Teaching Statistics Without Mathematics An Innovation Approach to Public Administration Education

Teaching Statistics to Adult Learners 1
Teaching Statistics to Mid-Career Adult Learner Graduate Students in Public
Administration and Public Health Programs
A Modified Innovative Paradigm
By
Michael W. Popejoy, M.B.A., Ph.D., M.P.H., M.S.
Fellow, Royal Society for Public Health (UK)
Adjunct Professor of Public Health
Department of Health Promotion and Disease Prevention
Robert Stempel School of Public Health and Social Work
Florida International University
Miami, Florida, USA
And
Associate Graduate Faculty
MSA Program in Research Administration
Clinical Research Administration
Global Campus
Central Michigan University
Mt. Pleasant, Michigan USA
Charlotte, North Carolina, USA
May 2013
Abstract
This paper presented to the Roundtable Discussion Session at the Annual Great Lakes
Conference on Teaching and Learning at Central Michigan University in Mt. Pleasant,
Michigan reports the results of an experimental course developed to teach a modified
statistics course to a small cohort of adult (mature) graduate students in public
administration. These students were mid-careerists in administrative or management
positions in the public sector with an average of 15 to 20 years of experience each. The
theme of the course, and this paper, is on whether or not statistics can be taught
successfully with minimal mathematical applications while emphasizing statistical

analysis interpretation leading to better informed administrative and managerial decisions
in public sector organizations. The limited sampling frame notwithstanding, the cohort
reported a high degree of satisfaction with the modified course, and reflected their belief
that the modified course would help them understand statistical data and knowledge
related specifically to understanding statistical analysis. Further, the students reported
that in the years spent in their careers, none had been mandated to undertake on-the-job
studies requiring them to perform statistical analysis themselves; rather they saw
themselves as consumers of statistical reports and needed more exposure in how to read
and interpret them. An untested frame is whether or not students retained knowledge
longer when the course was presented with minimal mathematical formulas and formula
problem solving. However, despite the potential controversy, a change in curriculum
content and teaching methodology is recommended due to the desired end result which is
to provide mid-career administrators with immediate information that can support
decisions and learning that could endure over time. This new approach supports the
primacy of practice over theory as it may relate to specific cohorts of students—the adult
learner in mid-career management and administrative positions in the public sector who
generally are not considering doctorate studies.
Introduction
Statistics can arrive in two sets of clothes: one is applied mathematics and the other as
applied information for decision makers. For the mathematician/statistician, it is all about
the process (statistical analysis) of arriving at an answer based on collected raw data and
application of appropriate statistical analysis procedures based on currently accepted
statistical theory. For the decision maker, it is more about using the end product of

statistical analysis, the output of the process, to arrive at a satisfying outcome—a well
grounded decision based on the best evidence.
In pedagogy, one has to close the gap between what the statistician does and what the
decision maker needs. If the decision maker has confidence in the work of the statistical
analyst, then the painful details of the “theoretically correct” process is not needed, and
indeed, may not be well understood anyway; and, the analytical details may complicate
further an already complex decision. The decision maker needs an answer to the raw
quantitative data collected, and a clearly understandable interpretation in order to inform
the decision. Although, once data is collected and analyzed appropriately, even the
interpretation may be left up to the decision maker if people in those positions are well
prepared to understand and interpret statistical output.
For instance, an SPSS (Statistical Program for the Social Sciences) output page(s)
with graphics should be enough information for the decision maker to interpret how the
raw data was handled and what decisions may be reliably made from the analysis. How
much is it necessary for the decision maker to know what data was collected, how it was
collected, how it was analyzed if he/she knows how to interpret the output and has
confidence in the skill of the analyst in developing the study plan? Does the decision
maker need to be well grounded in the mathematical formulae underlying the computer
printouts? Further, the decision maker can partner with the statistician in assisting with
what outcomes are desired in terms of the input data needed. Indeed, in many statistics
textbooks, the authors often advise students confronted with complex statistical tasks to
consult with a statistician first before proceeding with a research plan (Neill, 2008;
Rosner, 2006; Tabachnick and Fidell, 2001).

