Programs

In the US, instructional programs are encoded by 6-digit numbers curated by the US Department of Education. The US standard encoding format is a two-digit number followed by a period, followed by a four-digit number, for example, 14.0102. MIDFIELD uses the same numerals, but omits the period, i.e., 140102, and stores the variable as a character string.

This article in the MIDFIELD workflow.

  1. Planning
  2. Initial processing
  3. Blocs
  4. Groupings
  5. Metrics
  6. Displays

Definitions

program

US academic field of study. Can be used to indicate a specialty within a field or a collection of fields within a Department, College, or University. Programs are denoted by the Classification of Instructional Programs (CIP), a taxonomy of academic programs curated by the US Department of Education (NCES 2010).

CIP

Classification of Instructional Programs, a taxonomy of academic programs curated by the US Department of Education (NCES 2010). The 2010 codes are included with midfieldr in the data set cip.

cip6

Character variable in the term and degree data tables of program observations. Values are 6-digit CIP codes.

Method

We search the cip data set included with midfieldr using a variety of techniques to obtain the set of 6-digit CIP codes for the programs under study. We assign custom program names to codes or groups of codes.

Taxonomy

Academic programs have three levels of codes and names:

Specialties within a discipline are encoded at the 6-digit level, the discipline itself is represented by one or more 4-digit codes (roughly corresponding to an academic department), and a collection of disciplines are represented by one or more 2-digit codes (roughly corresponding to an academic college).

For example, Geotechnical Engineering (140802) is a specialty in Civil Engineering (1408) which is a department in the college of Engineering (14).

To illustrate the taxonomy in a little more detail, we show in the table the programs assigned to the 2-digit code 41, “Science Technologies, Technicians”. This 2-digit grouping is subdivided into 5 groups at the 4-digit level (codes 4100–4199) which are further subdivided into 9 programs at the 6-digit level (codes 410000–419999).

Table 1: CIP taxonomy
cip2 cip2name cip4 cip4name cip6 cip6name
41 Science Technologies, Technicians 4100 Science Technologies, Technicians, General 410000 Science Technologies, Technicians, General
41  ↓ 4101 Biology Technician, Biotechnology Laboratory Technician 410101 Biology Technician, Biotechnology Laboratory Technician
41  ↓ 4102 Nuclear and Industrial Radiologic Technologies, Technicians 410204 Industrial Radiologic Technology, Technician
41  ↓ 4102  ↓ 410205 Nuclear, Nuclear Power Technology, Technician
41  ↓ 4102  ↓ 410299 Nuclear and Industrial Radiologic Technologies, Technicians, Other
41  ↓ 4103 Physical Science Technologies, Technicians 410301 Chemical Technology, Technician
41  ↓ 4103  ↓ 410303 Chemical Process Technology
41  ↓ 4103  ↓ 410399 Physical Science Technologies, Technicians, Other
41  ↓ 4199 Science Technologies, Technicians, Other 419999 Science Technologies, Technicians, Other

A 2-digit program can include anywhere from four 4-digit programs (e.g., code 24 Liberal Arts and Sciences, General Studies and Humanities) to 238 4-digit programs (e.g., code 51 Health Professions and Related Clinical Sciences).

And 4-digit programs include anywhere from one 6-digit program (e.g., code 4100 above) to 37 6-digit programs (e.g., code 1313 Education).

Unfortunately, some disciplines can comprise more than one 4-digit code. For example, the programs that comprise the broad discipline of Industrial and Systems Engineering encompass four distinct 4-digit codes: 1427 Systems Engineering, 1435 Industrial Engineering, 1436 Manufacturing Engineering, and 1437 Operations Research. Hence the importance of being able to search all CIP data for programs of interest.

Load data

Start.   If you are writing your own script to follow along, we use these packages in this article:

library(midfieldr)
library(data.table)

Loads with midfieldr.   Prepared data, adapted from (NCES 2010). View data dictionary via ?cip.

Inspect the cip data

First glance.

