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A FRAMEWORK FOR MANAGEMENT
INFORMATION SYSTEMS EVOLUTION
AND
CASE STUDY
by
PETER FRANCIS DIGIAMMARINO
B.S., University of Massachusetts(1975)
SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF
SCIENCE
at the
MASSACHUSETTS INSTITUTE OF
TECHNOLOGY
June, 1977
Signature of Author .........................................Alfred P. Sloan School of Management, May 12, 1977
Certified by ..................................................Thesis Supervisor
Accepted by ................................................Chairman, Departmental Committee on Graduate Students
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A FRAMEWORK FOR MANAGEMENT
INFORMATION. SYSTEMS EVOLUTION
AND CASE STUDY
by
PETER FRANCIS DIGIAMMARINO
Submitted to the Alfred P. Sloan School of Management on May
12, 1977 in partial fulfillment of the requirements for the
degree of Master of Science.
ABSTRACT
This thesis describes several concepts relating to computerbased management information systems. Environmental factorsand guidelines that lead to the evolution of a successfulsystem are presented. A comprehensive framework for manage-ment information systems evolution is then proposed alongwith an example of its use through a case study.
Use of the proposed framework demonstrates its utility in anactual case, without leading to a definitive statement con-cerning its universal applicability.
Thesis Supervisor: JOHN J. DONOVAN
Title: PROFESSOR OF MANAGEMENT SCIENCE
Thesis Advisor: STUART E. MADNICK
Title: ASSOCIATE PROFESSOR OF MANAGEMENT
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ACKNOWLEDGEMENTS
This thesis could not possibly have been completed
without the magnanimous support and assistance received from
friends, relatives, advisors and colleagues too many to list
in entirety. Professor John Donovan provided invaluable
guidance from the beginning right through to the very end.
Paul Schaller, Lee Freeman, Luther Goodie, and Edward McCabe
all deserve special recognition for their help with the case
study and their support and constructive comments throughout.
My brother, Paul, also receives credit for his valuable
assistance. My parents, for giving endless support and for
sharing their worldly wisdom with me have earned a long
awaited word of thanks. My wife, Margaret Owen, with whom
I have begun to share the pleasures and pains associated with
pursuit of both a career and happiness, will now, finally,
experience life divorced from academia. For her I reserve
the highest form of gratitude.
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A Framework for ManagementInformation Systems Evolution
And Case Study
TABLE OF CONTENTS
Page
Abstract 2
Acknowledgements 3
Table of Contents 4
List of Illustrations 6
Chapter 1 Management Information Systems 7
1.1 Concepts and Misconceptions 9
1.2 Frameworks for MIS Evolution 12
Chapter 2 Proposed Framework 14
2.1 Needs Assessment 16
2.2 Design 26
2.3 Approach 32
2.4' Actualization 39
2.5 Evaluation 42
2.-6 Summary of Framework 45
Chapter 3 Case Study 47
3.1 Needs Assessment 47
3.2 Design 61
3.3 Approach 63
3.4 Summary of Case Study 74
Bibliography 81
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Page
Appendices 86
A. Organization of Departmentof Education 86
B. Organization of Bureau ofInformation Systems 87
C. Current Subsidy Formula 88
D. Three Proposed Changes toSubsidy Formula 92
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TABLE OF ILLUSTRATIONS
Figure Title
1 Proposed Framework
2 Evolution of A Management InformationSystem
3 Gorry and Scott Morton Framework ofDecision Making
4 - Information Characteristics by DecisionArea
5 Current Subsidy System
6 Proposed Subsidy System
7 District Data Base
8 Subsidy Components by County and District
9 Frequency Distribution by District
10 Macros Used to Compute Subsidies
11 Commands to Calculate Subsidy Allotments
12 Macros Used to Compute SubsidieslAlternative Formula)
13 Subsidy Components by County and Dietrict(Alternative Formula)
14 Frequency Distribution by District.(Alternative Formula)
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1 MANAGEMENT INFORMATION SYSTEMS
Computer based Management Information Systems (MIS) play
an important role in virtually all contemporary organizations.
A definition of management information systems, put forth by
Kennevan,(40) summarizes the key dimensions of the concept:
MIS - "An organized method of providing past,present and projection information re-lated to internal operations and externalintelligence. It supports planning, con-trol and operational functions of an org-anization by furnishing information inthe proper time frame to assist in thedecision process.
A summary of issues relating to management information systems,
eminating from Kennevan's attempt at a definition, prefaces
the body of this paper in order to establish common ground
for the unveiling of ideas relating to them.
An MIS is used to process data for some purpose in an
organization. Data used for a specific purpose is referred
to as "information". This purpose is one that probably existed
before an MIS was present and one that would continue were an
MIS nonexistent or made unavailable. The existence of an
MIS is justified only to the extent that the functions or
activities to which it applies are made more efficient, more
effective or simply easier to perform as a result of its
presence. An MIS is a service to its users and is not a
product in itself or, stated in another way, the MIS is a
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means to an end and is not an end in itself.
Kennevan highlights the realm of influence of an MIS.
Support of planning, control and operations implies an impact
on all phases of organizational activity. In reality, however,
a given MIS will.probably be oriented more toward a particular
one of Anthony's (4) three divisions of business functions
(planning, control, and operations) than another.
An MIS is capable of storing, retrieving and processing
historical data, assimilating present data and projecting
information about the future. Information internal and
external to the organization may be handled by the system.
There was a time when it was envisioned that an MIS would
be capable of managing all information related to an organization
in any way. An MIS of this sort is often referred to as
a "Total MIS". The concept of the "Total MIS" has been much
criticized in the literature (1,15,31,39,52). Contemporary
systems are oriented to specific portions of organizational
activity and deal with only a subset of all possible internal
and external information.
Kennevan suggests that an MIS provides timely information
to be used in decision making. In addition there is a notion
of supplying information in the format most appropriate to a
given situation and a corresponding means of insuring that
the information is delivered to the right place.
These broad characterizations of an MIS yield an ideal-
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istic perception of a system that aids in all phases of
organizational activity by supplying the right information,
to the right people, in the right format and at the right
time. This abstraction of an MIS is a useful conceptualization
as it accurately depicts the important dimensions of such
systems.
The growth in scope, breadth and complexity of management
information systems has given rise to a vast array of new
technical and operational problems, particularly in the
early stages of MIS development. The ability to deal with
these problems will determine whether or not information systems
will ever reach their predicted potential utility in the
business world.
1.1 CONCEPTIONS AND MISCONCEPTIONS
Management Information Systems have proliferated at an
astronomical rate in recent years, primarily as a result of
increasing technological capabilities and decreasing costs
of computer hardware. There appears to be an endless stream
of activities to which an MIS can be applied. This enthusiasm
is personified by Ackoff (1) when he refers to a "romantic
relationship between analysts and the most glamorous instrument
of our time, the computer." The analyst must remember, however
that the goal is to institute a vehicle capable of providing
more effective and efficient operations and not simply to
computerize the current system.
Ackoff points to several misconceptions that have been
the source of misguided attempts to institute management
information systems. Some of his revelations are considered
here as they are appropriate caveats to any attempts to bring
an MIS to life:
"Management suffers from a lack of relevantinformation in most important decision making."
This once accepted tautology is portrayed as a false pretense
by Ackoff (and later by Argyris (6)). A more accurate version
of the concept that is supported in this thesis runs as follows:
"Management suffers from an abundance ofirrelevant information in most importantdecision making."
The MIS designed to provide management with all possible in-
formation is bound to aggravate rather than ameliorate the
plight of decision makers. An MIS should be designed to pro-
vide filtered ACCESS to relevant information rather than being
the source of endless and useless reports.
"Designers of management information systemscan best determine users' information needsby asking him what information he requiresto make decisions."
The problem raised with this statement is that users
often do not know what information they need (on a regular
or ad--hoc basis) until they need it. Both Ackoff and Burg-
staller (10) point out that traditional attempts to discover
user needs through personal interviews and questionnaires
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leads to confusing and often contradictory results. Users
will generally either play it safe and ask for "the works"
(in which case the analyst responds by nobly trying to pro-
vide him with more than everything) or the decision maker will
claim that he already has all the information he can possibly
use, leaving MIS designers with no recourse but to guess at
what information to provide.
The final warning concerns the MIS users' intimacy with
the system. Traditionally users have been shielded from the
mechanics and logic of the MIS in order to protect them from
having to learn its esoteric details. Such a relationship
allows the user to be manipulated by the system. As a result,
the MIS cannot be adequately controlled or evaluated by the
user. Users ought to be comfortable with their MIS and should
be encouraged to ask questions concerning its processing
and results to insure correctness and relevance to the tasks
at hand. The introduction of an MIS into an organizational
environment can be an extremely emotional and complex problem
in human engineering. A high degree of interpersonal competance
is demanded of the MIS administrator in order to lead a smooth
transition to a new system.
Many authors have identified and made frequent reference
to four factors which, to a large extent, determine the fate
of even a superbly designed system (6,14,27,39,43,52):
-Top management support
-Clear statement of system objectives
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-Active and continuous user involvement
-Minimal degree of complexity and changeto organizational activity-
Empirical evidence has shown that when these factors are entirely
ignored, a system is sure to fail. "Successful" systems are
found to have been concerned with at least some of these factors.
In the framework proposed in Section 2, these issues play an
integral role in MIS evolution.
1.2 FRAMEWORKS FOR MIS EVOLUTION
Several frameworks and models that attempt to portray
the process of MIS evolution have appeared in the literature
(2,7,8,12,16,22,23,24,26,32,50,54). (Traditionally this
process has been labeled "MIS design" or "MIS implementation".
Here, and throughout this thesis, the term "MIS evolution"
is used in order to convey a more "start to finish" flavor
to the subject.) These have primarily been produced by
academicians who have tracked successful and unsuccessful
management information systems in order to identify factors
that lead to a successful system. Their frameworks represent
an effort to illustrate linear step sequences that provide
a means of visualizing MIS evolution from a global perspective
but fail to provide adequate guidelines for future endeavors.