In the training of adult, mid-career, administrative graduate students, who are most
often already well established in their careers as public decision makers, the faculty focus
may be too often on the process, and too seldom on how the results of the process will be
utilized. Indeed, the training and background of the faculty teaching statistics may be the
determining factor in how graduate courses are taught—applied mathematics statisticians
will most likely focus on process (equations and the mathematics process), whereas the
applied decision maker will most likely focus on interpretation of the analytical outputs
allowing the computer program to crunch the numbers based on the data collected by the
analyst (Hawk and Shaw, 2007 citing Merriam and Caffarella, 1999; Noddings, 1998).
Hawk and Shaw (2007) write, “We believe that most faculty in higher education
initially adopt a teaching style that merges (1) the ways they prefer to learn and (2)
approaches to teaching they saw as effective for their own learning in higher education
programs” (p.1). Consequently, Hawk and Shaw propose that the faculty either are
unfamiliar with learning style methods or are uncomfortable experimenting with learning
styles other than their own preference because it “takes them out of their own comfort
zone” (p.1).
It is possible that the mathematical processes are continuing to be taught both in the
classroom and in the textbooks mainly because the current generation of textbook writers
and university faculty learned statistics themselves prior to the development of
sophisticated computer programs that do all the hard, time consuming computational
work. Teachers may teach the way they were taught (the old school) rather than adapting
their pedagogy to the new demands of modern administrative managers, and the tools that
are now available as desktop decision support systems.

The question of the pedagogical approach in the curriculum may focus on whether it is
critical or how important it is to the quality of the decision that the decision maker must
comprehend the exquisitely complex mathematical processes when in reality it is the
outcome of the decision that is most important to the decision maker in public
administration (and public health). Are we emphasizing the wrong things for the wrong
audience based on what that particular audience may need? Are we emphasizing the
wrong things based on how today’s teachers were taught by yesterday’s teachers?
Another issue of consideration in graduate education is how long, once learned, will
the detailed statistical analytical (mathematical formula based) processes be retained by
decision makers who are not normally expected to perform the analysis themselves as
part of their daily responsibilities? “Use it or lose it” is the old adage about retention.
Would it be more probable that decision makers, who are not analysts working daily with
the numerical aspects of analyses; retain more of some ideas of interpretation longer than
they would the mathematical processes that they do not use and have not seen since
sweating out the required graduate course?
This paper shows that exposing a modified statistics curriculum of applied statistics
utilizing minimal mathematics to a graduate class for public administrators, through an
informal survey, it was revealed that 100 percent of the students were mid-career to upper
level decision makers (managers/administrators) in their public organizations who used
statistical information frequently but were never expected to do the analysis themselves.
Some of these student objections may be based on math phobia or simply inadequate
mathematics background; however, the more significant objections seem to come from
adult learners who simply do not believe in wasting time learning techniques, processes,

or theories that are of not an immediate benefit to their educational goals based on their
occupational requirements. When the focus of the course is directly related to the above,
adult student acceptance of the course rises significantly; as does faculty evaluations.
So, are we teaching the wrong things to the wrong groups under the guise of requiring
“tools” courses for academic programs designed for public administrators? This paper
adds some modest support to a curriculum reevaluation of how statistics are presented to
decision makers as compared to how they would be presented to students planning on
becoming statistical analysts.
One aspect of how these courses are presented that is not being considered in this
paper is the probable fact that some courses are held out to be “gatekeeper” courses that
in a sense serve to wash out students who cannot gain sufficient knowledge of the
material (such as mathematics) to make a passing grade in a required course for the
degree. This particular feature of a graduate program then lies with faculty preferences
and prejudices, not pedagogical methodology best serving theory.
Pedagogy on Adult Education
It is important to distinguish between teaching methodology that works well for young
people (with virtually no professional managerial or administrative work experience from
which to draw upon) and what works well for the adult learner who is returning to school
for advanced education or continuing education directly related to career retention and/or
advancement. Most adult learners are either changing careers or are expecting to advance
in careers that they already have attained a certain level – usually the mid-career level.

