How These Analyses Work

This page documents exactly how each analysis is computed, derived directly from the source code. Use it to interpret results correctly and spot anomalies.

1. Building a Student Group

A student population is built in three stages: (1) identify candidates — any student who ever appeared in the focal major; (2) classify outcomes — determine what happened to each candidate relative to the major; (3) filter and label — include the desired outcome groups and assign labels. The result (a tibble with student_id, population_label, outcome, entry_pathway, origin, entry_method, entry_status, relevant_until) is passed to every downstream analysis.

Outcomes

• ongoing — still declared in the focal major in the most recent data term.
• graduated — received a degree in the focal major in their last focal term.
• switched_out — left the focal major but remained at UNM. Detected two ways: (1) a formal declaration of another major after their last focal term in cedar_programs; (2) any enrollment record in cedar_students after their last focal term, even without a re-declaration.
• stopped_out — all declared candidates not accounted for by ongoing, graduated, or switched_out. No UNM enrollment or major record after their last focal term.
• chose_elsewhere — appeared only as a pre-major; never declared the focal major, but did declare a different major afterward.
• left_undeclared — appeared only as a pre-major; never declared any major and has no later major record in the available data. Displayed as Stopped before declaring.

Entry pathway (<code>entry_pathway</code>)

How the student arrived at the focal major — computed by get_entry_pathways() :

• direct — first major at UNM was the focal major (no prior declared major or pre-major).
• switched_in — had a non-focal declared major before declaring the focal major.
• pre_major — appeared as a focal pre-major before (or instead of) declaring.

Entry classification columns

• entry_method (classify_entry_method()) — first_program: no prior major record of any kind before this unit; switched_in: had at least one prior major record; unclear: first unit record is at the earliest available term, so prior history is unobservable.
• entry_status (classify_entry_status()) — whether the student’s first record in this unit was as a pre_major or a declared major.

Enrollment window (<code>relevant_until</code>)

Each non-ongoing population student carries a relevant_until term: their last_declared_term (last term with a declared, non-pre-major focal record). Course enrollments after that term are excluded from all analyses. A student who was History for 2 terms, then switched to Business for 8 terms, contributes only the 2 History terms to the analysis. Ongoing students have relevant_until = NA (no restriction).

Worked example — dept = HIST, default scope (declared + pre-major)

student_id	program_name	program_type	is_pre_major	outcome	result
S001	History	Major	FALSE	ongoing	✓ included
S002	History	Second Major	FALSE	ongoing	✓ included (Second Major counts)
S003	History	Major	TRUE	chose_elsewhere / left_undeclared	— excluded unless pre-major scope is included
S004	English	Major	FALSE	—	— excluded (different dept)
S005	History	Minor	FALSE	—	— excluded (Minor)

What programs belong to a department? The selector uses cedar_programs$dept_code, which is assigned during transformation from program/catalog and subject lookup tables with a final identity fallback when no explicit mapping exists. Mapping issues are surfaced under Admin → Data & Usage → Mappings; questionable fallbacks also appear in the Pathways scope bar.

2. Roadblocks — DFW as a Predictor of Leaving

For each course, compares the fraction of group students who did not return the following term among those who got a DFW grade versus those who passed. The gap between those rates is the key signal.

Step 1: Classify outcomes (per student per course per term)

registration_status_code	final_grade	classified as
DG / DW (late drop)	any	dfw — this is the W in DFW; most withdrawals post as late-drop status rows
RE / RS / RR	D, D+, D–, F, W, RD, RF	dfw
RE / RS / RR	A–C, CR, P, S, RA–RC, RCR	pass
RE / RS / RR	I, AUD, NR, or other	excluded — ungraded, no signal
DR (early drop)	any	excluded — a drop before the deadline posts no grade; registration churn, not an academic outcome
WL / other	any	excluded

Step 2: Determine whether each student returned the following term

For each student in each term, we check whether they appear in cedar_students in the next fall or spring. Summer is not counted — skipping summer is normal and not a stop-out.

Graduate correction

Students who earned a degree in term T are not counted as stopped out for that term, even though they don’t appear in term T+1. Without this correction, every graduate who finished their program would be misclassified as a stop-out. The correction uses cedar_degrees$term to identify graduation terms.

