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Evaluating how education faculty spend their time at a private research university

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EVALUATING HOW EDUCATION FACULTY SPEND THEIR TIME
AT A PRIVATE RESEARCH UNIVERSITY

by

Michelle Silver Lee

____________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

December 2012

Copyright 2012 Michelle Silver Lee

ii
DEDICATION
This dissertation is dedicated to all mothers and women who juggle multiple
lives as wives, moms of young children, daughters of aging parents, professionals
with careers, mentors of young minds, team moms, room moms, and most of all, my
true friends who inspired me during my journey. I would like to especially dedicate
this dissertation to my mother, Mrs. Jae Young Lee and my Godmother, Dr. Ji Y.
Park, who both have been inspirational and essential to my successful completion.
For the two of them, I am forever indebted. Let this be the evidence that to
accomplish great things, we must not only act, but also dream; not only plan, but also
believe. Anything is possible and can be accomplished with balanced life if you put
your heart and mind to it. And if you believe.

I can do all this through Him who give me strength. (Philippians 4:13, NIV).

iii
ACKNOWLEDGEMENTS
Writing this dissertation has been a long journey and one of my most
challenging and meaningful accomplishments in my life. I could not have done this
without the help and support of many important individuals in my life. This finished
dissertation is the result of countless hours staring blankly at a computer screen,
searching for references in the library, studying in numerous classes to identify and
engage in the topic, writing many drafts that were read and reviewed by trusty
mentors, many students daily reminding me why I need to finish, bosses
understanding the research and dissertation processes, a dissertation committee
standing by me as I progressed through the years, a mother’s sacrificing time and
leisure, a group of family and friends stepping up to the baby-sitting duties, and last
but not least a husband and two sons enduring it all by my side.
This product of several years of coursework and focused research is not the
result of something I did alone. It's the result of God's plan, the people He put in my
life and the experiences (good and bad) He led me through. I would like to thank Dr.
Melora Sundt for serving as my dissertation chair. Through it all, she stood by me
patiently. I would also like to thank Dr. Tatiana Melguizo and Dr. Lee Cerling for
serving on my dissertation committee. Without their feedback on numerous versions
of my drafts and encouraging me to bring more depth into this study, I would not
have been able to complete this journey. My gratitude and appreciation also goes to
the Doctoral Support Center for their support and excellent services.

iv
I would also like to thank and acknowledge my family, especially my
parents. They have instilled the values of hard work and the importance of good
work ethics in my personal character. I dedicate this study to my mother who
sacrificed so much for the good of her children and grandchildren. Most importantly,
I would like to thank and acknowledge loves of my life, my biggest cheerleaders and
motivators, Jacob and Marcus. I am beyond blessed to have the most handsome,
adorable, brilliant, compassionate, articulate, understanding, hilarious, and well-
rounded sons who have taught me courage and unconditional love. They inspire me
to be better in life. Lastly, I would like to acknowledge my husband Eric, my partner
in life who teamed up with me to bring the most wonderful children to this world.

v
TABLE OF CONTENTS
Dedication ii
Acknowledgements iii
List of Tables vii
Abstract viii
Chapter One: Overview of the Study 1
Introduction 1
Statement of the Problem 8
Purpose of the Study 11
Research Questions 15
Significance of the Study: Institutional vs. Public Accountability 16
Definition of Terms 22
Organization of the Dissertation 23
Chapter Two: Literature Review 24
Review Problem and Purpose of Study 24
The Nature of Professorial Work 26
Theoretical Framework 57
Figure 1. Theoretical framework on faculty behavior and
productivity. 61
The Work of Faculty in Research Universities 69
Reporting on Faculty Productivity 72
What is Productivity Index (PI) and why was it created? 74
Conclusion 76
Chapter Three: Methodology 79
Research Questions 80
Research Design 81
Chapter Four: Results 100
Descriptive Statistics 104
Reporting of Results and Analysis of Data 109
Results for Research Question #1 109
Results for Research Question #2 112
Results for Research Question #3 114
Results from Interviews: How did PI evolve? 115
Conclusion 118

vi
Chapter Five: Conclusions and Implications 121
Research Findings and Discussion 122
Implications and Future Research 135
Conclusion 138
References 140
Appendix: University of Southern California School of Education 153
Productivity Index

vii
LIST OF TABLES
Table 1: Research, Teaching and Service Activity Points 83
Table 2: Index Captions for Research Category 88
Table 3: Index Captions for Teaching Category 90
Table 4: Index Captions for Service Category 92
Table 5: Research Questions Methodology 98
Table 6: Demographic Codes 103
Table 7: Demographic Information 105
Table 8: PI Scores 107
Table 9: ANOVA Tables 111

viii
ABSTRACT
Defining and measuring faculty productivity are among the most central
issues for quality and accountability in higher education today, and it is the subject
this study seeks to illuminate. This study first examines how the productivity of
faculty in the School of Education at a private university differ according to different
faculty characteristics and according to faculty rank. It then examines which
activities are highly valued at this organization by determining the relative value of
each activity, providing a benchmark to evaluate whether faculty are indeed
spending their time on the activities that enhance the mission of the school and the
institution. Finally, the study examines the percentage of faculty participating in
activities that are considered highly productive and what proportion of faculty time is
spent on these activities.
I examine the Productivity Index (PI) of faculty from the School of Education
at a private research university to answer these questions. Data from the PI were
collected over the six years the measure was in use. This study will contribute to the
existing body of research on faculty work by examining the use of this measurement
tool and the resulting data collected by that tool to further understand ways in which
faculty work may be reported and valued, and to identify variances in faculty work
and time spent which may be influenced by various faculty characteristics. This
study is a secondary analysis of longitudinal data collected by the PI. As the
research questions above were largely comparing groups, the study uses a
quantitative approach.

ix
This study is significant in adding to the understanding of the factors and
characteristics of productive faculty which will in turn provide insight to the facts
and myths regarding faculty productivity. This study examines how faculty spend
their time (behavior) and how they are motivated to spend time on certain activities
versus other activities (motivation). This study also identifies which characteristics
effect behavior and motivation, and in turn, how those characteristics influenced
productivity. Significantly, what was found, using statistical analysis, was that
teaching was the most highly valued activity among research, service and teaching.
This was true for both the tenured and tenure-track faculty and special faculty.

1
CHAPTER ONE
OVERVIEW OF THE STUDY
Introduction
Education is an important endeavor in the United States and the world at
large. At the post-secondary level, higher education is not only charged with the
edification of a steady productive workforce, but also the generation of new
knowledge and its applications through research in all fields (Boyer, 1990). Our
nation’s commitment to education comes with a large price tag. An immense
amount of money, 7 billion dollars in California alone (Department of Education,
State of California, Budget Summary 2008-2009), and resources are invested in
institutions of education, both public and private, each year.
According to the National Center for Education Statistics (NCES), in 2007-
2008, there were more than 4,300 colleges and universities in the United States
(NCES, 2009). Over 60% of these are private institutions. Private institutions of
higher education are funded by a variety of sources, including student tuition and
fees, government grants and contracts, and private gifts (NCES, 2009). The diversity
of these funding sources creates a wide collection of constituents interested in the
results produced by private institutions of higher education. In the 2005-2006
academic year, about 29% of private institution funding was from student tuition and
fees, leaving approximately 70% of the more than $150 billion received by private
not-for-profit private institutions from additional sources including over 14% of their
total revenue from government sources (NCES, 2009).

2
Through tax payer monies, government agencies are significant patrons of
higher education. Public and private institutions of higher education receive revenue
from federal, state, and local governments in the form of appropriations, contracts,
and grants in amounts which topped $140 billion for 2005-2006 including more than
$30 billion allocated to federally funded research and development centers
administered by universities and colleges. In addition to government revenue
funding, federal legislation has also created student grant and loan programs which
allocated an estimated $68 billion of funds for tax year 2008, provided mostly by
agencies and institutions outside the federal government, to assist students in paying
tuition and fees totaling over $97 billion in 2005-2006 (NCES, 2009). All together,
this totals more than $208 billion in annual government-sponsored spending on
higher education with private institutions accounting for over 30% of the top 120
institutions receiving the largest revenue amounts from the federal government
(NCES, 2009). The federal amounts spent are 4.7% of the net cost of the U.S.
government’s operations of $2.5 trillion (FRUSG, 2004). According to Rasell and
Mishel (1990), the U.S. spends comparatively more than other countries on higher
education.
Given the numerous sources of higher education revenue and the increasing
complexities of government spending, it seems logical that accountability measures
would be required to protect the effectiveness and integrity of these sizeable annual
expenditures. How are these colleges and universities spending the money? The
largest expense in many organizations is personnel, and this holds true for colleges

3
and universities as well (Middaugh, 2001). Personnel costs can often be as much as
80 to 90 percent of an institution’s budget (Meyer, 1998). Faculty and staff represent
a significant investment of resources for an institution. For example, in 2007 there
were 1.37 million people employed as faculty or “professors” in the more than 4,300
college and universities in the United States (NCES, 2009). Of the more than a
million faculty and instructional staff reported in 2005-2006, over 680,000 (56.7%)
were considered full-time (NSOPF, 2004). A faculty member can be expected to
serve an average of 22 years with compensation ranging from $61,000.00 to
$120,000.00, with full-time faculty at private institutions earning a higher average
base salary than faculty at public institutions (NSOPF, 2004).
Introducing the topic of study- what is a professor?
So what is a professor? A professor is a senior lecturer and/or researcher,
usually in a college or university (Middaugh, 2001). Professors are qualified experts
who give lectures and seminars in their field of study, such as the basic fields of
science or literature or the applied fields of engineering, medicine, law, or business,
and provide service to the institution they belong and community nearby (Middaugh,
2001). As a member of the faculty at a college or university, professors also may
perform research in their fields and train graduate students. These tasks represent the
three traditional areas of professorial work – teaching, research, and service
(Colbeck, 2002c).
Faculty must incur substantial costs of their personal resources – time,
money, and effort – to earn a station in the academic world. The educational

4
preparation for a college professor is different from other professions in both length
and nature. The standard preparation for faculty who teach and conduct research at
most four-year research institutions is the Doctor of Philosophy (Ph.D.), or another
terminal degree (e.g. Ed.D., J.D, DBA, MD, etc.) (Bowen & Rudenstine, 1992). The
terminal doctoral degree has been called the “union card” for university instruction
and research (Bowen & Rudenstine, 1992) since reaching the highest ranks of the
professorate is nearly impossible without it. After earning a terminal doctoral
degree, which normally takes from four to seven years to complete after earning at
least a bachelor’s degree (Bowen & Rudenstine, 1992), it is possible to continue at a
university as a postdoctoral fellow, usually in research capacity although some
institutions may have teaching opportunities as well.
This post-doctorate experience in the field improves one’s credentials and
can lead to a tenure-track position. More recently, terminal doctoral degrees have
become a requirement for full-time faculty at research universities in the United
States (Bowen & Rudenstine, 1992). The majority of full-time faculty (79.6%) have
achieved tenure or are in a tenure-track position (NSOPF, 2004). Faculty who are
promoted and earn tenure at an institution are essentially guaranteed a position for
their lifetime. To earn tenure, faculty are typically required to perform at a high
level of productivity for several years. However there are concerns that productivity
declines once the security of tenure is achieved (Tierney, 1998). Supporting and
sustaining the productivity of the professorate across all stages of a career is a key

5
leadership and organizational issue in higher education (Baldwin, Lunceford, &
Vanderlinden, 2005).
How do faculty spend their time?
How do professors spend their time? Faculty work is traditionally divided
into three main areas: teaching, research, and service. Faculty tend to spend their
time devoted to various tasks related to these three domains. At the private
institution’s professional School of Education evaluated for this study, it is expected
that faculty distribute their time and effort such that 40 percent of their time is spent
on teaching activities, 40 percent on research activities, and the remaining 20 percent
on service activities. This is known as a 40-40-20 faculty workload profile for
tenured and tenure-track faculty. For non-tenure-track faculty (clinical and research),
faculty workload profile will most likely be 80-20. Depending on the type of
position, 80 percent of their time will be spent on teaching or research and 20 percent
on service.
Holding the numerous faculty within an institution of higher education
accountable can be a daunting task. In addition to the challenges presented by
monitoring a large and diverse population of professors, there is further difficulty in
defining productivity and creating standards for faculty responsibilities (Middaugh,
2001). To define faculty productivity, Massy and Wilger (1995) asked numerous
professors what ‘productivity’ meant to them and received equally numerous
descriptions often depending upon the respondent’s vantage point and
predispositions. They adopted an economic view of productivity in which