A characteristic common to most frameworks found in
the literature is that they leave no recourse at each step
but to go on to another step. This has negative impact if
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at some point it may be more appropriate, for one reason
or another, to abort the effort entirely. Few models
incorporate explicit checkpoints which call for reexamina-
tion of needs, objectives, feasibility and costs in order
to enable either management, user or analyst to terminate
the project.
Guidelines which account for the dynamic nature of the
evolutionary process and which focus attention on critical
phases of evolution are needed in order to give a coherent,
structured, and parsimonious foundation for MIS development.
A framework encompassing these guidelines must consider
communication between analysts, designers and users; it
should reflect a concern for human emotional behavior and
the importance of power; as well as document the steps
leading to MIS development. Finally, a set of guidelines
must be validated with repeated use to demonstrate that it
has utility'in the real world.
The framework presented in Section 2 is an attempt to
impose a structure that captures these essential points and
that will be useful in actual cases. Just as there is no one,
universally accepted definition of management information
systems, however, there is no single framework or set of
guidelines that are applicable in all instances. In essence,
then, this is a presentation of what appears to be a reason-
able methodology for MIS evolution in many cases, and evi-
dence that this is true in a particular application.
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2 PROPOSED FRAMEWORK
There are five major divisions or phases that comprise
the proposed framework (see figure 1). Within each phase
there is an associated set of activities and outcomes that
determine the function of that phase in the evolution of an
MIS.
G NEEDS DESIGN PPROACH ACTUALIZATIOND VALUATIONASSESSMEN
Figure 1Five Phase in MIS Evolution
The Needs Assessment (also called Pre-Design) phase
includes: becoming familiar with the user environment;
identifying problems, inefficiencies and bottlenecks; and
specifying characteristics and functions of a more desireable
environment. In the Design phaseobjectives and characteris-
tics of a new system are determined, and a plan for its
implementation developed. Approach refers to the identifi-
cation of and selection from available hardware and soft-
ware technology, breadboarding of a system prototype and
preparing to embark on the implementation effort. Actualiza-
OUTCOME I
NEEDS ASSESSMENT
OUTCOME
**'" AN
NJECTWES O
NEW SYSTEM
ACTIVITY OUCOME
KAROWARE &SOFTWARE
AWJLAB.E TOSUPPORT
DESIGN APPROACH
EVOLUTION OF A MANAGEMENT INFORMATION SYSTEM
FIGURE 2
ACTIVITY OUTCOME
EVALUATION
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tion includes programming and implementation of system
components, installing subsystems as they become ready,
testing, debugging, documentation and initial use. User
feedback and comparison to stated objectives provide the
basis for the Evaluation phase.
The framework is enhanced by extensions made along
three dimensions. First, each phase is further divided
into steps. Second, each step is portrayed as a combina-
tion of both an activity -and the result of an activity (or
an outcome) that is used as input to the next step. Finally,
checkpoints are strategically implanted to allow escape
from the process at key stations. A representation of the
enhanced framework appears in figure 2 and will serve as the
basis for discussion throughout the remainder of this
section.
The phases of evolution are now addressed in detail.
Certain vocabulary -is employed throughout the discussion.
The term "Analyst" refers to the individual or team of
individuals responsible for monitoring and guiding MIS
eVolution. "Users" or "Clients" are the individuals who
will employ or benefit from the system when it is completed.
"Management" may also be users but more generally repre-
sents the supervisors or overseers of users. "Top Manage-
ment" are the highest level of managers exercising direct
control over the user environment.
2.1 NEEDS ASSESSMENT
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The objective of the model's first phase is to insure
that the correct problem is addressed. It is difficult to
determine with certainty that one problem is any more
"correct" than another in terms of mandate for change. The
issue is, more precisely, that systems designers and
analysts must be working with a set of problems that are
in accord with those perceived as in need of attention by
individuals who work in the area. It is equally important
that the analyst's assessment of needs verify those perceived
by users. Initially, the analyst's diagnosis will often
deviate significantly from the user's assessment. For this
reason the analyst must be familiar with existing operations
from the user's point of view. The analyst who deals with
users, rather than exclusively with managers of users, learns
about needs from the proper perspective and will be more
likely to end up addressing the right problems.
STEP 1
In Step 1 the analyst become intimately familiar with
the prevailing user environment. This requires spending
considerable time and effort getting to know what duties
are performed, how they are accomplished, what decisions are
made, and why different functions are important. These
activities should result in a coherent and cogent descrip-
tion of the current system. This is referred to, in
literature, as formulating a "descriptive model" (27).
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ACTIVITY OUTCOME
DESCRIPTIONEXAMENE OFEXISTING EXISTING
'ENVIRDNMENT ENVIRONMENT
(DescriptiveModel)
STEP,- I
With the construction of this model the analyst acquires a
thorough understanding of the existing environment.
Users play an important role from the outset of MIS
evolution. The analyst's primary source of information is
the user. When a descriptive model is derived, the analyst
is encouraged to confront the user with an interpretation of
the environment and to incorporate criticism and suggested
modifications if necessary. Since the initial system serves
as the basis for change, it is critical that the descriptive
model represent an accurate and realistic summary of the way
duties are currently performed. User validation of the model
insures a higher degree of accuracy.
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Unfortunately there are no procedures, short of becoming
a part of the user environment for an extended period, that
guarantee the analyst will acquire the knowledge needed to
formulate a descriptive model. The most popular approach is
to conduct a series of personal interviews. As mentioned
earlier and as elaborated by Rockart (52) and Burgstaller (10)
the questions asked in an interview can do more harm than
good, and often result in contradictory or confusing informa-
tion. An alternative or adjunct to an interview incorporates
the use of an instrument or questionnaire designed to help
elicit information from the client. Questionnaires have
only recently begun to emerge (see 5, 10, 33 for discussion
and examples) and are largely untried and untested.
In general it has been determined that questions which
ask (either in person or via an instrument) "What informa-
tion do you need?" tend to emit vague and often meaningless
responses. Burgstaller indicates that more information is
gained from responses to such questions as: "What do you
(the user) do?" followed by "What important decisions do
you make?" and finally "What information do you require to
make these decisions?". The latter leads more readily to
substantive discussion concerning user functions and cor-
responding information needs.
The analyst, in order to be successful, must initiate
information gathering activities such as: interviews,
questionnaires, group discussions, direct observation and
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investigation. Regardless of the techniques used the results
of these activities should include the following outcomes:
1. establishment of working relationshipbetween user and analyst
2. realistic model or description ofexisting environment
3. statement of user information requirements
STEP II
The descriptive model of the environment should now be
studies to uncover inefficiencies, inconsistencies, redun-
dancies, bottlenecks and other problems with the existing
system.
ACTIVITY OUTCOME
ANALYZE DENTIFICATIONEXISTING 1 OF PROBLEMSENVIRONMENT INEFFICIENCIES
ETC.
STEP - II
The analyst must pull together user comments and combine
them with results from his own systematic evaluations to
compile a list of problems. At this point, no solutions
should be proposed. This is strictly a process of problem
identification. A list of all identified problems should be
drawn up, as this will form the basis for determining the
objectives and characteristics of a better system, in the
next step.
The analyst should take care to separate problems from
one another and to recognize that some problems are, in fact,
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the cause or result of others. The objective of this
activity is to isolate each issue in its most basic form
so that processes and functions requiring attention are
explicitly identified.
Once compiled, the list of problems should be discussed
with users in order to allow them to add or delete items as
well as to speak out on the relative importance of each.
Concensus amoung analyst and users results in a final list
that contains a full accounting of problems in a prioritized
order. This approach will stimulate discussion and draw
attention to areas that will later be the focal point of a
new system.
In the second step, then, the analyst: engages in a
systematic analysis of the existing environment from the
descriptive model, compiles a list of problems in prior-
itized order, and discusses this list of problems with the
user population. The result of these activities is a menu
of problems that will be addressed in the next step.
STEP III
The activities in Step III use the list of problems
and an understanding of the business environment to derive
a statement of how the existing system would appear if all
the noted problems were resolved.
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ACTIVITY OUTCOME
ASSESS DESCRIPTIONOF
EXISTING -MORE DESIREABLEENVIRONMENT
ENVIRONMENT (NormativeModel)
STEP - III
The result is a.picture of a "better world" or what is more
commonly referred to as a "normative model" (38). The
normative model serves as the basis for formulating explicit
goals and objectives of a new system. It is a statement of
how functions would be performed, decisions made, information
received, retrieved and transmitted in an ideal world.
There is bound to be more than one reasonable proposal
for the way things ought to appear in a perfect system. All
alternatives should be dutifully considered. If more than
one procedure appears to be warranted then perhaps a mechan-
ism for including multiple approaches is required. For
example, if certain information is needed in both a regular
and ad-hoc basis by different users then both avenues of
access may be arranged in a more desireable system.
In order to maintain a perspective that will facilitate
in this procedure, it is useful to determine the generic
nature of the problem. Two mechanisms have been developed
to assist in this effort. The first is a framework of
decision making presented by Gorry and Scott Morton at
MIT (28) and later supported and extended by several other
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authors (21, 39, 45). Figure 3 shows a taxonomy of
decision types identified by their characteristics along
two dimensions: degree of structure and context of organi-
zational activity.
DECISION CATEGORIES
Strategic Management OperationalPlanning Control Control
Plant . Task InventoryStructured Location Scheduling Monitor
Semi- Capital Product BondStructured Acquisition Budgets Trading
Merger; Hiring R&DUnstructured Acquisition Managers Activities
Figure 3
Gorry/Scott Morton Frameworkof Decision Making
DecisionTypes
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'Abross the top of the figure are Anthony's (4) three
decision categories (strategic planning, management control,
and operational control) and the rows correspond to Simon's
(57) classification of decision types. Gorry and Scott
Morton point out that traditional management information
systems have tended to cluster in the structured/operational
end of the continuums,
The-usefulness of the Gorry-Scott Morton framework here,
is in its ability to; first, provide a perspective to the
problem area; second, to help identify, in general terms,
the likely characteristics of the problem area and its
position relative to the short and long term activity of the
organization; and third, to help determine the probability
that similar situations have been addressed by others, It
is useful to search out such cases in order to study their
utility in other environments.
