Program Indicator Frequently Asked Questions

A: Program indicators are intended to support a comprehensive understanding of unit performance by providing transparent, consistent measures of historical activity and outcomes. These indicators serve as a resource for benchmarking, strategic planning, decision-making, and ongoing assessment at the unit, college, and institutional levels.

A: Given that we would expect those two things to be related, no. If these were supposed to be independent, we would likely go about them in a different manner.

A: Post-graduation employment outcomes are currently being collected through Career Services' 12Twenty survey platform. Institutional Effectiveness and Analytics (IEA) is partnering with Career Services to evaluate and integrate available employment placement data into the program indicators. The first inclusion of these data is anticipated as part of the February data update, pending completion of validation and reporting processes.

A: These important considerations will be addressed through a process that enables programs to submit qualitative information to accompany the program indicator data. Qualitative descriptions may be provided for the comprehensive, instructional, and research components, offering additional context for interpreting the reported metrics.

See "Instructional SCH" in Program Indicators Definitions.

A: Course size within a unit is reflected in several instructional metrics, such as SCH per Instructional FTE and Budget per SCH. The qualitative narratives supplement these indicators by providing insight into the underlying factors and operational considerations that influence course size.

A: Except for retention rates, data reflect all student careers. Note that because they are joined to other data using academic org rather than academic group (college owner), most graduate students belong to the departmental home rather than Graduate Studies.

A: Student career data is consolidated for each program indicator to keep the total number of program indicators manageable. 

A: For this exercise, a “unit” was first defined by having a financial hierarchy, an HR hierarchy, and an academic org hierarchy. We then used Career, Program, Plan data from the University Registrar to crosswalk courses, students and degrees to the “unit”.

A: The current program indicators being used are:

Instruction

• 4-year instructional share percentage change
• 4-year all-majors share percentage change
• Instructional SCH as a percent of total
• All-majors percent of total
• Instructional SCH/Instructional FTE
• Instructional Original State Aided Budget/Instructional SCH
• Degrees Awarded/Majors
• Undergraduate First-year Retention (5-year average)
• Net Tuition less Instructional Original General State Aided Budget

Research 

• Academic Analytics Scholarly Research Index (SRI) score when comparing units to Public AAU Institutions. If a unit has multiple Scholarly Research Index (SRI) scores available, they were averaged unless directed differently by the Dean or their designee.
• Average sponsored awards for FY21-25, including the NU Foundation, for purpose codes Research, Teaching and Public Service divided by total state appropriated budget.
• Growth of sponsored research awards from FY21 to FY25, including NU Foundation. Noting this was calculated by taking the dollar growth over that time period as a percentage of total growth for the institution. 
• Normalized federal research expenditures as defined by the AAU membership policy – FY15-24.
• Normalized highly prestigious awards, fellowships and memberships as defined by the AAU membership policy – LTD to 2024.
• Normalized book publications as defined by the AAU membership policy – FY15-24.
• Normalized citations as defined by the AAU membership policy (using Academic Analytics as a proxy for InCites) – FY15-24.

A: There are 9 Instructional program indicators that are equally weighted and used to calculate an Instructional score and there are 7 Research program indicators that are equally weighted and used to calculate a Research score. The Instructional program indicator and Research program indicator are averaged (equally weighted). 

A: The initial program indicators were developed through a collaborative process involving campus, academic, and research leadership. This approach ensured that the measures reflected institutional priorities while incorporating perspectives from multiple areas of the university. Beginning in the upcoming academic year, a formal governance structure will be established to oversee future enhancements, methodology changes, and ongoing data stewardship.

A: The instructional data are sourced from the University’s systems of record: SAP and Peoplesoft. Research data are drawn from NURamp, Academic Analytics, National Center for Science and Engineering Statistics (NCSES) HERD Survey, and the National Center for Education Statistics (NCES) IPEDS Data Center.  