6
productivity is a ratio of outputs to inputs, and concluded that the idea of
productivity in the mind of the faculty can be contradictory to this more economic
view in that faculty aim to maximize results often without a cost/benefit analysis and
even if the additional results mean additional cost. In an analysis of faculty
workload and productivity, Meyer (1998) took issue with the current measures of
workload and productivity since “measures of workload can capture only how
faculty time is spent, not how well it is spent” and “measures of productivity have
yet to be developed in any area except for research productivity” (p. 51).
Institutions have very different ways to evaluate faculty productivity. For
example, salary schedules for teachers are a nearly universal feature of public K-12
school districts. Data from national surveys show that nearly 100% of public school
teachers are employed in school districts that use salary schedules for pay setting
(Podgursky & Springer, 2007). Salary schedules are determined by the degrees
earned plus number of units earned as well as number of years in service. In large
school districts the pay of thousands of teachers in hundreds of schools – from
kindergarten up to secondary teachers in math and science – is typically set by a
single district schedule (Podgursky, 2006). These salary schedules for teachers
contrast with the situation in most Higher Education Institutions.
Since the 1990s, the management of faculty productivity has become one of
the most important issues in higher education, resulting in numerous studies of
faculty workload and the legislative mandates influencing faculty evaluation (Bess,
1998; Burke, 2001; Cage, 1995; Colbeck, 2002a; Dirks, 1997; Doyle, 2006; Gullatt

7
& Weaver, 1995; Layzell, Lovell, & Gill, 1994; Massy & Zemsky, 1994; Porter &
Toutkoushian, 2006). By 2000, there were 30 states which required higher education
performance reporting, with at least 24 states indicating requirements for faculty
workload and most states tying funding and/or budgeting for higher education to
performance reporting focusing on areas such as undergraduate access, two to four
year transfers, faculty workloads, new student preparations and time to degree
(Burke, 2001). These measures typically have a greater affect on the administration
of public higher education institutions and are likely in response to rising costs and
declining resources at the state level. The cost of higher education has been
increasing rapidly in recent years (NCES, 2009).
Rising costs are caused by several factors, including increases in fringe
benefits, new technology, more staff and faculty, certain internal processes, a general
increase in population, and a general decrease in the availability of funding from
state budgets to both public and private institutions (Meyer, 1998). It costs nearly
$29,000 on the average for each year of an undergraduate education (NCES, 2009).
To cover rising costs, parents and students are asked to pay increasingly higher
tuitions. Since the personnel budget often constitutes 80 to 90 percent of an
institution’s expenses, state legislatures and the public are more and more interested
in ways to increase the productivity of faculty (Meyer, 1998).
Private higher education institutions are less restricted by unions and state
legislative funding, and they can design instruments to reward faculty for spending
time in activities that will enhance the mission of the school and the university as a

8
whole. This study will focus on one of these examples. A School of Education at a
private university which created an instrument to measure how faculty spend their
time, and provided incentives for faculty to spend time in activities that promote the
mission of the school and the university will be examined.
Statement of the Problem
The objective of this study is to use the Productivity Index (PI) developed by
a professional school in a private university to examine how faculty report spending
their time. The PI is an annual measure of how faculty are spending their time, used
to determine raises – one outcome of how faculty spend their time
1
. This study will
look at faculty productivity in the School of Education at a private university.
Government agencies, students and their families, and numerous other private
sources make substantial investments in higher education. Colleges and universities,
in turn, make sizeable investments in the faculty they employ. The considerable
monetary amounts represented by these investments put pressure on faculty to
produce in order to provide a meaningful return on the expenditures made (Bowen &
Rudenstine, 1992). A variety of factors in the late 1980s, such as faltering regional
economies, rising taxes, and stagnant wages, along with appreciably higher costs of
higher education generated a public and governmental concern in the productivity of
college personnel (Meyer, 1998).

1
Productivity Index (PI) will be explained in great detail below.

9
Interest in the work of faculty intensified in the early 1990s, when lawmakers
across the United States began to make requirements, such as the number of hours
spent in classroom teaching, for undergraduate education at U.S. public colleges and
universities (Cage, 1995). The result of this accelerated interest in faculty
productivity has manifested in the increasing pressure over the past two decades for
higher education to provide evidence of its effectiveness (Doyle, 2006). Legislators
wanted to know why so many classes were being taught by teaching assistants, and
were astonished to find that a typical professor in the State Universities of New York
spent less than nine hours a week in a classroom (Cage, 1995). Similarly, the state
governments in Connecticut, North Carolina, Massachusetts, Washington and Ohio
have called for professors to spend more time with undergraduates (Cage, 1995).
Varying strategies to improve undergraduate instruction and learning have also been
implemented in Florida, Maryland, Tennessee and South Carolina, with lawmakers
targeting faculty work and productivity, particularly as they pertain to teaching and
learning (Colbeck, 2002a).
Public criticisms and ratification of state policies have sparked a debate
between faculty and administrators centering on policy makers, who are quick to
challenge how effectively faculty time is being used, and faculty, who emphasize
their long working hours as they spend much of their time in research and
preparation outside of the classroom (Dirks, 1997). Concentrating on faculty solely
as teachers, many critics seem to have an outdated picture of what a faculty member
is (Middaugh, 2001) and their policy suggestions work to decrease the time faculty

10
devote to other responsibilities, particularly research, in favor of more time spent
teaching. In some respects, these policies are attempting to reverse a shift from the
teaching-oriented mentality of faculty of the past that has gradually but surely shifted
to the institutional research demands of the present (Green, 1998). This shift
particularly represents the scholarship expectations of large research universities.
Despite various pieces of state legislation aimed at accountability in teaching, salary
increases and promotion of faculty are still largely rooted in research and publication
productivity (Fairweather, 2005; Sutton & Bergerson, 2001).
External critics assert that faculty interested in research are not interested in
teaching and therefore abandon students (Clark, 1997a). Both Marsh and Hattie
(2002) and Stack (2003) challenged this notion by comparing student evaluations of
teaching to the relative amount of research performed by the faculty member being
evaluated. Marsh and Hattie (2002) used publication counts as the measure of
research productivity and found no correlation between faculty research and student
evaluations. Stack, in an attempt to control for the quality of research, used citation
counts as the measure of research productivity and discovered a positive correlation
between faculty research and student evaluations of teaching. These results suggest
that research which is meaningful enough to be cited by others can improve the
quality of teaching and that research responsibilities should not be considered in
conflict with teaching responsibilities. The results are also contrary to the idea that
faculty may only perform well in one area, showing that faculty can excel at both
teaching and research. Melguizo and Strober (2007) created a framework

11
maximization of prestige. They used the data set from the 1999 National Study of
Postsecondary Faculty (NSOPF) to look at the relationship between faculty salary and
prestige. They hypothesized that monetary rewards are higher for faculty activities that
confer institutional prestige; and indeed, the results of their study are consistent with the
theory that faculty members are financially rewarded for enhancing institutional prestige
(Melguizo and Strober, 2007).
Purpose of the Study
One can conclude from the preceding discussion that the main obstacles to
assessing faculty productivity are the definition of productivity itself, the stability (or
lack thereof) of a faculty member’s productivity over time and among different
institutions or type of position, and the idea of productivity quality versus quantity.
This study will look at the information collected through a reporting/measurement
tool of the kind of activities faculty participate in order to hold them accountable for
their time to see how these activities align with what is considered productive by the
organization.
My first research question will examine how faculty in the School of
Education spend their time on an annual basis; how does productivity vary according
to different faculty characteristics (gender, ethnicity and rank); and how does the
productivity vary according to faculty rank? My second research question will look
at which activities are highly valued at this organization. This will be determined
based on the relative value of each activity on the PI and will provide a benchmark to
evaluate whether faculty are indeed spending their time on the activities that enhance

12
the mission of the school and the institution. My third research question will examine
the percentage of faculty participating in activities that are considered highly
productive according to the PI and what proportion of faculty time is spent on these
highly productive activities?
I will be examining a Productivity Index of faculty from the School of
Education at a private research university to answer these questions. Data from the
Productivity Index have been collected over six years.
What is the Productivity Index (PI)?
The Productivity Index was developed as part of a larger “academic
scorecard” to be used in communicating an annual report of “metrics of excellence”
to the University administration, in particular the Provost’s Office (O’Neil,
Bensimon, Diamond, & Moore, 1999). Borrowing from a business framework called
the “balanced scorecard” that was previously published in Harvard Business Review,
a faculty committee set about creating simple, practical metrics to respond to the
Provost’s request for a wide array of information and data which included the quality
of students, the quality of faculty, the quality of academic programs, and the nature
and efficiency of school operations. The scorecard approach they created involves
reporting information according to goals and measures from different perspectives:
Academic Management Perspective, Stakeholder Perspective, Internal Business
Perspective, and Innovation and Learning Perspective (O’Neil, Bensimon, Diamond,
& Moore, 1999).

13
The goals and measures were chosen to align with university and school
priorities with an attempt to keep the indicators simple yet meaningful (O’Neil,
Bensimon, Diamond, & Moore, 1999). In the area of faculty performance, another
annual memo from the Provost outlines the standards for allocating raises based on
merit (there are no cost of living raises) and includes faculty consultation in making
assessments of merit. While the memo identifies these basic principles as guides to
the process, each school within the University is given the freedom to define their
own indicators of merit and design their own measures (Bensimon & O’Neil, 1998).
The faculty productivity information gathered and reported is used along with
current budgeting data to determine whether or not a faculty member receives a raise
each year. Additionally, the School of Education occasionally may use surplus funds
to grant bonuses for distinctive contributions (Bensimon & O’Neil, 1998).
Characteristics of the Productivity Index (PI)
Prior to 1997, the process for reporting faculty performance in the School of
Education was a generic report created by each individual faculty member in which
they itemized their accomplishments in the areas of Research and Scholarship,
Teaching and Advisement, and Service. The reports were then reviewed by an
elected faculty committee and each professor was assigned points in the three areas –
up to five points each in Research and Scholarship and Teaching and Advisement,
and up to three points in Service (Bensimon & O’Neil, 1998). This system had some
significant problems. The guidelines were so loose that no two faculty reports looked
alike and faculty made their own judgments about what kinds of accomplishments to

14
report, a situation which made comparisons impractical. Also, there was no
connection between faculty productivity and institutional objectives and, further, the
review committee did not have well-defined criteria to assign the points in each area
making it difficult to reach agreement (Bensimon & O’Neil, 1998). A diverse ten-
person committee with a wide range of experience and roles in the School of
Education was organized to address these issues and update the faculty performance
reporting within the school.
The committee was asked to align the new productivity report with three
priorities: 1) enhancing external reputation; 2) pursuing mission and strategic themes
of the school; and 3) being consistent with the university’s strategic plan (Bensimon
& O’Neil, 1998). The committee reviewed various indicator-based reports and based
the new reporting system on one such indicator-based report from the University of
Iowa that was then extensively revised to match the values and goals of the School of
Education (Bensimon & O’Neil, 1998). The committee requested continuous
consultation from the entire faculty by sending drafts of the new report to all faculty
with the opportunity to provide feedback. After eight such revisions, Version 9 of
the performance report was implemented in the spring of 1997 for what was
originally a three-year trial (Bensimon & O’Neil, 1998). What is now known as the
Productivity Index was in place at the School of Education for more than ten years.
The Productivity Index brings together individual productivity and
institutional objectives by assigning value weights to each discrete item reported that
make clear which activities are more important to the school. This point system

15
explicitly communicates the relative worth of activities based on how essential they
are to achieving the priorities of the school, which are primarily to “increase the
school’s prestige and increase revenues while increasing quality” (Bensimon &
O’Neil, 1998, p.29). Activities that are identified as critical to reaching these goals
are awarded higher points regardless of which area (research, teaching, service) the
item is associated with.
This study uses the Productivity Index to identify how faculty in the School
of Education report spending their time, define the characteristics of the most
productive faculty across the three responsibility areas (teaching, research, and
service), and detect what percentage of faculty participate in activities considered
highly productive according to the PI. The Productivity Index data were collected
based on an annual calendar from January to December. The information gathered
through the Productivity Index is then analyzed to determine if any significant
patterns of faculty productivity exist. This study is a secondary analysis of
longitudinal data produced by the Productivity Index. In the data analysis, SPSS is
used to find the correlation and regression probability of the Productivity Index data
collection. (Note: the Productivity Index has evolved and is no longer in use at the
School of Education.)
Research Questions
The research questions for this study are:
1. How do faculty in the School of education spend their time on an annual
basis?

