document .resume ed 074.999 author. clark, david g ...document .resume ed 074.999 he 004 079 author....
TRANSCRIPT
DOCUMENT .RESUME
ED 074.999 HE 004 079
AUTHOR. Clark, David G.; And OthersTITLE Introduction to the Resource Requirements Prediction
Model 1.6.INSTITUTION Western Interstate Commission for Higher Education,.
Boulder, Colo. National Center for Higher EducationManagement Systems.
SPONS AGENCY National of Education (DHEW), Wishingtone D.C.Task Forcr' a Lab. and Center Transition.
REPORT NO TR-,34APUB DATE 73NOTE 77p.
EDRS PRICE MF-$0.65 HC-$3.29DESCRIPTORS *Budgeting;'*Educational Administration; *Educational'
Finance; Educational Planning; *Higher Education;*Management Systems; Planning; Program Budgeting
ABSTRACTThe Resource Requirements Prediction Model (RRPM) 1.6
is an instructional cost simulation model for use in all types ofpostsecondary institutions including community colleges, vocationalschools, and large and small 4-year institutions with or withoutmajor research activities. The model provides institutions with atool with which to analyze various institutional alternatives for theutilization of a limited set of resources. In addition, RRPM 1.6generates information necessary for the preparation of instructionalprogram badgets4 Institutional data, either historical or projected,may be put into the model. The model then calculates the program costinformation and implied resource requirements to undertake any givenseries of programs. RRPM generates 4 different types of reports: (1)
organizational unit reports providing line-item budgets for variousorganizational units within the institution; (2) program budgetreports indicating the discipline or department contributions tovarious instructional programs; (3) institutional summary, reports;and (4) formatted display reports that show all parameter data forthe institution. (Author/HS)
FILMED FROM BEST AVAILABLE COPY
RESOURCE
NT DUC °1RN T® THE
IUUEL 1.6
tionalnter for
Hi herucation
ManagementS temsa WICHE
ti
-dub_
TH
Technical Report 34A
U S DEPARTMENTOF HEALTH
EDUCATION & WELFAREOFFICE OF EDUCATION
S DOCUMENT HAS BEEN REPROED EXACTL. AS RECEIVED FROMPERSON ORORGANIZATION °RIC-riNG IT POINTS
OF VIEW OR OPINt JAS STATED DO NOT NECESSARILYREPRESENT OFFICIAL OFFICE OF EDUCATION POSITION OR POLICY
FILMED FROM BEST AVAILABLE COPY
National Center for Higher Education Management Systemsat WICHE
Executive Director, WICHE:
Robert H. Kroepsch
Associate Director, WICHE, andDired.or, National Center forHigher Education Management Systemsat WICHE:
Ben Lawrence
Assistant Director, NCHEMS:Gordon Ziemer
Director, Research and Development Program:Robert A. Wallhaus
An Equal Opportunity Employer
The Western Interstate Commission fore Higher Education(WICHE) is a public agency through which the 13 westernstates work together
. . to increase educational opportunities for westerners.to expand the supply of specialized manpower in theWest.
. to help universities and colleges improve both theirprograms and their management.
. . . to Tm the public about the needs of, higher educa-tion.
Director, Applications and ImplementationProgram:
Robert A. Huff
Program Associate:John Ivfmter
Communication Associate:Joanne E. Arnold
The Program of the National Center for Higher EducationManagement Systems at WICHE was proposed by statecoordinating agencies and colleges and universities in theWest to be under the aegis of the Western Interstate Com-mission for Higher Education. The National Center forHigher Education Management Systems at WICHE pro-poses in summary:
To design, develop, and encourage the iMplementation ofmanagement information systems and data bases includingcommon data elements in institutions and agencies of highereducation that v(,;,11:
provide improved information to higher education administration at all levels.
facilitate exchange of comparable data among institu-tions.
facilitate reporting of comparable information at thestate and national levels.
THE RESOURCE REQUIREMENTS PREDICTION MODEL 1,.6
INTRODUCTION TO.
THE RESOURCE REQUIREMENTS 'PREDICTION MODEL 1.6
Technical Report No. 34A
David G. ClarkRobert A. Huff
MiChael J. HaightWilliam J.Collard
1973
This study, is part of a, program supportedby the:National InstitOte,of'EducatiOn.
National Center'lor Higher:EdLcation Management Systems at'Westerp':Interstate Commission 'for'Higher Education
Dra*r P Bbulder'i Colorado 80302
An Equal Opportunity Employer
ThiS document has been reviewed and apprOved for publication by thestaff and Technical Council of the National. Center for Higher EducationManagement Systems at WICHE.. This publication does not necessarilyreflect official positions or policies of the National Institute ofEducation,'NCHEMS, or WICHE.
This edition of Introduction to the Resource Requirements PredictionModel 1.6 supersedes all previous draft editions of the same title.
ABSTRACT
The Resource Requirements Prediction Model (RRPM) 1.6 is an instructional
cost simulation model for use in all types of postsecondary institutions
including community colleges, vocational schools, and large and small four-
year institutions with or without major research activities.
RRPM 1.6 is a more fle)(ible and usable analytic tool than earlier NCHEMS cost
simulation models. While it is an evolutionary product, RRPM 1.6 does not
negate institutions' past experience in the area of instructional cost simu-
lation. ,Almost all of the data that nave been collected for either the
Resource Requirements Prediction Model 1.3 or the Cost.. Estimation Model can
be readily used with RRPM 1.6.
RRPM 1.6 provides institutions with a tool with which to analyze various
institutional alternatives for the utilizatioh of a limited set of resources.
RRPM 1.6 may also provide a useful point of departure for those institutions
wishing to adapt a cost simulation model to their own specific institutional
needs.
RRPM 1.6 generates information necessary for the preparation of instructional
program budgets. Institutional data, either historical or projected, may be
put into the model. The model then calculates the program cost information
and implied resource requirements to undertake any given series of programs.
RRPM 1.6 generates foUr different types of reports, any or all of which may
be requested by the user. These ihclLide:- (1) organizational unit reports
providing line -item budgets for various organizationalunits within the
institution, (2) program budget reports indicating thedisciplihe or depart.--
ment Contribution's to varidus:instroctionalprograMs.,:(3) institutional
summary reports, and (4) formatted display repdrtS that show all parameter
data for the institution...
The RRPM 1.6 programs have loeen written in ANS COBOL and'are designed for
use on systemS having the ANS COBOL compiler and a minimum of '50K bytes 'of
:core. storage..
WARRANTY
The user is referred to the RRPM 1.6 System Documentation for a detailed .
description of the computer programs and their limitations. NCHEMS has
released these programs as Type IA software. .A complete description of a
Type I proj.oam product is contained in the warranty section of the RRPM 1.6
System Documentation'.
r'.:HEMS certifies that the RRPM 1.6 programs meet conventional ANS COBOL
programming standards and will meet the performance characteristics indicated
in the RRPM 1.6 System Documentation. If such is not the case, NCHEMS will
make appropriate program modifications and distribute such changes to
institutions that have earlier versions. NCHEMS will assume .no responsibility
for other modifloations of RRPM 1.6 prOgrams.
Users of RRPM 1.6 should understand that the large amount of flexibility
afforded institutions in selecting such conventions as cost allocation pro-
cedures', student and faculty definitions, and levels of aggregation make
interinstitutional RRPM output comparisons difficult.. Only if very careful
attention is given by' all cooperating campuses to the use of standard
definitions and other conventions can output comparisons be made.
ACKNOWLEDGMENTS
Development of the Resource Requirements Prediction Model (RRPM) at the
National Center for Higher'Education Management Systems has been a long and
arduous process. Many experimental prototypes were developed along the way;
Many concepts were tried and evaluated. The latest RRPM version, as described
in this document, has resulted from the work and contributions of. many people
over the past several years. While NCHEMS feels that RRPM 1.6 represents a
significant improvement over past cost simulation models, it should be clearly
understood that the NCHEMS staff who developed this model could not have been
successful without other individuals work which served as a point of de-
parture.
The original conceptualization that has served as a basis for all RRPM develop-
ment NCHEMS was contributed by Dr. George Weathersby, formerly of the Office
of Analytical Studies at the University of California. Earlier versions
of RRPM were developed under,the leadership of NCHEMS staff members Dr. Warren
Gulko and Mr. James Martin. Eight participating NCHEMS institutions served as
official pilot test sites for earlier prototypes, and staff from those pilot
institutions contributed significantly to the art and science of resource
requirements simulatiOn modeling in higher education. Anotherforerunner of
RRPM 1.6 was the. Cost Estimation Model (CEM) developed by the NCHEMS staff.
and Mr.Colby Springer of Systems Research, Inc.,in Los Angeles.
TABLE OF CONTENTS
INTRODUCTION1
Section
I. PURPOSE 2
II, OVERVIEW OF THE RRPM 1.6 PROGRAM LOGIC 3
INDUCED COURSE LOAD MATRIX DEVELOPMENT 26
IV. RRPM 1.6 SOFTWARE DESCRIPTION 28
V. RRPM 1.6 COMPUTATIONAL FLOW 30
VI, GLOSSARY OF COMMONLY USED TERMS 33
REFERENCES 34
APPENDIX I. RESOURCE REQUIREMENTS PREDICTIONMODEL 1.6 FLOW CHART 35
APPENDIX II. RESOURCE REQUIREMENTS PREDICTIONMODEL 1 . 6 INPUT FORMS . . . . 41
LIST OF TABLES
Table
1. Induced Course Load Matrix:Field of Study by Student Level
2. Instructional' Work Load Matrix:Major by Student Level ...
LIST OF FIGURES
......... 11
Figure
1.Resource Requirements Prediction Model 1.6:FTE Faculty and Faculty Salary Computation 31
2. Resource Requirements Prediction Model 1.6:Non-FacultySalary Cost Computation . . ... . 32
INTRODUCTION TO
THE, RESOURCE REQUIREMENTS PREDICTION MODEL 1.6
The Resource Requirements Prediction Model (RRPM) 1.6 is an instructional
cost simulation model for postsecondary institutions. RRPM 1.6 is intend-
ed to be NCHEMS's primary cost simulation model for institutional use The
model has been designed for use in all types of postsecondary institutions'
including community colleges, vocational schools, and large and small four-
year institutions with or without major research activities. It may be seen
as an evolutionary model that has grown out of institutional experiences with
previous NCHEMS products.; specifically RRPMF1.3 and the Cost Estimation
Model (CEM).
The experience gained over the past two years with cost simulation models has
pointed the way for further refinements that has made RRPM 1.6 a more flexible
and usable analytic tool. It is important to note that, while t is an
evolutionary product, RRPM 1.6 does not negate an institutions's past experi
ence in the area of cost simulation. Almost all of the data that have been
collected for either the Resource Requirements Prediction Model 1.3 or the
Cost Estimation Model can be readily.used with RRPM 1.6. This moi4e1 combines
many positive aspects of RRPM 1.3 and CEM while avoiding.some of the difficult
problems that ,each of those models'presented institutional users.
supplant both RRPM 1.3 and CEM.
RRPM 1.6 will
The concepts of cost simulation in higher education have received consider-
able attention over the past two years. One of the primary purposes of
RRPM 1.6 is to generate information necessary for the preparation of instruc-
tional program budgets. Institutional data, either historical or projected,
may be put into the model. RRPM 1.6 calculates the program cost information
and implied resource requirements to undertake a given series of programs.
Another purpose of RRPM 1 6 is to provide institutions
tool with which to analyze various institutional alternatives for utilization
of a limited set of resources. The model has been designed as a long-range
planning tool to aid higher-level management in rapidly determining resource
implications of different policy and planning changes. Employing the model
in this experimental mode, the user may, ask a series of "what if' questions
related to admissions policies, implications of curriculum changes, and
operational parameter sensitivity analysis.
with a flexible
Also
RRPM
is
for those institutions with the analytical and programing capability,
6 provides a nof departure for their ow modeling efforts.
hoped that RRPM 1.6 is sufficiently flexible to permit adaptation to
specific institutional requirements without modification of the computer
programs. However, in some cases institutions will want to change the
format of reports or other items. Toward this end, the model has been
constructed in a modular manner that makes modification of reports, etc.
relatively convenient.
II OVERVIEW OF THERRPM 1 6 PROGRAM LOGIC
The reader wi
pages 37 to 3j
I to refer to the:RRPM1'.6 flow chart on
Asithe following section.
The logic flow of RRPM 1.6 may be appropriately divided into six phases:
1 Institutional definitions
2. Induced Course Lcad Matrix (ICLM) specification
3. Calculation of full-time-equivalent (FTE) instructional facultyand salaries
4. Calculation of discipline costs other than faculty salaries
5. Calculation of costs other than general academic instruction
Preparation of organizational reports, program budget andplanning parameter reports, and summary report.
Note that a aeries of cirClednumbers is shown on the RRPM 1.6 flow chart
These numbers indicate data that must, be provided by the user in order to
run model. On the following pages...,,each of those user inputs and its
relationship to the model'S 'calculations. will 'be distUssed.
Phase I
0 Definitions
When using RRPM 1.6, the institution may provide several definitions.
The definitions given will appear on the output reports that the
model generates. If no definitions are given, the model will insert
a series of identifiers in their place. The seven definitions that
may be provided are
Organizational Levels
(Up to e.g. , discipline, department, school)
2. Course Levels
(Up to 7; freshman, sophomdre, junior, senior,
graduate 1,, graduate II, other)
3. Student Levels
(Up to 7; e.g., freshman, sophomore, junior, senior,
graduate I, graduate II, dther)
4. Instruction Types
(Up to 5; e.g., lecture, discussion,laboratory,
independent study, other)
Faculty Ranks
(Up to 6; e.g., professor, associate professor,
assistant professor, instructor, teaching assistant,
other)
Staff' ategories
(Up to '4; :e.g. administrative assistant, secretary,
student help, other)
... Other Expense Types,
(Up to 7. supplies, travel, print ,
Ang, telephone, rentals,' miscellaneous):
The exact prodedure for establishing the definitions is contained
in the input sheets for RRPM 1.6.
Field of Study Titles
Once the definitions have been completed, the fields of study and
their titles, which the model will display in its reports, must be
specified. Afield of study may be a degree program, vocational
program, field of interest, or any group of students who might
logically-be viewed as a homogeneous group for the purposes of analysis.