The second mechanism is presented by Keen and Scott
Morton (39) and involves the description of information
characteristics according to the decision area (see figure
4). Examining information needs along these dimensions
provides insight that can be used to generate specifications
of a normative model. Notice that each dimension is por-
trayed as a continuum. Different uses of the same informa-
tion require that information to meet different standards
along the various dimensions.
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Strategic Management OperationalPlanning Control Control
Accuracy low ( _ _ high
Level of Detail aggregate ( : detailTime Horizon future / >,present
Information Frequency of Use infrequent ( ) frequentCharacteristics Source external ( internal
Scope wide ( - narrow
Type qualitative < ') quantitative
Age older : : current
Information Characteristics by Decision Area
Figure 4
Some MIS experts argue that the normative model should
be constructed without the aid of a descriptive model. In-
stead of building a descriptive model, followed by a normative
model and then determining how to change the descriptive into
the normative; one could ignore the way functions are CURRENTLY
performed and deal only with the way things SHOULD be performed.
This strategy manages to avoid simply computerizing the current
system because the current system is never studied. Under-
standing the manner in which functions are currently per-
formed is a basic step in the framework presented here. If
the normative model is eliminated or ignored it is still pos-
sible to construct an MIS, provided that there is a (manual)
system to start with, Whether or not to include the normative
model and if so whether or not to preceed it with a descrip-
tive model is highly situation dependant. There are occasions
when excessive tampering with existing methods are not possible
and only mechanization of already inbread procedures is
tolerable (See (55) for an example), Conversely, there are
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times when prevailing operations are so blatantly counter
productive that there is nothing to be gained from formulating
a description of current activities, The general case is pre-
sented here Cusing both descriptive and normative models in
sequence) with the. understanding that, on occasion, one or
the other is best left unused,
2The activities that lead to a normative model and a
specification of information characteristics are: design of
a system in -which the problems identified in step II are
resolved; information involved along several dimensions; and
efforts to locate and study similar systems that exist
elsewhere.
fonstruction of the normative model is the last step in
the :Needs Assessment phase. The combined objective of the
activities and outcomes making up this phase have been to
acquire a first hand familiarity with the user environment;
to know and understand prevailing functions, decisions and
activities; to identify problems; and to specify the
characteristics of a more desireable system. Taken together,
these steps are an organized means of establishin a basis
for hange, and steps to transform the current system into
one that is more desireable can now begin within the context
of Ilentified needs,
2.2 JESIGN
In this phase, results from Needs Assessment activities
are used to construct a viable system design and a plan of
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action to see that a new system will be successfully com-
pleted. Throughout this phase it is again critical to
maintain extensive interplay between user and analyst. For
this reason, the formation of a "design team" is advisable,
Such a team includes at least one analyst and at least one
user from the target population, The user plays an active
role in the design process and must be capable of speaking
on behalf of his constituency, In addition, he will help
acquire new information as it is needed and is at least
partially responsible for keeping other users up to date as
to the progress of the design process. (A comprehensive
discussion of the MIS team concept is presented by Alloway
(4)).
STEP IV
The design team's first task is to examine the normative
model in order to explicitly state the goals and objectives
of the new system.
ACTIVITY OUTCOME
ANALYZE GOALS ANDNORMATIVE _ OBJECTIVES
MODEL OF NEWSYSTEM
STEP - IV
These statements detail what the system will do, what char-
acteristics the new system will take on and how it will be
used. The descriptions should be as specific as possible
in order to give a complete picture of the expected results,
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Two important missions await the team once a set of
system objectives have been determined. The specifications
must be presented to top management for their approval and
support followed by a similar presentation addressed to
users.
Top management support has been identified earlier as a
primary determinant of system success. Management must be
convinced that there is a clear statement of purpose
(another prime contributor to success), that objectives are
both realistic and beneficial to the organization and that
the proposed system will be an asset to the organization,
Top management support alone does not guarantee
success. General Motors', Chevrolet division, found that
even though ton management vigorously and enthusiastically
supported the development of a Corporate Information System
(CIS), the system was less than a success primarily because
only upon completion were users consluted to test if design
objectives were useful (39, 53). Users too must be given the
opportunity to react to statements of intent prior to system
design. Valuable feedback may be obtained as well as
increasing the likelihood of formulating an acceptable
design.
The generation of objectives is an iterative process.
Initial specifications may meet with disfavor or may require
alteration and enhancement before approval and support are
secured. Once established, the objectives will be used
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throughout the rest of the evolutionary process to judge
whether or not the system is developing according to plan.
Upon completion, the end product will be correlated with
objectives to help reach a verdict of system success or
failure.
The first evaluation of the project's future is
conducted prior to planning a definite course of action,
If the objectives are such that either there is no way to
achieve them or if it would take an inordinate investment of
personnel, time and dollars to do so, then there is cause
for concern. In either of these two cases it is wise to
scale down the extent of the proposed system so that it is
possible to complete within a reasonable budget using
existing technology. In the event that this is impossible
to do, the system may best be postponed. It is the role of
the analyst to provide the design team with an accurate
assessment of costs and technologies that will be necessary
to see the system through.
It can happen that a decision to abandon the system
will have to be made. .If after analysis of descriptive and
normative models there is no basis for change, if top
management support is unable to be obtained or if the system
is impossible to build at reasonable cost then it is
probably best to terminate at this point, before investing
large sums of money and time into a doubtful system.
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The main objectives of Step IV are, in summary, to;
1. clearly define the objectives of the system
2. engage the support of top management
3, insure that the system will do what users want
4. insure that the objectives can be met and atreasonable cost
STEP V
Given that a decision is made to continue with the
evolutionary process, the next step is to construct a
plan of action.
ACTIVITY OUTCOME
PLAN COURSE ORDER &OF _ RIORITIES
ACTION OF SYSTEMCOMPONENTS
STEP - V
The proposed system should be broken down into component
parts or modules. A modular approach is more manageable
than one that attempts to bring up an entire system all at
once, In an accounting environment, for example, an MIS for
the controllers department might include the following
modules: general ledger, order entry, accounts payable,
and accounts receivable. Building systems for each module
and then tying them together is a reasonable strategy,
An extension to the modular approach calls for a
hierarchical implementation of modules (17). With this
strategy, the nucleus or most fundamental module is built
first and then components that depend upon it are brought up
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next. In this way the fundamental components of the system
(the kernel) are completed, tested and made operational
before modules that link to them are developed. This
procedure is repeated at the next level and further until
the entire system has been completed. This approach has
been successfully applied to operating systems (46) and
more recently to the development of management information
systems (17, 18, 19, 20).
Other factors may influence the order and size of
system modules. External forces or pressing organizational
needs may cause certain modules to receive higher priority
than others. Such cases must be dealt with carefully.
Planning to deliver a subsystem without its underlying or
supporting modules can lead to frustration and disillusion-
ment of end users.
Another activity in the Design phase is to set up a time
table. Modules are planned for completion according to a
schedule that includes time allocations and deadlines for
documentation, testing, debugging and data collection in
addition to actual programming.
The systems design and time tables should also be
approved by both top management and prospective users, This
represents a promise to deliver a particular service at a
given time and, hence, care should be taken to present a
realistic plan,
-32-
2.3 APPROACH
Two parallel processes follow the MIS design. The
first is to identify hardware/software technology that could
support the system and the second is to breadboard a system
prototype using the most available hardware and software.
These two processes are discussed independantly in the
following sections,
2.3.1 HARDWARE/SOFTWARE CONSIDERATIONS
There is, today, virtually an unlimited menu of
hardware and software technology from which to'choose that
which best suits the needs of the planned system. Unfortun-
ately there is no way to itemize them all and even if there
were it is difficult to compare any more than a few alter-
natives. In some environments there is little or no
flexibility in this selection process. The organization may
be committed to dealing with a particular vendor or to using
already available tin house) technology, The following
technology search is constrained by these factors.
STEP VI
There are many ways to learn about technology alter-
natives that may be applicable to the system.
ACTIVITY OUTCOME
IDENTIFY HARDWARE &EXISTING SOFTWARE
TECHNOLOGY AVAILABLE TOSUPPORT NEWSYSTEM
STEP - VI
-33-
The first source of information is usually simply experience.
Having worked with information systems and exposure through
work or school provides a basicknowledge from which a techno-
logy search can be launched. Finding other organizations
that have implemented similar systems will also be helpful.
Whether other installations have met with success or failure,
analysis of their attempts can give valuable insights. Vendors
are another source of information. Issuance of a Request For
Proposals (RFP) is sure to attract attention and a deluge of
information from commercial enterprises. Technical consultants
can also furnish information, albeit at considerably higher
cost. Finally, texts, journals, conferences, special reports
and industry catologues that document the advantages and dis-
advantages of various technologies are readily available.
The decision to purchase software is, increasingly,
becoming more costly and time consuming than determining
which hardware to employ. This is a by product of an era
which is characterized by rapidly decreasing hardware costs
and continuously increasing complexity and costs of software.
Most management information systems that are appearing in
organizations today utilize a Data Base Management System
(DBMS) (51) as their underpinning. There are a number of
commercially available DBMS packages that vary greatly in
cost, size, and approach. The design team should select a
DBMS that is sufficient to meet the needs of the system
-34-
without incurring -unnecessary overhead. Key dimensions to
consider when purchasing a DBMS are: flexibility, conceptual
view, overhead, performance, security, simplicity and reliability
(13,47).
Some software houses and time sharing services support
several hardware/software configurations. These services can
be used to support the final system or used exclusively as
a testing ground in order to experience alternative systems
first hand (see Section 2.3.2). A management information
system might permanantly operate in a commercial time sharing
environment in order to relieve the organization from having
to maintain its own technology. If this alternative is con-
sidered, factors to be concerned with are: reliability,
security, availability, efficiency, overhead, cost, technical
support, and ability to fine tune machinery or software of
the available systems.
STEPS VII & VIII
After identifying available hardware and software techno-
logies, an evaluation of alternative combinations to determine
that which is best suited to the needs of the proposed system
is undertaken. There is no algorithm to guaranty the selection
of the best components for a given system. Consideration of
cost, capabilities of the components and of the organization
that will use thea, ease of use, flexibility, upward compatibility,
and even intuition all contribute to the final selection.