Most of these adult learners are already successful and are interested in earning the
highest grades in courses they feel offer relevant work-related and/or life-related content.
They are less interested in theory or theory building foundations that are at best only
remotely related to their goals for returning to school. The rigors of theory take a back
seat to applied concepts. The exception, of course, may be the adult learner in a doctoral
program. The very nature of a doctoral program is designed to be grounded in theory,
advance theory through original research, push new paradigms, and learn research design
and analytical techniques to prepare the student for careers in research and/or academia.
However, the far majority of masters’ students are not potentially future professors or
researchers—they are being trained as decision makers in the applied sense and they need
information they can use tomorrow at work. Consequently, all course work for these
students needs to focus on adapting the theoretical foundations of any subject into an
applied context that adult students can relate to and use in their current and/or future
careers. But, how many professors can claim directly related work experience in the
fields they teach?
The expectations these adult students bring to the classroom are quite different than
that experienced in teaching younger students. Some of these differences may be
attributed to generational differences, and others may be more related to the stage in their
careers, and what they are seeking in terms of new knowledge that would be directly
related to their immediate work environments. Adult students want to get results from the
substantial investment in time (from both family and work) and money that they make to
return to school, usually for a graduate degree in management or administration (MBA,
MHA, MPA, MPH, MSN in administration, etc.).

This is possibly why at the University of Phoenix, courses are called workshops, and
professors are called facilitators, and the facilitators, although required to hold doctorates,
are also required to have significant occupational experience in any field they teach so
that the workshop discussions can be focused to a limited degree on theory that is broadly
applied to practice more intensely rather than what is often the opposite approach taken at
a research university.
There is little pretense in formal adult education that the adult student is interested in
studying, in depth, why something works the way it does, rather, they simply want to
know how to apply what works in their immediate work environments. So, the role of the
facilitator is to facilitate that application of theory to practice in concert with the adult
student-participants in the workshop.
In teaching statistics to adult learners, they may well ask “why do I need to do the
mathematics when I have a computer that will crunch the numbers and provide me with
output that then I can make a decision?” Further, “why can’t I just ask my staff
statistician to work out the details and send me a bullet report that I can be trained to
understand?” Traditional statistics teachers may counter that it is better and safer to know
the theory behind a process than just being an “operator.”
Unhappily for traditional statistics teachers is that the adult learner is less interested in
how to do statistics than in what statistics can do for them. Many statistics textbooks
today recognize the ease of doing the computations with computers, yet insist that
everyone should learn the basics by working out the formulas by hand so that they can
gain a “deeper” understanding of statistical analysis (Neil, 2008). This deeper level of
understanding is not what is being demanded by today’s adult learner who is seeking an

expedient application of any technique being taught. They then perceive whether or not it
is worth their investment in time and money to take the course and this is based on what
is in it for them—immediately. Tabachnick and Fidell (2001) argue in an earlier edition
of their textbook, Using Multivariate Statistics that you can be taught to skillfully drive a
car without necessarily knowing all that is going on under the hood.
This approach to teaching and learning is also the same when teaching in a research
university’s off campus compressed time format graduate degree programs such as
Central Michigan University’s College of Extended Learning which offers an MPA
degree among others. These specialized programs that are restricted to adult learners
attract the same adult learner with the same goals and motivations as does the
nontraditional graduate degree programs such as those offered at the University of
Phoenix and Strayer University; among a host of others.
As more universities are beginning to offer off campus programs and online programs
(due to excessive demand from students and due to the attractiveness of these programs
financially to universities competing for enrollment) attracting a different student than the
more traditional on campus programs tend to attract; the curriculum, and the faculty who
choose to teach in these new formats must face some challenges in modifying the
curriculum content, pedagogical approach and methodology to meet the demands and
goals of this new category of student.
These same students, who will likely be reluctant to invest their time and money in a
degree program that does not meet their needs, even if the program does meet the needs
of other types of students; and by the way, specifically meets the needs of faculty who are
entrenched in teaching the way they were taught. Further, it is unlikely that the traditional

faculty will be satisfied with negative student evaluations of their teaching, and of their
course content, when they continue to fail to understand and adapt to this new category of
student.
If we need to question a change in how courses are delivered, based on student
demand, we need only ask why University of Phoenix alone enrolls 345,000 students
(Wasley, The Chronicle of Higher Education, August 8, 2008. p.A1). And, Central
Michigan University’s College of Extended Learning is a significant cash cow for the
university as is most all adult learning programs are to their parent universities. Indeed,
some adult learner programs, such as the graduate programs (MBA, MHA, MBA/MHA,
and MSL) at Pfeiffer University in Charlotte, North Carolina, subsidize their parent
institution offering traditional degree programs to such an extent that the parent campus
would cease to exist quickly without the adult degree programs student enrollment
revenues (personal communication, Dr. Joel Vikers, Dean Inis Gibbs, 2007).
The annual budget at Pfeiffer University absolutely depends on sufficient enrollment
of adult learners in their Adult Studies Program (undergraduate) and their Graduate
Program, not the enrollment on main campus (where the traditional undergraduate
students attend) (personal communication, Dean Inis Gibbs, 2007).
So, given the financial imperatives created by this new student demand, and given the
faculty evaluations from students, it is important that curriculum content changes and
changes in how courses are taught are going to be increasingly necessary for university
programs to remain competitive, indeed in some cases to survive. And, given the
decreasing levels of financial support from both state and Federal sources, it is even more