Example:
Field of Study. Titles
History Degree Program
VocationalMelding:RrOgram
Nondegree Evening Students
Undeclared
Discipline. Titles (Organizational Level 1)
Titles must be given for the teaching departments or disciplines
that will be 'used in the'mode:y: The diSciplinesH(or departments)
chosen arethe costCenters'that become the basis for calculating
the unit cost (e.g cost Ter credit hour) and the average cost per
student
Example:
Discipline Titles
History Evening Extension
Biology Senior Seminar
Auto Mechanics
Department Titles (Organizational Level 2)
Data from teaching departments or disciplines may be aggregated within
the model during the preparation of reports. Typically, the Organi-
zatior ,1 Level 2 input form is used to specify how disciplines are
to be aggregated into departments or how departments are to be
aggregated into divisions.
Example:
Department Titles
Department Discipline
History .4 History
Biology BiologyBotanyZoology
Humanities EnglishPhilosophyReligibut StudiesForeign LanguagesLiterature
School/College Titles (Organizational Level 3)
Just as disciplines may oe!:..aggregated into deprtments, so departments:
may be combinecLintO'sthopls or colleges The title of each school or
college is ttated,!and the departmentt that comprise that, schobl'
or college are,specified thrOUgh, this inpUt.
Example:
School/College Tities
School/College Departments
Arts History
Sciences Biology,
Humanities. Social: Sciences
School of Business AccountingFinanceMarketingManagement
Faculty of Education Education
Phase II
Once the organizational Characteristics of -the institution have been defined
and all the needed titles ha-ve-been specified'a series of relationShips
between'prggrams and teaching 'departments, or discjplines must be established.
That is, after the organizationaljinits, ha've:been identified, it is
necessary tin determine how:those organizational -units provide jOstruCtional
. services to students in different programs This 'relationship may be
statA'in terms of units (e.g., credit hours) taken by students in different
programs from the various organizational units providing instruction. The
Induced Course Load Matrix specifies those relationships,
ICLM Data (Average Student Credit
Hour Load by Field of Study, by Student
Level, by DisciOline, by Course Level)
The relationships between programs and instructional disciplines are
established through the use of an Induced Course Load Matrix (ICLM).
In its simplest form the ICLM indicates the average number of units
(e.g., credit hours) taken by a typical student in each field of study
(program) from each discipline or department.
Example:
ICLM for Lower Division History Students
Average Semester HoursDiscipline and Course Level Taken Annually
Lower Division History 11.8
Upper Division History 4.2
Lower Division Biology 3.9
Lower Division Fine Arts 3.7
Lower Division Business 6.4
30.0
The institution must provide the model with the ICLM data for each
field of study, each student level, each discipline, and each 'course
level,- The ICLM for a hypothetical''institution: is shoWnon,-page 9.
While the credit hour is the most common unit of measure, such other
measures ,as contact hours, courses, or subjetts may be used The
unit chosen will depend upon what is deemed appropriate by the insti-
tution:and upon data 'availability.
Enrollment by Field of Study,
and by Student Level
Once the Induced Course,Lbad 'Matrix has been defined, it is necessary,
'to,specifythe total.number'of students in each program (field of
study) These enrollments are input to the modeL
Example:
Enrollments
Field of Study Enrollment
Lower Division History 143
Upper Division History 186
Graduate Division History 52
The enrollments for each field of study at each student level are
multiplied down through the columns of the Induced Course Load
Matrix, resulting in an Instructional Work Load Matrix (IWLM) as
shown on page 11. The IWLM indicates the total number of credit
hours taken by all students in each field of study from each instruc-
tional department or discipline.
LJJ
j
TABLE 1
INDUCED COURSE LOAD MATRIX*
(Semester Hours)
FIELD OF STUDY BY STUDENT LEVEL
History Biology Fine "rt
LD UD GD LD UD GD LD UD GD
Business
LD UD GD
CO
11.8 6.7 4.5 6.0
4. 11.3 4.5 3.9 2.1 2.3
18.3
>")CS)00
CO
CO
O- J
CZ/
CZ/CO
- J
CZ/
CZ/CO
4.1 12 4.2
5.8 13.7
2.1 20.4
3.7 2.7 2.7 1.8 10.9
6.5 3.0 3:5 2.9 6.3
6.4 2.8 2.3
2.6 4.2
.3:
4.6
Total 'AnnualSemester Hour 30.0 30.0 30.0 30.0 30.0 30.0
Load
30.0,
*FTE student load equals 30.0 annual semester hours.
LD = Lower Division UD = Upper Division
9
4.3
7.6 7.4
4.1 1.3
2.0
10.3
19.3
1.7
1.0
30.0 30.0
4.6
2.3
5.4
12.8
4.3
30.0
19
6.1 4.7
2.8
3.5
1.4 1..3-
1.1
10.3
1.7 21.2
30.0 30.0
= Graduate Division
Example:
IWLM for Lower Division History Students
Discipline &Course Level
L.D. History
History
L.11.01.F9Y
L.L. Fine Arts'
L.-EL Business
All Courses
Avernqe Cred;flours Taken(From ICLM)
11.8
4.2
3.9
3.7
6.4 x.
30.0
Total CreditHours Taken
Enrollment (From IWLM)
143 = 1,687
143 601
143 558
143 = 529
143 = 915
143 = 4,290
When the numbers in each, row of the IWLM are summed, the result is
the total numberof credit hours that an instructional discipline
or department must provide in order to meet the needs of students
from all fields of study. Hence, given the enrollment mix shown at
the top of the INLM on page 11, the history department would need to
generate 4,912 credit hours of lower division instruction, 6,438 credit
hours of upper (division tmstruction, and 952 credit hours of graduate
level instruct are.
Phase. III.
The credit hour demandLplaced:on each department, given a mix of students
and their typical course demands, has now been defined. The, remainder of
the calculations deallwith '.how those Credit hour demands are to be'met.
the model calculates the number of full-time-equivalent (FTE) faculty and
their associated salaftes for each instructional discipline (or department)
TABLE 2
INSTRUCTIONAL WORK LOAD MATRIX
(Semester Hours )
MAJOR BY STUDENT LEVEL
FTE ENROLLMENTS
143 186 52 121 94 45 85 61 17 180 206 124
4,
Hi story
LD UD GD
Biology
LD UD GD LD
Fine Arts Business
UD GD LD UD GD TOTAL
1,687
763
529
811 423 510 262 828 391 4,912
234 367 95 195 464 126 414 1,257 583 6,438
952 952
1,512 357 251 22 972 .4,435
702 1,288 122 824 347 3,283
197 918
502'
915 521
484
327 169 9.26 108 721 3 283
156 329 130 536 628 288 161 3,436
328 328
278 47 103 2,304 227 4,421
218 207 34: 774 2,122 3,839
350 2,629 2,979
Total Annual4,290 5,580 1,560 3,630 2,820
iemester HourLoad
1,350 2,550 1,830 510 5,400 6,180 3,720 39,420
at each course level. There are two methods of determining the:number of
facUlty required. The first involves the use of a::1:1productiyity ratio"
that is the average number of credit hours produced by each FTE faculty:
member teaching'at a course level within a discipline. This approach is
referred tofas th&"ShOrt method:" the institution may
provide data on a number of institutional parameters-including average
section size by instruction type, the number of contact hours by tYPe-of
instruttion and average faculty,work load by instruction tYpe--and
approach:the calcUlation of FTE facuityand their salary dollars by
a longer Method. This long method requires considerably more data and
effort but proVides information on the humber of faculty required by
instruction type at aieel of detail not attained through use of the
short method. Inputs (Danc1 are used for the short:Method of cal-
culating :FTE: fatuity requfrements., Input FaCulty, Salary SChedule
by Discipline, is used for both the long and:shOrt methods. Inputs 0through 76 are used for the long method only
0: Productivity Ratio - Credit Hours to FTE Faculty
by Discipline, by Course Level` (Short Method Only)
When using the short method for the calculation of FTE teaching
faculty, the user must provide a series of productivity ratios
that state the-number of credit hours produced by an FTE faculty
teaching exclusively at one level of instruction.
Example:
Productivity Ratios (Credit Hours/FTE Faculty)
Lower Upper GraduateDiscipline Division Division Division
History
Botany
ElementaryEducation
359 277 162
200 155 77
326 311 190
12
The number of FTE faculty required at each level of instruction is
determined-by dividing the productivity ratio for a given course
level of a specific discipline into the total nuMber of credit hours
to be produced in the corresponding discipline at that level.
Example:
Calculation of FTE Faculty for Lower Division History
4,912 Student Credit Hours359 Student Credit Hours/FTE Faculty
Faculty Rank Distribution by Discipline,
by Course Level '(Short Method Only)
13.66 FTE Faculty
This input specifies the rank mix ofthe fatulty who will teach
at each course level.
Example:
Faculty -Rank. Distribution for Lower Division HistoryDiscipline
Faculty Rank FacultyRank Distribution FTE Faculty by Rank
Professors .10 x 13.66 = 1.37
Assoc. Professors .20 x 13.66 = 2.73
Ass't. Professors 30 x. 13.66 = 4.10
InstrOctors .20 x 13.66' 2.73
Teaching Ass'ts. 20 x 13.66; 2.73
1.00 13.66
Faculty Salary Schedule by Discipline,
by Rank (Both Short and Long Methods)
Having determined the number of faculty in each rank at each course
level in each discipline, the model requires that faculty salary
schedules be input for each discipline.
13
Example.:
Faculty Salary Schedule for History Discipline(All Course Levels)
Professors, $17,000
Associate Professors $14,500
Assistant Professors $11,500
Instructors $ 9,500
TeachingAssistants $ 6,800
The number of faculty at each rank is multiplied by the corresponding
salary. Thus, the model calculates, by course level, and rank, the
total FTE instructional faculty and their salaries.
If the long method is chosen for the calculation of FTE faculty required
within each discipline, the model calculates the same Instructional Work
Load Matrix used for the short method. Rather than specifying a produc-
tiyity ratio, the user must gather and input other data that begin with
Input
Ratio of Student Contact Hours (SCTH)
to Student Credit Hours ,(Sa)
by Discipline, by Course Level (Long Method Only)
This ratio establishes the relationship between the number of hours
students spend in the classroom (SCTH) and the number of credit hours ,
(SCH) they receive. Some disciplines will have a ratio of one SCTH
to one SCH, while others will have "noncredit" labs or discussions,
in which case the ratio will be greater than one.
Example:
Ratio SCTH to SCH
Discipline .anO TotalCourte level SCTH
Lower DivisionHistorY ' 4;912.,
-0wer DivitiOnBiology
TotalSCH
4,912
7,406 4,435
RatioSCTH/SCH
LOU
1.67
The next two inputs provide the bas1S for determininthe total number,
of class meetings required within each discipline at'each course
level by Instruction type.
Distribution of Student Contact
Hours by Discipline, by Course Level,
by Instruction Type (Long -Methdd'.0nly)
The total, number Of.:..studentoontabtjpurs::.(SCTH)::by
course level,:mustbeAistributed across the different instruction
types.
Discipline,Course Level, and Contact HoursInstruction Type; By Instruction Type CohtaCt Hours ,Contact Hours
Example:
DiStribUtipn of SCTH By: Instruction Type
Total Discipline Distribution of
Lower DiOsionHistory 7 Lecture
Lower DivisionBiology - Lecture
Lower DivisionBiblogy - Lab
1.00
4,435
2,971 7,406 .40
15
0 Section Size by Discipline, by Course Level,
by Instruction Type (Long Method Only)
Having determined the total number of SCTH that must be generated
for each discipline at each course level by instruction type, one
can determine the total number of class meetings (CMTG) by dividing
the average section size (by discipline, by course level, by instruction
type) into the corresponding SCTH figures.
Example:
CalcUlation of CMTG by Instruction Type
Discipline Average
Course Level, and Total SCTH by Section Size by CMTG by
Instruction Type Instruction Type Instruction Type Instruction Type*
Lower DivisionHistory - Lecture :4,912 ...- 40 = 123
Lower DivisionBiology - Lecture 4,435 + 60 = 74
Lower DivisionBiology Lab 2,971 20 149
*Rounded to nearest integer
Ratio of Faculty Contact Hours (FCTH) to
Class Meetings (CMTG) by Discipline, by Course
Level, by Instruction Type (Long Method Only)
The next step is to calculate the total number of faculty contact
hours (FCTH) required in order to meet the instructional needs
of the disciplines. The ratio of class meetings to FCTH (by discipline,
by course level, by instruction type) adjusts for those class situations
where there may be team teaching or where the faculty member is not
meeting with the class at all times.
16
Example:
Calculation of Total FCTH Requirements
DisciplineCourse Level, and CMTG by Ratio FCTH/CMTG by Total FCTH byInstruction Type Instruction Type Instruction Type Instruction Type
Lower DivisionHistory - Lecture 123 x 1.00 = 123
Lower DivisionBiology - Lecture 74 x 1.00 = 74
Lower DivisionBiology - Lab. 149 x 2.00 = 298
0 Faculty Work Load by Discipline, by Course Level,
by Instruction Type (Long Method Only)
Once the total faculty contact hour requirements are known, the
typical FTE faculty work load, stated in faculty contact hours (FCTH),
must be specified for each type of instruction at each course level.
The total FTE faculty by discipline, by course level, and by instruction
type is then calculated.
Example:
Calculation of FTE Faculty.
DisciplineCourse Level, and Total FCTH by FTE Faculty FTEInstruction Type Instruction Type Work Load (FCTH) Faculty
Lower DivisionHistory - Lecture 123 .: 9 13.66
Lower DivisionBiology Lecture 74 9 8.22
Lower DivisionBiology - Lab 298 i. 15 19.94
17
Discipline Faculty Rank Distribution by
Discipline, by Course. Level, by
Instruction Type (Long Method Only)
Finally, the distribution of teaching faculty for each type of
instruction at each course level within each discipline must be
indicated.
Example:
Discipline Faculty Rank Distribution
Lower Division History
Lecture
FacultyRank
DistributionTotalFaculty
Faculty
Rank
Professors .10 x 13.66 = 1.37
Associate Professors .20 x 13.66 . 2.73
Assistant Professors .30 x 13.66 = 4.10
Instructors .20 x 13.66 = 2.73
Teaching Assistants .20 x 13.66 = 2.73
1.00 13.66
Lower Division Biology
Lecture
Professors .20 x 8.22 . = 1.64
Associate Professors .30 x 8.22 = 2.47
Assistant Professors .30 x 8.22 2.47
Instructors .20 x 8.22 = 1.64'
1.00 8.22
Lab
Teaching Assistants 1.00 x 19.94 = 19.94
Given all of the' Aata:specified:in Inputs 0 through(E), FTE
facUltY (by Aiscipline,:by course level, by instruction type, by
rank) will be calculated. The same faculty salary schedule (for
18
all course levels) used in the short method is used to determine
the total salaries by discipline, by course level, and by rank
in the long method.