-35-
ACTIVITY OUTCOME
EVALUATE SELECTIONSTEP VII AVAILABLE CRITERIA
TECHNOLOGY
ARE
YES ANY COM-INATIONS
V IAB
SELECT FROM COMMITMENTSTEP VIII AVAILABLE TO AVAILABLE
TECHNOLOGY TECHOLOGY
A general approach is to establish criteria along relevant
dimensions and to exclude systems that fail to qualify in
every case from further consideration. One from the remain-
ing alternatives is chosen based on the relative importance
of performance along particularly critical dimensions.
There are two caveats to bring up at this time. First,
unless the organization is risk seeking, it is best to select
technology that has a demonstrated ability to do the job.
New or untried technology that will present the organization
with unneeded risk and potential for failure should be avoided.
There have been many instances where substantial investments
have been lost as a consequence of using new and previously
untested equipment.
The second warning concerns the next checkpoint in the
framework. It may turn out that either no technology capable
-36-
of meeting the requirements of the planned system exist or,
if it does, that is too expensive. In this case the objec-
tives of the system may need to me modified, the design
changed, or the technology selection criteria relaxed. The
remaining alternative is to postpone the project until the
technology becomes available, the costs decrease or the
problem changes.
With this step a go/no-go decision is finalized and a
commitment to a specific technology made. The design and
technology are ready to come together in an implementation
effort that commences in the next phase (Section 2.4).
2.3.2 BREADBOARDING OF SYSTEM PROTOTYPE
While the technology search is in progress it is advis-
able to simultaneously implement a small scale prototype of
the system. There are several reasons for pursuing this
strategy. First, a prototype is a relatively inexpensive
means of determining whether or not the final system will be
compatible with what users are expecting to receive. When
completed the prototype is shown to future users to get
an expression of their initial reaction, identify
potential problems with the design, and generate user
interest in the final system. It is far easier to incor-
porate changes demanded from experience with a prototype
than from reaction to the end product. Second the
-37-
prototype allows those who will eventually be responsible for
full scale implementation to go through a learning phase that
will better prepare them for work on the larger version.
Familiarity with data management systems, query languages,
graphic display screens and other "new" technology is acquired
while losing no time and at minimal cost. This kind of learn-
ing and experimentation is much more costly and constrained
if done during "live" implementation. Third, the prototype
will be the foundation for the final system if it is implemented
using the same technology as that which is ultimately selected
for the full scale version.
isSYSTEM
No 0 ON RIGHTRACK
BREAD BOARD SYSTEM TOSYSTENM BE
PROTOTYPE DEMONSTRATED
The technology used for the prototype should be that
which is most available and at the lowest cost, while pro-
viding sufficient capability. In house computer resources,
short term arrangements with a time sharing service, potential
vendors, and university facilities are all possible testing
grounds. Although differences between the prototype and
-38-
actual system will exist (primarily in terms of operational
costs and efficiencies if nothing else), the decision to invest
resources in the final system are based on much better inform-
ation concerning characteristics and benefits of alternative
technologies from having developed a prototype. A prototype
can, then, be effectively employed to significantly reduce
risk, and increase the reliability of a decision to adopt a
particular technology.
The prototype should be tested in the user environment
on a small scale to determine whether or not the design team
and target population are on the right track. The third
checkpoint in the framework is to assess the compatibility
of the approach, as manifested in the completed prototype,
with user expectations. If discrepancies exist they must be
reconciled before continuing with any further development.
If the prototype is consistent with stated objectives but
ellicits an unfavorable response from users then there is
cause for concern. The objectives must be reexamined and
made to more accurately reflect user demands.
The completion of a prototype and a commitment to a
specific technology leads to a transition into the next phase
of evolution, Actualization. It should be reaffirmed here,
that there are two parallel processes that make up the Approach
phase: steps that lead to selection of technology; and steps
to complete a system prototype. There is a good deal of
-39-
interplay between these two processes. Ideally, the
prototype should be brought up using the technology that
is most likely to be chosen for the final system. In
addition, the prototype development should be monitored by
those responsible for selecting the technology so that they
will have as much information as possible upon which to
base their decision.
When choosing modules, subsystems or applications to
be brought up in the prototype environment, care should be
taken to select those that are both easy to do and that will
be sufficiently interesting and demonstrable to users.
Users impressed with the prospective utility and effective-
ness of the final system, because of experience with a
prototype, will have an easier time assimilating the final
version upon its completion.
2.4 ACTUALIZATION
Actualization refers to the programming, documentation,
data collection, installation, testing debugging and finally
use of the system as scheduled during the Design phase.
This is often the most time consuming and aggravatingly tedi-
ous phase of system evolution. It is, however, important that
the routine activities not be treated lightly or that their
role in the evolutionary process be underplayed. Many systems
have had superb designs but were never actualized effectively
-40-
due to lack of resources allocated to the implementation effort.
ACTIVITY OUTCOME
STEP IX
During implementation, Step IX, it is possible to con-
front a situation where a particular design criteria is unable
to be met in a satisfactory manner. In such cases it is
evident that a design change is mandated. For this reason,
the design team should continue its association with the
project through the later stages of evolution.
As modules of the system are completed they are installed,
Step X. Initial, trial useage can begin as soon as enough of
the system has materialized. System modules are often released
ACTIVITY
INSTALLATION
OUTCOME
STEP X
to the user environment before enough of the system has been
completed. The reason systems are released too soon is invariably
IMPLEMENTATION NEW SYSTEM
USEABLE SYSTEM14
-41-
associated with a failure to meet stated time commitments.
If the system is initially planned for release at a particular
time but only a few modules are actually completed at that
point, it is more appropriate to extend the deadline than to
release a portion of the system prematurely. When it is
apparent that a module is going to be late, efforts to expedite
should be set in motion, the implementation schedule revised,
and any problems identified and reconciled. The importance
of well founded time tables set up in the planning step becomes
clear in this phase of evolution. It is better to have been
realistic earlier on, than to require repeated deadline exten-
sions during implementation.
The first users of the system should be carefully selected.
The more progressive users who have displayed an ongoing
interest in the project should be given the first opportunity.
Such users are more likely to have a positive attitude and
thus a higher probability of accepting the system. The impact
of first impressions must also be given serious consideration.
If initial experiences are frustrating and unproductive the
system image will be forever marred.
The degree of operational change directly resulting from
the installation of a new system should be minimized. The
less individuals have to alter their current work habits and
routines, the easier it is for them to accept the system and
to incorporate it as part of their usual activities. Top
-42-
management support, at this stage, is invaluable. If
users are aware of such support, then use of the system is
required to continue in good standing with management.
However, this can be carried to an extreme. If management
insists that the system be assimilated at an unreasonable
rate (change occurs too fast) or if the system is not
functionally useful (due to, perhaps, a poor design) then
even a mandate from the chief executive will result in
defensive, unaccepting behavior by users. Either lack of
top management support or overzealous support can severely
impair the likelihood of success.
These two points, complexity of organizational change
and top management support, combine with extensive user
involvement and a clear statement of purpose to round out
the list of environmental factors that greatly influence
the evolutionary process. Concern for these matters and use
of this framework go hand in hand.
2.5 EVALUATION
Evaluation is perhaps the most difficult phase in the
evolution of an MIS. All too often it is ignored entirely
in favor of yielding to pressures requiring that attention
be given to other matters. Successful evaluation depends on
1) prior definition of system objectives, 2) a means of
monitoring progress towards meeting predefined goals, and 3)
a formal review process when the system is complete (39).
-43-
ACTIVITY OUTCOME
PROPOSALS FORFURTHER REVISIONS
EVALUATION -AND DEVELOPMENT(Return to stepsI-III)
STEP - XI
If the reasons for building the system are not clearly
specified then it is impossible to determine, ex-post,
whether or not the system rates as a success. An advan-
tage to using a modular approach is apparent here. If
every module has a specific purpose then each can be
evaluated individually as they are completed. Success of
the entire system, then, is some function of the success
rate of its modules. (e.g. if 80% of the modules are a
success then the project may, by some standards, be claimed
a success).
Delivery of an MIS is a service rather than a product.
Products can generally be assessed in terms of value as a
net of benefits minus costs or with a rate of return
analysis, as with most capital investments. However, an MIS
rarely results in cost displacement or cash generation.
Thus, there is no ready means of measuring contribution to
the organization in a traditional manner. Justifying the
investment in an MIS is largely an act of faith to begin
with. Rarely is an MIS initiated as the result of a
promise to reap a minimum payoff, (this is one reason why
-44-
it is often difficult to secure top management support for
the system). Support based on cost-benefit ratios is often
misguided, since costs are invariabily underestimated and
benefits intangible (eg. it is difficult to quantify the
value of better information and better decisions).
Success has traditionally been measured by frequency and
length of system use. This, however, has proved an unreliable
indicator. A heavily used system may be cumbersome, ineffec-
tive and superfluous whereas a lightly used system may provide
valuable information at strategic times. Other attributes of
systems have been proposed as success factors by Keen (38).
These include accuracy, timeliness, simplicity, learning, new
needs, new jobs, follow on projects, new problems, new person-
nel, and changes in organizational structure.
Keen and Scott Morton have further elaborated on the
issue of evaluation in their recent book on decision support
systems (39). Much of their material is based on earlier
work done by Keen (38) and is equally relevant to the evalu-
ation of management information systems. Alloway (5)
Carlson (11), and Ginzberg (27) have also made contributions
in this area. One note common to all reports is that a verdict
of success or failure is entirely dependant upon who is per-
forming the evaluation. The designers and builders of a
system are more likely to rate the system a success than are
users or management.
-45-
Use and evaluation of the system are bound to result in
the identification of system failings, a need for new features
and, perhaps, major extensions and revisions. It is difficult
to sort out those changes which fall under the rubric of
system maintenance from those which are simply bug fixing or,
at the other extreme, those that are actually independant
systems. In any case there is a likely need to reexamine
the now existing system to identify new problems, inefficien-
cies and the like. From this phase, then, there is a direct
line to Step II in the overall framework: Analyze the
Existing Environment. The framework can again be used in its
entirety if sufficiently new needs are established.