Below are some notes on source systems and processing steps:

1. Student plans (majors) come from Peoplesoft and are frozen by NU at the beginning of the fall and spring terms and end-of-term for summer. For each student, each attached plan of type “MAJ” was included, including non-primary majors. For academic year counting, a person’s major(s) is/are identified as the major(s) they held in the last term enrolled in a given academic year (“last one in wins”).
2. Enrollment headcounts also come from Peoplesoft beginning-of-term snapshots and are similarly assigned using the last enrolled term in a given academic year. These count only primary majors to provide unduplicated headcounts and are provided for informational purposes only.
3. Degree counts are also sourced to Peoplesoft, but from end-of-term snapshots. All major plan codes were counted. Unlike enrollments, all degrees earned during an academic year were counted, not just the last degree earned. For this reason, there is duplication of people when multiple plans were present or multiple degrees were awarded. (This is identical to how degree counts are formulated for CCPE).
4. Retention Rates are sourced from Peoplesoft using fall beginning-of-term snapshots. It includes unduplicated entering cohorts of first-time, first-year, full-time degree-seeking undergraduates in UNL admin campus programs. Re-enrollments are determined as those enrolled in the following fall in any capacity as long as it is either a UNL admin campus or UNL delivery campus enrollment.
5. Appointments are sourced to SAP for HR org and Activity Insight (Watermark) for apportionment reporting. The apportionment data include all tenure line and specialized (promotable, but not tenure-eligible) faculty, and lecturers, temporary lecturers, post docs, senior research associates, and academic administrators (M1). There is a row for each appointment and corresponding FTE and apportionments that a faculty member held. These data were used to determine instructional FTE by departmental unit.
6. Primary Appointments are also sourced from SAP but are summed at the primary position level (i.e., FTE is totaled, and apportionments are weighted and summed by FTE). These data are used for assigning instructed SCH to departmental units.
7. Instructional SCH is sourced from primary appointment HR org units (SAP, above), Peoplesoft, and CourseLeaf. Each section carries the SCH and all instructors (and their proportions of overall instruction). The instructional SCH is the percentage of effort by section SCH and is tied to the primary appointment home (tenure home for tenure-track faculty for consistency with research program indicators).
8. Course SCH (Department) comes from Peoplesoft and is tied to the academic org of the departmental unit that owns the course/subject. SCH for cross-listed courses are designated in Peoplesoft based on which course/subject/meeting the registration ties to.
9. Funding data comes from SAP in special budgetary views prepared by Business and Finance. State-aided budget for this exercise is limited to original budget. It is further broken out by General State Aided (GSA), Differential Tuition (DT), Programs of Excellence (POE) and Other fund subtypes.
10. Sponsored Awards data comes from NuRamp and was assigned to units based on the percent credit from the routing forms. 
11. Scholarly Research Index (SRI), Books, Citations and Highly Prestigious Awards data comes from Academic Analytics benchmarking data using the AAD2023 data release. 
12. Research Expenditures data comes from the National Center for Science and Engineering Statistics (NCSES) HERD Survey, which is calculated using expenditures from SAP and is mapped to units using the NuRamp routing forms and is reported annually by the UNL Research & Innovation team. 
13. USDA AFRI obligations data comes from usaspending.gov. 
14. T/TT Headcount data used for normalization of the AAU P1 indicators comes from the National Center for Education Statistics (NCES) IPEDS Data Center and is reported annually by the UNL Institutional Effectiveness and Analytics team. 

All data were joined at the lowest unit level (lowest level key) in the NU Finance, HR, and academic org hierarchies. Limited merges are made where units are distributed across colleges or IANR/EVC (e.g., CEHS and IANR departments).

A: Apportionment is used to calculate the FTE allocations within each unit, e.g., determining Instructional FTE. It is also used to calculate the “instructional budget” by multiplying the percentage of FTE that is allocated towards “instruction” by the original total state aided budget to create an estimated “Instructional Budget”.

A: Appointment is used to calculate the FTE allocations within each unit. Appointment allocations are required to determine the FTE in a unit. It is also used to determine the assignment of course SCH. Appointment is also used to allocate instructional SCH. (See Q5 above.)

A: Original General State-Aided budget (General State Aided, Differential Tuition, POE and all other types of State Aided) is multiplied by the apportionment allocated toward Instruction to calculate an approximated “Instructional Original General State-Aided Budget”. 