It is important to note once again the difference between the long
and the short methods calculating FTE faculty and:: faculty costs.
Clearly, the data required for the short metnad are considerably
less :difficult to obtain. However, the user .does not have the
ability to test directly the sensitivity of certain parameters
(e.g., class size and faculty work load).
The determination of FTE faculty, their rank distribution, and their
associated salaries has now been completed.
Phase IV
The model calculates direct instructional costs other than teaching
faculty salaries. All non-teaching-faCulty costs are collected for the
discipline as a whole and are then allocated to each of the course leVels
on the basis of faculty salaries, or FTE fatulty, or student credit hours
(SCH), or a specific course level designation.
Example:
Biology Discipline Expenses and Allocation
Expense Type Allocation Basis
Chairman's Salary FTE Faculty
Supplies Student Credit Hours
Travel Faculty Salaries
Lower Division LabSupplies Specific to Lower Division
FTE Chairman and Chairman. Salary
For purposes of calculation of teaching faculty costs, the depart-
ment chairman category is used to display the chairman's administra-
tive costs. The user must specify whether there is a chairman within
the discipline and his salary. This is done through the use of Input
Form 0 in which the faculty salary schedule for the discipline was
identified. If a department chairman is teaching classes in addition
to his regular duties as chairman, his FTE assignment as chairman
is correspondingly reduced.
Example:
History Discipline Chairman
FTE Chairman = .5
Annual Salary $20,000
Chairman's Salary = $10,000
Allocate to Course Levels by FTE Faculty
0 FTE Staff by Discipline, by Category
The number of staff by category is input together with the average
salary for each category. The number of staff may be input as a
constant, and/or as a function of FTE faculty, and/or credit hours,
and/or FTE chairman.
Example:
Calculation of Discipline FTE Staff (all course levels)
History Discipline Secretaries
(.2 x 30 FTE Faculty) + (1.0 x 1.0 FTE Chairman)
= 7.0 Secretaries
Secretary Salary Rate $ 5,000
Secretary Salaries (7) $35,000
Allocate to Course Levels by FTE Faculty
20
18 "OthF,"" Expense by Discipline, by Type (all course levels)
Esttnating equations are used for other expenses of each instructional
dise,:line. Such expenditures may be input as a constant, and/or
as a unction of total faculty, ..and/or total support staff, and/or
FTE(dmairman, and/or student credit hours, and/or total faculty
salarles, and/or total staff salaries.
example:
Calculation of "Other" Discipline Expense
History Discipline Instructional Supplies
$2 000 + ($.50 x 6,000 SCH) + ($50.00 x 30 FTE Faculty)
$6,500.00
Allocate to Course Levels by FTE 7acultY
Biology Discipline Instructional Supplies
$10,000 + ($2.00 x 4,000 SCH) . $18,000
Allocate to Course Levels by Student Credit Hours
As noted above, the costs associated with Inputs 10 (1), and 0are allocated to each course level on the basis of faculty salaries, or
FTE faculty, or student credit hours (SCH), or a specific course level
designation.
Allocation of Costs Other Than.
General Academic Instruction by
Discipline,' by Course Level
The user may wish:toallotate costs, other than diTect instructionalcosts to specific discipline and course leVe1 cost centers. This
may be done:through the use ofInpUtA2).': :The,modei does not
determine the amount of such allocations. That must be doneexter-'..
nal to the model.
Example:
Allocation of Support Costs
$20,000 of Library Operating Expenditure toGraduate Division History
$10,000 of Science Supply Store OperatingExpenditures to Upper Division Biology
This completes the collection of direct instructional cost data for
each discipline by course level.
Phase V
Having determined the total cost of producing a certain number of credit
hours in a given discipline at a given course level, the model calculates
the cost per student credit hour (SCH) by dividing the total number of
credit hours produced into a given discipline cost center. If the long
method was taken, the cost per student contact hour (SCTH) is also
calculated.
Example:
Calculation of Unit Costs
Lower Division History
Cost Per SCH
Cost Per SCTH
Lower Division Biology
$103,2014,912 SCH
$103 2014,912 SCTH
Cost Per SCH = $141:2SCH
$149,4157,406 SCTH
$19.63Cost Per SCTH
$21.01
$21.01
Up to this point, all the cost information has dealt solely with the
operations of a specific organizational unit, typically a discipline or
department. That information provides the beginning point for calculating
the cost per student by level of student in various programs.
Recall the Induced Course. Load Matrix shown on page 9. The ICLM describes
for each program the average number of credit hours taken by a typical
student, at a given student level, from each discipline, at each course
level. The average direct instructional cost per student may be calculated
by summing the number of credit hours a student takes in each discipline,
at each course level, multiplied by the cost per credit hour in the
respective instructional disciplines at each course level.
Example:
Calculation of Average Annual Cost Per Lower Division History Student
Discipline& CourseLevel
ICLM for Avg. L.D.History Major inSemester Credit Hrs.
Cost PerCredit
Hour
Total
DisciplineContribution
Lower Division 11.8 x $21.01 = $247.92History
Upper Division 4.2 x $37.20 = $156.24History
Lower Division 3.9 x $33.69 = $131.39Biology
Lower Division 3.7 x $34.71 = $128.43Fine Arts
Lower Division 6.4 x $20.90 = $133.76Business
Average Annual Cost Per Student $797.74
The total costs of a program are calculated by multiplying the number of
students times the average cost per student.
23
Example:
Calculation of History Program Costs
Total DirectStudent Average Annual Instructional
Enrollments Cost Per Student Cost
Lower Division 143 x $ 798 = $114,114
Upper Division 186 x $1,113 $207,018
Graduate Division 52 x $1,711 $ 88,972
$410,104
Estimating Equations for Costs Other
Than 'General Academic Instruction
RRPM 1.6 uses a series of estimating equations for costs other than
general academic instruction. These may correspond to research,
public service, and the various support activities of the NCHEMS
Program Classification Structure (Gulko, 1972). However, the model
will accept any series of definitions and estimating equations that
conform to specific institutional needs. The costs associated with
activities other than direct instruction may be input as a constant
and/or as a funCtion of enrollment, and/or student credit hours, and/or
.FTE faculty, and/or FTE staff, and/or total faculty salaries, and/or
total staff salaries, and/or total instructional. budget. Up to
9,889 estimating equations may-be used.
Example:
Support Costs
Libraries Budget = $ 52,000 + ($2.00 x Student Credit Hours) -I,
($100 x FTE Faculty)
Audio/VisualServices Budget = $ 36,000 + ($3.00 x Enrollment) +
($10.000 x FTE Faculty)
ExecutiveManagement $171,000
Phase VI
RRPM 1.6 generates four different types of reports, any or all of which
may be requested by the user. The first type is a series of organizational
unit reports that provide line-item budgets detailing the personnel and
dollar requirements for various organizational units within the institution.
The three levels of aggregation that might typically be chosen for the
organizational unit reports are (1) discipline, (2) department, and (3)
school/college. However, the level of aggregation is defined by the user
and may be tailored to institutional needs.
This report also provides planning parameter information for the lowest
organizational unit. Information on such parameters as productivity
ratios, total credit hour production, cost per credit hour, faculty rank
distribution, faculty salaries by rank, FTE staff and staff salaries, etc.,
is displayed.
The second series of reports shows program budgets for the institution.
The reports indicate the number of students enrolled in each program, the
cost per student, and the total cost of each program.
The third report is a summary for the institution as a whole. It displays
a breakdown of expenditures by institutional, activity; e.g., general
academic instruction, research, libraries. This list of activities may
correspond to the NCHEMS Program Classification Structure, or it may be
adapted by the userto replicate the :institution's own chart-of-accounts
cost centers.
The fourth report is not intended for general display. It is a formatted
display of all parameter data on the file for a given simulation.
III. INDUCED COURSE LOAD MATRIX DEVELOPMENT
The Induced Course Load. Matrix (ICLM) is one of the cornerstone concepts
for instructional cost simulation, for it defines the relationships between
programs and instructional disciplines or departments. When using RRPM 1.6
an institution must define the ICLM as an initial step in the implementation
process. An institution will normally build an historical ICLM that reflects
the course consumption patterns for the given budgetary period th:i, the
institution is seeking to have the model replicate. Once that historical
ICLM has been developed, it may be used as. a point of departure for
predicting future resource requirements of different alternatives and
under different assumptions.
To build an historical ICLM, the institution must first prepare an histor-
ical Instructional Work Load Matrix (IWLM). This is done by analyzing.
student registration records for a specific period of time, counting the
number of credit hours (units) taken by each student in each program at
each student level, from each instructional discipline or department at
each course level. Going back to the IWLM shoWn on page 11, one can
determine through analysis of the student records for the 143 FTE lower
division history majors that:those students took a total of 1,687 credit
hours of lower division history, 601 credit hours of upper division history,
558 credit hours of lower division biology, 529 credit hours of lower
division fine arts, 915 credit hours of lower division business.
Once the historical IWLM has been built, the total number of credit hours
taken by all students in each program is divided by the respective enroll-
ments in those programs. This gives the historical Induced Course Load
Matrix. If the enrollments shown at the top of the IWLM on page 11 were
divided through the columns of that matrix, the result would be the ICLM
shown on page 9.
A series of generalized computer programs has been written to assist
institutions in preparing an ICLM. These programs are available from
the National Center for Higher Education Management Systems.
Building an ICLM requires a number of important considerations. If
historical program cost information is desired, the ICLM used may be
quite different from one that is used for projective purposes. Some
specific considerations include:
Defining programs (e.g., student major, degree program, field of
study, curricular path)
Defining the instructional unit (e.g, course, discipline, depart-
ment, division)
Defining student levels (e.g., lower division, upper division
graduate division)
Defining course levels (e.g., lower division, upper division,
graduate division),
Determining the number of terms to be used for the historical IWLM
(e.g.,'single semester vs. two-semester average)
Defining the number of students for calculation of the ICLM from
the WLM (e.g., FTE vs. headcount students)
The use to be made of the ICLM will determine, in large measure, how it
is to be prepared. Two documents, Induced Course Load Matrix Generator:
System Documentation (Haight and Manning, 1972) and Instructional Program
Budgeting in Higher Education (Clark and Huff, 1972), discuss ICLM pre-
paration in greater detail.
IV. RRPM 1.6 SOFTWARE DESCRIPTION
NCHEMS has attempted to simplify and reduce the computer requirements for
implementation of RRPM 1.6 in comparison to earlier NCHEMS cost simulation
models. One of the primary difficulties of RRPM 1.3 was the large computer
capacity necessary to implement the model. RRPM 1.6 has been designed for
use on computer systems having 50K bytes of core storage. RRPM 1.6 has
been written entirely in ANS COBOL, whereas early NCHEMS models required
both COBOL and FORTRAN capabilities.
The RRPM 1.6 software is- comprised of two basic modules. The first is an
edit/update/calculate program that checks the input data for errors in
formatting, etc. It gives a series of warning and error messages for such
data errors as incomplete data sets, invalid discipline identifiers, incorrrect
degree program identifiers, and alpha characters in numeric fields. If no
serious errors are detected, calculations are completed as described pre-
viously.
The second module is a series of report programs which produce, from the
figures generated in the first module, reports requested ,by the user.
The model may be dimensioned up to the following limits:
Programs or Majors 9,999
Instructional Disciplines 9,999
Student Levels 7
Course Levels 7
Faculty Ranks 6
Staff Categories 4
"Other Discipline Expense Types 7
Types of Instruction (Long Method Only)
Costs Other Than General AcadeiriC Instruction 9,889
More specific information about the operating characteristics of the
model is contained in the RRPM 1.6 System Documentation.
Two additional systems have'been developed at NCHEMS that will aid in
implementating RRPM 1.6. The first is an Induced Course Load Matrix
Generator (Haight and Manning, 1972) that will prepare an Induced Course
Load Matrix from institutional student records. The programs are
sufficiently flexible to permit use with almost any machine-readable
student record system.
The other available system is the Cost Finding Principles (Ziemer, et
1971) software, which will aid institutions in crossing institutional
financial information over to the-Program Classification Structure. The
same software is also used for allocating indirect or support program costs
to primary program cost centers, namely those. activities involving
instruction, research, and public service.
A third set of programs is currently being developed to aid institutions
in the preparation of faculty7related input data for the model. This
system, the Faculty Data Generator, will be available sometime in mid-1973.
Additional information on these systems may be obtained by writing 'to.
the National Center for Higher Education Management Systems.
29
RRPM 1.6 COMPUTATIONAL FLOW
Pages 31 and 32 illustrate the computational flow of RRPM 1.6. The
example shoWTOS for one course level of a single discipline-. Moreover,
this example is stddents only. Ih a foUrIear:
institutional situation would exist wLire some .upPer
for loWpr division
crossovers
division students Would be taking lower diVision courses and so:forth-.