# Loads with midfieldr
cip
#> Index: <cip6>
#>       cip2                                                  cip2name cip4
#>    1:   01 Agriculture, Agricultural Operations and Related Sciences 0100
#>    2:   01 Agriculture, Agricultural Operations and Related Sciences 0101
#>    3:   01 Agriculture, Agricultural Operations and Related Sciences 0101
#>   ---                                                                    
#> 1580:   54                                                   History 5401
#> 1581:   54                                                   History 5401
#> 1582:   99                         NonIPEDS - Undecided, Unspecified 9999
#>                                   cip4name   cip6
#>    1:                 Agriculture, General 010000
#>    2: Agricultural Business and Management 010101
#>    3: Agricultural Business and Management 010102
#>   ---                                            
#> 1580:                              History 540108
#> 1581:                              History 540199
#> 1582:    NonIPEDS - Undecided, Unspecified 999999
#>                                             cip6name
#>    1:                           Agriculture, General
#>    2:  Agricultural Business and Management, General
#>    3: Agribusiness, Agricultural Business Operations
#>   ---                                               
#> 1580:                               Military History
#> 1581:                                 History, Other
#> 1582:              NonIPEDS - Undecided, Unspecified

All variables in cip are character strings, which protects the leading zeros of some CIP codes.

# Names and class of the CIP variables
cip[, lapply(.SD, class)]
#>         cip2  cip2name      cip4  cip4name      cip6  cip6name
#> 1: character character character character character character

The number of unique programs.

# 2-digit level
sort(unique(cip$cip2))
#>  [1] "01" "03" "04" "05" "09" "10" "11" "12" "13" "14" "15" "16" "19" "22" "23"
#> [16] "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37" "38"
#> [31] "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "54"
#> [46] "99"

# 4-digit level
length(unique(cip$cip4))
#> [1] 394

# 6-digit level
length(unique(cip$cip6))
#> [1] 1582

A sample of program names uses a random number generator, so your result will differ from that shown.

# 2-digit name sample
sample(cip[, cip2name], 10)
#>  [1] "Education"                                                   
#>  [2] "Foreign Languages, Literatures and Linguistics"              
#>  [3] "Business, Management, Marketing and Related Support Services"
#>  [4] "Engineering"                                                 
#>  [5] "Family and Consumer Sciences, Human Sciences"                
#>  [6] "Engineering Technology"                                      
#>  [7] "Health Professions and Related Clinical Sciences"            
#>  [8] "Business, Management, Marketing and Related Support Services"
#>  [9] "Health Professions and Related Clinical Sciences"            
#> [10] "Physical Sciences"

# 4-digit name sample
sample(cip[, cip4name], 10)
#>  [1] "Allied Health Diagnostic, Intervention Treatment Professions"          
#>  [2] "Applied Horticulture, Horticultural Business Services"                 
#>  [3] "Ophthalmic and Optometric Support Services and Allied Professions"     
#>  [4] "Specialized Sales, Merchandising and Marketing Operations"             
#>  [5] "Engineering-Related Fields"                                            
#>  [6] "Teacher Education and Professional Development, Specific Subject Areas"
#>  [7] "Allied Health Diagnostic, Intervention Treatment Professions"          
#>  [8] "Leatherworking and Upholstery"                                         
#>  [9] "Health, Medical Preparatory Programs"                                  
#> [10] "Research and Experimental Psychology"

# 6-digit name sample
sample(cip[, cip6name], 10)
#>  [1] "Soil Sciences, Other"                                 
#>  [2] "Health, Medical Physics"                              
#>  [3] "Adult Literacy Tutor, Instructor"                     
#>  [4] "Environmental Design, Architecture"                   
#>  [5] "Advanced, Graduate Dentistry and Oral Sciences, Other"
#>  [6] "Dental Materials (MS, PhD)"                           
#>  [7] "Drafting and Design Technology, Technician, General"  
#>  [8] "Chemical Engineering Technology, Technician"          
#>  [9] "Social Science Teacher Education"                     
#> [10] "Sports and Exercise"

filter_cip()

Subset the cip data frame, retaining rows that match or partially match a vector of character strings.

Arguments.

Equivalent usage.   The following implementations yield identical results,

# First argument named, CIP argument if used must be named
x <- filter_cip(keep_text = c("engineering"), cip = cip)

# First argument unnamed, use default CIP argument
y <- filter_cip("engineering")

# Demonstrate equivalence
same_content(x, y)
#> [1] TRUE

Output.   Subset of cip with rows matching elements of keep_text. Additional subsetting if optional arguments specified. Examples follow.

When search terms cannot be found

If the keep_text argument includes terms that cannot be found in the CIP data frame, the unsuccessful terms are identified in a message and the successful terms produce the usual output.

For example, the following keep_text argument includes three search terms that are not present in the CIP data (“111111”, “^55”, and “Bogus”) and two that are (“050125” and “160501”).