16
a. How does productivity vary according to different faculty
characteristics (gender, ethnicity and rank)?
b. How does the total productivity vary according to faculty rank?
2. What are the highly valued activities of the faculty at this organization
according to the PI?
a. Which category among teaching, research and service did the
faculty spend most of their time?
3. What percentage of faculty participate in highly valued activities
according to the PI? What proportion of faculty time is spent on these
highly valued activities?
a. To what extent do ratios align with the standard 40-40-20 faculty
workload for Tenured and Tenure-Track faculty and 80-20 for
Special faculty? Is any category "over represented" or "under
represented" based on what we'd expect for that group (i.e.,
compared to the standard)?
Significance of the Study: Institutional vs. Public Accountability
Tenured faculty in higher education institutions enjoy the benefits of respect,
enormous autonomy, the authority of shared governance, and the security of lifetime
employment (Trower, 1999). As stewards of their discipline and the institution, they
hold the capacity to influence the reputation, prominence, and visibility of their
institution, both collectively and individually. In receiving tenure and a lifetime
commitment from the institution, faculty are then beholden to the institution to

17
remain productive throughout their career. The annual obligation to pay tenured
salaries is often seen as a constraint to budget flexibility for institutions with tenure
systems and has influenced the increased trend of hiring non-tenure track or contract
faculty (Benjamin, 2002; Holub, 2003; Ward, 2003).
Before tenure is granted, the institution can use tenure as an incentive in
holding faculty accountable for their level of productivity since the evaluation of
teaching and research over several years is the cornerstone for awarding tenure
(Defleur, 2007). Depending on the higher education institutional type, there are
consequences for unproductive faculty. For example, faculty can be denied tenure,
not be promoted, be demoted, salary decreased and sometimes they are ‘forced’ to
retire. After the guarantee of tenure is bestowed, however, without additional
incentives, keeping faculty accountable for their productivity can be more difficult as
faculty are allowed to function independently and to exercise their own judgment in
the pursuit of teaching and research activities, and administrators find the post-tenure
review process too time-consuming and expensive in comparison with its benefits
(Custer, Foster, & Martin, 1999).
Most institutions do not require post-tenure review of faculty productivity,
and there is little recourse to remove the “deadwood” of the few who become
unproductive (Tierney, 1998). Opponents to tenure argue that a lifetime guarantee of
employment is a disincentive to be productive (Trower, 1999). Without a serious
documented offence that warrants termination more subtle consequences, such as
denying funding or proposals, may eventually make the stagnate faculty

18
uncomfortable enough to seek a tenured position at another institution (Trower,
1999). In response to the decade-old call for accountability in higher education,
however, post-tenure review to assess continued faculty productivity appears to be
the best compromise between the existing tenure process and its abolishment
(Custer, Foster, & Martin, 1999; Tierney, 1998; Trower, 1999). In the School of
Education being studied, the Productivity Index is administered to the entire faculty
regardless of tenure status and is used to determine annual merit-based raises and
potential bonuses for all members of the faculty. This study contributes to the
existing body of research on faculty work by examining the use of a measurement
tool (the PI) and the resulting data collected by that tool to further understand ways
in which faculty work may be reported and valued, and to identify variances in
faculty work and time spent which may be influenced by various faculty
characteristics.
Among other questions, this study examines productivity for the entire
faculty of the School of Education, regardless of current tenure status or type of
position. This study will also contribute to the research of faculty productivity by
determining which characteristics are prominent in highly productive faculty in a
professional School of Education. The reason for studying a School of Education is
due to the tendency of professional schools to have a variety of faculty ranks:
tenured, tenure-track, non-tenure-track (clinical, research, etc.). Therefore, a
professional school with a faculty productivity index, which is on a point system,
will provide a more comprehensive overview of which factors and characteristics are

19
conspicuous among productive faculty. Examining the Productivity Index will be the
best way to quantify and measure tangible productivity particularly as it relates to the
goals most valued by the institution.
As previously discussed, tightening budgets and a widespread call for
accountability has resulted in greater scrutiny of faculty work. The public and
legislative perception is that faculty are only responsible for teaching. The higher
education establishment has not done a good job of communicating the diverse range
of faculty responsibilities (Layzell, 1994; Middaugh, 2001). According to Trower’s
(1999) discussion with public officials, people simply do not believe that faculty
work 50-60 hours per week and they feel that faculty have a “what’s in it for me”
attitude. Faculty productivity, accountability, workload, and effectiveness are all
becoming “buzz words” across college and university campuses in America. The
public demand for outcomes seems to affect research and teaching institutions alike;
both public and private higher educational institutions are implicated (Middaugh,
2001).
The public’s focus on education intensifies when the job market gets tougher
and the economy slows down. Increasingly, higher education seems to be the key to
stable jobs and better career paths (Gullatt & Weaver, 1995). Calls for
accountability and studies of faculty workload are the result of heightened attention
to the cost of higher education and its related benefits. Postsecondary education is
progressively more important for access to better job opportunities; at the same time
increases in tuition are putting colleges/universities out of reach for many (Meyer,

20
1998). Both sets of concerns amplify the pressure to find ways to improve
productivity in higher education (Middaugh, 1998).
Often, the public must depend on regional accreditation boards such as the
Western Association of Schools and Colleges (WASC) to seek institutional
accountability, set better standards, and demand an explanation of their spending
(Middaugh, 2001). Parents want to make sure that they are spending their money
wisely for themselves and their children. WASC is one of six regional associations
that accredit public and private schools, colleges, and universities in the United
States and is responsible for approving the continued accreditation of the university
being studied. These accreditation bodies have adjusted their institutional review
process to focus more heavily on the assessment of teaching and learning outcomes,
as well as other faculty activity related to the institution’s mission as a direct
response to the public and legislative pressures and criticisms (Middaugh, 2001).
The focus of WASC has evolved towards requiring proof of productivity in results
specific to the goals and values espoused by the institution.
Traditional independent governance of each American university campus has
resulted in the development of site-specific sets of productivity measures that serve
only the internal evaluation needs of a particular institution (Gullatt & Weaver,
1995). The public, government, and other interested constituents are demanding to
see a uniform scale of assessment tools and a standard reporting system across all
college and university campuses to observe and compare results (Middaugh, 2001).
The Productivity Index, although developed specifically for the professional School

21
of Education that is the focus of this study, adopts a comprehensive approach that
has the potential to be used at other schools and institutions. As a reasonably
complete catalog of various faculty activities, the weighted values associated with
each activity could be adapted to match the priorities of virtually any institution or
school. While these modifications would customize the index to be site-specific, the
resulting scores could be somewhat standardized to compare productivity relative to
the institutional values.
Critics of higher education suggest that placing the emphasis on conducting
academic research at universities depreciates undergraduate education through an
increase in class size, increase in teaching assistants offering lectures, and restricting
undergraduate access to faculty by drawing faculty engaged in research out of the
classroom (Cage, 1995; Colbeck, 2002b; Middaugh, 2001; Porter & Umbach, 2000).
Even though the performance of academic research requires the allocation of faculty
time and attention that might otherwise be directed toward undergraduate education,
research activities also create benefits for not just the faculty or institution, but for
the overall discipline and its students (Marsh & Hattie, 2002). It is in the best
interest of institutions of higher education to effectively communicate the various
products of faculty work to their many constituents (Middaugh, 2001). With this
study, we learn the factors and characteristics of productive faculty which will in
turn provide insight to the facts and myths of faculty productivity.

22
Definition of Terms
Faculty productivity. The measurement of research, teaching and service or
contribution output in tangible and intangible ways in different institutions and
disciplines.
Productivity Index (PI). Data provided by a professional School of
Education to examine faculty productivity by a weighted point system.
Research Universities (RU/VH: Research University - very high research
activity). According to the Carnegie Foundation, RU/VH institutions offer a full
range of baccalaureate programs and are committed to graduate education through
the doctorate programs. They give high priority to research and award 50 or more
doctoral degrees each year. They also receive annually $40 million or more in
federal support.
Faculty load profile. Percentage of 100% effort assigned to teaching,
research and service according to a faculty member – the ratio is not always formally
articulated.
Non-Tenure Track (NTT)/ Special Faculty. Faculty who are not on the
tenure-track. There are both full-time and part-time NTT faculty. Depending on the
institution, NTT are mostly teaching faculty; or only research specific faculty. Most
of them do not have any service obligations to the institution.

23
Organization of the Dissertation
Chapter 1 of the study has presented the introduction, the statement of the
problem, the purpose of the study, the research questions to be answered, the
significance of the study, and the definitions of terms.
Chapter 2 is a review of relevant literature. It addresses how faculty work and
productivity in higher education is defined. Measures of productivity, factors
influencing faculty productivity including gender, age, ethnicity, tenure status and
faculty rank, and strategies for improving productivity among faculty are presented,
and tied to the theoretical framework.
Chapter 3 presents the methodology used in the study, including the research
design (population and sample, instrumentation, dependent variables and
independent variables). This chapter goes on to describe the procedures for data
collection and the plan for data analysis.
Chapter 4 presents the findings of the study.
Chapter 5 discusses and analyzes the results, culminating in conclusions and
recommendations.

24
CHAPTER TWO
LITERATURE REVIEW
This chapter studies faculty productivity in higher education at research
universities including (a) reporting on faculty productivity, (b) measuring faculty
productivity as it relates to the three areas of teaching, research, and service as well
as implications for compensation and tenure, (c) differences in productivity among
faculty according to institutional mission, faculty rank, and discipline, (d) theories on
faculty work (Blackburn and Lawrence, 1995) and motivation theories as the
conceptual framework of this paper and (e) strategies that may improve faculty
productivity. This chapter focuses on individual faculty characteristics and
environment. Moreover, how faculty behave (how faculty spend their time) and what
motivates faculty to be productive (which activities to spend time on) are explored.
The research on faculty behavior and motivation and defining productivity will also
provide the background for this study.
Review Problem and Purpose of Study
The objective of this study is to use the Productivity Index (PI) developed by
the School of Education in a private university to examine how faculty report
spending their time. The PI is an annual measure of how faculty report spending their
time, and is used to determine raises by rewarding faculty. Data from the PI collected
over a six year period will be used to examine how faculty report spending their time
in order to determine which common characteristics, if any, exist among productive

25
faculty, and to detect what percentage of faculty participate in activities considered
highly productive according to the PI.
For the purposes of this study, faculty workload, or how faculty spend their
time are used as a measure of productivity using a weighted index (the Productivity
Index) of specific tasks and outputs related to the three domains of teaching,
research, and service. This study analyzes information from the PI in order to
establish the extent to which faculty spend their time in accordance with the school’s
expected 40 (research) -40 (teaching) -20 (service) Tenured and Tenure-Track
faculty workload versus 80 (teaching) -20 (service) Special faculty workload, and to
see how the reported activities align with what is considered productive by the
organization.
In conducting a review of the literature, it is apparent that before the late
1960s the productivity of individual faculty was not widely studied research topic.
Prior to that time, it appears that the discussion of teaching effectiveness and
research production was still at the institutional level. The passage of the Higher
Education Act of 1965, which created the foundation of the federal financial aid
system, and the increasing number of postsecondary institutions in the 1970s, meant
that more and more public funds were being devoted to higher education. According
to data from the National Center of Education Statistics (NCES, 2005), the number
of postsecondary institutions grew by 70% between 1949 and 1980, growing from
1,851 institutions in the 1949-1950 academic year to 3,152 in the 1979-1980
academic year. The number of public institutions more than doubled during the

26
same time period, growing from just under 35% of all institutions in 1949 to over
47% by 1980 (NCES, 2005). Tax revolts of the 1980s followed by recession in the
early 1990s, increases in cost of health and other social services, and increased
enrollment in higher education together with increased student eligibility for federal
financial aid, all combined to force states to contain spending (Allen, 2004; Ward,
2003). The increase of government funding applied to education and the growing
number of institutions under public governance prompted closer scrutiny of the
internal workings of higher education, particularly the work of faculty (Cage, 1995).
The Nature of Professorial Work
The types of faculty contributions – teaching, research, and service – have
remained fairly consistent over time. The discussion among various administrators
revolves around which of these three areas, if any, should be the primary focus of
faculty performance, with state governments in particular concerned that time spent
on teaching and instruction is disproportionately smaller compared to time spent on
research activities (Cage, 1995; Clark, 1997a; Colbeck 2002a). Determining the
balance and proportion of work in the three classic professorial tasks – teaching,
research, and service – may vary across institutions and disciplines. There are
several different institutional types according to the classification system of the
Carnegie Foundation. According to the foundation’s website, The Carnegie
Classification of Institutions of Higher Education™ was originally developed in
1970 and was most recently updated in 2005.