Step 3: Compute rates and gap

student_id	BIOL 2310 outcome	returned next term?
S001	pass (A)	yes
S002	pass (B)	no
S003	dfw (F)	yes
S004	dfw (W)	no
S005	dfw (W)	no

pass_stopout_rate = 1/2 = 0.500 (S002 didn’t return)
dfw_stopout_rate = 2/3 = 0.667 (S004, S005 didn’t return)
stopout_gap = 0.667 − 0.500 = 0.167
p_value: chi-squared test on the 2×2 contingency table (outcome × returned). Skipped if either group has fewer than 5 students — result is NA.

3. Course Timing — When Students Take Each Course

Computes where population students took each course along the selected x-axis. The default x-axis is total-credit bands, so transfer and continuing students are compared by credits earned rather than by calendar year or first observed term.

X-axis choices

• Total credits: 0–30, 31–60, 61–90, 91–120, 121+ credits earned, including transfer credit.
• UNM credits: the same bands, using institutional credits attempted only.
• Relative term: 1st, 2nd, 3rd… observed enrolled term for each student.
• Classification: Freshman, Sophomore, Junior, or Senior at the time of enrollment.

How relative term is defined

Relative term 1 is the first term in which the student has a registered course record in cedar_students. It is not necessarily their first semester at UNM or their first semester in the program. It is row_number() over distinct enrolled terms, sorted by UNM term code.

Skipped semesters

The counter only increments for terms with actual registered enrollment. Gaps are invisible. A student enrolled in Fall, absent in Spring, enrolled in Fall has relative terms 1 and 2 — not 1 and 3. There is no concept of “missed term 2” in this model.

Summer terms

By default, summer does not advance the counter. Summer courses are pinned to the relative term of the immediately preceding fall or spring. A student taking a summer course between their 2nd and 3rd fall/spring semesters has those summer courses recorded as relative term 2. The current Pathways UI does not expose an Include summer toggle, so the app always uses the default non-summer-advancing behavior.

Denominator

Each cell is a percentage: students who took the course at that x-axis position divided by students observed at that position. For relative term, that means students whose enrollment record reached that term. For credit bands and classification, it means students with any enrollment in that band or class.

4. Course Pairs — Common Sequences

Finds ordered pairs (A → B) where group students took Course A in one relative term and Course B in a later term, within a configurable term gap.

Exact computation

1. Assign relative terms, then self-join enrolled records on student_id where relative_term_B > relative_term_A and relative_term_B − relative_term_A ≤ max_term_gap and course_A ≠ course_B.
2. Count distinct students per (course_A, course_B) pair.
3. pct_a_to_b = students who took both ÷ students who took A.

5. Course to Major — Course Associations Before/Into the Unit

This subtab has two related but different scopes. The Course + Instructor Associations table is department/course scoped: the selected Pathways population is used to resolve the focal department and subject prefixes, but the table itself is computed from all enrolled students in those focal courses. The Courses Before Major Entry heatmaps are population scoped: they use only students in the selected population and look backward from each student's first focal program record.

Course + Instructor Associations

1. Resolve focal subject prefixes from the selected population's department codes. Department codes are not assumed to be course prefixes; they are translated through cedar_lookups$subject_lookup.
2. Filter cedar_students to registered enrollments in those focal subjects. The level, campus, Gen Ed only, and date-range controls apply here.
3. For each enrollment, find the student's first cedar_programs record in the focal department (program_type %in% c('Major', 'Second Major')). A student is eligible for that course term only if they had no department major or pre-major before or during the enrollment term.
4. Later entered means the first focal-department program record appears after the course term. This includes students whose first focal record is a pre-major or a declared major.
5. Rows are grouped by subject_course + course_title + instructor_name, so topics courses with the same number but different titles stay separate. Distinct students are counted within each group; totals across visible groups are group memberships, not unique headcount, because one student can take multiple focal courses.