important for the traditional research universities to seek out the adult learner student
base and strive to meet their unique needs.
The pedagogical or teaching methodology changes being suggested in this paper can
be applied to the entire curriculum in general, but specifically, this paper concentrates on
how statistics is being taught, or how it should be taught, to contemporary adult learner
graduate students today.
Students learn in different ways which forces faculty in higher education to reevaluate
any assumptions that all students learn the same way and that the faculty member’s own
preferences and prejudices for learning are broad enough to facilitate the learning needs
of most or all the students in a course. In order to achieve more effective learning, faculty
must embrace the attitude of expanding their learning activities to accommodate a wider
field of adult learning styles (Hawk and Shah, 2007, p.2).
In the Kolb Experiential Learning Model (2005), learners are segmented by learning
preference and then Kolb designs learning activities to accommodate the different
personalities. The segments are (1) divergers who have strong imaginative ability, are
good at seeing things from different perspectives, are creative, and work well with
people; (2) assimilators who have abilities to create theoretical models, prefer inductive
reasoning, and would rather deal with abstract ideas; (3) convergers have a strong
practical orientation, are generally deductive in their thinking, and tend to be
unemotional; (4) accommodators like doing things, are risk takers, are in the here and
now, and solve problems intuitively.
A second, but related learning model is the Gregorc Learning/Teaching Style Model
that defines learning style as “distinctive and observable behaviors that provide clues

about the mediation abilities of individuals and how their minds relate to the world and,
therefore, how they learn” (Gregorc, 1979, p.19).
Gregorc claims that individuals have natural predispositions for learning along four
bipolar, continuous mind qualities that function as mediators as individuals learn from
and act upon their environments (adult, mature, administrators/managers). These mind
qualities are abstract and concrete perception, sequential and random ordering, deductive
and inductive reasoning, and separative and associative relationships. The Gregorc Style
Delineator (GSD) provides metrics on how individuals measure up to these four
dimensions. The GSD is commercially available (www.gregorc.com) and can be self-
administered, self-scored and self-interpreted.
Both of these learning/teaching models are only two of many models currently in
vogue in educational research, however, it is imperative that higher education faculty in
all disciplines and in all course delivery methods, who normally are not in the field of
educational research, should be aware of and increase their adaptability in teaching based
on these models. It gets back to the thesis of teaching the right things to the right
audience in the right manner to ensure both learning and student satisfaction with the
educational experience into which the student has invested time and money.
The conclusion in educational research is that no one instrument or model can capture
all the learning styles in one neat package. It does require higher education faculty to
rethink their pedagogical processes based on the student audience that is presented at the
time. It should be easy to conclude that undergraduate students, masters’ students, and
doctoral students should all be approached differently since each category has different

life experiences, different expectations from the educational experience, and different
immediate needs in terms of how their education will be put to use.
Hawk and Shah (2007) propose that the use of learning style instruments and adapting
the curriculum and delivery to the student model (i.e., adult learner) results in the
following: (1) higher levels of adult student satisfaction with the learning in a course; (2)
higher levels of academic performance by adult learners in a course; (3) deeper, more
lasting adult student learning in the course and beyond the course; (4) increase in the
ability of adult learners to learn in different ways in a course and beyond the course; (5)
higher levels of academic performance for the adult students than the use of just one
learning style (probably that preferred by the individual professor) (pgs. 14-15).
A Review of Selected Textbooks in Statistics
A review of selected textbooks in statistics was undertaken to compare how key
statistical processes were being presented by different authors. Each textbook fits rather
well into a specific learning style for the adult learner and based on prior learning or
background especially currency in intermediate to advanced mathematics. It is probably
true that textbooks are selected by professors based on their specific teaching style and
desired curriculum content—more mathematics or less mathematics as a potential
measure of perceived rigor—as opposed to what is best for the students expected in the
course.