2.6 SUMMARY OF FRAMEWORK
The proposed framework represents a systematic approach
to the evolution of a management information system. The five
phases of the process include: Needs Assessment, Design,
Approach, Actualization and Evaluation. Each phase is com-
posed of several steps, where each step consists of a set of
activities and outcomes. The outcome of an activity is gen-
erally used as input to the next activity. The eleven steps
are complemented by three checkpoints that are used to deter-
mine whether or not evolution should continue. The Approach
Phase consists of two distinct parallel processes: the
development of a system prototype, and the selection of
technology to support the final system. There is iteration
-46-
between some steps and a liberal "bouncing around" is
encouraged at certain stages.
The framework is developed within the context of four
key concepts that dramatically influence its utility. These
are: there must be a clear statement of system goals and
objectives; there must be substantial user involvement
throughout evolution; top management must actively support
the system; and the degree of organizational change imposed
by the system must be minimal.
-47-
3. CASE STUDY
The guidelines presented in the previous section are
now applied to an actual case study in order to demonstrate
their utility in a particular setting. The organization
used in this example is the Pennsylvania State Department
of Education. An organization chart of the department
is found in Appendix A. The Bureau of Information Systems
is the focal point of discussion. An organization chart
for the Bureau is found in Appendix B.
3.1 NEEDS ASSESSMENT
Four days of on site visits by two analysts were
used to conduct the Needs Assessment phase. Formal inter-
views, informal discussions and group meetings were con-
ducted at all levels of the organization to get an inside
view of the existing environment.
Description of Existing Environment (Step I):
The Bureau of Information Systems is a service center
and receives ad-hoc requests for information, reports,
and analyses in addition to being responsible for com-
piling and distributing standard, institutional reports
to other branches of the department on a regular basis.
The Bureau's Division of Computer Services (see Appendix B)
maintains over one hundred and fifty computer programs
(written predominantly in COBOL) that are aggregated
into 70 applications. Example applications include:
-48-
Annual Financial Accounting, School Attendance, Trans-
portation, and Federal Registrar Reports. The application
studied here bears the rubric "Act-580" and is one of
seventeen subsidies used to distribute state funds to local
school districts. The application is of interest because
of the problems associated with its use and because it is
an example of an application that may benefit from infor-
mation systems support.
The School Subsidy Program (Act-580) has been in use
since 1965. At that time a formula was constructed to
calculate the amount of dollars to be distributed among
the 505 school districts throughout the state (the actual
number of districts has fluctuated over time and currently
stands at 505). Act-580 is the largest of the seventeen
subsidies, accounting for over 80% of all subsidy funds
distributed to school districts throughout the state.
The total dollars allocated through the subsidy currently
sum to nearly $1.25 Billion.
Act-580 has three main goals. First; a prediction
of total school subsidy disbursements is required by the
Governor's Budget Office on an annual basis. The budget
figure represents an amount of money expected to be dis-
tributed to school districts at the end of the fiscal year.
A prediction for the coming fiscal year is, ideally,
available on July first. In order to plan ahead, the
Budget Office requires that a prediction for two years
-49-
hence be provided and that this be updated with a second
estimate after a year has passed. Estimates a-re provided
by the Division of Educational Statistics (see Appendix B)
and are based on the current school subsidy formula.
Estimates have tended to be within .2% - .5% of actual
subsidy amounts.
The second goal of the subsidy system is to assist
local school district planners by providing them with an
indication of the state dollars they can expect to receive
for instructional expenditures.
The third goal is to provide a means of "testing"
alternative subsidy formulas in order to establish more
"equitable" means of distributing funds. Periodically
(roughly every three years) the formula undergoes extensive
analysis and revision. Reworking consists of generating
alternative formulas, determining what the distribution of
funds would be with the new formulas, analyzing the results,
presenting results to others for discussion and, often,
generating still other formulas. When a formula that meets
with the approval of the legislative body is established,
it becomes the new mechanism for determining subsidy awards.
This process continues from January to July or until a
suitable formula is found. The process can continue into
November if no consensus is reached until then. Several
hundred formulas are tested over the course of the six to
-50-
nine months. In the current year (1977), nearly 100
formulas have been tested from mid-January to mid-April.
Up to six formulas are under study at one time and on the
average two to three are "in process". Proposed formulas
are generated by the legislative General Assembly, the
Budget Office, the Governor, the Teachers Union and the
public. When a formula has been submitted for testing to
the Statistics Division, the proposal originator would like
to learn its bottom line and distributional impact imme-
diately, but typically must wait for one to three days
while a computer program to "test"the formula is written
and run. Results of a test run are returned to the statis-
tics office and from there are passed on to the proposal
originator. When presented to other parties for discussion
the results generally lead to political debate and, often,
another proposal to be tested. An illustration of this
process is shown in figure 5.
It is this goal that serves as the focal point of
this case study. The current subsidy formula is detailed
in Appendix C. Basically it consists of four components;
a base subsidy, a density factor, a sparsity factor and a
poverty factor. There are currently ten separate data
series used to compute the amount of subsidy. Proposed
changes to the formula include raising and lowering constants
and multipliers, restructuring conditional allotments, and
altering the actual mathematics. Several new data series
-51-
Figure 5Current Subsidy System
STATISTICIAN
DATA PROCESSINGPERSONNEL
PROGRAMTO
TESTPROPOSEDFORMULA
-52-
have been proposed as additional components and it has been
suggested that some data series be dropped or extensively
modified, (see examples of proposed formulas in Appendix D).
Previous reworking of the formula has resulted in
several ammendments that attempt to correct inequities.
For example, the addition of a density factor required
subsequent inclusion of modified density, super density,
and modified super density when it was realized that density
levels change over time and that some school districts have
excessively large density levels. Qualifiers and guarantees
are also common. Currently, there is a written guaranty
that no district shall receive a subsidy less than that
received in 1972.
The decision to adopt a new formula is entirely poli-
tical in nature. Legislators support a proposed formula
only if a test run shows that their districts are treated fairly
relative to others. In general, a large number of proposals
must be considered before consensus is reached. Special
interest groups also play an important role, as they apply
pressure to the members of the General Assembly. The
Teachers Union is a notable example of a group currently
exerting such influence. Teachers are prepared to strike if
an unacceptable subsidy formula is adopted.
Analysis of Existing Environment (Step II):
Having become familiar with the existing environment,
-53-
through the development of a descriptive model in the
previous step, it is discussed with representatives of the
Department, and is analyzed to uncover problems, inefficien-
cies and bottlenecks. Identified issues are presented below
in order of importance as perceived by the analysts, the user
responsible for mediating between submitters of proposed
formulas and the computer personnel, the data processing
staff, the assistant director and the director of the Bureau
of Information Systems.
The lack of continuity in formula generation, analysis
and regeneration is the most severe constraint in formula
development. The submitter of a proposal must wait up to
several days for the results of a test run. This time lag
requires the proposal originator to refrain from selling his
ideas to others until test results are available. Valuable
time is lost in this delay and there is a corresponding loss
in momentum from a policy making perspective. Associated
with this issue is a tendency to confuse alternative for-
mulas. It is difficult to keep track of proposed formulas
when there are any more than a few for which test run results
are unavailable. The time to generate, test run, analyze,
present, discuss and modify a proposal requires at least a
week. Those involved have expressed much concern for this
bottleneck and have identified an urgent need to expedite
the process.
-54-
A ramification of the excessive amount of time to
process a single proposed formula is manifested in terms
of the total time it takes to reach a final decision to
adopt a new formula. Ideally the estimated subsidy amount
should become part of the Governor's budget by the first
of July. However, the final formula may not even have been
agreed upon until as late as November, causing problems for
even the highest level of authority.
In a parallel with the time to process dilemma is an
issue of completeness. Fine tuning of proposed formulas
is time consuming and, hence, generally is not done. Thus,
a great many variations on a theme remain unexplored.
Similarly, proposals that would take a great deal of effort
to test are never even proposed. The number of proposals
considered, then, is limited.
Currently, the data processing center budgets close to
one full man year of effort to accomodate the computational
requirements of Act-580. Included in this time is, re-
programming needed to generate test runs, acquiring and
often rekeypunching data needed by a given formula (even
if the data is used for another application it must generally
be reformatted or resorted to run under Act-580), and
actually running the new formulas (this is usually done at
night since day time computer resources are running at full
capacity).
-55-
The burden upon the data processing staff due to Act-580
is substantial and ever growing as formulas tehd to only
increase in number and complexity.
The data processing staff also find themselves facing
confusion and frustration when a large number of alterna-
tives are "in process" simultaneously. According to one
statistician, it recently took a one half hour conversation
with a programmer to identify which proposed formula the
statistician had.called to discuss.
The director of statistics often find this response
time to be inadequate. Rather than wait for a proposal to
be processed by the computer center, he will take an entire
day and a handful of staff to work through a proposed
formula manually.
Finally, the current mode of operations can lead to
costly mistakes. Typically, a formula will be tested
using a modified version of the current subsidy program.
The danger is that the wrong version of the program will
be discarded after testing. This actually happened recently
and lead to inaccurate final estimates being submitted to
the budget office.
The list of problems in prioritized order are summarized
below:
-56-
Summary of Problems with Present State Computational Systemto Handle Act-580
1. lack of continuity and momentum in formula generation
and analysis
2. proposed formulas take too long to process (one to three
days)
3. total time needed to select a new formula is too long
(six to nine months)
4. not enough proposals are considered (100-200)
5. it is difficult to fine tune proposals
6. it takes a full man year of DP support to handle Act-580
7. it is complicated and confusing to multi-process several
proposals
8. new components to the formula require extensive time
to collect and process data that may or may not already
be used by other application
9. there is a lack of available computer resources
10. manual processing is tedious and time consuming
11. formula testing can lead to mistakes and erroneous
data used for policy making
12. it is difficult to know whether or not a given formula
has already been tested
-57-
Before developing the specifications of a subsidy
system in which the noted problems are resolved (i.e.
construct a normative model), consider the application in
terms of the two mechanisms presented in Section 2.1.