27
Recognizing the great diversity of institutional types and missions, the
classification seeks to identify groups of similar institutions for the purposes of
comparing like institutions in the research of higher education. The 2005
classification system includes the basic classification categories of Associate’s
Colleges, Doctorate-granting Universities, Master’s Colleges and Universities,
Baccalaureate Colleges, Special Focus Institutions, and Tribal Colleges. Doctorate-
granting Universities, which are classified in the subcategories of RU/VH: Research
Universities (very high research activity), RU/H: Research Universities (high
research activity), and DRU: Doctoral/Research Universities, which according to the
“research” titles of their classifications tend to make the creation of research and
scholarly activity their primary mission. It is this type of institution that receives the
most criticism for putting research ahead of teaching, whereas the Associate’s
College (typically a two-year community college) performs little research and
focuses almost exclusively on teaching.
In recent years, research and publications have been the focus of faculty
workload in the Carnegie-defined research universities (RU/VH). With funding for
public research institutions tied to state budgets and the rising cost of attending
college, the call for accountability has been echoed across the nation. However, few
studies seem interested in what, if any, effects such mandates have on private
research universities regarding faculty productivity as a function of evaluating
spending. Despite being somewhat out of reach of many of the legislated
requirements for accountability in higher education, which are focused at state

28
systems, most private institutions also are concerned about how productive their
faculty are (Gullatt & Weaver, 1995).
In many ways, it would seem that private institutions have complicated
challenges in determining spending effectiveness. Private institution revenues tend to
come from funding sources that should clearly demonstrate accountability. In
addition to the differing missions of various institution types which may influence
the balance of faculty workload in teaching, research and service, there is also the
potential for differing measures of what it means to be truly productive in each area
based on institutional mission and specific discipline, as well as faculty rank and
type of faculty position (Layzell, 1996; Middaugh, 2001; Milem, Berger, & Dey,
2000). These additional factors will be explored below.
Defining Productivity
It is not easy to define faculty productivity. The term “faculty productivity”
relates to return on investment, which is problematic in higher education as the
relationship between inputs and outputs, tangible and intangible, is multifaceted and
not always clear (Layzell, Lovell, & Gill, 1994). In some of the ways discussed
below, it is possible to objectively measure elements of faculty productivity, but as a
matter of continuous quality improvement, the definition and measurement of faculty
work is often vague and subjective.
With the three components of faculty workload profile (teaching, research,
and service), it is sometimes hard to determine how faculty spend their time. Faculty
activities, such as research publications, instruction, various services and committee

29
activities, and administration are common indicators of faculty productivity and
success. However, quantification of achievement in these areas still remains
troublesome since there are no numerical standards with which we can compare.
With respect to teaching, productivity might be measured by the student
ratings, number of courses or students taught (Ehrlich, 2003; Middaugh, 2001).
However, this measurement does not capture the effectiveness of the teaching, or in
other words, whether the students learned anything. With respect to research,
productivity might be measured by the number of articles and books published or
dollars of grant money received. However, this does not necessarily evaluate the
significance or quality of the research in contributing to advances within the field
(Braxton & Del Favero, 2002; Stack, 2003; Toutkoushian, Porter, Danielson, &
Hollis, 2003). Finally, with respect to service, productivity can be measured by the
number of committees, mentoring and development events a faculty participates in,
but this may not estimate the true impact of such participation in strengthening the
communities of the institution (Ward, 2003).
The meaning of productivity also differs across faculty in different
disciplines and institutions (Layzell, 1996; Middaugh, 2001; Milem, Berger, & Dey,
2000). Faculty in the arts and humanities may focus on disseminating information
and understanding esoteric events and creative works which results in a greater time
spent teaching while faculty in the sciences and engineering may focus on proving
new theories and developing practical applications which results in a greater time
spent in research (Middaugh, 2001). Similarly, institutions such as community

30
colleges and small liberal arts colleges focus on teaching undergraduates while
research universities focus on promoting and supporting scholarly research in a
variety of areas.
Faculty link productivity to quality, and they tend to focus more on benefits
rather than the relationship between costs and benefits (Massy & Wilger, 1995).
Faculty do work hard, with an average of over 53 hours of work each week (NSOPF,
2004), and they understand the need to be productive (Middaugh, 2001). Much of
the focus has been on how many of these hours, or the percentage of time, faculty
devote to certain task areas, such as teaching, but time spent is not a true measure of
actual productivity by itself and researchers still seek to answer to important
questions such as how do we know faculty are doing those things, what are the
measurable outcomes, how productive is the individual and the like (Middaugh,
2002).
Also, these measures typically “examine data at the group level or
organizational level and neglect differences in individuals, or they examine data at
the individual level and ignore the impact of group membership” while never taking
a multi-level perspective (Porter & Umbach, 2000, p.1). What variables are most
prominent in the evaluation of faculty? How do we measure quality? At the center of
much of the productivity debate is the perception of lawmakers and the public at
large that the only responsibility of faculty should be teaching undergraduates, and
the failure of higher education institutions to effectively communicate what faculty
do and how well they do it (Middaugh, 2001). These challenges are central to

31
defining and measuring faculty work across the three task areas of teaching,
research, and service. As will be discussed below, productivity in the areas of
teaching, research, and service can be defined very differently and even within each
domain there can be variation as to the best definition and measurements. Given the
varied tasks and outcomes in each area, it would be impractical to rely on a single
measure to determine the productivity of a faculty member, department, or
institution. While various approaches are reviewed below, it is clear that multiple
measures are needed to gain a complete and accurate representation of faculty
productivity.
Teaching. Teaching is not simply giving instruction in the classroom,
although, in recent years the amount of time faculty spend giving classroom
instruction has become controversial due to the questionable amount of hours
actually spent in the classroom (Cage, 1995). Lawmakers tend to focus only on the
actual time spent in the classroom (Colbeck, 2002b). Outside of the classroom,
faculty are also responsible for designing, revising, and continually evaluating the
current curriculum of courses they are teaching, which may include adding, deleting,
or revising courses, all in support of teaching and instruction endeavors. This process
is tedious and time consuming (Astin, 1993). Preparation takes more time than actual
instructional time in the classroom (Mooney, 1993). Usually, curricular design is the
responsibility of a group of faculty, although individual faculty are responsible for
accomplishing the department’s or program’s goals within each class (Astin, 1993).
Each of these tasks – preparing materials, reviewing and grading work, designing

32
and organizing course content – can fall under the label of teaching or instruction,
and the majority are performed away from the classroom (Astin, 1993). The
administrators and lawmakers who focus on time in the classroom alone define
teaching in very narrow terms.
One of the most prominent policy issues in higher education has been the
regulation of faculty instructional hours, especially as demands have grown on state
budgets (Porter & Umbach, 2000). State legislators have begun focusing attention on
increasing the productivity of faculty, particularly at state-supported universities, as
an alternative to increasing state spending (Layzell, 1996). Porter and Umbach
(2000) point out that some legislators believe that significant cost savings would
result if faculty, especially faculty at research universities, were required to do more
teaching. The authors cited a study conducted at the University of Maryland that
concluded the College Park campus alone could save $20 million each year if all
full-time faculty were required to teach five courses each year. Such a requirement
to teach additional courses would presumably decrease the amount of time faculty
have for scholarship and service as each individual course requires a great deal of
preparation and planning.
Faculty must update their courses each term, revise reading lists, and prepare
lectures or instructional activities not only to conform to departmental policies, but to
incorporate new findings in their area of study (Teodorescu, 2000). According to
Marsh and Hattie (2002), instructors involved in research are likely to be better
informed in their discipline and use their own research to clarify, update, and amend

33
their teaching. In addition to all of this course design and preparation, the tasks
related to instruction have recently been expanding to include other faculty
interactions with students outside of the classroom such as advising and conducting
instructional and classroom research (Huber, 2002). The push to require more time
teaching and courses taught can unwittingly decrease the quality of teaching as
faculty must reduce their time in scholarly pursuits and are unable to keep up with
advancements in their field.
To insure quality and effectiveness in teaching, instructors are typically
required to go through an evaluation process, receiving feedback from students
regarding the overall course and curriculum (Stack, 2003). However, the evaluation
process is usually limited to a student satisfaction survey and is not necessarily
indicative of the quality of the instructional experience or the success of any learning
outcomes since the evaluation is based on perception, and therefore must be
interpreted carefully (Stack, 2003). When there is substantive feedback from
students, the challenge for faculty is incorporating the evaluation results in their
future courses, which will again involve designing, revising and evaluating in a
repeating cycle (Austin, 1993).
The question of what is considered productive in the area of teaching has
many answers. For some studies, “what is productive” means the results of student
evaluations of teaching (Marsh & Hattie, 2002; Stack, 2003), while others focus on
the percentage of total time or specific number of hours spent in the classroom,
number of courses taught, or number of credit hours taught (Ehrlich, 2003). There

34
have also been recommendations to implement teaching portfolios that would
include the professor’s philosophy about teaching and an analysis of the degree to
which the professor’s behaviors align with that philosophy as a means of assessing
productivity (Murray, 1997).
It seems clear that teaching is important; especially to state legislatures as
well as to the public they serve (Middaugh, 2001). However, the discussion
continues in research related to this assessment movement, and between state
governments and higher education administrators regarding the best way to measure
teaching productivity and effectiveness. Most of the measures of faculty productivity
in teaching focuses on the amount of time faculty devote to instruction, but few
attempt to quantify whether the teaching was indeed “productive” by assessing the
somewhat intangible outcomes of student learning (Middaugh, 2001). What remains
to be learned is how teaching can be evaluated holistically. How do instructional
hours correlate to the outcome of student learning? Studies have revolved around
teaching hours, number of courses taught, with some analysis of student evaluations.
However, few if any have looked at quality of teaching over time or identified
characteristics of teaching productivity among faculty.
Research. The requirement to be productive in the area of research can vary
across institutions and is largely dependent on institutional mission (Massy and
Wilger, 1995). For some higher education institutions, particularly those classified as
research universities by the Carnegie Foundation, faculty’s highest priority is often
research since, by definition, the creation of new knowledge through research is a

35
primary goal of the institution (Middaugh, 2001). At research universities, Massy
and Wilger (1995) assert that faculty bracket their teaching, giving it and the
undergraduate students a satisfactory amount of attention, while directing the bulk of
their creative energies and efforts to research. Tenure-track faculty at research
universities are expected to do the kind of research that contributes to their field in
order to receive promotion and tenure (Fairweather, 2005). This includes the
scholarly activities of producing reputable articles and books, text books, citations of
their work in other researchers’ publications, survey findings, etc. (Braxton, 2002).
However, the requisite amount and type of research may vary depending upon the
institutional mission, faculty rank, type of faculty position and the norms of the
discipline (Layzell, 1996; Middaugh, 2001; Milem, Berger, & Dey, 2000).
In the pursuit of any type of scholarship, faculty must identify an area of
scholarly work that is both personally interesting and professionally rewarding
(Massy & Wilger, 1995). The predilection to perform research is determined by a
variety of factors, the most important being the intrinsic motivation of the individual.
Some faculty genuinely enjoy the search for new knowledge and devote many hours
during weekends and evenings to perform research (Massy & Wilger, 1995).
Selecting a research niche not only depends on the faculty’s interests, but also trends
in the discipline, institutional support, and available resources for funding (Layzell,
1996). Faculty must design appropriate research programs utilizing the methods,
tools, and theories that will produce worthwhile results.