RESOURCE REQUIREMENTS PREDICTION MODEL 1.6FTE FACULTY AND FACULTY SALARY COMPUTATION
Lower Division Enrollments
300 Type Z Students
200 Type Y Students
100 Type X Students
Induced Course Load
Matrix (ICLM)
Programs
Instructional Workload Programs
Matrix (IWLM) Z
900
1,800
cc:0'5b
Lai
33
4.,
,ca.9,.0
2
3
700 400
500 1,400
300 1,200 1,800
1,500 3,000 4,500
3,700
3, 300
U)
Productivity
Ratio200 FTE
SCH FacultyTotal FTE Faculty for
Lower Division Disc. #1
10
Rank Distribution
For Lower Division
Discipline #1
Prof.=.20
Assoc. Prof. -.30Asst. Prof.=.50
%L.D.TDiscipline #1
SCTH InLecture
=. 66 2/3
RatioFCTH/CM
= 2/1
Total Lower Division
Student Credit Hours
(SCH) in Discipline #1
Total Lower Division
Student Contact Hours (SCTH)in Discipline #1
SCTH:
Lecture
2,000
Class Meetings(CMTG):
Lecture
SCTH:
Lab
Class Meetings(CMTG):
Lab
Long Method
31
ulty forDisc. #1
Rank DistributiorFor Lower Division
Discipline #1
Prof.=.20Assoc. Prof,=.30Asst. Prof,=.50.,
Class Meetings(CMTG):Lecture
SCTH:
Lab
20
Class Meetings(CMTG):
Lab
Short Method
NOTE: ABBREVIATIONS
SCTH: STUDENT CONTACT HOURS
FCTH: FACULTY CONTACT HOURS
CMTG: CLASS MEETINGS
SCH: STUDENT CREDIT HOURS
Rank. Distribution:
LectureProf.=.40
Assoc. Prof.=.60
Faculty Contact Hours(FCTH):Lecture
40
FTE Faculty:Lecture
FTE Faculty
By Rank:Lecture
4z-0
Schedule(cn Salary
g Prof. = $18,000
ocB
Assoc. Prof.= $15,000a Asst. Prof. -. $10,0001
Prof. =Assoc. Prof. =
Faculty Contact Hours(FCTH):
Lab
40
FTE Faculty:Lab
FTE FacultyBy Rank:
Lab
Asst. Prof. = 5
Faculty
Workload:Lab
= 8 FCTH
Rank Distribution:
Lab
Asst. Prof.=1,00
Long Method
Total FTE Faculty By
Rank for. Lower. Divis-
sion Disc. #1
Prof, = 2Assoc. Prof. = 3Asst. Prof. = 5
Total Faculty SalariesFor. Lower Division
Disc. #1
2 x $18,000 = $36,0003 x $15,000 = $45,0005 x $10,000 = $50,000
$131,000
NCHEMS 9/72
To Disc. #1Lower Divsion
Cost Center
RESOURCE REQUIREMENTS PREDICTION MODEL 1.6NONFACULTY-SALARY COST COMPUTATION
NOTE: TOTALS FOR DISCIPLINE #1 ALL LEVELS
FTE Faculty 20Total Student Credit Hours (SCH) 4,000
Cha irman'sSalaryRate
$20,000 Chairman'sSalary
FTEChairman
.5 $10,000
Discipline41Lower Division
Cost Center
Unit Costs forDiscipline #1 Lower
Division Instruction
StaffSalaryRate
= $5,000
CreditHourICLM
Prograri
StaffSalaries
Discipline #1All Levels
Per Credit
Hour = $80.00
$30,000
Allocate toLower Division
By FTE Faculty(10120)
Per Contact
Hour = $53.33
Enrollments
100 Type X Stud&
1200 Type Y Studer
300 Type Z Studer
Equation
$6,000
+($200x FTE)
Faculty
+($2xSCH)
Cost of Supplies andOperating Expense
Discipline #1.All Levels
Total =$160,000
t Costs forine #1 Lower
)n Instruction
;redit
it = $80.00
intact
r= $53.33
CreditHourICLM X
Programs
Y Z
1
Co
.9 2 2n.
'0' .9° 330
Eli
Enrollments
100 Typ_e.X Students
200 Type Y Students
30p Type Z Students
CoSt Per. Student
(ContributionOnly From Discipline
#1 Lower Division
Instruction)
X Student =$560
"Student = $160
Z Student = $240
Program Costs
(Contribution OnlyFrom Discipline #1
Lower Division
Instruction)
X = $ 56,000
Y = $ 32,000
Z = $ 72,000
32
EXAMPLE OF ESTIMATINGEQUATION FOR COSTS OTHER THANGENERAL ACADEMIC INSTRUCTION
NOTE: INSTITUTIONAL TOTALS
Enrollment = 2,000 StudentsFTE Faculty = 150FTE Instructional Staff = 40Student Credit Hours (SCH) = 30,000Direct Instructional Budget = $2,500,000
INSTITUTIONAL ESTIMATING EQUATION
$100,000 Constant
+ ($100 X Student Enrollment)r-
+($1,000 X FTE Faculty) --+ ($50 x FTE Instructional Staff)o-
:1-($10 X SCH)
+(.2 x Direct Instructional Budget)r-
Institutional Administra-tion, Libraries, Physical
Plant Operation andOther Support Costs
$100,000
200,000
150,000
2,000
300,000
500,000
Total =
$1,252,000
NCHEMS 9/72
Z.., GLOSSARY OF.423MMONLY US :,TERMS
lost Center-- A defined activity (such as instruction in a discipline)
to which a variety of Specificallyidentiftable costs are attached.
Induced Course Load Matrix (ICLM) - A table defining the relationships
between the instructional programs and the teaching disciplines or
departments that provide instructional services for those programs.
The ICLM displays the average number of credit hours taken at various
course level§ in each instructional discipline by the typical student
in each program at each Student level.
Instructional Work Load Matrix OWLM) - A matrix indicating the total
number of credit hours demanded by all students in each program at
each student level from each of the instructional disciplines or
departments at each course level.
Student Credit Hour (SCH) -,A measure of, progress toward some academic
onjective by a student. This may be a semester credit hour, a quarter
credit hour, a course unit, or any measure of progress toward an
educational objective.
75iching Faculty Those FEE faculty nor parts*Df FTE faculty who in-
:s'i7fact students in, classroom sessions or other or,gani20',Situations
amtL:who' perform other tasks in preparation for and in support ofacademi
qlr vocational laStrUction.
Glass MeetingrUMTG) - One:si.ession ofa class meeting one hour.
ruity Contact Hour (FCTH) - One faculty member'meetingwith a
tnass one hour.
-Student Contact Hour (SCTH) - One student in a class meeting one
hour.
33
REFERENCES
Clark., David, and Huff, Robert. Instructional Program Rudgking in HigherEducation: Boulder, Colo: Western Interstate Commission for HigherEducation, 1972.
Gulko, Warren W. ProgralmClassificatiOn Structure-. Boulder, Colo.: WesternInterstate Commission for Higher Education, 1972.
Gulko, Warren W., and Hussain, K. M. A Resource Requirements Prediction Model(RRPM -l): An Introduction to the Model. Boulder, Colo.: Western InterstateCommission for Higher Education, 1971.
Haight, Michael, and Manning, Charles. Induced Course Load Matrix Generator:System Documentation. Boulder, Colo.: .Western Interstate Commissionfor Higher Education, 1972.
Huff, Robert A., and Manning, Charles. Higher Education Planning an ManagementSystems: A Brief. Explanation. Boulder, Colo.: Western InterstateCommission for Higher Education, 1972.
Hussain, K. M. A Resource Requirements Prediction Model (RRPM-1): Guide forthe Project Manager. Boulder, Colo.: Western Interstate Commission forHigher Education, 1971.
Hussain, K. M. and Martin, James, eds. A Resource Requirements PredictionModel (RRPM-1): Report on the Pilot Studies. Boulder, Colo.: WesternInterstate Commission for Higher Education, 1971.
Ziemer, Gordon; Young, Michael; and Topping, awes. Cost Finding Principles:and Procedures. Boulder, Colo.: Western Interstate Commission for Hitler.Education, 1971.
34
APPEFIDIX I
RESOURCE REQUIREMEITTS PREDICTION MODEL 1.6
FLOW CHART
Def
initi
ons
Org
aniz
atio
n Le
vels
Cou
rse
Leve
ls*
Stu
dent
Lev
els*
Inst
ruct
ion
Typ
esF
acul
ty R
anks
Sta
ff C
ateg
orie
sD
isci
plin
e E
xpen
seT
ypes
Fie
ld o
f Stu
dy T
itles
*
Dis
cipl
ine
Titl
es*
Dep
artm
ent T
itle
Sch
ool T
itles
1CLM
DA
TA
Ave
rage
Stu
dent
Cre
dit
0H
our
(SC
H)
Load
by F
ield
of S
tudy
by S
tude
nt L
evel
by D
isci
plin
eby
Cou
rse
Leve
l
EX
AM
PLE
S:
co 0
iL-a
;
Indu
ced
Cou
rse
Load
Mat
rix(I
CLM
)
by F
ield
of S
tudy
by S
tude
nt L
evel
by D
isci
plin
eby
Cou
rse
Leve
l
Inst
ruct
iona
lW
ork
Load
Mat
rix(I
WLM
)
by F
ield
of S
tudy
by S
tude
nt L
evel
by D
isci
plin
eby
Cou
rse
Leve
l
1AtO
t4A
1-0E
NtE
li bR
1-1I
GH
ER
Etit
itAfi
tikM
AN
AG
EM
EN
TS
YS
TE
MS
AT
WIC
HE
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S. P
RE
DIC
TIO
N M
OD
EL
1.6
LOG
IC F
LOW
Pro
duct
ivity
Rat
ioC
redi
t Hou
rs to
FT
E F
acul
tyby
Dis
cipl
ine,
ly C
ours
e Le
vel
1WLM
(SC
TH
)
by F
ield
of S
tudy
by S
tude
nt L
evel
by D
isci
plin
e
by C
ours
e Le
vel
Cla
ss
Mee
tings
(CM
TG
)
by D
isci
plin
eby
Cou
rse
Leve
l
by In
stru
ctio
nT
ype
Fac
ulty
Con
tact
Hou
rs(F
CT
H)
by D
isci
plin
eby
Cou
rse
Leve
l
by In
stru
ctio
nT
ype
Org
aniz
atio
nal
Leve
ls'C
ours
e Le
vels
.S
tude
nt L
evel
sIn
stru
ctio
n T
ypes
Fac
ulty
Ran
ksS
taff
Cat
egor
ies
Oth
er E
xpen
se.
Typ
es1. 2. 3.
Dis
cipl
ine
Dep
artm
ent
Sch
ool
1. 2. 3. 4. 5. 6. 7.
Fre
shm
an
Sop
hom
ore
Juni
orS
enio
rG
radu
ate
1G
radu
ate
2O
ther
1. 2. 3. 4. 5. 6. 7.
Fre
shm
an
Sop
hom
ore
Juni
orS
enio
rG
radu
ate
1G
radu
ate
2O
ther
1. 2. 3. 4. 5.
Lect
ure
Dis
cuss
ion
Labo
rato
ryIn
depe
nden
tS
tudy
Oth
er
1. 2. 3. 4. 5. 6.
Pro
fess
orA
ssoc
iate
Pro
fess
orA
ssis
tant
Pro
fess
orIn
stru
ctor
Tea
chin
gA
SS
ISla
ntO
ther
1. 2. 3. 4.
Adm
inis
trat
ive
Ass
ista
nts
Sec
reta
ries
Stu
dent
Hel
pO
ther
1. 2. 3. 4. 5. 6. 7. ,
Equ
ipm
ent
Sup
plie
sT
rave
l
Prin
ting
,
Tel
eph
one
Ren
tals
.M
isce
l la
heel
ls
*Out
put o
f IC
LM G
ener
ator
0-5
2
aa a,
rt,
-a cii
,..,
ca.
43..,
FT
E F
acul
ty
by D
isci
plin
eby
Cou
rse
Leve
lby
Ran
k
Fac
ulty
Sal
arie
s
by D
isci
plin
eby
Cou
rse
Leve
lby
Ran
k
Nov
em
bed
72
Fac
tors
1 1
Con
stan
tF
TE
Fac
ulty
Cre
dit H
ours
FT
E C
hairm
an
Fac
tors
.C
onst
ant
FT
E F
acul
tyF
TE
Oth
er S
taff
FT
E C
hairm
anC
redi
t Hou
rsF
acul
ty $
Sta
ff $
sai ay
chai
rman
Cha
irman
Of0
-4%
O14
0,
1.E
stim
atin
g E
quat
ion
2.E
stim
atin
g E
quat
ion
3.E
stim
atin
g E
quat
ion
4.E
ttiiii
atin
g E
quat
ion
1.E
stim
atin
g E
qUat
jnq
2.E
stim
atin
g E
quat
ion
3.E
stim
atin
g E
quat
ion
4.E
stim
atin
g E
quat
ion
5.E
stim
atin
g E
quat
ion
6.E
stim
atin
g E
quat
ion
7.E
stim
atin
g E
quat
ion
FT
E S
taff
bY41
Plif
lF,
byC
ateg
ory
FIn
teff
by D
isci
plin
e,by
Cat
egor
y
FT
E S
taff
by D
isci
plin
e,by
Cat
egor
y
FT
E S
taff
by D
isci
plin
e,by
Cat
egor
y
sfirg
h'j
',;s
.E
Itil
of
....._
-§ta
fttal
iKeS
lijr
Dis
Cib
line,
by C
ateg
ory
Sta
ff S
alar
ies
by D
isci
plin
e,by
Cat
egor
y
Sta
ff S
alar
ies
by D
isci
plin
e,by
Cat
egor
y
Ailo
caie
LaC
ti td
Cou
rse
Leve
ls
by F
acul
ty S
alar
ies,
FT
E F
acul
ty, S
CH
, or
Spe
cific
Cou
rse
Leve
l
064
cO:ii
iaaw
,3Y
010
4111
{1.5
iky
Typ
o
Oili
er E
xpeb
seby
Dis
cipl
ine,
by
Typ
e
Oth
er E
xpen
se
by D
isci
plin
e, b
y T
ype
Oth
er E
xpen
seby
Dis
cipl
ine,
by
Typ
e
Oth
er E
xpen
se
by D
isci
plin
e, b
y T
ype
Oth
er E
xpen
seby
Dis
cipl
ine,
by
Typ
e
Oth
er E
xpen
se
by D
isci
plin
e, b
y T
ype
Allo
cate
Eac
h to
Cou
rS.0
,,
by F
acili
ty S
alar
ieS
,
FT
E F
acul
ty, S
CH
,
or S
peci
fic _
Cou
rse
Leve
l /
Dis
CI0
11he
Cos
t Cen
ters
By
Cou
rse
Leve
l
Org
aniz
atio
nal R
epor
ts I
1.D
isci
plin
e2.
Dep
artm
ent
3.S
choo
l
Nov
embe
r/72
Cos
t Per
Stu
dent
Cre
dit H
our
by D
isci
plin
eby
Cou
rse
Leve
l --C
ost P
er S
tude
ntC
onta
ct H
our
by D
isci
plin
eby
Cou
rse
Leve
l
ICLM
Cos
t Per
Stu
dent
by P
rogr
am
by S
tude
ntLe
vel
Pro
gram
Oild
?et
Fac
tors
Con
stan
tE
nrol
lmen
tS
tude
nt C
redi
t Hou
rsF
TE
Fzc
ulty
ITE
Oth
er S
taff
Fac
ulty
$5,
Off
$lij
itruc
tiona
l Bud
get
/I
/E
stim
atin
g E
quat
ions
for
Cos
ts O
ther
Tha
nG
ener
al A
cade
mic
Inst
ruct
ion
Est
imat
ing
Equ
atio
n C
ost C
ente
rs
SO
MM
ary
Rep
ort
Nov
embe
r/72
APPENDIX II
RESOURCE REQUIREMENTS PREDICTION MODEL 1.6
INPUT FORMS
RR
PM
1.6
INP
UT
FO
RM
S
INT
RO
DU
CT
ION
RR
PM 1
.6 in
put f
orm
s, w
hich
aid
the
user
in p
rovi
ding
info
rmat
ion
to th
e co
mpu
ter-
base
d m
odel
, hav
e be
en d
esig
ned
so a
s to
be
larg
ely
self
exp
lana
tory
. Eac
h in
put f
orm
has
a s
peci
fic
and
uniq
uepu
rpos
ean
d th
at p
urpo
se h
as b
een
expl
icitl
y ex
pres
sed
on e
very
for
m f
or th
eus
er's
con
veni
ence
. How
ever
, eve
ry in
put f
orm
has
cer
tain
com
mon
char
acte
rist
ics.