Analysis of the problem area with respect to the Gorry-Scott
Morton framework for decision making and then in terms of
the Keen-Scott Morton table of information characteristics,
gives an insight into the system's generic nature. The
selection of a formula to determine subsidy allotments is
clearly not a structured task (refer to figure 3). There is
nc way to impose a straight forward algorithm or linear
program to insure that an optimum formula is selected.
Neither is it a totally unstructured process, as there are
implicit heuristics and rules that govern the final selection.
For example, no district can receive less than it did last
year, and if a particular form of aid is received once
(e.g. sparsity aid) then it shall always be received. Semi-
structured, then, appears to be the appropriate classifica-
tion. The decision category lies somewhere in the range
between strategic planning and management control. This
combination (semi-structured/management control) places
the system in a class that has had only little history of
successful information systems support (39). In some ways,
though, this example is remarkably similar to the Westing-
house case discussed by Scott Morton (55), particularly
-58-
with respect to the issues of: time to select an alternative,
the number of alternatives to consider, and the thoroughness
of formula testing. The information system developed in that
case provides valuable lessons for this system, but will be
left for the interested reader to explore in detail.
Analysis of data used in Act-580, along the dimensions
presented by Keen and Scott Morton (39) reveals that the
various data series used in this application span over a
considerable range. In fact, it is clear that the current
system uses data of inconsistent quality, accuracy, and
age (see Appendix C). Focusing upon the goals of the
subsidy program, a general picture of information charac-
teristics can be formulated as seen below:
-59-
Act 580 Information Characteristics
DataCharacteristics
Accuracy
Level of Detail
Age
Source
Scope
Type
Ballpark figures are sufficient for
budget estimates
School District and State Wide-no need
to be too detailed (i.e. school level)
Past data is used when present data is
unavailable; present data is used
instead of estimating future figures
Internal
Wide
Quantitative
Time Horizon Future
-60-
Description of More Desireable Environment (Step III):
The director of the Information Systems Bureau and
both the head statistician and the data processing manager
expressed their perception of an ideal Subsidy System in
terms of an interactive facility. This facility would
enable easy entry of new formulas and provide estimates of
total dollar and distributional impact. The focal point of
such a system would be a station for incoming proposals to
be entered (e.g. via display screen or computer terminal),
run immediately, and results returned instantaneously.
Incoming proposals would be assigned an identification code
to keep track of who made the request and to maintain a
history of proposals. It is unlikely that members of the
general assembly would benefit from direct access to the
system. Rather, it is more appropriate to assign an in-
dividual (in the Statistics Division) the responsibility to
run proposals from a central sight as they come in by phone,
in person or by memorandum.
Information available for formula testing should include
any data kept at the district level. A district data base
would be maintained by the data processing staff for this
purpose. If a proposed formula includes district data used
by some other application it should be available through
the district data base. New data should be added as needed
to increase the alternatives open to formula generation.
-61-
Information available for formula testing should
include any data kept at the district level. A district
data base would be maintained by the data processing staff
for this purpose. If a proposed formula includes district
data used by some other application it should be available
through the district data base. New data should be added
as needed to increase the alternatives open to formula
generation.
Results of test runs should also be available in hard
copy form for distribution to interested parties. Such
printouts can come from either a screen copier or from a
line printer.
This systen, once implemented, would require only
minor reprogramming and maintenance efforts by the data
processing staff from year to year. Their primary respon-
sibility would be to provide an accessible reliable and
integrated base of data around which the Subsidy System will
function.
3.2 DESIGN
Analysis of Normative Model and Viability of SystemGoals (Step IV and Checkpoint I)
The goals-of the proposed system as derived from an
analysis of the descriptive model are:
- to provide continuity and momentum to the searchprocess
- to allow more proposals to be considered
-62-
- to allow fine tuning of formulas
- to provide immediate and unambiguous feedback fromtest runs
- to provide a means of viewing results by schooldistrict, legislative district or in aggregateby county and state
- to keep track of proposals and who has generatedthem
- to easily incorporate new variables into the formula
- to allow components to be dropped from the formula
- to substantially reduce the clerical workload intesting formulas
- to substantially reduce the need for continuous dataprocessing support
- to ultimately arrive at more equitable subsidydistributions
Given the demonstrated success of similar systems (18,
21,27, 55), these objectives appear to be both reasonable
and viable. Readily available technology shoudl be suffi-
cient to attain them.
Support for these design objectives was easily secured.
The director of information systems, the head statistician,
and data processing personnel all displayed keen interest
and enthusiasm for the proposed system. A commitment to
provide resources and personnel- support during systems
development and implementation was made by top management.
System Components and Plan of Action (Step V):
The following activities should be performed to
establish a complete system and in the the time frame shown,
-63-
relative to the start of system development:
- stabilize district database (weeks 1-8)
- develop or acquire software environment from whichformulas can be tested and results displayed(weeks 3-15)
- establish human interface to the system (weeks 12-20)
- resolve procedural and logistic details (weeks 18-22)
- establish procedures and capabilities to maintaindistrict database (weeks 18-24)
- provide a means of tracking and storing proposedformulas (weeks 12-20)
- document supporting software and operating procedures
- evaluate system modules (as completed)
The nucleus of the system is an integrated database of
all information maintained at the district level. The
ability to access and manipulate this data gives the flexi-
bility needed to test and track alternative formulas. An
illustration of the proposed system is shown in figure 6.
The system modules were recognized and approved by the
Bureau of Information Systems. The time tables and scheduled
dates for completion are currently under consideration.
Approval of a final schedule is expected soon.
3.3 APPROACH
Identify and Evaluate Available Technology (Steps VIand VII)
The Bureau of Information Systems, despite its heavy
-64-
Figure 6Proposed School Subsidy Information System
T/O VIA KEY-BOARD OR DIS
CIAN
ResultsProp-osals
-65-
use of computer resources, does not currently own and
operate its own equipment. Due to increasingly computa-
tional requirements and an increasing desire to control
accessibility, activity and operations, the Bureau has
requisitioned and received approval for funds to acquire
its own computer facility. A request for proposals has
been issued and a decision to purchase a medium sized
computer is emminent. The hardware must be capable of
supporting a commercial data management system and be
able to operate in both a batch and interactive mode. In
the opinion of the analysts, at least two vendors, IBM and
UNIVAC, are capable of supplying the required resources.
The chosen facility will most probably be in the range of
IBM 370/138. A DBMS that uses a hierarchical conceptual
view of data is preferred by the user environment due to the
relationships that exist between county units, counties and
school districts. These relationships are of more relevance
in other applications than in the case of the School Subsidy
system. ADABASE, SYSTEM 2000, and TOTAL are all being
considered for this- role, (see (51) for a description of the
various DBMSs).
Checkpoint II and Selection from Available TechnologyStep VIII)
It appears that sufficient hardware and software re-
sources are available to meet the system objectives detailed
-66-
in the Design Phase. The underlying selection criteria are
currently being developed in conjunction with the user
environment. Final selection of hardware and software tech-
nology will be made in due course.
Breadborad System Prototype:
In order to test the viability of the Act-580 design
presented above, and to become better acquainted with the
computational requirements of the system, a prototype was
developed. The prototype will be shown to users and tested
with actual proposed formulas to determine whether or not
the system will be useful when complete.
A partial view of the complete District Database under-
lying the prototype is shown in figure 7. Essentially,
there are a set of attributes (columns) associated with each
of the 505 school districts (rows). Janus, an experimental
host language supported by MULTICS, is used to provide access
to the data from an interactive computer terminal.
The prototype uses a relational schema (13) on hardware
that is unlikely to be chosen for the final system. The
relational approach is well suited to the needs of this
system but is generally unavailable in the form of a
commercial product. It is used here as it is a convenient
vehicle for the purposes of demonstration and because it was
readily available and at low cost. It is expected that the
capabiliteis shown here can be transferred to the hierarchi-
-67-
cal schema supported by the DBMS ultimately chosen to
underlie the final system. The hardware (Honeywell 6180)
is employed for similar reasons; it was readily available
and at lost cost, while it provides ample computational
capability.
display mktval,incomeafdcpovertyaie,pop_per areawadm sort on cnty,distnamewith title="Figure 7"/District Database"/(Partial Listing)",blocking,1n1=95 for 1 to 20
Figure 7District Database(Partial Listing)
afdc povertywadm
aie popper-area
BERMUDIAN SPRINGS SDCONIEWAGO VALLEY SDFAIRFIELD AREA SD
00 GETTYSBURG AREA SDLITTLESTOWN AREA SD
UPPER ADAMS SDALLEGHENY VALLEY S DAVONWORTH UNION S DBABCOCK S DBALDWIN WdHITEHALL S D
BETHEL PARK S DBRENTWOOD BORO S DCARLYNTON S DCHARTIERS VALLEY S DCHURCHILL AREA S D
CLAIRTON CITY S DCORNELL SDDEER LAKES SDDUQUESNE CITY S DEAST ALLEGHENY S D
39539100846525004080340014283330047609500
46331800979708005252560087217500
261263700
2458523006952190092123200
210492600202320500
998656008790330062813900-67410400123556200
2383235756461739111069137096898130087391
266846535395446845563402561499804
208852198
1838114665226608379830529
150528372136422434
4208247934362599482994133340773973860528
distname mktval I ncome
84369
23241
40
68129
6862
236
92103277206
88
592232127482315
198294167424
89
2452521541,76525
272235I431379184
1071385300812604
14753302371957
89354041210571941263
1642350330591723844183472124
10699690
109590782180118353984672340616701731
31467142228624371678820639574434449
79215
54101162
771433
922380
4803
29909809579620823584
50173484
34260052156
18293269100144232362
19772822213633729950
106432221341167255690
23061632362518324095
-69-
Abbreviation
distname
-mktval
income
c:fdc
poverty
Ale
pop-per-area
Meaning (reference Appendix C)
the school district name
the market value of the district'sreal-estate
total income earned by wage earnersin the district
number of pupils receiving aid tofamilies with dependent children
the number of poverty pupils in thedistrict
last year's actual instructionalexpenses
the population per square mile
weighted daily average membership
(Figure 7 continued)Explanation of column Headings
-70-
The data shown in figure 7 is operated on according
to the rules of the current formula (Appendix C) to arrive
at the subsidy payments shown in figure 8. Figure 9 shows
a partial frequency distribution of subsidy allotments
by district. The vast majority of districts receive
between .04% and .21% of the total subsidy disbursements.