36
A reputation for scholarly excellence can result in an increased capacity to
attract research funds and high quality graduate students to the program (Grunig,
1997). A graduate program whose faculty actively produce research is likely to
attain a more favorable reputation than lower producing counterpart programs
(Grunig, 1997). Research universities have put a higher emphasis on research than
any other institutional goals as demonstrated by the research-heavy review process
for determining faculty promotion and compensation (Fairweather, 2005). And they
expect to see the tangible results of quality work in the form of increased grant
funding, partnerships with renowned faculty, and greater prominence for the
institution, not just the mere execution of staid research studies and publications
(Massy & Wilger, 1995).
Once funding and a topic are secured, the long process of research can begin.
At this point, faculty must design their study, obtain approval from review boards,
and enlist the assistance of colleagues and students in collecting and analyzing
information and data (Massy & Wilger, 1995). To produce significant results,
faculty must devote time to developing and / or implementing valid and reliable
measures, maintaining the integrity of sample selection and data collection, and
carefully interpreting the results of their efforts. The discovery of original concepts,
and the confirmation or revision of existing theory in turn advance the discipline and
renew the process of research funding and support to answer new questions.
Currently, faculty at research universities are expected to publish the results
of their work in scholarly, peer reviewed journals (D’Souza, 1991). Many authors

37
debate the merits of requiring publication for tenure and promotion decisions;
however, publication is a firm requirement for now (D’Souza, 1991). Articles, book
chapters, monographs, and complete books must be researched, written, rewritten
and then submitted to journals or publishers to be reviewed by a panel of experts
who judge them on merit of contribution to the field and rigor of methodology.
Publications may get accepted or rejected and it may take months or even years for
the publication to appear in print (D’Souza, 1991; AAUP, 1994).
Similar to teaching, research encompasses a variety of tasks. Trying to
compress the measure of research into a single value is difficult, since there are many
different modes of research from presentations to journal publications and books to
amount of grant dollars generated (Porter & Umbach, 2000), and the public focus on
teaching is at odds with the reward structures that support research over teaching
(Boyer, 1990; Sutton & Bergerson, 2001). Many use a “straight count” of all
publications as the measure of research productivity, but this inevitably leads to
additional analysis to qualify the quality of the publication by looking at the
discipline, publication type, the prestige of the journal, or average number of
citations as an indication of impact (Braxton & Del Favero, 2002).
As with teaching, there are a number of ways to look at a faculty member’s
work in research with continuing debate about which items most accurately measure
productivity. What remains to be learned is a way to quantify and measure merit in
research across all research universities. The tension between legislation pushing for
more instruction time versus institutions pushing for prestige and recognition in the

38
research community will be an ongoing battle until a tangible way of measuring
teaching and research is presented in a quantifiable dimension. Melguizo and Strober
(2007) concluded that spending more time on teaching has a negative effect on
salary. Although the negative effect was very small to no effect, it is still concerning
finding. Studies of research productivity tend to discuss the tenure process and
include counts of items published (Antony & Ravelin, 1998; Bland, Center, Finstad,
Risbey, & Staples, 2006; Creamer, 1998; Grunig, 1997; Toutkoushian, Porter,
Danielson, & Hollis, 2003). However, only a few have looked at quality of research
over time (Baldwin, Lunceford, & Vanderlinden, 2005; Bland & Berquist, 1997;
Green, 1998) or identified characteristics of research productivity among faculty
(Hughes, 1998).
Service. Generally, faculty are expected to provide service both within the
institution and outside the university (Neumann & Terosky, 2007). University-wide
activities may include serving on committees that guide the program, department,
school or university (Ward, 2003). These may include admissions committees,
curriculum committees, search committees, faculty committees, and / or task forces
to study and resolve internal issues (AAUP, 1994). External service obligations vary
by discipline. For example, education faculty may help in a local K-12 school,
business faculty may offer their time to a non-profit organization, and engineering
faculty may offer their expertise to solve a company’s technical problem. A faculty
member in the arts or humanities may serve on a board for an arts organization or a
faculty member may be invited to serve in a nationally recognized prestigious

39
organization (Neumann & Terosky, 2007). Service obligations of this nature are not
compensated but are considered to be included in the faculty’s obligations to his / her
institution, profession, or society (AAUP, 1994).
The amount and type of service required is also dependent on institutional
mission, faculty rank, type of faculty position and specific discipline (Ward, 2003).
Given the public and institutional demands to provide teaching and research, faculty
at research universities are likely to have much less of their time and effort left to
devote to service activities. Few studies have focused on service when discussing
faculty productivity. Faculty commitments to service tend to increase upon
receiving tenure as faculty are asked to “pitch in” to help meet an eclectic list of
institutional service needs often without recognition for these under-researched
service responsibilities (Neumann & Terosky, 2007).
Changes in Productivity
Another problem with assessing faculty productivity is that the balance of
work in the three areas can change. In general, there has been a shift in priorities of
the profession over many decades. Initially, faculty work included teaching as the
majority of one’s responsibility (Middaugh, 2001). However, especially in research
universities, the idea of faculty work has transitioned to doing more research and
bringing outside funding sources to the institution (Green, 1998; Melguizo &
Strober, 2007; Middaugh, 2001). In addition to this widespread change, the
distribution of faculty workloads may vary according to the type of institution, the

40
type of faculty position, or over the course of a career as a faculty member ascends
the ranks.
Faculty Rank and Age. At the height of the productivity debate, Chait
(1995) reported that the existing data showed no evidence of a decline in research
productivity with increasing rank or with the achievement of tenure. Furthermore,
Banks (1997) asserted that “tenure is an assurance of academic quality and
institutional integrity. It is not a barrier to academic productivity or to responsible
management” (p. 6). Regardless, there is evidence that the nature of faculty work
can change over the course of a career. On average, research productivity declines
with age, though senior faculty tend to spend roughly the same amount of time
teaching as younger faculty, and the decline in research production may have more
to do with a diversification of service responsibilities and a focus on quality (Bland
& Berquist, 1997).
The research-heavy review process for promotion and tenure requires that
junior faculty produce traditional published research in the first several years of their
career (Fairweather, 2005). While there are many studies that focus on the early
career of faculty, once tenure is achieved, little has been studied regarding faculty
workload and productivity in the middle years of their academic life (Baldwin,
Lunceford, & Vanderlinden, 2005). It seems clear that the balance of faculty work in
teaching, research, and service can evolve over one’s lifetime, as faculty go through
personal and professional development and changes, but how their productivity is
affected over time has not been adequately studied.

41
Type of Appointments. Depending on the employment relationship between
a faculty member and their institution, the expectations of work across the areas of
teaching, research, and service may fluctuate. With the focus on creating published
research as well as teaching for tenure and tenure-track faculty, many institutions
have met the demands of student enrollment by hiring contract or non-tenure-track
faculty whose main priority is teaching (Fairweather, 2005; Middaugh, 2001). This
has been an increasing trend in higher education to the point that some state officials
and higher education trustees have called for the abolishment of the tenure system
itself (Trower, 1999).
Since those on the tenure-track and those who are not are hired for different
reasons and purposes, does this create separate expectations and requirements for
productivity in the different types of positions? While tenured and tenure-track
faculty are expected to conduct and publish research, non-tenure track faculty, who
are hired for specific responsibilities such as teaching, may publish fewer articles and
work fewer hours (Bland, Center, Finstad, Risbey, & Staples, 2006). With differing
expectations among tenured faculty who face a multitude of responsibilities in
teaching, research and service versus specific contract-driven requirements for non-
tenure track faculty, a one-size-fits-all approach to productivity assessment is
impractical.
Type of Institutions. A faculty member’s discipline and the type of
institution they work will impact the distribution of the professor’s workload. The
importance of work in each area can differ greatly according to institution size and

42
type (Middaugh, 2001). For example, a large public research institution may stress
the importance of generating new research in their faculty evaluations, whereas a
small liberal arts college might instead highlight the significance of teaching
(Middaugh, 2001). Historically, faculty in community colleges teach more than
those at research universities (Middaugh, 2001).
Faculty at research universities are generally expected to secure grants and
publish research, unlike their community college counterparts who typically have no
such expectations (Meyer, 1998). There are also varying institutional characteristics
that can support or hinder a productive environment for faculty (Bland, Center,
Finstad, Risbey, & Staples, 2006). Since the balance of work across institutions does
not remain stable, with liberal arts colleges devoted to teaching in the relative
absence of research, and graduate or research universities dedicated to the production
of scholarly work while balancing research and teaching demands, it seems unlikely
that one universal definition of what it means to be a productive faculty member can
be developed.
Productivity Quality versus Quantity. Another obstacle to successfully
evaluating faculty productivity is the idea of productivity quality versus productivity
quantity. If over the same ten-year period, one faculty member publishes just one
paper that wins a Nobel Prize, and another faculty member publishes 25 papers that
simply restate and confirm the research of others, would both be considered equally
“productive”? As previously discussed, attempts to quantify faculty productivity
may overlook significant intangible or qualitative characteristics by excluding other

43
related tasks in taking straight counts of time spent in the class or articles published
and not examining desired outcomes of teaching and research. In the measures most
often used, it is clear that a certain level of work was accomplished, but the impact
and meaningfulness of the work remains elusive (Middaugh, 2001). Appropriate
indirect measures, for example, such as counts of how many times a faculty’s
published work has been cited by others, can be used to evaluate the significance and
not just the volume of faculty work.
According to Dirks (1997), faculty tend to be suspicious of efforts to improve
productivity, such as increasing the student-faculty ratio, and other primarily
quantitative reforms. They see inputs and outputs as being related to each other and
changes can only affect quality (Dirks, 1997). A decrease in number of students in a
classroom may mean that the faculty have to offer more classroom sessions to
maintain revenue. More classroom sessions, in turn, will result in more preparation
time but less individual time with students. Critics of this argument note that there is
little evidence in faculty definitions of productivity to suggest that faculty search
actively and continually for ways to improve teaching quality (Banks, 1997).
Measurement of separate faculty activities such as research publications,
instruction, various service and committee activities, and administrative duties are
common indicators of faculty productivity and success (Middaugh, 2001). However,
quantification of achievement in these areas still remains challenging. The level of
participation and proportion of these activities are also extremely sensitive to the
institutional type and discipline (Boyer, 1990; Colbeck, 2002a). Public or private,

44
two-year or four-year, research or liberal arts, science or humanities – these
characteristics give each institution and department a unique mission and goals
which translate to fairly wide variations in the importance of one area of faculty
work over another. Since institutional, and even departmental, objectives differ, the
literature contains numerous individualized measures of faculty productivity that are
specific to the college or university being studied and may not be transferable to
other settings.
Although government oversight has mandated reporting on these issues in
many state college systems, Cooper and Hensley (1993) indicated that no empirical
evidence exists regarding the status of faculty productivity reporting systems across
U.S. institutions of higher education. According to reporting mandates in about 34
states, the focus tends to be on the number of hours faculty work each week, and the
number of hours spent in the classroom (Allen, 2004; Cage 1995). The lack of
evidence regarding how effective such measures have attributed by poor
conceptualization of faculty productivity, measurement tools with problematic
validity and reliability, hasty implementation, and a failure to identify aspects of
faculty productivity that are most important among faculty (Allen, 1999).
Gullatt and Weaver (1995) assert that the literature is confounded by
differing measures of faculty productivity and various criticisms of the reporting
systems. A later study (Burke, 2001), also concluded that the implementation of state
accountability measures in higher education have had little or no effect, or have not
generated enough information to perform an assessment in over half the states

45
surveyed. Gullatt and Weaver report that evaluation of faculty productivity has been
conducted by both institutions and funding sources, to ensure accountability for
outside sources of funding provided to higher education and for internal institutional
measures. Many organizations responsible for assessing and awarding the
accreditation of organizations, as well as state public officials, such as the National
Governors’ Association, are interested in the assessment of faculty productivity as a
significant part of the activities of post-secondary education (Middaugh, 2001).
Again, as with different types of universities and disciplines, each state government
may have a different focus when defining and evaluating faculty productivity
(Colbeck, 2002a).
Different studies have operationalized different definitions about faculty
productivity. In the current era of accountability and in response to external
pressures, studies have looked at the number of hours per week worked by faculty
and the number or percentage of those hours spent in classroom instruction (Cage,
1995; Middaugh, 2001). Meanwhile other studies continue to look at publication
and citation counts to measure faculty productivity (Green, 1998; Hagedorn & Sax,
2004; Stack, 2003). However, these definitions tend to be fairly similar as they
relate to the areas of teaching and research, and are often found to be influenced by
the same factors including faculty rank, institutional mission, academic discipline
and type of faculty position (Layzell, 1996; Middaugh, 2001; Milem, Berger, & Dey,
2000).