Courses Before Major Entry heatmaps

1. Use population$first_unit_term as each student's entry anchor. This is scoped to the focal program and avoids accidentally anchoring switchers to their prior non-focal major.
2. Build lag terms by walking backward through the sorted non-summer term sequence. T-1 is the prior regular term, T-2 two regular terms back, and so on.
3. Count population students who took each course at each lag. Split courses into focal subjects versus other departments.
4. Compute pct_of_majors as students in that course-lag cell divided by the selected population size, and pct_converted as those students divided by all students enrolled in the same course/term slots. The heatmap currently visualizes pct_of_majors.
5. The heatmaps use Min N and Heatmap lag only. Campus, level, Gen Ed only, and date-range controls are association-table filters and do not affect the heatmaps.

6. Major Changes

Detects when a student’s primary declared major changed from one observed primary-major record to the next, then summarizes those transitions for the selected student group.

Banner/MyReports fields used by this tab

Major Changes uses normalized CEDAR tables, but the values come from specific Banner/MyReports fields. These derivations matter for interpretation:

• cedar_students$term comes from class-list Academic Period Code. The movement cards use each student's minimum observed cedar_students$term as their first observed class-list enrollment. This is not a formal Banner matriculation/start-term field; it is the first term CEDAR sees that student in a class-list enrollment row.
• cedar_student_term_credits is derived by CEDAR from cedar_students. It stores observed UNM credits by student_id and term, including cumulative attempted credits and cumulative completed credits from credit-earning grades. Movement card credit medians use this class-list-derived table, not Academic Studies UNM credit totals.
• cedar_programs$term comes from academic-studies Academic Period, converted to a CEDAR term code.
• cedar_programs$program_name, program_type, major_code, and program_code come from the academic-studies program columns such as Major, Second Major, Major Code, Second Major Code, and Program Code. CEDAR expands those wide Banner columns into one row per student-program-term.
• cedar_programs$is_pre_major is computed by CEDAR from program naming/code patterns. A pre-major to full-major progression inside the same program is treated as a status progression, not a major-change event.
• cedar_programs$student_population comes from academic-studies Student Population and is used to label students as Always UNM vs Transfer.
• Credit positions do not come from the Banner cumulative fields. Institution Credits Attempted and Overall Credits Attempted are reported as of the moment the data was pulled and stamped onto every historical row, so a student's freshman record can read their final credit total. Measured across a full historical re-pull, they change from term to term only 16% of the time, and they overstate the position at a student's first term by a median of 84 credits. Every credit figure on this tab is instead built by build_credit_timeline() from observed class-list credits, plus a transfer block recovered as the gap between the two cumulative fields — a difference taken at one instant, so it survives the freeze. Transfer credit is attributed to the student's start, and students whose UNM history predates the data window are flagged rather than averaged in.

Step 1: Detect change events

Source: detect_major_changes() in R/cones/major-changes.R.

1. Filter cedar_programs to program_type == “Major” rows for the population students only.
2. Sort by student_id, term. Use lag() to get each student’s program in the prior term (prev_major) and their prior academic level (prev_level).
3. Flag a change when program_name != prev_major AND (is.na(prev_level) | student_level == prev_level). The level check excludes transitions between undergraduate and graduate programs — a History BA student enrolling in Law School is not a “major change” in the undergraduate sense. First records are not change events because prev_major is missing.
4. Each flagged row becomes one change event with: student_id, change_term, from_major, to_major, and the student’s credit position at the change — unm_credits_before_change (UNM only) and total_credits_before_change (UNM + transfer). Both are attempted hours, so they aren’t deflated by W/F grades from the abandoned major.
5. Which term’s position? A major change typically posts to Banner the term after the student actually switches, so the figure reported is the position at the end of prev_term — how far along they were when they switched, not after the paperwork caught up.
6. Where the credits come from. Not from the Banner cumulative columns: those are stamped as of the data pull onto every historical row, move across a student’s own terms only 16% of the time, and overstate a first-term position by a median of 84 credits. build_credit_timeline() builds the position from observed class-list credits plus a transfer block recovered as the gap between the two cumulative columns. Events whose student has UNM history predating the data window are flagged credits_position_valid = FALSE and excluded from credit averages — avg_credits_before_major() reports how many per major in n_excluded_position.