The
se c
omm
on c
hara
cter
istic
sar
e as
fol
low
s:
1. R
EC
OR
D I
DE
NT
IFIE
R
Thi
s is
a f
our-
char
acte
r m
nem
onic
cod
e w
hich
uni
quel
y id
en-
tifie
s ea
ch in
put r
ecor
d fo
rm f
rom
oth
er ty
pes
of r
ecor
ds. F
orex
ampl
e, "
DE
FN"
is th
e re
cord
iden
tifie
r fo
r th
e de
fini
tion
reco
rd a
nd "
MA
JR"
is th
e re
cord
iden
tifie
r fo
r th
e m
ajor
title
reco
rd, e
tc. T
hese
cod
es h
ave
been
pre
prin
ted
on th
e fo
rms
for
user
con
veni
ence
and
are
fou
nd in
pos
ition
s 1
to 4
.
2. I
TE
RA
TIO
N I
DE
NT
IFIE
R (
OPT
ION
AL
)T
his
is a
two-
char
acte
r fi
eld
that
iden
tifie
s th
eou
tput
iter
atio
n(i
.e.,
year
or
case
). T
his
fiel
d is
not u
sed
by R
RPM
.
3. S
EQ
UE
NC
E F
IEL
D
Thi
s is
an
eigh
t-ch
drac
ter
fiel
d th
at is
pro
vide
dfo
r th
e us
er's
conv
enie
nce.
It a
llow
s th
e us
er to
seq
uenc
e th
e da
ta r
ecor
ds,
thus
allo
win
g th
e us
er to
upd
ate
data
rec
ords
eas
ily.
It s
houl
dbe
not
ed th
at in
put d
ata
for
RR
PM 1
.6ca
n be
sub
mitt
ed in
ara
ndom
way
(i.e
., no
req
uire
d se
quen
ce).
4. I
DE
NT
IFY
ING
NU
MB
ER
SIt
sho
uld
be n
oted
that
dis
cipl
ines
, dep
artm
ents
, maj
ors,
and
colle
ges
(or
scho
ols)
mus
t be
assi
gned
uni
que
iden
tifyi
ngco
des.
Eac
h su
ch id
entif
ying
cod
e co
ntai
nsa
stan
dard
alp
hach
arac
ter
and
four
cha
ract
ers
as il
lust
rate
d be
low
:
Dis
cipl
ine
code
s ar
e: D
0001
, D00
03, D
0004
,et
c.D
epar
tmen
t cod
es a
re: T
0001
. T00
02. T
0003
, etc
.M
ajor
cod
es a
re: P
0001
. P00
02. P
0003
. etc
.C
olle
ge/S
choo
l cod
es a
re: S
0001
, S00
02, S
0003
.et
c.
It is
impe
rativ
e th
at th
e id
entif
ying
cod
es a
ssig
ned
to th
ese
orga
niza
tiona
l uni
ts r
emai
n co
nsis
tent
for
all
inpu
tto
the
mod
el.
5. D
EC
IMA
L P
OIN
T
The
inpu
t for
ms
cont
ain
deci
mal
s fo
r th
e co
nven
ienc
e of
the
user
. How
ever
, no
deci
mal
s ar
e ke
yed
into
the
inpu
t rec
ord
sinc
e th
ey a
re im
plie
d in
the
RR
PM p
rogr
ams.
6. S
IGN
ED
NU
ME
RIC
VA
LU
ES
Onl
y a
few
val
ues
may
be
sign
ed. T
hey
are
indi
cate
d by
-'-
prin
ted
over
the
trai
ling
digi
t. If
ent
ered
, the
sig
nm
ust b
e a
punc
h ov
er th
e tr
ailin
g di
git.
Onc
e da
ta h
ave
been
inpu
t. th
ey w
ill r
emai
n st
atic
exc
ept i
f in
tent
ion-
ally
cha
nged
. To
chan
ge d
ata
alre
ady
inpu
t to
the
mod
el. t
heus
er is
re-
quir
ed o
nly
to in
put t
he li
nkin
g in
form
atio
n su
chas
car
d ty
pe, i
tera
-tio
n nu
mbe
r, c
ours
e le
vel,
etc.
, and
then
the
spec
ific
data
to b
e ch
ange
d.If
a d
ata
fiel
d is
left
bla
nk (
not z
ero)
, the
syst
em w
ill n
ot m
odif
y th
eda
ta f
ield
.
RE
CO
RD
IDE
NT
IFIE
R
RIP
IM2
34
58
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S F
RE
DIC
TIO
N M
OD
EL
CO
NT
RO
L R
EC
OR
D
RE
QU
IRE
D IN
PU
T
PA
GE
OF
DA
TE
Itera
tion
Out
Itera
tion
Nam
e
910
11
Dat
e
12 1
3 14
15
16 1
7 18
19
20 2
1 22
23
24 2
5 26
27 2
8 29
30
31 3
2 33
34
Inst
itutio
n N
ame
35 3
6 37
38
39 4
0 41
42
43 4
4 45
46
47 4
8 49
50
51 5
2 53
54
55 5
657
58
59 6
0 61
62
63 6
4
Cal
cula
tion
Itera
tion
War
ning
Add
ition
alLi
nes
Met
hod
InO
ptio
nF
ile O
ptio
nP
er P
age
6566
6768
6970
7173
74
75
Seq
uenc
e
76 '7
7 78
79
80
ITE
RA
TIO
N O
UT
Thi
s da
ta it
em u
niqu
ely
iden
tifie
s th
e se
t of
data
to b
e cr
eate
d an
d sa
ved
forf
utur
e ite
ratio
ns.
CA
LC
UL
AT
ION
ME
TH
OD
Thi
s da
ta it
em id
entif
ies
the
calc
ulat
ion
met
hod
to b
e us
ed f
or a
ll di
scip
lines
. Spe
cifi
cally
iden
tifyi
ng a
calc
ula-
tion
met
hod
for
a di
scip
line
is a
ccom
plis
hed
thro
ugh
the
Sala
rySc
hedu
le I
nput
She
et. T
he c
alcu
latio
n co
des
are
(L)
for
long
met
hod
and
(S)
for
shor
t met
hod.
If
this
is le
ft b
lank
. (S)
is a
ssum
ed.
ITE
RA
TIO
N I
NT
his
iden
tifie
s th
e se
t of
data
(pr
evio
us it
erat
ion)
to b
eus
ed in
cre
atin
g th
e ite
ratio
n ou
t
WA
RN
ING
OPT
ION
To
supp
ress
war
ning
mes
sage
s, e
nter
any
non
-bla
nk c
hara
cter
.
AD
DIT
ION
AL
FIL
ET
o in
dica
te th
at d
ata
will
be
subm
itted
on
a se
cond
dev
ice,
inse
rt a
ny n
on-b
lank
char
acte
r.
OPT
ION
LIN
ES
PER
PA
GE
Ent
er th
e nu
mbe
r of
line
s pe
r pa
ge d
esir
ed o
n th
e ou
tput
rep
orts
. (D
efau
lt=
55.
Min
imum
= 3
0).
RE
CO
RD
IDE
NT
IFIE
R
E 23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DE
FIN
ITIO
N R
EC
OR
D
OP
TIO
NA
L IN
PU
T
IPA
GE
OF
ID
AT
E
Def
initi
onD
efin
ition
Cod
e
7:8
910
Def
initi
on N
ame
12 1
3 14
15
16 1
7 18
19
20 2
1 22
23
24 2
5 26
27
Def
initi
onA
bbre
v.
28 2
9S
eque
nce
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith u
ser-
supp
lied
nam
es a
nd a
bbre
viat
ions
of
sele
cted
data
ele
men
ts. T
hese
labe
ls a
re u
sed
by th
e sy
stem
for
linki
ng a
nd id
entif
ying
dat
a el
emen
ts f
or e
ither
cal
cula
ting
and/
or r
epor
ting.
If th
e us
er c
hoos
es n
ot to
red
efin
e ce
rtai
n da
ta e
lem
ents
, the
syst
em w
ill u
se th
e de
fini
tion
code
s di
spla
yed
belo
w f
or th
e D
EFI
NIT
ION
AB
BR
EV
.
DA
TA
DE
FIN
ITIO
NC
OD
EM
AX
IMU
MN
UM
BE
R1
Org
aniz
atio
nal H
iera
rchy
(D
efau
lt: I
= D
ISC
IPL
INE
,O
RG
N-
33
2D
EPA
RT
ME
NT
, 3 =
SC
HO
OL
/CO
LL
EG
E)
2. C
ours
e L
evel
s*C
RL
V-7
73.
Stu
dent
Lev
els*
ST L
V-7
74.
InS
truc
tion
Typ
esIN
ST-5
55.
Fac
ulty
Ran
ks-F
RN
K-6
66.
Sta
ff C
ateg
orie
sSC
AT
-44
7. D
isci
plin
e E
xpen
se T
ypes
EX
PN-7
7
Opt
iona
l out
put o
VIC
LM
Gen
erat
or
July/72
RE
CO
RD
IDE
NT
IFIE
R
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
FIE
LD O
F S
TU
DY
TIT
LES
*
RE
QU
IRE
D IN
PU
T
PAG
EO
F
DA
TE
Maj
or
7 8
910
11
Maj
or
Maj
or N
ame
12 1
3 14
15
16 1
7 18
19
20 2
1M
ajor
Nam
e
2223
2425
2627
Seq
uenc
e
2829
30
31
Maj
or
32
3334
3536
3738
39 4
0 41
42
Maj
or N
ame
4344
4546
4748
P
4950
51
5253
5455
5657
5859
60 6
1 62
63
6465
6667
6869
7374
75 7
6 77
7879
80
Thi
s in
put,
whi
ch is
use
d fo
r re
port
ing,
pro
vide
s th
e sy
stem
with
title
s fo
r al
l maj
ors
defi
ned
to th
e sy
stem
.
If th
e na
me
fiel
d is
left
bla
nk, L
lch
maj
or n
ame
will
be
a co
mbi
natio
n of
the
maj
or n
umbe
r an
d th
e w
ord
"maj
or."
For
exa
mpl
e, if
no n
ame
isgi
ven
for
maj
or n
umbe
r 10
56. t
hen
the
nam
e "M
AJO
R-1
056"
will
be
used
EX
AM
PLE
:
P 78
9 10
11
0Y
12 1
3 14
15
16 1
7 18
19
20 2
1 22
23
24 2
5 26
27
*Opt
iona
l out
put o
f IC
LM
Gen
erat
or
July
/72
RE
CO
RD
IDE
NT
IFIE
R
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E T
ITLE
S*
(Org
aniz
atio
nal L
evel
-1)
RE
QU
IRE
D IN
PU
TI
PA
GE
OF
DA
TE
Dis
cipl
ine
78
910
11
Dis
cipl
ine
28 2
9 30
31
32D
isci
plin
e
49 5
01 5
1 52
53
Dis
cipl
ine
Nam
e
12 T 33 54
1314
1516
17 1
8 19
20
2122
2324
2526
27D
isci
plin
e N
ame
3435
3637
38 3
9 40
41
4243
4445
4647
48D
isci
plin
e N
ame
tS
eque
nce
5556
5758
59 6
0 61
62
6364
6566
6768
69
73 7
4 75
76
77 7
8 79
80
Thi
s in
put,
whi
ch is
use
d fo
r re
port
ing,
pro
vide
s th
esy
stem
with
title
s fo
r al
l dis
cipl
ines
def
ined
to.th
e sy
stem
.
If th
e na
me
fiel
d is
left
bla
nk, e
ach
disc
iplin
ena
me
will
be
a co
mbi
natio
n of
the
disc
iplin
e nu
mbe
r an
d th
e w
ord
-dis
cipl
ine.
" Fo
r ex
ampl
e,if
no
nam
e is
giv
en f
or d
isci
plin
e 10
56, t
hen
the
nam
e "D
ISC
IPL
INE
-105
6" w
ill b
e us
ed.
EX
AM
PLE
:
07
89
10 1
1E
iV
LS
12 1
3 14
15
16 1
7 18
19
20 2
1 22
23
24 2
5 2:
'-; 2
7
*Opt
iona
l out
put o
f IC
LM
Gen
erat
or
July
/72
RE
CO
RD
IDE
NT
IFIE
R
E 23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DE
PA
RT
ME
NT
TO
DIS
CIP
LIN
EO
RG
AN
IZA
TIO
NA
L R
ELA
TIO
NS
HIP
(Org
aniz
atio
nal L
evel
-2 to
.
Org
aniz
atio
nal L
evel
-1)
OP
TIO
NA
L IN
PU
T1
PAG
EO
F
DA
TE
Dep
artm
ent
Dep
artm
ent N
ame
T 78
910
1112
13 1
4 15
16 1
718
19 2
0 21
22
2324
25 2
6 27
Dis
cipl
ine
Dis
cipl
ine
Dis
cipl
ine
Dis
cipl
ine
Dis
cipl
ine
D
2829
30
3132
3334
35
3637
3839
40
41 4
243
44 4
5 46
4748
49 5
0 51
52
Dis
cipl
ine
Dis
cipl
ine
Dis
cipl
ine
Dis
cipl
ine
DD
DS
eque
nce
5354
55
5657
5859
60
6162
6364
65
66 5
768
69 7
0 71
72
73 7
4 75
76
77 7
8 79
80
Thi
s in
put,
whi
ch is
use
d fo
r re
port
ing,
pro
vide
s th
e sy
stem
with
the
need
ed in
form
atio
n fo
r lin
king
rel
ated
form
of
orga
niza
tiona
l uni
t est
ablis
hed
with
in th
e in
stitu
tiona
l hie
rarc
hy (
i.e..
disc
iplin
e to
dep
artm
ent)
.
The
nam
e of
the
leve
l with
in th
e or
gani
zatio
nal h
iera
rchy
(i.e
., di
scip
line,
dep
artm
ent)
may
be c
hang
ed w
iR
EC
OR
D.
EX
AM
PLE
:
PLI
/SI
T 0
R Y
I16
17
18 1
9 20
21
22 2
3 24
25
26 2
77
89
1011
1213
1415
0I
028
2930
3132
3334
3536
37
orga
niza
tiona
l uni
t (s)
to a
hig
her
th in
put r
ecor
d I.