46
Strategies for Improving Productivity
Given the known challenges and difficulties presented for groups of faculty
in maintaining productivity, intervention on the part of the institution or the
individual faculty can be instrumental to the success of the professor. The
independent nature of the academic profession may discourage such intervention, but
strategies such as mentoring and monetary incentives can significantly improve
faculty productivity and success.
Mentoring. Many institutions of higher education have provided support,
interventions and professional development strategies for tenure-track faculty who
are under the shadow of the tenure clock. Mentoring seems to be the most prevalent
method of support in preparing junior faculty for the tenure review process.
Mentoring typically involves tenured senior faculty guiding tenure-track junior
faculty to a career path that will help them earn tenure.
Bland and Bergquist (1997) note that “many senior faculty are confident in
their teaching and research skills, and they possess a deep sense of commitment to
their institution, highly inculcated values, a vital network of professional colleagues,
knowledge of the academic enterprise, and an ability to manage multiple
simultaneous projects” (p. iii).The authors also found that senior faculty tend to
value collaboration and differing perspectives and feel quite “generative,” desiring to
coach and support the next generation of faculty at their institutions. Mentoring can
energize senior faculty while assisting junior professors in understanding
methodology, work ethic, and nuances of the discipline or institution. Through

47
mentoring, faculty are able to see their career in new ways, often leading them to
desire expanded and diversified roles in their institution (Bland & Bergquist, 1997).
Mentoring assists the development of the academic profession to such a great
extent that “by not mentoring, we are wasting talent. We educate, and train, but don’t
nurture” (Wright & Wright, 1987, p. 207). As a means to promote institutional
culture, provide access to communication networks, and offer professional
stimulation to both junior and senior faculty members, mentoring is a practical and
powerful tool (Luna & Cullen, 1995). Mentoring fosters professional growth and
revitalization, which in turn empowers faculty as individuals and colleagues (Boice,
1992). Luna and Cullen (1995) assert that when junior faculty are paired with
mentors, teaching and research improve; job satisfaction and institutional
socialization are also greater. Not only do junior faculty become empowered through
the assistance of a mentor, but mentors themselves also feel rejuvenated through
cooperation and collaboration (Luna & Cullen, 1995).
Mentoring is developmental, continuous and may deal with an assortment of
faculty career needs over time. Luna and Cullen (1995) reported that “faculty
involved in mentoring are more likely to have opportunities to develop not only
professionally but also personally over the span of their careers” (p. 3). In many
studies, the benefits of mentoring programs and the successes of those who have
experienced mentoring are emphasized. Also emphasized are the needs for
mentoring to fit the culture and environment of the educational institution and for

48
faculty involvement in the design and implementation of strategies and plans for
mentoring.
Although informal mentoring programs are often found in tenure granting
institutions, no existing body of literature provides a synopsis or analysis of these
programs. Empowering junior faculty through mentoring requires careful planning to
incorporate the educational institution’s needs as well. Although most mentoring
programs have similar steps, purposes, and activities, programs need to be
customized to meet the goals of the mentees, the mentors, and the institutions.
Campus awareness should be raised about the importance of faculty mentoring and
the need to establish a mentoring program with faculty assistance and input.
Well-organized mentoring programs should include an established purpose
and goals, assessment of the institution’s policies, matching and training for both
mentees and mentors, and development of measures to evaluate and adjust the
program. However, well-organized, formal mentoring programs are rare and some of
those that exist have not determined assessment outcomes in terms of the mentees,
mentors, and institutional goals and objectives. Future research should work to
identify successful faculty mentoring programs and the characteristics that made
them successful. (Luna & Cullen, 1995).
The concepts of quality improvement and professional development have
been incorporated into higher education because faculty productivity and
accountability have come into question within the last decade. For years, business
and industry have applied the philosophy and principles of mentoring to attract,

49
retain, and promote junior employees, and mentoring has improved individual and
corporate performance and effectiveness (Luna & Cullen, 1995). In translating these
same mentoring concepts to higher education, strategies, guidelines, and professional
development programs have been developed and implemented to empower faculty
through mentoring. The concept of mentoring embraces a philosophical commitment
to faculty and emphasizes their importance to their own educational institutions.
Luna and Cullen (1995) synthesized the literature on mentoring in terms of
conceptual frameworks, mentoring arenas, and roles and functions of mentors and
mentees. Their research discusses the dynamics of mentoring for empowering faculty
members as leaders and the importance of mentoring women and minorities in
academe. The researchers’ results point to the different faculty experiences of
women and minorities in higher education in terms of scholarship, advising
assignments, teaching loads and service to the community, profession, and
institution. Assisting female and minority faculty members in understanding the
organizational culture is very important in helping them succeed in the tenure
process. Well-planned faculty mentoring models develop and empower faculty and
ultimately benefit the institution (Luna & Cullen, 1995).
For faculty women who must balance career and family responsibilities,
mentoring can be crucial. Female faculty who have successfully received promotion
and tenure can provide guidelines and advice on what helped them get through the
‘up-and-out’ tenure process. Parents, especially mothers, need all the support they
can get just to maintain their daily responsibilities. Since the probationary years of

50
the tenure process typically occur at the same time as peak child-bearing years, many
women abandon their academic careers, either permanently or temporarily to raise
families, and women are still underrepresented in the senior ranks of faculty (Finkel,
Olswang & She, 1994). Without a mentoring and a professional development system
for mothers seeking tenure, gender issues will become more significant in the future
of higher education institutions. Maack and Passett (1994) asserted the importance
of mentoring in assisting tenure-seeking female and minority faculty members in
feeling comfortable with the academic environment. They also concluded that
additional research is needed to study the specific benefits of mentoring programs for
female and minority faculty members at tenure granting institutions.
Compensation and Financial Incentives. Monetary incentives in the form
of a contract salary and tenure can be powerful motivators for faculty to achieve and
maintain increased levels of productivity. However, as mentioned before, decisions
related to promotion and tenure providing additional income for faculty are largely
based on research productivity which does not address the state legislatures’
concerns regarding productivity in teaching (Green, 1998; Meyer, 1998). One
cannot discuss faculty productivity and workload without defining and examining
faculty compensation. Faculty compensation is a critical management tool for
increasing faculty productivity, improving efficiency, and enhancing an institution’s
public image (Sutton & Bergerson, 2001).
The factors that determine faculty compensation include academic rank,
faculty productivity, discipline market pay, ability to obtain external grants, seniority

51
or length of service, service in administrative positions, professional service, and
teaching and guiding students. The best predictor of salary within an institution and
within any rank are an individual’s years of experience, with the number of articles
published as the second best predictor (Sutton & Bergerson, 2001). This indicates
that faculty compensation will increase not only if faculty productivity in publishing
increases, but also simply as time passes.
This correlation between compensation and productivity gives faculty a
tangible, monetary, incentive to perform. While the passage of time and the
production of published articles leads to increased compensation for faculty, the rise
in salary and benefits is usually also related to the receipt of tenure. Tenure is also
based on years of experience and number of articles published. The measurement of
faculty productivity is especially important when it comes to awarding tenure since
this involves a review of what an assistant professor on the tenure-track has produced
and accomplished during the past several years of their career in terms of
publication, teaching and service. As Gullatt and Weaver (1995) found, many larger
institutions are more likely to use faculty reporting when determining promotion,
tenure and merit. As such, the influence on salary and compensation related to
faculty productivity should not be overlooked.
Some state governments have tied institutional funding to productivity
benchmarks which include requirements for faculty work. However since these
conditional funding initiatives are at an institutional or university-system level, the
budgetary inducement intended is too far removed even at the departmental level,

52
and appears to be ineffective in having any kind of influence on the productivity of
individual faculty members (Layzell, 1999; Middaugh, 2001). Faculty continue to
respond to the much more immediate incentives and pressures associated with their
personal contract and tenure status. As discussed, since promotion and tenure are
typically tied to research production, these monetary incentives are conducive to
results in research, but not necessarily in teaching or service.
Tenure and Promotion. As a system that is inextricably linked to trends in
faculty productivity, particularly related to faculty age and rank, it is necessary to
review the process of tenure and promotion and the subsequent productivity
requirements related to tenure. Academic tenure is a privilege unique to faculty
members. It is also the one privilege that a majority of tenured faculty members are
not willing to relinquish (Premeaux & Mondy, 2002). Tenure is essentially the
system by which faculty earn promotion and job security at an institution. Tenure is
a well-established and pervasive system in institutions of higher education. About
90% of all four-year institutions and 99% of four-year public universities have a
tenure system, with an estimated 60% of all professors nationwide having earned
tenure (Banks, 1997).
Tenure is the lifetime appointment of a faculty position at an educational
institution. Tenure originally began as a protection of academic freedom, allowing
professors who teach and publish controversial ideas to do so without fear of losing
their jobs. It is associated with more senior positions of academic rank, such as
professors and associate professors. An assistant professor on the “tenure-track”

53
must accumulate six to seven years of academic experience and demonstrate strong
academic productivity before being considered for tenure. The academic freedom
and job security have been weighted on opposing sides. The Supreme Court has
generally upheld the role of academic freedom as an essential protection of free
expression (Horwitz, 2004).
Tenure, the granting of permanent employment, was intended to protect
faculty from unwarranted or capricious firings, thereby protecting academic freedom.
Defining productivity among faculty is also a key component to the longstanding
tradition of tenure. Tenure is a topic that has produced much heated discussion in
recent years. Some argue that tenure is an outmoded concept, and, if institutions are
going to remain competitive, they need to be able to have more flexibility to hire and
fire faculty as student needs change. Others argue that tenure is vital to the protection
of academic freedom and that without tenure we will return to the days when faculty
were dismissed for teaching unpopular theories, ideas, and opinions. Higher
education institutions need to implement support systems for junior faculty seeking
tenure that have intervention and mentoring components.
For research universities, the removal of tenure will hurt the institution in the
long run as it is only a temporary solution to a potentially much bigger problem.
Without tenure as an incentive, the productivity of faculty could significantly decline
as contract faculty without any real commitment to the institution do the bare
minimum to collect their salary and / or frequently leave for better opportunities
elsewhere (Poch, 1993). Research universities and policy makers should attend to

54
this issue in higher education carefully to accommodate the needs of the students,
faculty, institutions and the public at large in the best way possible.
While nearly all four-year institutions have a tenure system and the majority
of faculty are tenured, a study by the National Education Association indicated the
number of full-time, tenured faculty in colleges across the nation was decreasing and
another researcher called faculty “underproductive, esoteric technophobes who teach
obsolete notions about business practice under the protection of an arcane tenure
system” (Premeaux & Mondy, 2002, p. 335). Despite such opponents to tenure and
their efforts to eliminate the practice, tenure remains a strong shield of life-time
faculty protection.
As a factor influencing productivity, tenure is a popular target. Attacks on
tenure have increased over the past several years with serious attempts to question
the existence of tenure systems since they are looked on as an excuse to avoid
accountability for faculty workload and productivity (Dirks, 1997). Tenure advocates
believe that tenure is necessary to attract the best candidates to the academic world,
because without at least job security they cannot compete with the attraction of
higher paying, private-sector jobs (Premeaux & Mondy, 2002). Regardless of which
viewpoint is correct, Premeaux and Mondy assert that awareness of the perspectives
of faculty is critical to understanding the current nature of academic tenure.
The security of tenure is considered an essential condition for maintaining
academic freedom and independence from political or partisan control in an
academic setting. Tenure in academia can be terminated only on grounds of serious

55
misconduct or incompetence. Such allegations have to be considered by a duly
constituted body. In academic appointments these bodies are often collegial boards
of peers or such overseeing councils as trustees and university senates. The
conditions as well as the duration of tenure and the manner of its guarantee vary.
Professors may have continuous or indefinite tenure. Security of tenure and freedom
from external control, particularly in the academic profession, are also insured by a
tradition of independence and an ethos of tolerance in addition to guarantees by law
and regulation (Mawdsley, 1999).
Productivity, particularly in the form of research publications is a key factor
in earning tenure. Green (1998) noted that in the 1990s, publication and scholarly
productivity had become the top ranked item in determining tenure and promotion.
A shift has occurred over the past few decades. In the 1980s, publication was ranked
second most important to such decisions, whereas in the 1970s publication
productivity was far from the main criteria in determining appointments, promotion,
and tenure (Green, 1998). This shift towards rewarding research productivity with
promotion and tenure may not fit with the current accountability climate that is
pushing for more classroom instruction hours.
Granting tenure on the basis of research publications has been widely studied
and discussed. Research has not always been the most important criterion, although
its importance has grown. From 1975 to 1984, Boyer (1990) found that the percent
of faculty stating that it was difficult to get tenure at their institution without
publications grew from 54% to 69%. It is common to hear that new assistant

56
professors have been told that publications are the only criterion for tenure and
promotion, and they must “publish or perish” in the tenure arena. Faculty do not
necessarily agree with this emphasis. Similarly, the promotion to full professor is
frequently dependent upon exceptional contributions to the field and is not typically
granted for normal levels of performance and/or a certain number of years of
unremarkable service (AAUP, 1994).
The evaluation of faculty work as well as the subsequent decisions made
based on that evaluation, both pre-and post-tenure will continue to be a fundamental
issue to the administration of higher education in the coming years (Boyer, 1990;
Tierney, 1998). What Ovington, et al (2003) call “the highly sensitive and
emotionally charged nature of the tenure acquisition process” cannot be ignored;
regardless of how longstanding and seemingly objective the policies and procedures
may be (p. 637). They also note that the importance of impartial, incontrovertible,
and justifiable criteria for tenure should not be disregarded. Much has been written
about the pitfalls of the tenure process, yet those assigned the responsibility to
evaluate, judge, and ultimately decide the fate of each academic career often lack
awareness and sensitivity to its true nature and implication (Ovington, Diamantes,
Roby & Ryan, 2003). Granting or denying tenure is a fine line to walk for the
administrator, since tenure often brings concerns of complacency and reduced
motivation to create and produce; on the other hand, denying tenure can result in the
threat of legal action (Ovington, Diamantes, Roby & Ryan, 2003).