# Unsuccessful terms produce a message
sub_cip <- filter_cip(c("050125", "111111", "160501", "Bogus", "^55"))
#> Can't find these terms: 111111, Bogus, ^55

# But the successful terms are returned
sub_cip
#>    cip2                                            cip2name cip4
#> 1:   05 Area, Ethnic, Cultural and Gender and Group Studies 0501
#> 2:   16      Foreign Languages, Literatures and Linguistics 1605
#>                                       cip4name   cip6
#> 1:                                Area Studies 050125
#> 2: Germanic Languages, Literatures Linguistics 160501
#>                          cip6name
#> 1:                 German Studies
#> 2: German Language and Literature

However, as seen earlier, if none of the search terms are found, an error occurs.

# When none of the search terms are found
filter_cip(c("111111", "Bogus", "^55"))
#> Error: The search result is empty. Possible causes are:
#>  * 'cip' contained no matches to terms in 'keep_text'.
#>  * 'drop_text' eliminated all remaining rows.

CIP data from another source

If you use a CIP data set from another source, it must have the same structure as cip: six character columns named as follows,

# Name and class of variables (columns) in cip
unlist(lapply(cip, FUN = class))
#>        cip2    cip2name        cip4    cip4name        cip6    cip6name 
#> "character" "character" "character" "character" "character" "character"

Assigning program names

Programs in MIDFIELD data sets are encoded by 6-digit CIP codes. As we’ve shown, multiple 6-digit codes can be considered specialties within a larger program with a 4-digit code or even a set of distinct 4-digit codes. Thus the program names in cip are generally inadequate for grouping and summarizing. User-defined program names are nearly always required.

Most studies require deliberate assignment of user-defined program names to CIP codes or groups of CIP codes.

Here we demonstrate the creation of a data frame with all 6-digit CIP codes in a study plus their user-defined names.

By searching cip, we can find that the 4-digit codes for the four engineering programs are: Civil (1408), Electrical (1410), Mechanical (1419), and Industrial/Systems (1427, 1435, 1436, and 1437).

We obtain their 6-digit CIP codes. The 4-digit names are appropriate here. Our task is to create a variable with custom program names.

# Changing the number of rows to print
options(datatable.print.nrows = 15)

# Four engineering programs
four_programs <- filter_cip(c("^1408", "^1410", "^1419", "^1427", "^1435", "^1436", "^1437"))

# Retain the needed columns
four_programs <- four_programs[, .(cip6, cip4name)]
four_programs
#>       cip6                                               cip4name
#>  1: 140801                                      Civil Engineering
#>  2: 140802                                      Civil Engineering
#>  3: 140803                                      Civil Engineering
#>  4: 140804                                      Civil Engineering
#>  5: 140805                                      Civil Engineering
#>  6: 140899                                      Civil Engineering
#>  7: 141001 Electrical, Electronics and Communications Engineering
#>  8: 141003 Electrical, Electronics and Communications Engineering
#>  9: 141004 Electrical, Electronics and Communications Engineering
#> 10: 141099 Electrical, Electronics and Communications Engineering
#> 11: 141901                                 Mechanical Engineering
#> 12: 142701                                    Systems Engineering
#> 13: 143501                                 Industrial Engineering
#> 14: 143601                              Manufacturing Engineering
#> 15: 143701                                    Operations Research

To make the assignments clear, our approach here will be to assign a new program column with NA values, then edit the new column values.

# Assign a new column
four_programs[, program := NA_character_]
four_programs
#>       cip6                                               cip4name program
#>  1: 140801                                      Civil Engineering    <NA>
#>  2: 140802                                      Civil Engineering    <NA>
#>  3: 140803                                      Civil Engineering    <NA>
#>  4: 140804                                      Civil Engineering    <NA>
#>  5: 140805                                      Civil Engineering    <NA>
#>  6: 140899                                      Civil Engineering    <NA>
#>  7: 141001 Electrical, Electronics and Communications Engineering    <NA>
#>  8: 141003 Electrical, Electronics and Communications Engineering    <NA>
#>  9: 141004 Electrical, Electronics and Communications Engineering    <NA>
#> 10: 141099 Electrical, Electronics and Communications Engineering    <NA>
#> 11: 141901                                 Mechanical Engineering    <NA>
#> 12: 142701                                    Systems Engineering    <NA>
#> 13: 143501                                 Industrial Engineering    <NA>
#> 14: 143601                              Manufacturing Engineering    <NA>
#> 15: 143701                                    Operations Research    <NA>

1. Use cip4name %ilike% to recode one value

The %like% function is essentially a wrapper function around the base R grepl() function. The %ilike% version is case-insensitive. You can view the help page by running (the back-ticks facilitate a help search for terms starting with a symbol):

# Run in Console
? `%like%`

In this approach, we search for one distinctive term only. We’re using abbreviations for compact output.