Philadelphia and Pittsburgh receive 2.63 and 16.94 percent
of the total subsidy respectively but are not shown in the
figure.
The subsidy components (base, density, sparsity and
poverty) are calculated using the formulas detailed in
Appendix C and as shown in the listing of macros in
figure 10. The macros are employed by the user in place
of typing in the formula each time an estimate is desired.
To calculate the density subsidy, for example, the user
types "create-density(density)". To arrive at a total
subsidy for a district the user sums four component parts
to the formula. The calculations are performed as shown
in figure 11 along with the calculation of total subsidy
payments, (computer responses are indented).
To test a new formula the user may change the macro
definitions shown in figure 10 and re-execute them. An
example of this procedure is shown in figure 12 where the
following changes are made to the current formula:
-71-
1. base subsidy maximum per WADM expenditure changed
to $900 from $750
2. district income, instead of market value used to
calculate the aid ratio
3. and the poverty multiplier is increased to $200
from $165
The results of this formula are shown in figures 13 and 14.
display cnty,ar nmfd=3,current_base,densitysparsity,poverty_dollarssub sort on cnty,distnamebreak on cnty tally currentbasedensitysparsity,poverty_dollarssubwith title="Figure 8"/Subsidy Components"/By County and District'/(Partial Listing)",1nl-95for cnty-1 I cnty-3 I cnty-7
Figure 8Subsidy Components
By County and District(Partial Listing)
cnty ar currentbasepoverty-dollars
density sparsity
BERMUDIAN SPRINGS SDCONEWAGO VALLEY SDFAIRFIELD AREA SDGETTYSBURG AREA SDLITTLESTOWN AREA SDUPPER ADAMS SD
APOLLO-RIDGE SDARMSTRONG S DFREEPORT AREA SDLEECHBURG AREA SD
ALTOONA AREA SCH DISTBELLWOOD ANTIS S DCLAYSBIJRG KIMMEL S DHOLLIDAYSBURG AREA S DSPRING COVE S DTYRONE AREA S DWILLIAMSBG COMMUNITY S
0.5910.5100.2290.3890.6190.557
0.7810.7160.6760.610
0.6280.6740.6480.5890.6820.7180.731
8114471210756
17217112917711096432
826106
5408683
171327068032141433401809256
10759141
67407611108253
611839263325517010541790207503904
15089273
21378968
507434959212120
24002
788123358
9317110861
51616
21660345231652
1685366914834
907
252063
1136030
5279900
126670
293071
326704851027555699601468540425
233805
0 968550 3664650 374550 273065
0 528165
00
7750000
22G760154530
458790
444510381154504591410631957359024420
780285
distname sub
9598561268234253032
13660801117038
995321
5959561
181800671930371480173
11338922
74018741150892736036
274151817709402095390683761
16580410
0
000ouo00
Uouo-00,0
00000000000,0
000600000
COU0000000
000000
0000OU0
000000OUD
ooooouuouo
oooouuouuuooo
oooooouuouo
c
00600000000000
0uoooooooooooooooouu
ooooouooooouuooo
uuouoooooooouuuu
oocouuuooouoooouuoouoouoououoooooo.
00000000000000000000000000
0000000000000OU00OU00oooooo
c6ouGuuuuooGououoouuu0000000G00
oooououuuooooooooouoooooooooo
Ln
oooooooouoooouooooooooouooouoooooooooouooI
oououoouuoouuoooooooo000000000000000
VI
00000000000000000000OU00000000
oouoooooooooooouooo-o
00000000OU0000
Ln
OU006000000000
00
0
0000000
coo0
L 'I.
9
tv
0 17
L06s zL
z9 zs -
ztz
_E
-z
z zI zOIZ-G
i21L
1-91STk
i-
Z-1
1-1-01-
0
W C-
:3- U,
:3 1---- 4--
w
-4-- Q
,
U:*
C-
C-
:3-il
C-
il
NI
uc-
-EL-
display-attribute Name,Definition in Macrodefinitions with ttl="Macros", lnl=89;
Macros
Name
createaid_ratio
createbase
createsparsity
Def i nit ion
*
'createattribute arg1 as1-((mktval/wadm)/(totmktval/totwadm))*0.5;change_attribute arglfor argl<0.1:0.1' with parameters argI
'createattribute argI as(aie/wadm)*ar*wadm for aie/wadm>750,otherwise 750*ar*wadm' with parameters arg1
'createattribute arg1 as(2-pop-per-area/50)*250*ar*wadm forpopper_area(100&popper_area>50, 250.*ar*wadm forpopper_area(=50, otherwise 0' with parameters arg1
'create_attribute TEMP as ar f.375;create_attribute argI aspop-perarea>10000&wadm<50000,wadm>50000&pop.perm.a rea>10000,wadm>50000&popper_area<10000,for pop-perarea(10000&wadm<50missing;delete_attribute TEMP'
or ar>.375, otherwise250. *TEMP*wadm for0.19*ale for(pop.permarea*0.19*aie)/10000. forwadm*(popper_area/10000)*250.*TEMP
000, otherwisewith parameters arg1
createpoverty 'createattribute argl as 165*poverty' with parameters argl
Figure 10Macros Used to Compute Subsidies
create_density
create aid ratio(ar)
createbase(current_base)
createdensity(density)
create-sparsity(sparsity)
create-poverty(poverty-dollars)
createattribute
create_a ttr ibute
sub as currentbase+density+sparsity+poverty-dollars
9,t a1syb s i, diy
dsa total_sybsidy
total,_subsidy
1537040160
Figure 11Commands to Calculate
AllotmentsSubsidy
a's. s tam,(svu )
display Name, Definitionvertical _attrspacingl=1
in Macrodefinitions with title="New Macros", 1n1=90,sort on Name for locatetext(Name,"_2")'=O
New M4acros
Name Defi ni tion
createaidratio_2 'create attribute argI1-((income/wadm)/(totifor argl<0.1:0.1' with
asncome/totwadm))*0.5;change_attribute arr1parameters arg1
create_base_2
create_density_2
create_poverty_2
createsparsity_2
'createattribute arg1 as(aie/wadm)*ar2*wadm for aie/wadm>900,otherwise 900*ar2*wadm' with parameters arg1
'create attribute TEMP as ar2 for ar2>.375, otherwise.375;createattribute arg1 as 250.*TEMP*wadm forpopper_area>10000&wadm<50000, 0.19*aie forwadm>50000&popperarea>10000, popperarea*0.19*aie/10000 forwadm>50000&popperarea(10000, wadmn*(popper_area/10000)*250.*TEMPfor pop~perarea<10000&wadm<50000, otherwisemissIng;delete_attrebute TEMP' wi th parrnotorr nrsl
'createattribute arg1 as 200*poverty' with parameters arerl
'createattribute arg1 as(2-popper_area/50)*250*ar2*wadm foirpopperarea<100&popperarea>50, 250.*ar2*wadm forpopperarea<=50, otherwise 0' with parameters arg1.
Figure 12Macros used to Compute Subsidies
(Alternative Formula)
display cnty,current_base_2,density2,sparsity2,poverty_dollars2,sub2 sort on cnty,distnamebreak on cnty tally currentbase_2,density2,sparsity2,poverty_dollars2,sub2with tItle="Figure 13"/Subsidy Components"/By County and District"/(Partial Listing)",1n1=95for cnty=l I cnty-3 I cnty=7
Firure 13Subsidy Components
By County and District(Partial Listing)
distname
BERMUDIAN SPRINGS SDCONEWAGO VALLEY SDFAIRFIELD AREA SDGETTYSBURG AREA SDLITTLESTOWN AREA SDUPPER ADAMS SD
APOLLO-RIDGE SDARMSTRONG S DFREEPORT AREA SDLEECHBURG AREA SD
ALTOONA AREA SCH DIST 7BELLWOOD ANTIS S D 7CLAYSBURG KIMMEL S D 7HOLLIDAYSBURG AREA S D 7SPRING COVE S D 7TYRONE AREA S D 7WILLIAMSBG COMMUNITY S 7
densIty2cnty current_base_2
10152541446609606798
21755G413289171072870
7646013
1674179725300115996201011562
11538361
71994591381324766890
294998519739262042333613395
16927310
22288639
910589659802295
25949
641820091
866510028
45201
19278546811726
1573364704595
858
226849
poverty_dollars2sparsity2
1134460
15507100
137089
410606
0000
0
00
8094900
215580146222
442751
396005880033400848001780049000
283400
117400444200
4540033200
640200
5338001620054600
1108007660089200296(00
945800
sub2
11755281514049796179
226626013526971261254
8365968
1797997771729116536841054790
12223762
79310441432205904165
307651820569962351707
790076
18542711
00 I
01
000
|01
000 I
00 I
000 I
00000 I
01
000000000000
I
000 I
01
'000 I
000 I
00 I
00000 I
000000
I00000
I
000000000
I0000000000000
I00000000
I000000000000000
0000000000000
I0000000000000000
II00000000000000
000000000000000000000I
ooouooouooouoooo000000000000000000000
IOUOOOOOOOOOOOOOOOOOOOOO
Iooo
00000000O0000000000000LO00000000U0U0000000
IU0000000000000000000000000000
0000000000000"000000000000
0000000U00000000
000000000
U0001
-8L-
6tz
Li
9 1
61
LI91it
LIII
01
68L99I0
Cu
0
-3o.14
0 /
:H
-H 4.x
N0E
-40-.04Q
-79-
The Act-580 system has evolved through the Needs Assess-
ment, Design and most of the Approach phases. Evaluation
of technology and review of the system prototype are
2urrently underway within the user environment. Commitment
to -a specific technology and to an approach will be estab-
1-ished -in the near future.