It seems that possibly the most successful textbooks for mid-career administrators are
ones that minimizes but not eliminates the mathematical relationships. For instance, an
author presents a sequence of statistical logic in a fairly common way such as
presentation of simple descriptive techniques such as mean, median, mode, and variation
and standard deviation before moving on into the more complex inferential processes.
This is a common approach even in the more complex textbooks such as Rosner’s text on
Biostatisics. But, also the author presents a straightforward narrative of what each process
is and proposes to do while showing the simplest mathematical formula notation
available (which Rosner does not do), then working out a problem inserting numbers into
the formula, then moving into solving the same problem using a computer statistical
package such as SPSS showing how to input the data and how to interpret the output.
For instance, in Neil J. Salkind’s book, Statistics for People Who (think they) Hate
Statistics, 3rd
edition (2008), he has adapted his approach in such a way as to minimize
mathematics, although not eliminating it entirely. However, when he illustrates a
mathematical formula to provide the foundation for a statistical procedure, he uses the
most simple and straightforward formula notation possible, while listing each expression
of the formula in plain language.
He follows this approach with a plain language explanation of what the formula is
doing, and why, and what the expected results are attempting to report to the analyst. He
then uses a table or other method of illustration to demonstrate how the formula is
worked out using real numbers from the provided example.

But, he is not yet finished—he follows this approach with a detailed description on
how to use SPSS (from data entry to procedure commands) to get the same result, and
how to read and interpret the SPSS output.
Consequently, Salkind takes students from a simple illustration of the mathematical
formula to an example of how to work out the formula by hand, to how to enter the same
data into SPSS, select the appropriate commands, and read and interpret the output from
the SPSS procedure. Along the way, he offers some theoretical background but does so
without confusing the reader who may want only to get to the answer needed—which
may only be how to enter the data into SPSS and how to interpret the output.
Salkind does not endeavor to take the student beyond a certain level such as the many
tests available and the more advanced multivariate statistical techniques. He indicates that
many such advanced techniques exist, but that they are beyond the scope of the book he
has written. His book may not seem rigorous enough for a graduate program, however, he
does a thorough job of taking a student from an assumed zero knowledge of statistics or
math or computer operation to a level of at least being able to do basic multiple
regression with F tests. Does a graduate student who is working as a manager or
administrator need more than this?
Indeed, Salkind’s book covers the same ground as Rosner’s book, but does so in a
more straightforward way assuming no prior knowledge or mathematical skill on the part
of the student.
Tabachnick and Fidell’s book, Using Multivariate Statistics, 4th
edition essentially
covers the same ground except they attempt to cover three major statistical packages,
including SPSS, and they offer a great deal more theoretical background, and more

detailed mathematics for the reader who wants a more comprehensive background.
Nevertheless, their book can be used as an operator’s manual for any of the three
packages, such as SPSS, demonstrating how to input data, what commands to use, and
how to interpret the output, without attending to all the theoretical details—but, they are
there if you want to refer to them.
A helpful little book is Stanton A. Glantz’s Primer of Bio-Statistics, 3rd
edition (1992)
which also blends somewhat simplified mathematical models with easy to follow
applications although Glantz does not cover computer applications preferring to
encourage the student to work out formulas by hand.
The courageous author, Derek Rowntree, in 2004 wrote a small book titled, Statistics
Without Tears: A Primer for Non-Mathematicians. He covers a sampling of statistical
information through early correlation and regression although he avoids any extensive
use of formulas or computer entry and output interpretation. He informs the reader that
his emphasis is on ideas and not on calculations—how to understand the key concepts of
statistics and use them in thinking statistically about whatever real-world problems you
find them relevant to. If you are a consumer of statistics (interpreting other people’s
reports), this may be all you need (p. 10).
There are also books that are designed to more or less teach fundamental statistics
while teaching how to use statistical packages such as SPSS. One such example is Using
SPSS to Solve Statistical Problems: A Self-Instruction Guide, 2001 (of which I am a
reviewer to the new edition), by David M. Shannon and Mark A. Davenport. This book
assumes no statistical, mathematical or computer background as it attempts to integrate
statistics and computer operations to solve problems. It comes with datasets on disks and