# Recode program using the 4-digit name
four_programs[cip4name %ilike% "electrical", program := "EE"]
four_programs
#>       cip6                                               cip4name program
#>  1: 140801                                      Civil Engineering    <NA>
#>  2: 140802                                      Civil Engineering    <NA>
#>  3: 140803                                      Civil Engineering    <NA>
#>  4: 140804                                      Civil Engineering    <NA>
#>  5: 140805                                      Civil Engineering    <NA>
#>  6: 140899                                      Civil Engineering    <NA>
#>  7: 141001 Electrical, Electronics and Communications Engineering      EE
#>  8: 141003 Electrical, Electronics and Communications Engineering      EE
#>  9: 141004 Electrical, Electronics and Communications Engineering      EE
#> 10: 141099 Electrical, Electronics and Communications Engineering      EE
#> 11: 141901                                 Mechanical Engineering    <NA>
#> 12: 142701                                    Systems Engineering    <NA>
#> 13: 143501                                 Industrial Engineering    <NA>
#> 14: 143601                              Manufacturing Engineering    <NA>
#> 15: 143701                                    Operations Research    <NA>

2. Use cip6 %like% to recode one value

In our second approach, we use the %like% function again, but apply it to a CIP code. Here we use the regular expression ^1408 meaning “starts with 1408.”

# Recode program using the 4-digit code
four_programs[cip6 %like% "^1408", program := "CE"]
four_programs
#>       cip6                                               cip4name program
#>  1: 140801                                      Civil Engineering      CE
#>  2: 140802                                      Civil Engineering      CE
#>  3: 140803                                      Civil Engineering      CE
#>  4: 140804                                      Civil Engineering      CE
#>  5: 140805                                      Civil Engineering      CE
#>  6: 140899                                      Civil Engineering      CE
#>  7: 141001 Electrical, Electronics and Communications Engineering      EE
#>  8: 141003 Electrical, Electronics and Communications Engineering      EE
#>  9: 141004 Electrical, Electronics and Communications Engineering      EE
#> 10: 141099 Electrical, Electronics and Communications Engineering      EE
#> 11: 141901                                 Mechanical Engineering    <NA>
#> 12: 142701                                    Systems Engineering    <NA>
#> 13: 143501                                 Industrial Engineering    <NA>
#> 14: 143601                              Manufacturing Engineering    <NA>
#> 15: 143701                                    Operations Research    <NA>

3. Use program := fcase() to edit all values

In this approach, we use the data.table function fcase(), an implementation of the SQL CASE WHEN statement. The data.table function %chin% is like %in%, but for character vectors.

# Recode all program values
four_programs[, program := fcase(
  cip6 %like% "^1408", "CE",
  cip6 %like% "^1410", "EE",
  cip6 %like% "^1419", "ME",
  cip6 %chin% c("142701", "143501", "143601", "143701"), "ISE"
)]
four_programs <- four_programs[, .(cip6, program)]
four_programs
#>       cip6 program
#>  1: 140801      CE
#>  2: 140802      CE
#>  3: 140803      CE
#>  4: 140804      CE
#>  5: 140805      CE
#>  6: 140899      CE
#>  7: 141001      EE
#>  8: 141003      EE
#>  9: 141004      EE
#> 10: 141099      EE
#> 11: 141901      ME
#> 12: 142701     ISE
#> 13: 143501     ISE
#> 14: 143601     ISE
#> 15: 143701     ISE

Verify prepared data.   study_programs, included with midfieldr, contains the case study information developed above. Here we verify that the two data frames have the same content.

# Demonstrate equivalence
same_content(four_programs, study_programs)
#> [1] TRUE

Reusable code

Preparation.   To provide a working example, we select the four engineering programs of the case study used throughout the articles (Civil, Electrical, Industrial/Systems, and Mechanical Engineering). We assume a prior search of cip yielded the relevant codes used here. Requires editing before reuse with different programs.

# Edit as required for different programs
selected_programs <- filter_cip(c("^1408", "^1410", "^1419", "^1427", "^1435", "^1436", "^1437"))

Programs.   A summary code chunk for ready reference. Requires editing before reuse with different programs.

# Recode program labels. Edit as required.
selected_programs[, program := fcase(
  cip6 %like% "^1408", "CE",
  cip6 %like% "^1410", "EE",
  cip6 %like% "^1419", "ME",
  cip6 %chin% c("142701", "143501", "143601", "143701"), "ISE"
)]
selected_programs <- selected_programs[, .(cip6, program)]

References

NCES. 2010. “IPEDS Classification of Instructional Programs (CIP).” National Center for Education Statistics. https://nces.ed.gov/ipeds/cipcode/.