graduates (timely completers) can be determined independent of other blocs.
Filter source student-level records for data sufficiency and degree-seeking.
Determine completion status.
Filter graduates (timely completers).
Filter by degree program.
The next step might be to subset the graduates if necessary to meet the needs of the metric. For example, the graduation rate metric requires graduates to be a subset of starters in the same program. We postpone this step until treating the metrics.
Reminder. midfielddata datasets are for practice, not research.
Load data
Start. If you are writing your own script to follow along, we use these packages in this article:
Load. Practice datasets. View data dictionaries via
?student, ?term, ?degree.
# Load practice data
data(student, term, degree)Loads with midfieldr. Prepared data. View data
dictionaries via ?study_programs,
?baseline_mcid.
Initial processing
Select (optional). Reduce the number of columns. Code reproduced from Getting started.
# Optional. Copy of source files with all variables
source_student <- copy(student)
source_term <- copy(term)
source_degree <- copy(degree)
# Optional. Select variables required by midfieldr functions
student <- select_required(source_student)
term <- select_required(source_term)
degree <- select_required(source_degree)Initialize. Use the term and
student data tables to obtain a data frame of student IDs
meeting the data sufficiency and degree-seeking criteria. Appled to the
practice data, this procedure yields the baseline_mcid data
frame derived in Blocs and included with
midfieldr.
# Working data frame
DT <- copy(baseline_mcid)
add_completion_status()
Add columns to a data frame of student-level records that indicate whether a student completed a degree, and if so, whether their completion was timely.
The input data frame must have columns for ID and the timely
completion term. We use add_timely_term() again, though
alternatively we could have retained the timely_term
variable from previous code chunks.
# Timely term required before completion status
DT <- add_timely_term(DT, term)
# Drop unnecessary columns
DT[, c("term_i", "level_i", "adj_span") := NULL]Arguments.
dframeData frame of student-level records keyed by student ID. Required variables aremcidandtimely_term.midfield_degreeData frame of student-level degree observations keyed by student ID. Default isdegree. Required variables aremcidandterm_degree.
Equivalent usage. The following implementations yield identical results,
# Required arguments in order and explicitly named
x <- add_completion_status(dframe = DT, midfield_degree = degree)
# Required arguments in order, but not named
y <- add_completion_status(DT, degree)
# Using the implicit default for the midfield_degree argument
z <- add_completion_status(DT)
# Demonstrate equivalence
check_equiv_frames(x, y)
#> [1] TRUE
check_equiv_frames(x, z)
#> [1] TRUEOutput. Adds the following columns to the data frame.
term_degreeCharacter. Term in which a program is completed. Encoded YYYYT. NA indicates non-completion.completion_statusCharacter. Completion status: “timely”, indicating degree completion no later than the timely completion term; “late”, indicating completion after the timely completion term; and “NA” indicating non-completion.
# Add completion status and supporting variables
DT <- add_completion_status(DT, degree)
DT
#> mcid timely_term term_degree completion_status
#> <char> <char> <char> <char>
#> 1: MCID3111142689 19941 19913 timely
#> 2: MCID3111142782 19941 19903 timely
#> 3: MCID3111142881 19951 19894 timely
#> ---
#> 76873: MCID3112785480 20123 <NA> <NA>
#> 76874: MCID3112800920 20153 <NA> <NA>
#> 76875: MCID3112870009 20003 <NA> <NA>Similar to the details described in the data sufficiency vignette,
add_completion_status() accepts Alternate
source names and uses Silent
overwriting when existing columns have the same name as one of the
added columns.
Closer look
Examining the records of selected students in detail.
Example 1. The student has a degree term, Spring 1997
(encoded 19963) indicating successful completion. Their
degree term does not exceed their timely completion term, Spring 1998
(encoded 19973), so their completion status is
“timely”.
# Display one student by ID
DT[mcid == "MID25783162"]
#> Empty data.table (0 rows and 4 cols): mcid,timely_term,term_degree,completion_statusExample 2. This student too has a degree term, Spring 2017
(encoded 20163) indicating successful completion. Their
degree term exceeds their timely completion term, Spring 2016 (encoded
20153), so their completion status is “late”.