would be a great workbook for the graduate student who needs to learn the basics with
minimal exposure to the sophisticated mathematical formulas.
Three textbooks for public administration include Kenneth J. Meier and Jeffrey L.
Brudney’s Applied Statistics for Public Administration, 4th
edition and Lawrence L.
Giventer’s Statistical Analysis for Public Administration, 2nd
edition. Meier and Brudney
use less mathematics and more clear expository explanation, whereas, Giventer’s
approach is far more mathematical in terms of working out formulas by hand since there
are no computer software applications.
Finally, Brian P. Macfie and Philip M. Nufrio’s Applied Statistics for Public Policy
(2006) uses minimal mathematical formulas while emphasizing the use of Polystat
(provided with the textbook as a CD). Many exercises are provided that demonstrate how
Polystat works and how to interpret the output to apply to public policy and public
administration case problems.
One other book, for those interested in doing health policy and health administration
analyses using Excel is James E. Veney’s book, Statistics for Health Policy and
Administration Using Microsoft Excel (2003). Here again, like Macfie and Nufrio, there
is virtually no mathematical formulas to work out since the emphasis is on clear
expository explanation similar to Rowntree’s approach but with emphasis on how to
enter, compute and interpret statistical analyses using Microsoft Excel. There are also
many examples to work with that are focused on health policy and health administration
environments.
However, by far, the majority of statistics textbooks cover in painful detail the
mathematical foundations for every statistical procedure. In fact, a statistics textbook

without mathematics or with minimal mathematical expression may be viewed as not
rigorous enough for a graduate degree program. This again reflects the faculty’s
preferences and prejudices over the role of rigor being based on reductionist principles
rather than being designed for a particular model of student.
For example, at Florida Atlantic University’s Ph.D. program in public administration,
reductionist principles takes on a life of its own when the prevailing attitude of the
faculty is that no dissertation will be accepted unless advanced multivariate statistics are
used in the methodology of the research. The statement went; “if it can’t be regressed, it
can’t be a dissertation at FAU.” This approach certainly keeps the methodologists
assigned to dissertation committees busy while limiting the scope and theory that can be
covered by a dissertation to one that can be reduced to a mathematical relationship. If
only all of social sciences was so easy to conceptualize.
However, in a Ph.D. program, this may not be a problem since the curriculum for
doctoral candidates indeed should be different and more theoretically grounded than the
curriculum for adult learner graduate students whose primary goals revolve around
getting a job or advancing a career in administrative practice in the public or private
sector. The perceived goals of the doctoral student revolve around theory building
research, and as such, the job of an academic practitioner would be expected to be more
grounded in theory than in practice. How often though is the curriculum of the masters
student and the doctoral student too similar, and also often, taught by the same faculty? If
this is so; is the faculty adapting their teaching methodology and course content to the
level of the student encountered?

In Florida International University’s (FIU) Stempel School of Public Health MPH
program, biostatistics is viewed as the most rigorous (hardest) course to pass to attain the
MPH degree despite the fact that few public health workers actually do statistics as much
as they use statistics. But, biostatistics is indeed a gatekeeper course making the MPH a
significantly difficult degree to earn since the course is taught at a level that would be
more expectant of a doctoral level program. The textbook used is Rosner’s Fundamentals
of Biostatistics, 6th
edition and it is virtually incomprehensible to any student without a
significantly sophisticated background in mathematics, and armed with great courage.
Further, there is no preparatory course that places the MPH student into a readiness phase
to handle the Rosner textbook.
At FIU, the Rosner textbook is deeply laden with complex examples of biostatistics
problems, but none are worked out in a simple easy to follow format. Again, the issue is
—does this approach appeal to the needs of the generalist public health worker in the
field or is this really substantial research preparation for the Dr.PH or Ph.D. student who
will be an academic or public health researcher? And, the professors teaching the
biostatistics courses are biostatisticians/mathematicians who are teaching it the way they
were taught in a previous generation (before the advent of powerful desktop computers).
Further complicating this course curriculum is that when it is taught online to
generalist MPH students in a distance learning program, the same course outline, syllabus
and textbooks are used that are used for on campus delivery. There is little or no
accommodation made for the online delivery or the needs and requirements of the adult
learner professional (working pretty much as a self-instructed learner) who is earning an
MPH degree to advance their career in health care delivery at one level or another. There