-3.4 Summary of Case Study
Trhis ?-ase study demonstrates that the guidelines pre-
Eanted in Section 2 can be effectively employed. This is
&ne within the context of the key environmental factors
-that -influence systems success. In the Needs Assessment
.phase an understanding of the existing environment was
rformulated. This was used to establish a clear statement
-of the proposed system's objectives in the Design phase.
4sers -actively participate in the construction of a des-
criptive model and design of a normative model to insure
hait ~the final system will be accepted and productive when
Conplete. Top management support is manifested by the
7promise to supply ongoing personnel and resource assistance
cthroughout system development. The proposed system has been
-kpt simple and uncomplicated to facilitate its assimila-
tiLon by-the organization during implementation.
-The -feature that distinguishes this framework from
76thers is its use of a system prototype. The one developed
in the case study conveys that a prototype, as an active
-80-
model, illustrates the capabilities of the future system.
This enables the analyst to determine the need.for any re-
finements to the system's design. In addition, the proto-
type may generate user interest and enthusiasm for the
final product.
This framework, through the incorporation of a system
prototype, enhances the process of MIS evolution. The
example presented in the case study is evidence of its
utili-ty Lin a particular application. It is expected that
future usage will reveal its utility in other applications.
-81-
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-85-
57. Simon, H.A. The New Science of Management Decision.New York, Harper & Row, 1960.
58. Sterling, Theodore, "Guidelines for Humanizing Com-puterized Information Systems", Communications ofof the ACM, vol. 17, no. 11, 1974.
59. Swanson, E.B. "Information Systems Approaches", Manage-ment Datamatics, 1976.
60. Thome, P.G., and R.G. Willard, "The Systems Approach:A Unified Concept of Planning. Aerospace Management,Fall/Winter, 1966.
61. Wilson, Ira G., and Morthann Wilson. Information,Computers and System Design, Wiley, 1967.
62. Young, Stanley. Management: A Systems Analysis.Scott, Forisman and Company, 1966.
COMMONWEALTH OF PENNSYLVANIADEPARTMENT OF EDUCATION
GROVER Mci AUG-L IVY7-52EE JUIIN Po thblER 7%0
SstACFO T A7
EtfCUTIVE? c 'PUt SECmAl
PASSBf~ 806 WN037HO IO 7uaow.1COI
PTATE AAS OF EOUCA TION
Pt A558L EVANS77 3787
PM)FIESSIONAL 1IANDANDE
PRACTICSAND r Amn71MAIVE MOFORA I'ON ANDP ATICESCDoM~imm%' ACTION OFFICE PUIKICAfTONSs
DA8E AUN, V " Ia rAl. AN)C ANN WTMER 'A.- GRFENAGIEA
FRANK 5 MANCHESTER 7 2t7j
DEPUTY COMMISSIOERMAARY I GERLACH 7 4ti5 ---
SATFn aTO EDUJCATION VAcert I e11SS 101EM UTV ACADEMT GEO001F 5AUElR5 j t. 2
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ASET. CosU(3686 FOR
CUT COMMCAIONR C6 101"A 6BYD 771 lS
PROJECT gIgMACM8CT CUMLCULU
1RVl.A I7 2177 '89
INTSEFDIATE UNITS -- C P II
SP ASsT FOREM1 CA TIGAL stAlCES
w Auf P, VOC ATIONA
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AUPPOSR
kIfOHN
3 3760
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HE SEEAB AR F27
MSERVICES
AU OF'" EDUCATION
ALI OfLDUCATION
AU OF/CTIONALTSERVICES
AU OfSERVICESO10SlIf08
OFFICE OF
OFFICE ofOMIClT@S ED
PAUL DAINf,7 3 22
OffoCs OFN408luPK9C 1CHOOL(1
MCHIEF4ANGA
MMF 8
J AMf, E E A. A
EDUCATIONAL
7 4234
AS' SA I7ITDEPUTV $FCM TANT
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ASSI COMMISSIONI
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7 ymE
FOR PUSI C AFF AINMARY 'DOFF
7 97 44
LEGILAIVIE FEDERAL LEASERVICES PN)GRAMS PATICIA DONOVANM *A TP KOCH JOAN 5CWAR77
7 2,44 7 7133
ER FOR HIGHER EDUCATION
JEROMf M 7'EGLER 7 5041
PUTT DotnlSSIONRIGL ADY5 HADY 7 7572
ASS? CommSOIgEIRMAROLD C)( W150p
COONDINATOWFOA
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CONkAD JONE$
7 1343
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7 5105
PROPOSED I JULY 1976
Appendix A
Appendix B
Bureau of Information Systems
DirectorSeon Cho
Assistant
IP.
Planning andCoordinating Unit
Data Base CoordinatorInformation Systems
Planning Specialist
BureauonE
Voainl dct2
Divisionof
Research
ResearchCoordinatingUnit
Researchand
Developmenl
Divisionof
EducationalSta sties
Dean Hartman
In-house TechnicalResource Function
a /Higher EStatisti
Bas c Ed.Statistics
PDE/Activities
PSERBActivities
VEMAIS
Data rtingCollection
d.
Foi/Dat aControl
Divisionof
Computer Services
Al Azar
-88-
Appendix C
The Subsidy Formula
A. The Base Subsidy ( 87% of total subsidy)
ApprovedBASE = MIN Instructional Expenditure
LK- WADM750per *ARWADM
*WADM
1. Approved Instructional Expenditure - totaldollars to be spent by a school district oninstruction.
2. WADM - Last year's Weighted Average DailyMembership (maintained by Child AccountingDivision)
Kind of Child
half day kinder-garten
full time K-6
secondary
3. AR - Aid Ratio
Weight (unchanged since1966)
0.5
1.0
1.36
District Market Value/WADMAR = 1 - State Market Value/WADM
Market Value - dollar value of district's real estate
computed yearly and certified by: State Tax Equalization
Board**. Most recent year's data used instead of calculating
future values. Announced 6/30/n for year n-l.
** currently defunct
a
-89-
Note - Income Level is being phased into Aid Ratio
formula in conjunction with Market'Value
(e.g. 75% MV & 25% Income)
- Only 85% of district Incomes are currently
accounted for due to acquisition errors.
AR is never less than .10 (i.e. district never
gets less than 750/WADM)
The .5 is a distribution constant used since
1966
4. BASE can only be greater than or equal to
amount received per WADM in 1971/72 (base year
rarely changes)
B. Population Factor - Density
1. Density - if district has 10000 people/sq.mile
it receives: $250 * mAX [AR, .375] * WADM
- square miles computed by Bureau of Internal
Affairs
- Number of people-converted to district from
1970 census
- $250 has been constant over time
2. Super Density - if district has > 50,000 WADMs
it receives: 19% of Actual Instructional Expense
- the 19% has been a constant over time
-90-
3. Modified Density - used for districts with
under 10000/sq.mile
population/sq.mile ,10,000 * Density
4. Modified Super Density - if population <
10000/sq.mile and has > 50,000 WADMs it
receives:
population/sq.mile50,000 * .19*Actual Instruc-
tional Expense
5. A district can receive only one form of
Density Aid
6. Modified densities insure that once Density
Aid received, it will always be received;
even if population distribution changes over
time
B. Population Factor - Sparsity
1. Sparsity - if < 50.people/sq. mile a district
receives: $250 * AR * WADM
No minimum AR
2. Modified Sparsity - if < 100 people/sq.mile a
district receives:
2 - population/sq.mile) * Sparsity50
-91-
D. Poverty
1. Poverty Allowance per pupil to district
a) # of students ( age 5-17)
belonging to families making
income <*2000 - comes from 1960 or 1970 census
b) # of students (age 5-17)
belonging to families receiving
) $2000 in Aid to Families with Dependent Children
( data comes from Welfare office on tape)
can use either present AFDC or 1972 AFDC
c) calculate a total number of poverty kids and
multiply by $165 to get poverty subsidy
d) all districts receive some poverty subsidy
-92-
Appendix D
Three Examples of Proposed Changes to Subsidy Formula
Scenarios - proposals submitted to, statistics group
- test results needed within 1 hr - 1 day
- Results shown by: school district, county,
legislative district.
I. Parameter Change to Basic Formula.
A. Change AR to include Income along with
market value
B. Change maximum amount per WADM in basic
formula from $750 to $900
II. Union Proposal
A. 1.
2.
- New Model Required.
Rank order districts by WADM expenditures
Establish median WADM expenditures(round to $50)
B. Create 4 categories below median, at $50
intervals
(i.e. M, M-$50, M-$100, M-$150, M- $200
where M = Median WADM expenditures)
-93-
C. Districts qualify for special subsidy based on
"local effort" defined as:
mills on (total taxes collected for school purposesmarket valve per district)
( market value of district)
D. 1. Rank order districts based on: mills on
market value
2. Establish the median mills on market value(mm)
E. For each district:
1. if district collects 30% greater than mmdistrict receives (median WADM)
2. if district collects 15-29% GT mediandistrict receives (median WADM -$50)
3. if district collects 15% below to 15% GT median,district receives (median WADM -$100)
4. if district collects 29% -14% below median,district receives (median WADM-$150)
5. if district collects 30% or more below mediandistrict receives (mediam WADM-$200)
F. Bootstrap: Any district that is spending less
than (median - $200) per EADM,and is making at
least a -median local effort (i.e. mils on market
value is GE. to Median) district shall be con-
sidered to be spending (median $200) per WADM.
III. Governor's Proposal - Adds New Components to Base
Formula
A. Urban Assistance Component
-94-
1. If within a school district, there exists
a City of General population GE 40 K,
district will receive: $5/capitaor
$8/capita ifPhiladelphia orPittsburgh
2. No school district will be allowed to
receive more than 60% of the total Urban
Assistance given to all districts
B. Aid to Low Wealth Districts Component
1. If Aid ratio. is greater than 0.7, and
there are no sparsity payments of any kind
within the district, the district will
receive per WADM:
$125 for AR & .78
$100 for .75 ± AR & .78
$ 75 for .72 i AR 4 .75
$ 50 for .70 t AR 4-.72
2. To qualify, district must have 4 mills on
market value.
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