# Display one student by ID
DT[mcid == "MID26696871"]
#> Empty data.table (0 rows and 4 cols): mcid,timely_term,term_degree,completion_statusExample 3. The student’s degree term is NA, indicating they did not complete their program. Thus completion status is NA as well.
# Display one student by ID
DT[mcid == "MID26697615"]
#> Empty data.table (0 rows and 4 cols): mcid,timely_term,term_degree,completion_statusGraduates
Defining “graduates” as timely completers, we filter to retain records for which completion status is “timely”. This data set comprises all timely graduates in the practice data.
# Graduates
DT <- DT[completion_status == "timely"]
# Filter for unique IDs
DT <- DT[, .(mcid)]
DT <- unique(DT)
DT
#> mcid
#> <char>
#> 1: MCID3111142689
#> 2: MCID3111142782
#> 3: MCID3111142881
#> ---
#> 40438: MCID3112692944
#> 40439: MCID3112694738
#> 40440: MCID3112730841Filter by program
In this case, “filter by program” means to filter by degree
program. Hence we join variables from degree to obtain
the CIP codes of the graduates.
Add variables. Left join with selected variables from
degree.
# Add degree CIP codes and terms
cols_we_want <- degree[, .(mcid, term_degree, cip6)]
DT <- cols_we_want[DT, on = c("mcid")]
DT
#> mcid term_degree cip6
#> <char> <char> <char>
#> 1: MCID3111142689 19913 090401
#> 2: MCID3111142782 19903 260101
#> 3: MCID3111142881 19894 450601
#> ---
#> 40538: MCID3112692944 20153 090101
#> 40539: MCID3112694738 20143 230101
#> 40540: MCID3112730841 20164 040401If our IDs are all graduates, there should be no NA values in the CIP column after joining. We check as follows, where the result is the integer number of rows with an incomplete case, i.e., an NA in either column. The result is zero.
# Verify no NAs in CIP column
sum(!complete.cases(DT))
#> [1] 0Filter. Join program labels and filter to retain the desired programs.
# Filter by program
DT <- study_programs[DT, on = c("cip6"), nomatch = NULL]
DT
#> cip6 program mcid term_degree
#> <char> <char> <char> <char>
#> 1: 141001 EE MCID3111142965 19901
#> 2: 141001 EE MCID3111145102 19893
#> 3: 141001 EE MCID3111146537 19913
#> ---
#> 3264: 141901 ME MCID3112618976 20153
#> 3265: 141001 EE MCID3112619484 20133
#> 3266: 141901 ME MCID3112641535 20143Filter. Filter to retain observations in the first degree term only. Multiple degrees are allowed but only if they occur in the first degree term.
# Filter by first degree term
DT <- DT[, .SD[which.min(term_degree)], by = "mcid"]
DT
#> mcid cip6 program term_degree
#> <char> <char> <char> <char>
#> 1: MCID3111142965 141001 EE 19901
#> 2: MCID3111145102 141001 EE 19893
#> 3: MCID3111146537 141001 EE 19913
#> ---
#> 3262: MCID3112618976 141901 ME 20153
#> 3263: MCID3112619484 141001 EE 20133
#> 3264: MCID3112641535 141901 ME 20143Filter. Drop unnecessary variables and filter for unique observations of ID and degree program label.
Reusable code
Preparation. The data frame of baseline IDs is the intake for this section.
DT <- copy(baseline_mcid)Graduates. A summary code chunk for ready reference.
# Gather graduates, degree CIPs and terms
DT <- add_timely_term(DT, term)
DT <- add_completion_status(DT, degree)
DT <- DT[completion_status == "timely"]
DT <- degree[DT, .(mcid, term_degree, cip6), on = c("mcid")]
# Filter by programs and first degree terms
DT <- study_programs[DT, on = c("cip6"), nomatch = NULL]
DT <- DT[, .SD[which.min(term_degree)], by = "mcid"]
DT[, c("cip6", "term_degree") := NULL]
DT <- unique(DT)