Prepare FYE data for multiple imputation — prep_fye

Constructs a data frame of student-level records of First-Year Engineering (FYE) programs and conditions the data for later use as an input to the mice R package for multiple imputation. Sets up three variables as predictors (institution, race/ethnicity, and sex) and one variable to be estimated (program CIP code).

Usage

prep_fye_mice(
  midfield_student = student,
  midfield_term = term,
  ...,
  fye_codes = NULL
)

Arguments

midfield_student: MIDFIELD student data table or equivalent with required variables mcid, race, and sex.
midfield_term: MIDFIELD term data table or equivalent with required variables mcid, institution, term, and cip6.
...: Not used for passing values; forces subsequent arguments to be referable only by name.
fye_codes: Optional character vector of 6-digit CIP codes to identify FYE programs, default "140102". Codes must be 6-digit strings of numbers; regular expressions are prohibited. Non-engineering codes—those that do not start with 14—produce an error.

Value

A data frame in data.table format conditioned for later use as an input to the mice R package for multiple imputation. The data frame comprises one row for every FYE student, first-term and migrator. Grouping structures are not preserved. The columns returned are:

mcid: Character, anonymized student identifier. Returned as-is.
race: Factor, race/ethnicity as self-reported by the student. An imputation predictor variable.
sex: Factor, sex as self-reported by the student. An imputation predictor variable.
institution: Factor, anonymized institution name. An imputation predictor variable.
proxy: Factor, 6-digit CIP code of a student's known, post-FYE engineering program or NA representing missing values to be imputed.

Details

At some US institutions, engineering students are required to complete a First-Year Engineering (FYE) program as a prerequisite for declaring an engineering major. Administratively, degree-granting engineering programs such as Electrical Engineering or Mechanical Engineering treat their incoming post-FYE students as their "starting" cohorts. However, when computing a metric that requires a count of starters—graduation rate, for example—FYE records must be treated with special care to avoid a miscount.

To illustrate the potential for miscounting starters, suppose we wish to calculate a Mechanical Engineering (ME) graduation rate. Students starting in ME constitute the starting pool and the fraction of that pool graduating in ME is the graduation rate. At FYE institutions, an ME program would typically define their starting pool as the post-FYE cohort entering their program. This may be the best information available, but it invariably undercounts starters by failing to account for FYE students who do not transition (post-FYE) to degree-granting engineering programs—students who may have left the institution or switched to non-engineering majors. In either case, in the absence of the FYE requirement, some of these students would have been ME starters. By neglecting these students, the count of ME starters is artificially low resulting in an ME graduation rate that is artificially high. The same is true for every degree-granting engineering discipline in an FYE institution.

Therefore, to avoid miscounting starters at FYE institutions, we have to estimate an "FYE proxy", that is, the 6-digit CIP codes of the degree-granting engineering programs that FYE students would have declared had they not been required to enroll in FYE. The purpose of `prep_fye_mice()“ is to prepare the data for making that estimation.

After running prep_fye_mice() but before running mice(), one can edit variables or add variables to create a custom set of predictors. The mice package expects all predictors and the proxy variables to be factors. Do not delete the institution variable because it ensures that a student's imputed program is available at their institution.

In addition, ensure that the only missing values are in the proxy column. Other variables are expected to be complete (no NA values). A value of "unknown" in a predictor column, e.g., race/ethnicity or sex, is an acceptable value, not missing data. Observations with missing or unknown values in the ID or institution columns (if any) should be removed.

Method

The function extracts all terms for all FYE students, including those who migrate to enter Engineering after their first term, and identifies the first post-FYE program in which they enroll, if any. This treatment yields two possible outcomes for values returned in the proxy column:

The student completes FYE and enrolls in an engineering major. For this outcome, we know that at the student's first opportunity, they enrolled in an engineering program of their choosing. The CIP code of that program is returned as the student's FYE proxy.
The student does not enroll post-FYE in an engineering major. Such students have no further records in the database or switched from Engineering to another program. For this outcome, the data provide no information regarding what engineering program the student would have declared originally had the institution not required them to enroll in FYE. For these students a proxy value of NA is returned. These are the data treated as missing values to be imputed by mice().

In cases where students enter FYE, change programs, and re-enter FYE, only the first group of FYE terms is considered. Any programs before FYE are ignored.

The resulting data frame is ready for use as input for the mice package, with all variables except mcid returned as factors.

Examples

# Using toy data
prep_fye_mice(toy_student, toy_term)
#>            mcid   race    sex   institution  proxy
#>          <char> <fctr> <fctr>        <fctr> <fctr>
#>  1: MID26060301  Asian Female Institution C   <NA>
#>  2: MID25995980 Latine Female Institution C   <NA>
#>  3: MID25997636 Latine Female Institution C   <NA>
#>  4: MID26086310 Latine Female Institution C   <NA>
#>  5: MID26000057  White Female Institution C   <NA>
#>  6: MID26614720  Asian   Male Institution J   <NA>
#>  7: MID26593796  White   Male Institution J   <NA>
#>  8: MID25848589  White   Male Institution M 143501
#>  9: MID25846316  White   Male Institution M 143501
#> 10: MID25847220  White   Male Institution M 143501
#> 11: MID25828870  White   Male Institution M 149999

# Other columns, if any, are dropped
colnames(toy_student)
#> [1] "mcid"        "institution" "race"        "sex"        
colnames(prep_fye_mice(toy_student, toy_term))
#> [1] "mcid"        "race"        "sex"         "institution" "proxy"      

# Optional argument permits multiple CIP codes for FYE
prep_fye_mice(midfield_student = toy_student, 
              midfield_term =toy_term, 
              fye_codes = c("140101", "140102"))
#>            mcid   race    sex   institution  proxy
#>          <char> <fctr> <fctr>        <fctr> <fctr>
#>  1: MID25977316  White   Male Institution B   <NA>
#>  2: MID26060301  Asian Female Institution C   <NA>
#>  3: MID25995980 Latine Female Institution C   <NA>
#>  4: MID25997636 Latine Female Institution C   <NA>
#>  5: MID26086310 Latine Female Institution C   <NA>
#>  6: MID26000057  White Female Institution C   <NA>
#>  7: MID26614720  Asian   Male Institution J   <NA>
#>  8: MID26593796  White   Male Institution J   <NA>
#>  9: MID25848589  White   Male Institution M 143501
#> 10: MID25846316  White   Male Institution M 143501
#> 11: MID25847220  White   Male Institution M 143501
#> 12: MID25828870  White   Male Institution M 149999