57
Theoretical Framework
This study is guided by several perspectives from the literature. First,
theories on faculty work will help us better understand faculty behavior. Second,
motivation theories provide a framework for what motivates faculty to be
productive- spend time on certain activities vs. other activities. These theories help
frame faculty productivity with respect to faculty and institutional characteristics in
addition to characteristics of the profession. The theoretical perspective from
Blackburn and Lawrence (1995) used in this study posits that characteristics of
individuals and their employing institution combined can lead to variations in faculty
behavior, productivity and motivation.
Theories on Faculty Work (Blackburn and Lawrence, 1995)
A deeper understanding of faculty behavior and a better exploration of how
they make decisions in prioritizing their workload will help to identify possible
barriers and facilitators to faculty productivity and how they spend their time. In this
study, two theories that are interrelated will be used to explore how faculty behavior
and motivation impact productivity and how they spend their time.
Faculty Behavior and Productivity. The first theoretical framework in this
study is Blackburn and Lawrence’s (1995) model of faculty behavior and
productivity. They rationalized their study of faculty work with faculty being the
target of critics in regards to their roles and responsibilities. Faculty members find
themselves in a conflicting set of expectations (Blackburn & Lawrence, 1995, p. 3)
and this causes pressure on them. A perspective of administrators or faculty on these

58
set of expectations brings different elements into the picture. Hence, Blackburn and
Lawrence (1995) articulated faculty individual characteristics and work
environments and analyzed somewhat reciprocal relationships among these elements
in an effort to explore faculty behavior and productivity.
Two main factors were found to impact faculty members’ behavior and
productivity: individual faculty characteristics and the environment. Individual
faculty characteristics are considered with regard to socio-demographic
characteristics (age, gender, race/ethnicity, etc.), career (academic discipline,
preparation of career, type of institution, type of rank, etc.), self-knowledge
(understanding of self-self-referent, etc.), and finally social knowledge (how
individuals perceive their environment). In terms of properties of environment, they
discussed three main features, environmental conditions (the structural and
normative features of the university), environmental response (different formal
feedback that faculty receive) and social contingencies (events that happen in faculty
members’ life which affect their work).
Self-Knowledge. Self-knowledge category includes self-perceptions of
faculty that are directly in relation to behaviors and products in their various
academic roles. Unlike socio-demographic characteristics, and career preparation,
these perceptions have a potential to change over one’s career as a result of
environmental conditions. Blackburn and Lawrence (1995) characterized self-
knowledge in five aspects: 1) interest - how faculty prioritize their work reflects their
interests; 2) commitment - faculty interest may transfer into commitment to an

59
activity, which can be a vow or a promise to succeed; 3) efficacy - faculty might
develop a sense of ownership and control of choices, options and opportunities; 4)
psychological characteristics - faculty personal dispositions may impact conduct of
teaching and learning activities and 5) satisfaction & morale - satisfaction with work
and career may relate to productivity (Blackburn & Lawrence, 1995).
Social Knowledge. Blackburn and Lawrence (1995) further analyzed the
elements of social knowledge and concluded that faculty build their beliefs based on
their experiences with colleagues, administrators, committee decisions, faculty
meetings, institutional roles and norms, professional association practices which all
constitute their social knowledge. The list of characteristics of social knowledge is
defined as: 1) social support - feedback from colleagues, administrators and students
may influence faculty decision to act; 2) material support - funding, time and
support for conducting research and/or other roles may have an effect on faculty
behavior; 3) perceived institutional preference - time allocated to teaching, research,
scholarship and service may be defined by how faculty perceive institutional
preference; 4) institutional values - faculty will be motivated by their perception of
what the institution honors, values and rewards (Blackburn & Lawrence, 1995).
Self and Social Knowledge versus Faculty Behavior. Blackburn and
Lawrence (1995) later presented these interactions between self and social
knowledge as well as environmental conditions to explain faculty behavior and
productivity. Figure 1 depicts this interaction and shows how such interaction can be
transformed into behavior. Their research explored and interpreted some of these

60
factors and their relationship to faculty behavior—whereas in this study, faculty
productivity and how they spend their time will be examined. To put it differently,
self-knowledge (perception of their knowledge, beliefs and resources), socio-
demographic characteristics (ethnicity, race, gender, age), and career characteristics
(years of experience in teaching, academic rank, prior experience) have
repercussions on the decision making process that go beyond the influence of
environmental conditions.
In Figure 1, the thick heavier arrows represent strong, direct effects of the
variables in one category on the variables in the category the arrow points to. The
thin arrows signify the weaker effects between several of the principal constructs. In
this model, the main impact on faculty behavior and productivity comes from social
knowledge, how faculty perceive their environment with regard to their work.
According to the framework, there are several factors that have direct effect on social
knowledge, self-knowledge and environmental responses. Self-knowledge includes
self-perceptions of various academic roles- teaching scholarship, research and
service. In this study, how faculty spend their time will be interrelated to how faculty
are motivated to participate in certain activities--productivity in teaching, research
and service.

61

Figure 1. Theoretical framework on faculty behavior and productivity. This figure
explains the relationship between variable above. Source: Blackburn & Lawrence
(1995).

The elements that shape faculty self-knowledge are socio-demographics (i.e.
ethnicity/race, gender, age) and career (academic rank, career preparation, or
teaching and learning related professional development activity) characteristics. In
other words, what faculty value in their teaching and research roles, how much they
see benefits of certain activities in their work life or whether they have positive
attitude toward work related issues may be formed based on the number of years in
teaching, type of schools they attend for their graduate education, their
race/ethnicity, their attendance to professional development and so on. According to
Blackburn and Lawrence (1995), a direct impact on social knowledge is

62
environmental responses, meaning that formal feedback is given to faculty on their
participation or non-participation, or they are recognized and rewarded for the
activities they value and spend the most time on.
Some of the key premises of this theoretical model are:
1. Universities / colleges are achievement-laden environments, where there
is continuous faculty, student, and administration performance evaluation.
2. Faculty make decisions through assessing themselves and their social
contexts, which leads to action.
3. Experience over time can be used to modify faculty members’
understanding of their work environments and their self-images.
4. Some self-referential thoughts might change depending on the feedback
and experience while others are fairly enduring (Blackburn & Lawrence,
1995).
These key premises provide a baseline in understanding faculty members’
work environment, how they change their self-understanding as well as their self-
referential thoughts. Eventually, these key premises may offer us an explanation of
the decision-making process for faculty participation in activities deemed valuable to
spend their time. Behavior in Blackburn and Lawrence’s (1995) model is defined as
“the specific activities a faculty member engages in as well as the levels of effort
expanded and productivity is the specific outcomes achieved by individuals” (p. 28).
In this study, faculty spending time in certain activities listed on PI is considered as
behavior. This behavior interacts with various factors such as their self-perception

63
(self-knowledge) and their perception of their environment (social-knowledge) in
relation to productivity and socio-demographic characteristics. With regard to self-
knowledge, faculty members’ understanding of productivity – their knowledge,
beliefs and attitudes – would give a more in-depth view regarding faculty spending
their time in activities deemed valuable, while some of the socio-demographic
characteristics like academic discipline, age, rank, gender, and ethnicity would bring
another perspective to the issue.
In their empirical work, Blackburn and Lawrence (1995) tested their
theoretical framework on faculty work and reported the following findings:
1. Socio-demographic Characteristics: Gender was found to be the only
predictor variable in research outcomes. Women faculty talked more with
their colleagues about their research. Gender also had a direct effect on
career and self-knowledge.
2. Career: Career age was found as a predictor meaning that senior faculty
publish more and give greater effort to teaching.
3. Self-Knowledge: Self-efficacy – as a researcher, teacher, and committee
member– mattered more than any other variables in this category.
4. Social-Knowledge: Social knowledge was the greatest predictor in faculty
work. Support and effort which faculty believed their institution desired
were two variables found significant in predicting faculty behavior.
Grants, having credible colleagues, department chairs, and resources were
some other variables that predicted faculty performance at work.

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Motivation Theories
Blackburn and Lawrence (1995) framed the theories on faculty work on what
affects faculty behavior and productivity (structural dimension) along with the
second theoretical framework, that guides this study, is motivation theories- how
various factors influence behavior and productivity (process dimension). Blackburn
and Lawrence’s (1995) theoretical framework proposes integration of the research on
faculty role performance and productivity with motivation. They discuss and group
motivation theories into two categories: noncognitive (Personality and Career
Development Theories, Reinforcement Theories, and Dispositional Theories) and
cognitive (Expectancy Theories, Attribution Theories, Efficacy Theories, and
Information-Processing Theories). They also focus on motivation in achievement
contexts, that is, situations in which there are performance outcomes that define
levels of success (Blackburn & Lawrence, 1995). This study focuses on two specific
motivation theories: Hertzberg’s Two-Factor Theory and Vroom’s Expectancy
Theory.
The idea of motivation has created a wide range of theoretical frameworks
centered around studying the causes of what drives people to do what they do or do
what they are supposed to do (Reiss, 2004). The observation of these factors is
influenced by different variables which might positively or negatively affect the
outcome and lead researchers to false or misleading results (Reiss, 2004). Blackburn
and Lawrence (1995) discussed the importance of motivation in relation to

65
productivity. Motivation is a field of humanistic science that has a direct effect on an
employee’s desire to complete a given task (Adair, 2006).
Non-cognitive and cognitive categories mentioned by Blackburn and
Lawrence (1995) lay a foundation for the importance of maintaining high motivation
amongst faculty in order to improve overall academic performance. Motivation
greatly affects employee productivity (Stacey, 2007). Motivation amongst faculty
members in higher education remains a common issue faced by the higher education
institutions (Weightman, 2008). Lack of motivation at the higher education level has
a direct impact on the productivity of professors and leads to low performance, high
turnover, and possible inappropriate activity (Weightman, 2008). Stacey (2007)
suggested that it is the nature of a learning organization to rely on cognitive,
constructivist, and humanistic psychology to understand the thought processes and
nature of human beings. In order to affect faculty motivation, administrators may
have to understand the theories that explain human behavior.
The theoretical approach to motivation is generally linked to the concept of
individual needs, driven by factors ranging from satisfying basic human needs (e.g.
hunger) to complicated human desire (e.g. attaining a status) (Adair, 2006).
Weightman (2008) noted that the wide range of motivation research makes research
into specific situations and observations more critical for administrators seeking to
motivate employees. Human behavior researchers have further divided motivation
into intrinsic and extrinsic motivation. Pintrich and Schunk (2001) developed a basic
understanding of intrinsic and extrinsic motivation. Intrinsic motivation is the driving