then exists a high failure and repeat rate to hurdle this barrier to the MPH degree. All
quantitative courses such as statistics, mathematics, quantitative analysis, economics,
finance and accounting are extremely difficult courses to deliver in an online
environment. A great deal of work needs to be done to improve how higher education is
going to accommodate the distance learner in mastering these abstract theory based
courses. It should be recommended that self-instruction workbooks be adopted for these
distance learning courses.
The question is again posed, why do research universities with established graduate
schools in any particular discipline choose to expand their program into an online format
when the entire graduate program is more geared to the on campus, traditional student?
The answer is simply two-fold, (1) student demand and (2) revenue generation due to
increased enrollment opportunities by expanding a program nationally and even
internationally through an asynchronous online delivery. Even the most traditional
universities are jumping on this bandwagon, and in many cases, they are using their
traditional professors and adjunct professors to deliver these courses.
The problem is that the traditional on campus faculty may be having some difficulty in
adapting to the unique students that are engaged in the on line environment—which are
predominately adult learners with immediate career goals in sight as their motives for
taking a new degree. They are less interested in grounded theory building approaches to
the discipline—especially if deep blue theory may have a negative impact on their
potential for graduating from the program.
It is doubtful that many professors have been exposed to any form of intensive
training and orientation in how to conduct on line delivery courses for distance students

who may already be well advanced in their careers. Both the University of Phoenix,
Strayer University and Central Michigan University offer intensive preparation before
they let any of the faculty loose in the online environment. The revenue potential is
simply too great to allow poor faculty performance to jeopardize the cash flow stream.
Results of an Experimental Course in Applied Statistics in a Cohort of Mid-Career, Adult
Learner Graduate Students in Public Administration (MPA)
An experimental course in the MPA Program of Nova Southeastern University
conducted in West Palm Beach late in 2007 was an attempt to modify the traditional
approaches to teaching statistics to adult learner, mid-career professionals in an MPA
cohort, and test its acceptability.
The course used the textbook, Applied Statistics for Public Policy (2006) by Brian P.
Macfie and Philip M. Nufrio. This textbook was well designed for this approach to
teaching statistics since it had minimal coverage of mathematical formulas with clear,
expository descriptions of statistical concepts and applications which were relevant to
public policy and public administration environments, and it had clear, easy to understand
instructions for using a simple to use statistical analysis package (Polystat) which was
included on a CD with the textbook for each student. SPSS would have been preferred,
however, the university did not have a site license for student/faculty use, and its cost to
purchase individually by students is prohibitive.

The major emphasis for the course, and this reflected in the required work products
from the students, was on each student’s individual work environment. Consequently,
each student made a presentation each week based on an analysis of statistical reports
from their own individual work areas. The evaluation of the statistical reports were
required to cover the relevance, readability (especially for reports generated for the
public), and flaws noted in the research design, analysis or reporting.
Although most of the evaluations of statistical reports focused on how descriptive
statistics were used in the work environment, and how they were reported, some
advanced inferential studies were reviewed as well. One student noted that her staff
statistician had developed a report for the general public that was so convoluted and
technically detailed that it was incomprehensible to most of the public administration
staffers let alone the general public. This report was stopped before it went to press and
distribution to the public. So, this particular report was critiqued with the idea of how to
make it more understandable for the end user—where that was the staff or the public.
Discussion and Conclusion
While realizing that any changes being recommended to a long established curriculum
content and teaching method for any academic discipline is going to be fraught with
controversy and opposition. Change is hard. The key point is to note that the changes
recommended by this paper are not for students in courses intended for those who will
one day be statisticians, biostatisticians, research designers, and/or doctoral level workers
in either academia or in practice. It is rather a recommendation for changing teaching