DE
FIN
ITIO
N
July
/72
RE
CO
RD
IDE
NT
IFIE
R
H
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
SC
HO
OL/
CO
LLE
GE
TO
DE
PA
RT
ME
NT
OR
GA
NIZ
AT
ION
AL
RE
LAT
ION
SH
IP
(Org
aniz
atio
nal L
evel
-3 to
Org
aniz
atio
nal L
evel
-2)
OP
TIO
NA
L IN
PU
T
PA
GE
OF
DA
TE
Sch
ool /
Col
lege
78
910
Dep
artm
ent
1112
T 28'2
9 30
31
Dep
artm
ent32
33
5354
55
5657
58
13 1
4 15
Dep
artm
ent
34 3
5 36
Dep
artm
ent
59 6
0 61
Sch
ool/C
olle
ge N
ame
1617
'18
19 2
0 21
2223
2425
26
27D
epar
tmen
tD
epar
tmen
tD
epar
tmen
t
T37
3839
40
4142
4344
45
4647
4849
50
51 5
2D
epar
tmen
tD
epar
tmen
t
Seq
uenc
e62
6364
.65
6667
6869
70
7172
73 7
4 75
76
77 7
8 79
80
inpu
t, w
hich
is u
sed
for
repo
rtin
g, p
rovi
des
the
syst
em w
ith th
e ne
eded
info
rmat
ion
for
linki
ng r
elat
ed o
rgan
izat
iona
l uni
t (s)
to a
high
erfo
rmof
org
aniz
atio
nal u
nit e
stab
lishe
d' w
ithin
the
inst
itutio
nal h
iera
rchy
(i.e
.,de
part
men
ts to
sch
ools
or
colle
ges)
.
The
nam
e of
the
leve
l with
in th
e or
gani
zatio
nal h
iera
rchy
(i.e
., de
part
men
t, sc
hool
/col
lege
)m
ay b
e ch
ange
d w
ith in
put r
ecor
d I,
DE
FIN
I-T
ION
RE
CO
RD
.
EX
AM
PLE
:
S0
78
9 -1
0 11
1.1
a12
13
14 1
511
1111
3111
13S
CH
0 0
L16
17
18 1
9 20
21
22 2
3 24
25
26 2
7
3233
34
35 3
6 37
July
/72
RE
CO
RD
IDE
NT
IFIE
RIT
ER
AT
ION
L
23
4L 5
6
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
ICLM
DA
TA
*
RE
QU
IRE
D IN
PU
T
PA
GE
OF
DA
TE
78
910
11
Cou
rse
Leve
l
19 2
0
Cou
rse
Leve
l
43 4
4
Hou
rs
Dis
cipl
ine
12 1
3 14
15
16
Cou
rse
Leve
l
21 2
2 23
24
Hou
rs
45 4
6 47
48
25 2
6
Cou
rse
Leve
l
Stu
dent
Leve
l
17 1
8
Hou
rs
2712
8 29
30
Hou
rs
49 5
05.
_ 5a
53
54
Cou
rse
Leve
l
31 3
2
Cou
rse
Leve
l
Hou
rs
33 3
4 35
36
Hou
rs
55 5
657
58
59 6
0
Cou
rse
Leve
l
37 3
8
Hou
rs
39 4
0 41
42
Seq
uenc
e
L 73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith!t
he in
form
atio
n fo
r de
velo
ping
an
Indu
ced
Cou
rse
Loa
d M
atri
x (I
CL
M).
RR
PM 1
.6su
ppor
ts a
fou
r-di
men
sion
al I
CL
M th
at is
by
maj
or, b
y di
scip
line,
by
stud
ent l
evel
, and
by
cour
se le
vel.
An
ICL
Mm
ay b
e de
fine
d as
the
aver
age
cred
itho
urs
take
n in
var
ious
dis
cipl
ines
at v
ario
us c
ours
e le
vels
by
a m
ajor
at v
ario
us s
tude
nt le
vels
. Stu
dent
and
cour
se le
vels
may
be
defi
ned
via
DE
FIN
ITIO
N R
EC
OR
D 1
.
EX
AM
PLE
:
P1O
10
78
9 10
11
12 1
3 14
15
16 ;
17 1
8
019
20
21 2
2 23
24
25 2
627
28
29 3
0*O
ptio
nal o
utpu
t of
ICL
M G
ener
ator
RE
CO
RD
.ID
EN
TIF
IER
ITE
RA
TIO
N
Stu
dent
Leve
l
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
EN
RO
LLM
EN
T B
Y F
IELD
OF
ST
UD
YB
Y S
TU
DE
NT
LE
VE
L*
RE
QU
IRE
D IN
PU
T
PA
GE
OF
DA
TE
Maj
orE
nrol
lmen
tS
tude
ntLe
vel
78
910
11
12 1
314
15
16 1
7 18
19 2
0
Stu
dent
Maj
orS
tude
ntM
ajor
Leve
lE
nrol
lmen
tLe
vel
Enr
ollm
ent
33,3
435
36
37 3
8 39
Maj
orE
nrol
lmen
tS
tude
ntM
ajor
Leve
lE
nrol
lmen
t
21 2
2 23
24
2526
27
Stu
dent
Leve
l
'40
4142
43
44 4
5 46
47 4
8
Maj
orE
nrol
lmen
t
49 5
0 51
52
53
28 2
9 30
31
32S
tude
ntLe
vel
54 5
5
Maj
orE
nrol
lmen
t
56 5
7 58
59
60 Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith th
e st
uden
t enr
ollm
ent f
or e
very
maj
or b
y st
uden
t lev
el. T
he e
nrol
lmen
t pro
vide
d is
eith
er h
eadc
ount
en-
rollm
ent o
r in
stitu
tiona
lly d
efin
ed F
TE
enr
ollm
ent.
EX
AM
PLE
:
I
78
9 10
11
LD
12 1
3' U
D
14 1
5 16
17
1819
20
*Opt
iona
l out
put o
f IC
LM
Gen
erat
or
87
21 2
2 23
24
25
July
/72'
RE
CO
RD
IDE
NT
IFIE
R
P
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E F
AC
ULT
YP
RO
DU
CT
IVIT
Y R
AT
IO
RE
QU
IRE
D IN
PU
TIS
HO
RT
ME
TH
OD
PA
GE
OF
DA
TE
Dis
cipl
ine
Cou
rse
Leve
lPr
oduc
tivity
Rat
io
78
910
1112
13
1415
16
1718
Cou
rse
Pro
duct
ivity
Cou
rse
Leve
lR
atio
Leve
l
36 3
738
39 4
0 41
42
4344
45
46
Cou
rse
Pro
duct
ivity
Leve
lR
atio
60 6
162
63
64 6
5 66
67
Cou
rse
Lev
el
1920
21
Pro
duct
ivity
Rat
io
I47
48
49 5
0 51
Pro
duct
ivity
Rat
ioC
ours
eLe
vel
22 2
324
25
26 2
728
29
Cou
rse
Pro
duct
ivity
Leve
lR
atio
52 5
354
55
56 5
7 58
55
Pro
duct
ivity
Rat
io
30 3
1 32
33
34 3
5 Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith in
form
atio
n ne
eded
for
cal
cula
ting
FTE
fac
tOty
req
uire
men
ts. I
t ind
icat
es th
eav
erag
e nu
mbe
r of
cre
dit
hour
s an
FT
E te
achi
ng f
acul
ty is
to u
ener
ate
for
a di
scip
line
ata
cour
se le
vel.
EX
AM
PLE
:
78
9 10
11
L0
39
0
12 1
314
15
16 1
7 18
19
U D
i0
27
7,01
020
21
22 2
3 24
25
26 2
7
July/72
RE
CO
RD
IDE
NT
IFIE
R
RM
1X
i1
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
NM
OD
EL
DIS
CIP
LIN
E F
AC
ULT
Y R
AN
K D
IST
RIB
UT
ION
RE
QU
IRE
D IN
PU
TS
HO
RT
ME
TH
OD
PA
GE
OF
DA
TE
Dis
cipl
ine
D 78
910
11
Cou
rse
Leve
l
12 1
3
Fac
ulty
Ren
kR
ank
Dis
trib
utio
n
16 1
7F
acul
tyR
ank
34 3
5
18 1
9 20
21
Ran
kD
istr
ibut
ion
Fac
ulty
Ran
k
22 2
3
Fac
ulty
Ran
k
36 3
7 38
39
40 4
1
Ran
kD
istr
ibut
ion
24 2
5 26
27
Ran
kD
istr
ibut
ion
'42
43 4
4 45
Fac
ulty
Rel
ic
28 2
9F
acul
tyR
ank
46 4
7
Ran
kD
istr
ibut
ion
30 3
1 32
33
Ran
kD
istr
ibut
ion
48 4
9 50
51
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put s
uppl
ies
the
syst
em w
ith d
ata
for
dist
ribu
ting
the
tota
ldi
scip
line
facu
lty to
six
type
s of
use
r-de
fine
d fa
culty
ran
ks. F
acul
ty r
anks
may
be
defi
ned
via
DE
FIN
ITIO
N R
EC
OR
D 1
.
EX
AM
PLE
:
Col
iL
I D
I7
89
10 1
112
13
PR
16 1
718
19
20 2
1LA
P2
00
22 2
324
25
26 2
77
00
28 2
930
31
32 3
3
July
/72
RE
CO
RD
IDE
NT
IFIE
RIT
ER
AT
ION
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E F
AC
ULT
YS
ALA
RY
SC
HE
DU
LE A
ND
CH
AIR
MA
N D
AT
A
PAG
EO
F
DA
TE
SA
LI2
34
Dis
cipl
ine
89
10 1
1
Cha
irman
Allo
catio
nM
etho
d
Fac
ulty
Ran
k
12 1
3
Fac
ulty
Ran
k
33 3
4
Cha
irman
FT
E
54 5
5 56
57 5
8 59
Fac
ulty
Sal
ary
Fac
ulty
Ran
kF
acul
tyS
alar
yF
acul
tyR
ank
Fac
ulty
Sal
ary
1415
16
17F
acul
tyS
alar
y
1819
20
Fac
ulty
Ran
k
2122
23
24F
acul
tyS
alar
y
2526
27
Fac
ulty
Ran
k
2830
31
r?cu
ltySa
lary
32
II
3536
37
3839
40 4
142
43 4
4 45
4647
48
49 5
0 51
52
53
Cha
irman
Sal
ary
Cal
cula
tion
Met
hod
60 6
1 62
63
6465
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith th
e di
scip
line
sala
ry s
ched
ule
for
the
six
teac
hing
facu
lty r
anks
and
cer
litie
r ch
airm
an-r
elat
ed d
ata.
The
chai
rman
's in
form
atio
n is
the
num
ber
or p
ropo
rtio
n of
FT
E c
hair
men
to b
e al
loca
ted
to a
dis
cipl
ine.
thei
r zt
vera
geS
alar
y.an
d th
e se
lect
edm
etho
d to
be
used
in a
lloca
tine
thei
r co
st to
cou
rse
leve
ls. T
he a
lloca
tion
met
hods
ava
ilabl
e ar
e: F
TE
fa.,,
111t
y(F
TE
); f
acul
ty s
alar
ies
(SA
L); s
tude
nt c
redi
t hou
rs (
SC
H);
and
spe
cific
cou
rse.
leve
l (le
ft ju
stifi
ed. 2
pos
ition
). T
he c
alcu
latio
nm
eAtt)
d is
iden
tifie
d by
the
cod:
s(L
) fo
r lo
ng m
etho
d an
d (S
) fo
r sh
ort m
etho
d. T
heca
lcul
atio
n m
etho
d ne
eds
to b
e id
entif
ied
only
if it
is d
iffe
rent
fro
m th
e ca
lcul
atio
nm
etho
d es
tabl
ishe
d on
the
cont
rol r
ecor
d.
EX
AM
PLE
:
78
9 10
11
012
13
14 1
5 16
17
18N
ote:
If n
o al
loca
tion
met
hod
is g
iNeu
. FT
E fa
culty
is a
ssum
ed.
IA(P
1920
I4
00.
1N
21 2
2 23
24
2526
27
95
00
28 2
9 30
31
32
July
/72
RE
CO
RD
IDE
NT
IFIE
R
N
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
iSC
IPLI
NE
RA
TIO
OF
ST
UD
EN
T C
ON
TA
CT
BU
RS
TO
ST
UD
EN
T C
RE
DIT
HO
UR
S
pTIO
NA
L IN
NT
IZ
Z:A
40
1LO
NG
Mki
ii0D
PAG
EO
F
DA
TE
Dis
cipl
ine
78
Cou
rse
Leve
l
Rat
io o
fC
ours
eC
onta
ct H
ours
Leve
lto
Cre
dit H
ours
910
11
12 1
314
15
16
Rat
io o
fC
onta
ct H
ours
to C
redi
t Hou
rs
Cou
rse
Leve
l
17 1
8
Rat
io o
fC
ours
eC
onta
ct H
ours
Cou
rse
Leve
lto
Cre
dit H
ours
Leve
l
Con
Rta
actit
oH
ours
Com
eto
Cre
dit H
ours
toite
l
27 2
829
30
3132
33
34 3
5 36
37 3
8.
.1
the
Thi
s in
put p
rOlt.
tEs
the
EX
AM
PLE
:
89
10 1
1I',
D
12 1
3
19 2
0 21
LI
1
Rat
io o
fC
onta
ct H
ours
to C
redi
t Hou
rs
39 4
0 41
23 Cou
rse
Leve
l
42 4
3
Rat
io o
fC
onta
ct H
ttl.tr
sto
Cre
dit H
ours
24 2
5 g
Rat
io a
tC
onta
tt H
ours
to C
redi
t Hou
rs
44 4
5 46
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
;0
Of
the
num
ber
of c
onta
ct h
otle
n-um
ded
of a
(O
ivip
titte
ata
cour
se ic
vd.
D
14 1
5 16
17.1
80
19 2
0 21
Not
e: T
his
need
be
ente
red
only
if o
ther
than
1.0
0. s
ince
the
sys"
tem
will
use
1.00
as
defa
ult.