This “Instructional Budget” is used in the following program indicators:

• Instructional Original General State-Aided Budget / Instructional SCH

• Total Tuition less Instructional Original General State-Aided Budget

A: Original General State-Aided budget (General State Aided, Differential Tuition, POE and all other types of State Aided) was used because it includes only permanent budget and does not include any temporary transfers that happen throughout the year, and it does not include carryforward.  Every unit might receive and/or send temporary budget throughout the year, which would create timing issues.

A: No. Total Realizable Base Tuition uses the only base tuition by residency by career level, which is then multiplied by the SCH for each course.  This methodology was adopted to establish a consistent and comparable data set. Accordingly, differential tuition and tuition remissions were excluded to maintain comparability across the analyzed institutions.

A: No. Total Realizable Base Tuition is used in the model and that calculation uses the base tuitions by residency by career level which is multiplied by the SCH by residency for each course.

A: Questions regarding unit data, methodology, definitions, or reporting should be directed to the appropriate data steward or subject matter expert. Please refer to the Data Ecosystem and Contact Directory section for guidance on identifying the appropriate contact based on the specific data source.

A: Yes, awards and expenditures data are credited to departments/units using the NuRamp routing form. 

A: Efforts have been made to recognize collaborations in the data, while at the same time ensuring data sum to the institutional totals so that percent of totals can be utilized in the program indicators. Awards and expenditure data are credited to units based on the NuRamp routing form, ensuring each unit gets their appropriate share of the productivity. Academic Analytics data are credited to the individual’s tenure org unit, which is how the benchmarking data are tracked and reported to AAU as well as how they are utilized to calculate the Scholarly Research Index (SRI) score. Shared authorship for articles and books is credited equally across participating faculty given there is no equivalent of a routing form for these outputs.

A: Research productivity program indicator output values typically increase throughout a faculty member’s career, resulting in most newer Assistant Professor level faculty contributing at lower value of measurable activities than that of Associate and Full Professor faculty. Units can view the portfolio of faculty at varying career levels for their unit as well as peers in Academic Analytics to assist with their narrative around the unit’s research productivity.

A: The Scholarly Research Index (SRI) is a methodology to provide comparative context for faculty or unit research activity compared to taxonomy peers. This comparison is based on the program indicators Academic Analytics collects and maintains for the peer analysis/benchmarking tools.

Academic Analytics calculates the unit level Scholarly Research Index (SRI) based on the mean Scholarly Research Index (SRI) scores of individual faculty members within the unit. Thus, the Scholarly Research Index (SRI) of each unit or other level of aggregation — program, department, broad field, institutions, etc. — is the average of the deduplicated faculty who comprise that unit. 

Program Indicators for the person-based Scholarly Research Index are:

• Total Journal Articles
• Total Awards
• Total Books
• Total Book Chapters
• Total Citations
• Total Conference Proceedings
• Total PI Grant Dollars
• Total US Patents
• Total Clinical Trials

Academic Analytics utilizes a weighting scheme for these program indicators, which varies across taxonomic classifications. For some departments at UNL, there is not a 1:1 match of disciplines resulting in more than one SRI score being available for that department. Currently, for units with multiple Scholarly Research Index (SRI) scores, those have been averaged across the disciplines. If there are departments where it does not make sense to average the Scholarly Research Index (SRI) scores, for example if one of the disciplines most closely matches the department at UNL, contact the R&I team to have those adjustments made. 

A: In the AAU Benchmarking module, select the department that you want the Scholarly Research Index (SRI) score for from the dropdown. 

Academic Analytics defaults to a nationwide peer group. Use the AAU and Sector filters to select AAU peers in the Public Sector and select APPLY. 

The blue text for institutions, departments, faculty and custom index will provide you with the data for that AAU Public peer group. The black text continues to report the nationwide data. The custom index value is the Scholarly Research Index (SRI) for AAU public institutions. 

A: See screenshot below.

A: No changes were made to the default weighting scheme for the discipline. For units with multiple disciplines mapped to a single department, the Scholarly Research Index (SRI) scores were averaged.