methods for courses populated by students who will be called upon soon after graduation
(or even those currently in managerial positions even as they are graduate students) to
make decisions based on analysis rather than design research models or engage in
statistical analysis.
It is important to note that maybe student acceptance and retention will be stronger
when the material taught and the method of teaching is more relevant to immediate
needs. Some may argue that this modified approach is “dumbing down” the discipline
being taught leaving future managers/administrators ill equipped to engage in designing
research methods or engaging in statistical analysis. This argument has merit if indeed the
majority of managers/administrators actually engaged in research or analysis in the
course of their daily core set of responsibilities. Fortunately, or unfortunately, this is just
not the case.
Thus, it becomes a waste of time and effort to continue to teach “old school” methods
to a new generation of decision makers who have new demands on their educational
experiences including content and presentation, and how the student will be evaluated.
This investment of time and money are carefully evaluated by the adult learner, and many
come away from the educational experience disappointed and disaffected by a theory
driven approach with limited or no relevance to their needs. Further, the adult learner is
more likely to become very vocal in terms of their disapproval of any program or
professor who fails to meet their needs as they perceive them to be, not as the professor
perceives them to be.
Further research is needed in both the educational environment (advanced
undergraduate and graduate programs in public administration/public health) and in the

practice environment from which the majority of the adult learners in mid-careers are
drawn. If our initial assumptions hold that adult learners are less interested in theory and
more interested in acquiring information they can use quickly, and if the curriculum
content and teaching methodology is changed in ways that make the educational
experience more relevant, then the student is both more satisfied and better prepared as a
manager/administrator, and faculty can expect to receive better evaluations for meeting
these perceived needs.
References
Angrosino, M.V. (1987). A health practitioner’s guide to the social and behavioral
sciences. Dover. Auburn House Publishing Company.
Bordens, K.S., Abbott, B.B. (2005). Research design and methods a process approach.
Boston. McGraw Hill.
Chan, Y.H. (2003). Biostatistics 101: data presentation. Singapore Med Journal. Vol
44(6):280-285

Chan, Y.H. (2003). Biostatistics 102: quantitative data-parametric & non-parametric
tests. Singapore Med Journal. Vol 44(8):391-396.
Field, A. (2005). Discovering statistics using SPSS, 2nd
edition. Thousand Oaks, CA.
SAGE Publications.
Givinter, L.L. (2008). Statistical analysis for public administration. Sudbury. Jones and
Bartlett Publishers.
Glantz, S.A. (1981). Primer of bio-statistics. New York. McGraw-Hill, Inc.
Gregorc, A.F. (1985). Inside styles: Beyond the basics. Maynard, MA: Gabriel Systems.
Hawk, T.F., Shah, A.J. (2007). Using learning style instruments to enhance student
learning. Decision Sciences Journal of Innovative Education. Vol. 5. No.1:1-19.
Kirkwood, B.R., Sterne, J.A.C. (2005). Essential medical statistics, 2nd edition. Malden,
MA. Blackwell Science.
Kolb, A.Y., Kolb, D.A. (2005). Learning styles and learning spaces: Enhancing
experiential learning in higher education. Academy of Management Learning and
Education, 4(2), 193-212.
Kuzma, J.W., Bohnenblust, S.E. (2005). Basic statistics for the health sciences, 5th
edition. Boston. McGraw Hill International Edition.
Macfie, B.P., Nufrio, P.M. (2006). Applied statistics for public policy. London. M.E.
Sharpe.
Meier, K.J., Brudney, J.L. (1997). Applied statistics for public administration. Fort
Worth. Harcourt Brace College Publishers.
Rosner, B. (2006). Fundamentals of biostatistics, 6th
edition. Thomson: Brooks/Cole.
Belmont, CA.
Rowntree, D. (2004). Statistics without tears a primer for non-mathematicians. Boston.
Allyn and Bacon.
Salkind, N.J. (2008). Statistics for people who (think they) hate statistics, 3rd
edition. Los
Angeles. SAGE Publications.
Shannon, D.M., Davenport, M.A. (2000). Using spss to solve statistical problems. Upper
Saddle River. Merrill Prentiss Hall.
Sullivan, L.M. (2008). Essentials of biostatistics in public health. Sudbury. Jones and
Bartlett Publishers.

Tabachnick, B.G., Fidell, L.F. (2001). Using multivariate statistics. Needham Heights.
Pearson Education Company.
Taleb, N.N. (2007). The black swan the impact of the highly improbable. New York.
Random House.
Trochim, W.M. (2005). Research methods the concise knowledge base. Cincinnati.
Atomic Dog Press.
Veney, J.E. (2003). Statistics for health policy and administration using microsoft excel.
San Francisco. Jossey Bass Publishers.

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