July
/72
RE
CO
RD
IDE
NT
IFIE
R
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E S
TU
DE
NT
CO
NT
AC
T H
OU
RD
IST
RIB
UT
ION
TO
INS
TR
UC
TIO
N T
YP
ES
RE
QU
IRE
D IN
PU
TI
1
LON
G M
ET
HO
D
F
PA
GE
OF
DA
TE
Dis
cipl
ine
Cou
rse
Leve
lIn
stru
ctio
nT
ype
78
910
11
12 1
314
15
Inst
ruct
ion
Typ
e
29 3
0
Inst
ruct
ion
Typ
eP
erce
nt
16 1
7.18
Inst
ruct
ion
Typ
eP
erce
nt
I1
31 3
2 33
Inst
ruct
ion
I nst
rudi
enT
ype
Typ
eP
erce
nt
19 2
0
I nst
ruct
ion
Typ
e
nI
21 2
2 23
Inst
ruct
ion
Typ
eP
erce
nt
34 3
536
37
38
Inst
ruct
ion
Typ
e
24 2
5
Inst
ruct
ion
Typ
eP
erce
nt
26 2
7 28
Seq
uenc
e
73 7
4 75
76
77 7
8 79
8
Thi
s in
put p
rovi
des
the
syst
em w
ith th
e ne
cess
ary
info
rmat
ion
for
dist
ribu
ting
the
tota
l num
ber
of d
isci
plin
e co
ntac
tho
urs
at a
cou
rse
leve
lto
the
five
type
s of
inst
ruct
ion.
The
fiv
e ty
pes
of in
stru
ctio
n ar
e de
fine
d by
inpu
t rec
ord
1,D
EFI
NIT
ION
RE
CO
RD
.
EX
AM
PLE
:
D 0
0 0
1 /
78
9 10
11
DD
40
12 1
314
15
16 1
7 18
19 2
06
0
21 2
2 23
July
/72
RE
CO
RD
IDE
NT
IFIE
R
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E S
EC
TIO
N S
IZE
RE
QU
IRE
D IN
PU
T_
LON
G M
ET
HO
D
Dis
cipl
ine
78
910
11
Cou
rse
inst
ruct
ion
Lev
elT
ype
12 1
314
15
Inst
ruct
ion
Typ
e
32 3
3
Sec
tion
Siz
e
16 1
7 18
19
Sec
tion
Siz
e
34 3
5 36
37
Inst
ruct
ion
Typ
e
20 2
1
Inst
ruct
ion
Typ
e
38 3
9
Sec
tion
Siz
e
22 2
3 24
25
Sec
tion
Siz
e
40 4
1 42
43
Inst
ruct
ion
Typ
e
26 2
7
Sec
tion
Siz
e
28 2
9 30
31
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith th
e av
erag
e se
ctio
n si
ze f
or e
ach
disc
iplin
e at
a c
ours
e le
vel f
or e
ach
type
of
inst
ruct
ion.
EX
AM
PLE
:
78
9 10
11
12 1
31
14 1
5F
l2
516
17
18 1
9L
AI
5
20 2
122
23
24 2
5
Not
e: T
his
inpu
t act
ually
spe
cifi
es th
e nu
mbe
r of
con
tact
hou
rs s
atis
fied
by o
ne c
lass
mee
ting.
Thi
s us
ually
is th
e sa
me
as s
ectio
n si
ze.
July
/72
RE
CO
RD
IDE
NT
IFIE
R
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E R
AT
IO O
F F
AC
ULT
YC
ON
TA
CT
HO
UR
S T
O C
LAS
S M
EE
TIN
GS
Tea
m T
each
ing
OP
TIO
NA
L IN
PU
TI
ILO
NG
ME
TH
OD
PA
GE
OF
rDA
TE
Dis
cipl
i ne
78
910
11-
Cou
rse
Leve
lIn
stru
ctio
nR
atio
of
Inst
ruct
ion
Rat
io o
fIn
stru
ctio
nR
atio
of
Typ
e
12 1
314
15
19 2
0
Inst
ruct
ion
Rat
io o
fIn
stru
ctio
nT
ype
FC
TH
to C
MT
GT
ype
FC
TH
to C
MT
GT
ype
FC
TH
to C
MT
G
16'1
7 18
Typ
eF
CT
H to
CM
TG
29 3
031
32
3334
35
21 2
2 23
Rat
io o
fF
CT
H to
CM
TG
36 3
7 38
24 2
526
27
28
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
iNn
with
the
info
rmat
ion
for
adju
stin
g th
e nu
mbe
r of
fac
ulty
con
tact
hou
rs f
or s
uch
thin
gs a
s te
am te
achi
ng.
It in
dica
tes
whe
ther
ther
e is
mor
e or
less
than
one
fac
ulty
mem
ber
assi
gned
to a
cla
ss s
ectio
n.
EX
AM
PLE
.
D 78
9 10
'11
12 1
3D 14
15
00
LI
A
16.1
7 18
19 2
0
Not
e T
hr; n
eed
be e
nter
ed o
nly
if ot
her
than
1.0
0. s
ince
the
syst
em w
ill u
se S
.00
asde
faul
t,
00
21 2
2 23
RE
CO
RD
IDE
NT
IFIE
RIT
ER
AT
ION
12
34:
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E F
AC
ULT
Y W
OR
KLO
AD
RE
QU
IRE
D IN
PU
T.
LON
G M
ET
HO
D.1
6
PA
GE
OF
DA
TE
Dis
cipl
ine
78
910
11
Cou
rse
Inst
ruct
ion
Leve
lT
ype
12 1
314
15
Inst
ruct
ion
Typ
e
32 3
3
Fac
ulty
Wor
kloa
d
16 1
7 18
19
Fac
ulty
Wor
kloa
d
Inst
ruct
ion
Typ
e
20 2
1
Inst
ruct
ion
Typ
e
34 3
5 36
37
38 3
9
Facu
ltyW
orkl
oad
22 2
3 24
25
Facu
ltyW
orkl
oad
40 4
1 42
43
Inst
ruct
ion
Typ
eF
acul
tyW
orkl
oad
26 2
728
29
30 3
1
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith th
eav
erag
e fa
culty
wor
kloa
d fo
r ea
ch d
isci
plin
e, w
hich
is u
sed
for
calc
ulat
ing
the
num
ber
of te
achi
ngfa
culty
req
uire
d fo
r ea
ch d
isci
plin
e at
a c
ours
e le
vel a
t an
inst
ruct
ion
type
.
0
1920
21
22 2
3 24
25
July/72
RE
CO
RD
IDE
NT
IFIE
R
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E F
AC
ULT
Y R
AN
K D
IST
RIB
UT
ION
LON
G M
ET
HO
D
PA
GE
OF
DA
TE
Cou
rse
Inst
ruct
ion
Fac
ulty
Ran
kF
acul
tyR
ank
Fac
ulty
Ran
kD
isci
plin
eLe
vel
Typ
eR
ank
Dis
trib
utio
nR
ank
Dis
trib
utio
nR
ank
Dis
trib
utio
n
DI
i
17
811
11
12 1
314
15
16 1
718
i9 2
0 °2
122
23
24 2
5 26
27
28 2
930
31
32 3
3
Fac
ulty
Ran
kF
acul
tyR
ank
Fac
ulty
Ran
k
Ran
kD
istr
ibut
ion
Ran
kD
istr
ibut
ion
Ran
kD
istr
ibut
ion
L1
11
1i
34 3
536
37
38 3
940
41
42 -
43 4
4 45
46 4
748
49
50 5
1
Seq
uenc
e
F73
74
7:: 7
6 77
78
79 8
0
Thi
s in
put
supp
lies
the
syst
em w
ith d
ata
for
dist
ribu
tinz
the
tota
l dis
cipl
ine
facu
lty a
t a c
ours
e ev
e] a
t an
inst
ruct
ion
type
to s
ix ty
pes
ofus
er-d
efin
ed f
acul
ty.
EX
AM
PLE
:
r-p
R7
00
78
9 10
11
12 1
314
15
16 1
718
19
20 2
122
23
24 2
526
27
28 2
930
31
32 3
3
July
/72
RE
CO
RD
IDE
NT
IFIE
R
-23
ITE
RA
TIO
N.
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E E
ST
IMA
TIN
G E
QU
AT
ION
SF
OR
ST
AF
F
OP
TIO
NA
L IN
PU
T
PA
GE
OF
DA
TE
1
Dis
cipl
ine
Cat
egor
yC
ode
78
910
11
12 1
3
Sta
ffS
alar
y
31 3
2 33
34
35
Allo
catio
nM
etho
d
36 3
7- 3
8
Con
stan
t_
1+
14 1
5 16
17
18
FT
E F
acul
tyS
tude
nt C
redi
t Hou
rF
TE
Cha
irman
Coe
ffici
ent ±
Coe
ffici
ent ±
Coe
ffici
ent ±
19 2
0 21
22
23 2
4 25
26
27 2
8 29
30
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith th
e eq
uatio
n fo
res
timat
ing
disc
iplin
e st
aff
othe
r th
an in
stru
ctio
nal f
acul
ty, I
tal
so p
rovi
des
the
syst
emw
ith th
e us
er-s
elec
ted
met
hod
for
allo
catin
g th
eco
st a
ssoc
iate
d w
ith th
e ot
her
staf
f to
cou
rse
leve
ls. T
he a
lloca
tion
met
hods
ava
ilabl
e ar
eFT
E f
acul
ty (
FTE
); f
acul
ty s
alar
ies
(SA
L):
stu
dent
cre
dit
hour
s (S
al);
and
spe
cifi
c co
urse
leve
l (le
ft ju
stif
ied.
2 p
ositi
on).
The
use
r is
allo
wed
to d
efin
e a
max
imum
of
four
cat
egor
ies
of "
othe
r'st
aff
(i.e
.. se
cret
arie
s, s
tude
nt a
ssis
tant
s, f
acul
tyon
sab
batic
al, c
lerk
s). S
eeD
EF
I-N
ITIO
NR
EC
OR
D I
.
EX
AM
PLE
:
07
39
10 1
112
13
10
_o14
15
16 1
7 18
Not
e: I
f no
allo
catio
n m
etho
d is
giv
en F
IT f
acul
tyis
ass
umed
.
50
19 2
0 21
22
36 3
7 38
July
172
RE
CO
RD
IDE
NT
IFIE
R
L2
34
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
DIS
CIP
LIN
E "
OT
HE
R"
EX
PE
NS
EE
ST
IMA
TIN
G E
QU
AT
ION
S
OP
TIO
NA
L IN
PU
T
PA
GE
OF
DA
TE
Dis
cipl
ine
78
910
11
FT
E C
hairm
anC
oeffi
cien
t±
-
30 3
1 32
33
34
Exp
endi
ture
Typ
eC
onst
ant
FT
E F
acul
tyC
oeffi
cien
t
12 1
314
15
16 1
7 18
19
20 2
1 22
23
24
Stu
dent
.Cre
dit H
our
Fac
ulty
Sal
ary
Coe
ffici
ent±
Coe
ffici
ent
-±
35 3
6 37
38
3940
41
42 4
3
Sta
ff S
alar
yC
oeffi
cien
t-±
44 4
5 46
47
FT
E S
taff
Coe
ffici
ent ±
25 2
6 27
28
29
Allo
catio
nM
etho
d
48 4
9 50
Seq
uenc
e
j73
74
75 7
6 77
78
79 8
0
Thi
s in
put p
rovi
des
the
syst
em w
ith th
e eq
uatio
n fo
r es
timat
ing
the
disc
iplin
e co
sts
othe
r th
anst
aff
(i.e
.. eq
uipm
ent.
supp
lies,
trav
el, e
tc.)
.It
als
o pr
ovid
es th
e sy
stem
with
the
user
-sel
ecte
d m
etho
d fo
r al
loca
ting
the
cost
asso
ciat
edth
e "o
ther
- ex
pens
e to
cou
rse.
leve
ls. T
heal
loca
tion
met
hods
ava
ilabl
e ar
e FT
E f
acul
ty (
FTE
); f
acul
ty s
alar
ies
(SA
L);
stu
dent
cre
dit h
ours
(SC
H);
and
spe
cifi
c co
urse
leve
l (le
ftju
stif
ied,
2 p
ositi
on),
The
use
r is
allu
wed
to d
efin
e a
max
imum
of
seve
n ty
pes
of "
othe
r" e
xpen
se. S
eeD
EFI
NIT
ION
RE
CO
RD
1.
EX
AM
PLE
:
78
9 10
11
12 1
31
85
5
14 1
5 16
17
18 1
9
Not
e: I
f no
allo
catio
n m
etho
d is
giv
en, F
TE
fac
ulty
is a
ssum
ed.
0
20 2
1 22
.23
24L
S 48 4
9 50
July
/72
RE
CO
RD
IDE
NT
IFIE
R
23
4
ITE
RA
TIO
N
5
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
ALL
OC
AT
ION
OF
CD
ST
S D
TH
ER
TH
AN
GE
NE
RA
L A
CA
DE
MIC
INS
TR
UC
TIO
N T
O D
ISC
IPLI
NE
S
OP
TIO
NA
L IN
PU
T
PA
GE
OF
DA
TE
Dis
cipl
ine
78
910
11
Cou
rse
Leve
l
12 1
3C
ours
eLe
vel
36 3
7C
ours
eLe
vel
60 6
1
Add
ition
alC
osts
14 1
5 16
17
18 1
9
Add
ition
alC
osts
38 3
9 40
41
42 4
3A
dditi
onal
Cos
ts
62 6
3 64
65
66 6
7
Cou
rse
Leve
l
20 2
1C
ours
eLe
vel
44 4
5
Add
ition
alC
osts
22 2
3 24
25
26 2
7
Add
ition
alC
osts
46 4
7 48
49
50 5
1
Cou
rse
Leve
l 128
29
Cou
rse
Leve
l
52 5
3
Add
ition
alC
osts
30 3
1 32
33
34 3
5A
dditi
onal
Cos
ts
54 5
5 56
57
58 5
9
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
am
eans
of
allo
catin
g_no
nins
truc
tiona
l cos
ts to
dis
cipl
ine
by le
vel o
f co
urse
.
The
se d
ata
if e
nter
ed, m
ay c
ome
from
som
e ot
her
syst
em. s
uch
as. C
ost F
indi
ng-P
rinc
iple
s.
L D
12 1
3'1
1- 1
5°
5 16
1°7
1°8
1°9
20 2
122
23
24 2
5 26
27
July
/72
RE
CO
RD
IDE
NT
IFIE
R
ES
Q
12
34
ITE
RA
TIO
N
56:
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
ES
TIM
AT
ING
EQ
UA
TIO
N F
OR
CO
ST
SO
TH
ER
TH
AN
GE
NE
RA
L A
CA
DE
MIC
INS
TR
UC
TIO
N
OP
TIO
NA
L IN
PU
T
PA
GE
OF
DA
TE
Est
imat
ing
Equ
atio
n N
o.
