A Basic Technique for Guiding Evidence‐Informed Problem Solving

• Published date: 01 September 2022
• Author: Harold C. Abrams
• Date: 01 September 2022
• DOI: http://doi.org/10.1111/puar.13451

A Basic Technique for Guiding Evidence-Informed ProblemSolving 893

Public Administration Review,

Vol. 82, Iss. 5, pp. 893–896. © 2021 by

The American Society for Public Administration.

DOI: 10.1111/puar.13451.

Harold C. Abrams

University of Texas Permian Basin

A Basic Technique for Guiding Evidence-Informed

ProblemSolving

Abstract:To help guide certain problem-solving efforts in a timely and relevant manner, public agencies should consider

the use of simple multiplicative component models. Such models involve basic algebra, arithmetic, practical logic, and

straightforward cross-tabulations and tables. Two actual case examples are presented. Additional diverse applications are

briefly discussed to illustrate the potential value of this tool to guide a number of evidence-informed efforts.

Evidence for Practice

• Simple MCMs involve basic algebra, arithmetic, practical logic, and straightforward cross-tabulations and

tables.

• Simple MCMs can help organize the presentation of data in a way that induces useful discussion on how

to improve diverse outcomes of interest from increasing public university enrollment to decreasing traffic

injuries among older drivers.

• Public agencies should consider the increased use of MCMs.

There is a quantitative device that many public

agency decision-makers, program managers–

staff, and analysts can use to gain useful

insights into certain management–policy problems

faced (e.g., to increase university enrollment, to

reduce traffic injuries, to decrease fossil fuel-related

carbon emissions, and to increase the number of

cadaveric organs for transplantation). This device

may sometimes be overlooked when it could be

useful in analyses that must be done on pressing

problems within the constraints of limited time, data

availability, and resources that characterize typical

analytical efforts in public agencies (Patton and

Sawicki1993). In certain situations, this device can

be used to help organize, inform, and guide group

discussions about potential intervention strategies

and additional analyses–reviews (e.g., more detailed

analyses of existing raw or aggregate data, special data

collection efforts, literature reviews).

This article will provide an overview of a device

known as the multiplicative component model

(MCM) (Herendeen1998) or the decomposition

method (Li and Baker1996). The focus will be on

the simplest form of MCM that can be useful and

does not involve sophisticated statistical techniques

beyond cross-tabulations and tables. Simple MCMs

are a kind of back-of-the-envelope model (Patton

and Sawicki1993). They involve algebra and

arithmetic along with practical logic. The device

can be very useful but is not generally considered

in typical quantitative courses required in Master of

Public Administration programs (Aristegueta and

Raffel2001; Horne2008).

Simple MCMs

What is a simple MCM? A simple MCM involves

formulating an equation that expresses an outcome

of interest as the product of two or more component

factors. For example, a day treatment program for

youngsters concerned about the outcome of care-

days produced in a year might formulate an equation

that casts the number of care-days generated as, by

definition, the product of three factors: the number

of days open, the enrollment level, and the attendance

rate among those enrolled.

For each year of interest, the program may generate a

display that shows the actual figures for each aspect.

This can shed light on the reasons for changes in care-

days produced over time, and support planning on

how to increase care-days in future years. In the next

section, this particular application will be illustrated

with an actual case example. This will also further

clarify some of the fundamental characteristics of a

simple MCM (including the notion mentioned above

that the equation needs to be true by definition).

Examples of studies–analyses using simple MCMs

include:

Harold Abrams is an associate professor

of public administration at the University

of Texas Permian Basin (UTPB). He is also

Chair of the Department of Social Sciences

and Coordinator of the Master of Public

Administration Program, which is one of

six program areas within the Department.

Prior to coming to UTPB, he served as the

analysis director for several health and

human services agencies in New York City

and Boston. Abrams' research focuses on

issues in health and human services policy

and management (including developing

cases of practical techniques of analysis for

assisting decision-making).

Email: abrams_h@utpb.edu

Viewpoint Article