Worked example — History student program history

student_id	term	program_name	student_level	prev_major	result
S001	202310	Psychology	Undergraduate	(none)	— first term, no change
S001	202380	Psychology	Undergraduate	Psychology	— same major
S001	202410	History	Undergraduate	Psychology	✓ change event: Psych → History
S001	202480	History	Undergraduate	History	— same major
S001	202710	Juris Doctor	Graduate/GASM	History	— level changed (UG→GR), excluded

Step 2: Derive focal majors

Source: mc_data reactive in R/modules/pathways.R.

Focal majors are the majors that define the selected student group — not all majors ever held by group members. A History population student who also declared Political Science should not make PolSci a focal major.

• Dept mode (e.g., HIST): all majors where dept_code == “HIST” and program_type %in% c(“Major”, “Second Major”) in cedar_programs.
• Specific majors mode: exactly the majors the user selected in the population filters.
• Preset mode: the program_names list from the population opt.

Step 3: Filter to focal changes

From the full set of change events, keep only rows where from_major %in% focal_programs OR to_major %in% focal_programs. This means a History population sees:

• Psychology → History (arriving to History) ✓
• History → Political Science (leaving History) ✓
• Political Science → Law (made by a History student, but neither side is History) ✗ excluded

Step 4: Build summary outputs

Source: mc_data() in R/modules/pathways.R, using change events from detect_major_changes().

• Inflow / Outflow table: count change events by to_major (arrivals) and from_major (departures) in focal changes, then filter to rows where the major is in focal_programs. Net = arrivals − departures.
• Common Pathways table: group focal changes by (from_major, to_major), count events, and compute median class-list-derived completed and attempted UNM credits through prev_term, the term before the change posted. Minimum threshold (default 3) removes rare pairs.
• Trend sparkline: per-term count of arrivals (to_major %in% focal, green) and departures (from_major %in% focal, red).
• Donuts: “Leaving for” = top to_major values among departures. “Arriving from” = top from_major values among arrivals. If the selected unit contains multiple focal majors, focal-to-focal changes can appear.

Step 5: Build major-status movement cards

The movement cards at the top of the tab are built separately from the legacy population entry columns. They use raw selected-unit program records from cedar_programs so each card has a clear event definition.

• First pre-major declaration: first selected-unit program record where is_pre_major == TRUE, when it occurs before any full-major record for that selected unit.
• Direct full-major declaration: first selected-unit full-major record when no earlier selected-unit pre-major record is observed.
• Pre-major to full major: students with both a selected-unit pre-major record and a later selected-unit full-major record. Terms are counted from first selected-unit pre-major record to first selected-unit full-major record.
• Left for another major: first focal-touching change event where from_major is selected-unit and to_major is outside the selected unit. Graduations are not included in this card.

Movement-card credits come from cedar_student_term_credits. The headline card value is median cumulative completed UNM credits through the event term; the detail table also shows cumulative attempted UNM credits from the same class-list source and transfer-inclusive attempted credits from Academic Studies.

Median terms uses term_diff(), which counts Spring/Fall steps only by default: Spring → Fall = 1, Fall → next Spring = 1, and summer is not counted as an additional term. Entry cards count from first observed class-list enrollment; conversion cards count from first selected-unit pre-major record; departure cards count from first selected-unit record.

Because first observed class-list enrollment is not a formal Banner start date, headline entry cards exclude records already present at the data-start term and records whose first selected-unit program record already has substantial class-list-derived attempted UNM credits. Those uncertain records remain visible in the movement detail table, but they are not summarized as new declarations. The Major Changes scope stripe reports these excluded entry students explicitly.

Worked example — Inflow / Outflow for a History department population

major	students arriving to	students leaving for elsewhere	net
History	47	31	+16
History / Pre-Law	5	12	−7

Only History-dept programs appear. The 47 arriving students came from other majors; the 31 departures went to other majors (shown in the “Leaving for” donut).

Methodology reflects: R/branches/population.R (group builder), R/cones/stopout.R, R/cones/pathway.R, R/cones/major-changes.R, and R/modules/pathways.R (display logic). Update this panel when cone logic changes.