78
910
Enr
ollm
ent
Coe
ffici
ent
±
Est
imat
ing
Equ
atio
n N
ame*
1112
13
"14
15 1
6.17
18
19 2
0 21
22
23 2
4 25
26
FT
E F
acul
tyS
tude
nt C
redi
t Hou
rC
oeffi
cien
t±
Coe
ffici
ent
+
35 3
6 37
38
39 4
041
42
43 4
4: "
45 4
6 47
Fac
ulty
Sal
arie
sS
taff
Sal
arie
sC
oeffi
cien
t ±
60 6
1 62
63
Coe
ffici
ent
64 6
5 66
67
Inst
ruct
iona
l Bud
get
Coe
ffici
ent ±
68 6
9 "7
0 71
72
48 4
9 50
51
52 5
3
Con
stan
t
27 2
8 29
30
31 3
2 33
34
Sta
ffC
oeffi
cien
t
54 5
5 56
57
58 5
9
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put p
rovi
des
the
syst
em w
ith th
e eq
uatio
n fo
r es
timat
ing
the
cost
for
inst
itutio
nally
defi
ned
orga
niza
tiona
l uni
ts o
ther
than
gen
eral
inst
ruct
ion
iac
adem
ic a
nd v
ocat
iona
l nst
ruct
on (
i.e..
librz
iry
cost
s, p
hysi
cal p
lant
cos
ts. e
tc.)
. The
use
r L
ial
low
ed to
def
ine
a: m
axim
um o
f 9,
889
equa
-tio
ns. I
f th
e us
er c
hoos
es to
inco
rpor
ate
a m
ajor
hea
ding
for
gro
upin
g-re
late
d eq
uatio
ns, h
e si
mpl
yin
dica
tes
this
by
a re
late
d nu
mer
ic v
alue
in th
e hi
gher
ord
er p
ositi
on (
posi
tions
7 a
nd 8
) an
d ze
roes
in th
e lo
w-o
rder
pos
ition
(po
sitio
ns9
and
10)
at, t
he e
quat
ion
num
ber.
Exa
mpl
e:2.
00re
sear
ch2.
02 in
divi
dual
res
earc
h2.
03 o
rgan
ized
res
earc
h
Not
e: E
stim
atin
g eq
uatio
n no
s. 0
.00
thro
ugh
01.1
0 ar
e pr
edef
ined
by
the
syst
em a
nd m
ay n
ot b
e us
er in
put.
The
est
imat
ing
equa
tion
nam
e is
req
uire
d in
ord
er to
cre
ate
this
rec
ord.
July
/72
RE
CO
RD
IDE
NT
IFIE
R
23
4
ITE
RA
TIO
N
56
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
BLA
NK
ET
PA
RA
ME
TE
R C
HA
NG
E
OP
TIO
NA
L IN
PU
T
PAG
EO
F
DA
TE
Cha
nge
Iden
tifie
r
78
910
Maj
or o
rD
isci
plin
e
Ran
k,S
tude
nt L
evel
, or
Cou
rse
Leve
l
11 1
2 13
14
1516
17
Inst
r.T
ype
18 1
9
Per
cent
age
Am
ount
Cha
nge
2021
22
23 2
4 25
(Rig
ht J
ustif
ied)
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s in
put a
llow
s pe
rcen
tage
incr
ease
s or
dec
reas
esac
ross
dis
cipl
ines
or
maj
ors.
The
use
r is
pro
vide
d w
ith th
e fl
exib
ility
of
eith
er c
hang
ing
a pa
ram
eter
acr
oss
all d
isci
plin
es o
r m
ajor
s at
any
or
all l
evel
(s),
or
chan
ging
apa
ram
eter
of
a sp
ecif
ic d
isci
plin
e or
maj
or a
t any
or
all
leve
l(s)
. Lev
els
cont
aini
ng a
ll as
teri
sks
will
be
cons
ider
ed a
s co
veri
ng a
llT
he p
aram
eter
s an
d th
eir
resp
ectiv
e co
des
and
leve
ls a
re a
sfo
llow
s:C
HA
NG
E-
MA
JOR
DIS
CIP
LIN
E S
TU
DE
NT
CO
UR
SE P
ER
SON
NE
L I
NST
RU
CT
ION
PAR
AM
ET
ER
IDE
NT
IFIE
R N
UM
BE
RN
UM
BE
RL
EV
EL
LE
VE
LR
AN
KT
YPE
I. S
tude
nt E
nrol
lmen
tE
NR
L2.
Pro
duct
ivity
Rat
io (
shor
t met
hod)
PRO
D3.
Fac
ulty
Sal
arie
sFS
AL
4. C
hair
man
Sal
arie
sC
SAL
x x x5.
Sup
port
Sta
ff S
alar
ies
SSA
Lx
6. C
redi
t to
Con
tact
Hou
r R
atio
(lo
ng m
etho
d)C
ON
Tx
7. S
ectio
n Si
ze (
long
met
hod)
SEC
Tx
8. F
acul
ty W
orkl
oad
(lon
g m
etho
d)L
OA
Dx
July
/72
RE
CO
RD
IDE
NT
IFIE
R
RR
P M
6
2 3
48
9
(0
5)
10 1
1 12
,13
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
FO
RM
AT
TE
D F
ILE
DIS
PLA
Y R
EQ
UE
ST
RE
CO
RD
PAG
EO
F
DA
TE
Itera
tions
Req
uest
edD
efin
ition
Dat
e R
eque
sted
(Y =
Yes
, N =
No)
(All,
Spe
cific
)D
EF
N =
15 1
6 17
18
19 2
0 21
22'
2325
26
27 2
8 29
30
Maj
or D
ata
Req
uest
edN
on-I
nstr
. Dat
a R
eque
sted
(Y =
Yes
, N =
No)
(Y =
Yes
, N =
No)
46 4
7 48
'49
50 5
153
54
55 5
6 57
58
ICLM
Dat
a R
eque
sted
(Y =
Yes
,. N
= N
o)
32 3
3 34
35
36 3
7
Line
s P
er P
age
Req
uest
ed
60 6
1 62
63
64 6
5 66
67
Dis
cipl
ine
Dat
a R
eque
sted
(Y=
Yes
, N=
No)
DS
39 4
0 41
42
43 4
4
Seq
uenc
e
73 7
4 75
76
77 7
8 79
-80
Thi
s re
ques
t rec
ord
allo
ws
the
user
to d
ispl
ay th
e co
nten
t of
the
RR
PM 1
.6 M
aste
r Fi
le. I
f no
req
uest
s ar
e pr
esen
t dur
ing
exec
utio
n, o
nly
limite
d da
ta c
once
rnin
g th
e fi
le w
ill b
e di
spla
yed
(i.e
.fi
le in
vent
ory)
. The
pro
gram
will
acc
ept m
ultip
le r
eque
sts.
The
se r
eque
sts
shou
ld b
ein
itera
tion
(IT
ER
) or
der
(i.e
., as
cend
ing
orde
r).
In o
rder
to c
ompl
ete
this
for
m, t
he u
ser
mus
t ide
ntif
y w
hat i
nfor
mat
ion
on th
e R
RPM
1.6
file
nee
ds to
be
disp
laye
d.T
o in
dica
te th
e nu
mbe
r
of it
erat
ions
(IT
ER
), u
se A
LL
for
all
itera
tions
via
nted
: and
for
a s
peci
fic
itera
tion.
indi
cate
the
two-
char
acte
rite
ratio
n id
entif
ier
used
whe
n
the
file
was
cre
ated
. For
the
defi
nitio
n, I
CL
M, d
isci
plin
e, m
ajor
or
non-
inst
ruct
iona
l dat
a us
e "N
" (o
r bl
ank)
for
no
data
dis
play
ed, a
ny o
ther
char
acte
r w
ill d
ispl
ay d
ata
Lin
es p
er p
age
allo
w th
e us
ers
to o
verr
ide
the
exis
ting
lines
per
pag
e. E
nter
a tw
o-di
git,
righ
t-ju
stif
ied
num
ber
grea
ter
than
29.
2641971000045400(507):
9341600000045200(507):
5M:373:GD:Hirsch:2BA74
RE
CO
RD
IDE
NT
IFIE
R
R R
IP M
12
34
11
6 1
68
910
0 6
1
)
11 1
2 13
RE
SO
UR
CE
RE
QU
IRE
ME
NT
S P
RE
DIC
TIO
N M
OD
EL
RE
PO
RT
S R
EQ
UE
ST
RE
CO
RD
PA
GE
_ O
FD
AT
E
Itera
tions
Req
uest
ed(A
ll, S
peci
fic)
16 1
7 18
19
20 2
1 22
23
Inst
itutio
n S
umm
ary
Rep
ort
(Y =
Yes
, NN
o)
Org
aniz
atio
nal B
udge
t Rep
ort
(Y -
= Y
es, N
= N
o)
50 5
1 52
53
54 5
5 56
57
58 5
9
26 2
7 28
29
30 3
1 32
33
34 3
5
Line
s P
er P
age
ILI
14E
.S =
-62
63
64 6
5 66
67
68 6
9
Low
est O
rgan
izat
iona
lLe
vel
36
Pro
gram
Bud
get R
epor
t(Y
= Y
es, N
= N
o)
38 3
9 40
41
42 4
3 44
45
46 4
7
Seq
uenc
e
73 7
4 75
76
77 7
8 79
80
Thi
s re
ques
t rec
ord
allo
ws
the
user
to s
peci
fy w
hich
of
thre
ere
port
s fo
r an
y or
all
itera
tions
sho
uld
be p
rodu
ced.
If
no r
eque
sts
are
pres
ent
duri
ng e
xecu
tion,
all
repo
rts
will
be
prod
uced
for
all
itera
tions
pre
sent
on
the
file
. The
prog
ram
will
acc
ept m
ultip
le r
eque
sts.
The
se r
equ:
sts
shou
ld b
e in
iter
atio
n (I
TE
R)
orde
r (i
.e..
asce
ndin
a or
der)
.t.
In o
rder
to c
ompl
ete
thk
form
, the
use
r m
ust i
ndic
ate
the
repo
rts
desi
red
and
for
wha
t ite
ratio
ns. T
o in
dica
te th
e nu
mbe
r of
iter
atio
ns (
ITE
R),
use
AL
L f
or a
ll ite
ratio
ns w
ante
d. f
or a
spe
cifi
c ite
ratio
n, in
dica
te th
e tw
o-ch
arac
ter
itera
tion
iden
tifie
r us
ed w
hen
the
file
was
cre
ated
. To
requ
est a
ny o
f th
e th
ree
repo
rts
(org
aniz
atio
n bu
dget
, pro
gram
bud
get,
or in
stitu
tiona
l sum
mar
y). e
nter
any
cha
ract
er o
ther
than
"N
" or
bla
nk.
To
supp
ress
any
of
the
repo
rts.
ent
er "
N'
blan
kl. T
o ov
erri
de th
e ex
istin
g lin
es p
er r
epor
t pag
e, e
nter
a tw
o-di
git,
righ
t-ju
stif
ied
num
ber
area
ter
than
29.
The
use
r sh
ould
indi
cate
the
low
est o
rgan
izat
iona
l lev
el r
eque
sted
on
the
orga
niza
tion
budg
et. T
he v
alid
ent
ries
are
(4)
inst
itutio
nal l
evel
son
ly (
3) le
vels
3 a
nd 4
; (2)
leve
ls 2
, 3 a
nd 4
: (1)
leve
ls 1
, 2,
3 an
d 4:
any
, oth
er e
ntry
or
blan
k gi
vesi
all i
nstit
utio
nal l
evel
s.
r nvivi otoi tiVA.L.L.JAbL.L UUFY
Advisory Structure for theNATIONAL CENTER FOR HIGHER EDUCATION MANAGEMENT SYSTEMS at WICHE
Robert Mautz(Chairman)Chancellor, State UniversitySystem of Florida
Thomas S. Smith (Vice-Chairman),President, Lawrence University".
Frank C. AbbottExecutive Director, ColoradoCommission on Higher Education
Rutherford H. AdkinsVice President, Fisk University
Fred E. BalderstonChairman, Center for Research inManagement Science, University of.California, Berkeley
Hale ChampionFinancial Vice PresidentHarvard , University
Donald IL ClarkChairman,' Higher Education Advisory..Committee to the Midwest
Kenneth CreightonDeputy Vice.President for FinanceStanford University
'red E. DavisFinancial Vice PresidentUniversity of Utah
James Eden (Chairman)Directsr, of ,.AdministrativcServices and Assistant to thePresident, University. ofRochester, River Campus
John F. Chaney (Vice-Chairman)Vice Provost for ManagementSystems, University of Colorado
W. K. Boutwell, Jr.Director, Office of Planning andEvaluation, Florida StateUniversity System
Mary, Jane CalaisVice President, Financial Affairsand Treasurer, The Junior CollegeDistrict of St. Louis
EXECUTIVE COMMITTEE
Man FergusonExecutive Director, New EnglandBoard of Higher Education
James FurmanExecuiive Coordinator, WashingtonCouncil on Higher Education
Richard GibbCommissioner of Higher EducationSouth'Dakota State Board of Regents
James F. GollattscheckPresident, Valencia CommunityCollege
George KaludisVice Chancellor, Operations andFiscal Planning, VanderbiltUniversity
Douglas MacLeanVice President for Management ServicesUniversity of Houston
Robert McCambridgeAssistant Commissioner for HigherEducation Planning, New York StateEducation Department
James MillerProfessor, Center for the Study ofIligher'Education, The Universityof Michigan
G. Theodore MitauChancellor, The Minnesota StateCollego Board
Gordon OsbornAssistant Vice Chancellor forManagement, State University ofNew York, Central Administration
Garland P. PeedAy -"ant Superintendent of
"-' .3s, State Center JuniorCk.',':ge District, California
Donald E. PercyVice President for Budget Planningand Analysis, University ofWisconsin System
Richard S. TakasakiChancellor, University of Hawaii,Manoa
TECHNICAL COUNCIL
Denis J. CurryDeputy Coordinator for InformationSystems, Washington Council onHigher Education
William DempseyAssistant to the President, StateUniversity of New York, Plattsburgh
Boyd W. Ilorne.Assistant Chief, Budget Planningand Administration, The California.State University and Colleges
.1Ians II. JennyVice President of Finance andBusiness, Director of AdministrativeComputer Services, College ofWooster
Marvin WachmanVice President for AcadeinicAffairs, Temple University.
Fred L. WellmanExecutive Secretary, IllinoisJunior College Board
Martin ZeiglerAssociate ProvostUniversity of Illinois
T. Harry McKinneyDirector, Planning Division Bureauof Higher Education, Michigan Departmentof Education
William R. Odom .Coordinator, PPBS,Development, Divisionof Community C011eges, Florida StateDepartment of'Education
Michael M. RobertsDirector, Management SystemsStanford University
Ron SappDirector, Office of AdministrativeSystems, Johns Hopkins University