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force within an individual that pushes or motivates them to undertake a course of
action for their own benefits (self-motivation). Extrinsic motivation requires external
motivators (e.g. raise, status, etc.) to convince an individual to pursue a task (Pintrich
& Schunk, 2001).
Hertzberg’s Two-Factor Theory. Frederick Hertzberg’s Two-Factor Theory
concludes that certain factors in the workplace result in job satisfaction, but if absent,
they do not lead to dissatisfaction but no satisfaction. The theory concentrates on
hygiene factors that affect an individual’s motivation and causes them to become
dissatisfied with their work (Hertzberg, 1968). Hygiene factors include extrinsic
factors such as salary, rank, and physical environment and intrinsic factors such as
self-fulfillment (Pintrich & Schunk, 2001). Hertzberg (1968/2003), a pioneer in the
field of motivation, specifically related to employees, theorized that environmental
factors lead to increases and decreases of an employee’s motivation. Hertzberg
(1968) theorized that extrinsic motivators were pertinent to the development of
employee motivation management. Hertzberg (2003) later found that job satisfaction
is closely linked to the physical work environment of an employee.
Hertzberg (1968) differentiated between motivators and hygiene factors.
Motivators are factors that create a long-term positive influence on an employee’s
attitude to particular tasks which give positive satisfaction (e.g. challenging work,
recognition, responsibility), whereas hygiene factors are aspects of an employee’s
work environment that create dissatisfaction that do not motivate if present, but, if
absent, result in demotivation (e.g. status, job security, salary, fringe benefits)

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(Hertzberg, 1968). In relation to this study, motivators may influence how faculty
spend their time. For example, tenured and tenure-track faculty may choose to spend
most of their time on research because of the fact that research is challenging work.
Research can also bring recognition to them at the institutional and national level.
However, hygiene factors may also influence how faculty spend their time as well
since lack of raises and promotion will definitely demotivate faculty behaviors.
Vroom’s Expectancy Theory. Victor H. Vroom (1964) defines motivation
as a process governing choices among alternative forms of voluntary activities, a
process controlled by the individual. The individual makes choices based on
estimates of how well the expected results of a given behavior are going to match up
with, or eventually lead to, the desired results. According to Vroom (1964),
motivation is a product of the individual’s expectancy that a certain effort will lead
to the intended performance, the instrumentality of this performance to achieving a
certain result, and the desirability of this result for the individual, known as valence.
Vroom’s expectancy theory takes an individual’s personal perception as a motivator
for performance.
The Expectancy Theory has been applied to the field of motivation in order to
understand the process that individuals use in their decision making (Oliver, 1974).
Expectancy Theory proposes that a person will decide to behave or act certain way
because they are motivated to select a specific behavior over other behaviors due to
what they expect the result of that selected behavior will be. In essence, the
motivation of the behavior selection is determined by the desirability of the outcome.

68
For example, if a faculty member expects to publish three articles per year at the top-
tier journals, their high expectation of themselves will propel them to achieve that
goal. That faculty will also spend time in activities (behavior) that will lead them to
meet the expectancy. However, at the core of the theory is the cognitive process of
how an individual processes the different motivational elements. This is done before
an individual makes the ultimate choice. The outcome is not the sole determining
factor in making the decision of how one will behave. The theory of expectancy
creates a framework that may be used for evaluation and assessment of faculty
motivation (Blackburn & Lawrence, 1995). Having clear expectations and rewards
for those expectations (or consequences for lack of expectations) will create balanced
assessment tool.
According to the theories of faculty work and motivation, faculty behavior is
closely related to their motivation. Blackburn and Lawrence (1995) proposed that the
main impact on faculty behavior and productivity comes from social knowledge, that
is, how faculty perceive their environment with regard to their work. In their
empirical work, social knowledge was also the greatest predictor in faculty work.
Support and effort which faculty believed their institution desired were two variables
found significant in predicting faculty behavior. Therefore, the environmental factors
(Hertzberg’s Theory) and individual’s expectancy (Vroom’s Theory) that a certain
effort will lead to desirability of results (personal perception) are the two motivators
in performance of faculty. This study examines how faculty spend their time
(behavior) and how they are motivated to spend time on certain activities versus

69
other activities (motivation) according to the Productivity Index from the School of
Education at a private university. This study also identifies which characteristics
affect behavior and motivation, and in turn, how those characteristics influenced
productivity. The following section examines how faculty productivity is reported.
The Work of Faculty in Research Universities
In the three areas of teaching, research, and service, the distribution of faculty
work varies, and a central question in assessing faculty productivity centers on
which, if any, of them should be given priority. Another challenge in determining
faculty productivity is the very nature of the faculty position (Middaugh, 2001).
Certain characteristics of the faculty role are unique to being a professor. One
characteristic unique to the professorial role is its enormous breadth. Professors are
to pursue truth, teach students, and serve society, but each of these tasks is endless in
scope (Bowen & Schuster, 1986). Compared to many other professions and
occupations, most faculty have discretion over the use of their time, the tasks they
choose to address, and the methods used to accomplish each task, especially in the
case of research and service (Middaugh, 2001). However, an institution will
typically set guidelines for the distribution of faculty responsibilities, or a faculty
load profile, which loosely sets the expectations and objectives required in each area.
The normal faculty load profile distribution for purposes of this study will be
40% teaching, 40% research and 20% service for full-time tenured and tenure-track
faculty. A faculty load profile can be tailored to individual faculty depending on
their rank and type of position, department, discipline and school. According to

70
Layzell (1999), at its most basic level, all faculty work involves the transfer,
discovery, and application of knowledge which may be similar to other knowledge-
based professions. However, Layzell (1999) also asserts that there are three factors
which distinguish the production and content of faculty work – a high level of
autonomy, asynchronous production, and preeminence of the discipline, described as
follows:
A high level of autonomy in the production process. Faculty are trained to
work independently beginning with their immersion in the culture of graduate
school. This carries forward into their professional lives, where faculty are
highly autonomous professionals who have significant freedom over the
mode of production in their instruction, research, and service activities. A
corollary to this is the fact that the working relationship between faculty and
their “customers” (e.g. students) is not as direct as in other professions (lack
of feedback and evaluation).
Asynchronous production. Most faculty do not work the typical 9 to 5
schedule that many other professions have. Outside of regularly scheduled
courses, faculty work can and does happen at any time. The advent of such
technologies as e-mail and the Internet have made this even more so,
whereby faculty can advise students, correspond with colleagues, or conduct
research at any time of day or night. This is also one of the least understood
aspects of faculty work for many non-academics, calling into question the
rigor of faculty work and productivity for these individuals.

71
The preeminence of the discipline in faculty work life. This is perhaps best
illustrated at major research universities where loyalty to the institution or
even to the school/college is clearly secondary to loyalty to the discipline and
its values and mores in determining faculty priorities and behaviors in the
work place. This is contrary to the culture of most other professional fields
(with perhaps the exception of medicine-although that may be changing with
the rapid spread of HMOs and other managed care plans), where
professionals are encouraged to conform to the values and traditions of the
organization by which they are employed. (Layzell, 1999, pp. 15-16)

Each of these characteristics can contribute to the difficulties in accurately
evaluating faculty productivity. Since faculty have a high level of autonomy, much
of the collection of productivity data relies on self-reporting from the faculty
themselves, giving rise to doubt and speculation from outsiders that the information
received is unreliable (Middaugh, 2001). The fact that faculty often do not adhere to
the traditional workday or workweek also makes measurement difficult. Work may
be done at odd hours, from home, or in informal settings so that it may not always be
strictly labeled as “work” to casual observers or even to the faculty themselves. The
third characteristic, citing faculty loyalty to the discipline is valued above the
institution, also creates stumbling blocks to assessing productivity as disciplines or
areas of study often have their own cultures which dictate requirements and
processes within the smaller academic community of a department (Clark, 1997a).

72
Even so, financial pressures from state governments and public calls for
accountability ensure that productivity measures will continue.
Reporting on Faculty Productivity
With the enormous amount of federal and state funding provided to
thousands of institutions each year and the large number of public colleges and
universities managed by the states, government oversight and reporting on the
internal methods of higher education has been mandated to provide accountability
(Allen, 2004; Burke, 2001; Cage, 1995; Colbeck, 2002a; Layzell, 1999; Middaugh,
2001; Ward, 2003). As discussed previously, state legislatures have included
measures and standards of faculty productivity in this reporting. Burke (2001) noted
that of 31 states surveyed, the majority stated that there had either been little to no
impact, or there had been an inability to assess impact.
In the years following legislation, additional cross-institutional data has been
generated by several nation-wide studies. Gullatt and Weaver (1995) gathered data
from 116 institutions representing all national accreditation regions regarding the
measurement and reporting of faculty productivity. Additionally, survey data was
reported from the appropriate governing board of all fifty states. Legislative
mandates to standardize faculty productivity measurement state-wide existed in 14
states. Ten states mandated state-wide faculty productivity measures without a
legislative statute, and the remaining 26 states did not have legislative or supervisory
board directives to standardize the measurement of faculty productivity at the time of
Gullatt and Weaver’s study. Regardless of state mandates, all 116 participating

73
institutions “reported an organized faculty productivity measurement process” (p. 8).
The researchers found that within U.S. institutions of higher learning, faculty
productivity encompasses multiple dimensions.
On average, at least four of the following six dimensions of productivity were
reported based on survey answers from the institution: (a) instructional, (b) advising,
(c) publication, (d) community service, (e) length of university service, and (f)
university service projects. Seventy-seven of the reporting institutions were public
institutions and thirty-nine were private. Again, based on survey responses from each
institution, it was found that a faculty productivity report was more likely to be used
for (a) promotion, (b) tenure, and (c) merit in institutions having larger academic
division status, larger student enrollment, and larger operating budgets. The authors
suggested further research in the areas of significance between availability of
professional development provided by institutions of higher education and the uses
of faculty productivity instruments. The results suggest that larger institutions may
find more benefits from productivity reports, perhaps to deal with a larger volume of
faculty in general, and that the use of productivity instruments warrants additional
study.
Another investigation (Meyer, 1998), examined data on faculty workload
over 15 states, several university / college systems, and three national studies. While
the majority of studies indicated that faculty work long hours (over 40 to 50 hours
per week), the time spent in the actual classroom usually represented a small portion
of work hours. However, depending on what types of activities were included in the

74
definition of teaching, the total amount of time spent on teaching or teaching–related
activities was larger. Using the percentage of total working time that is spent on an
activity is a common way to assess faculty’s effort, but the percentage of time
identified as “teaching” usually does not include all activities associated with
teaching. Longitudinal data from large-scale surveys administered to faculty and/or
administrators indicate that since the late 1970s, time spent teaching has declined,
and time spent in research has increased (Meyer; Green, 1998).
Meyer (1998) suggests that in order to increase public satisfaction,
institutions need to “adjust their missions to align more closely with public
expectations”, the reward structure for faculty must be realigned to support teaching,
and the role of research must be revised (p. 4). Meyer also suggests that “placing
students and their learning needs ahead of faculty preferences will have a profound
impact on all aspects of education” (p. 5). The study highlights the conflicting
nature of state accountabilities which tend to focus on teaching hours and
institutional promotion structures which reward faculty for their research
publications. The results suggest that a balance be found to effectively measure and
meet these competing standards.
What is Productivity Index (PI) and why was it created?
Despite the strong reservations from the faculty committee regarding the
value of quantitative measures of performance, later known to be the Productivity
Index (PI), the School of Education at a private university adapted a model
originally developed for business firms to satisfy the central administration’s need to

75
know how the company is doing and how the company measures up to others
(O’Neil, Bensimon, Diamond & Moore, 1999). The submission of the Annual
Performance Review (Metric of Excellence) was required by the provost’s office
from each school on campus for the fall budget meetings. The provost’s office
provided guidance, but each school had the responsibility of providing the metrics
that were most appropriate for its disciplines within each school.
The results were mixed. Some provided too many metrics, making it difficult
to focus on those key areas that define academic excellence; some provided
mechanisms which overemphasized input measures or relied upon external rankings
as the primary metric of excellence. Faculty dreaded this process each year not
because the report was not taken seriously but because it was seen to be irrelevant.
The performance indicators included were not connected to the decisions that had
been made about program development, enrollment management, or the allocation of
resources (O’Neil, Bensimon, Diamond & Moore, 1999).
This is when the School of Education at a private university took ownership
of the situation and decided to create a “metrics of excellence”. A faculty committee
was put in charge of creating a quantitative system that was to be used 3-5 years, and
which would enable the school to reflect on faculty accomplishments. The
committee turned to literature on organizational performance and assessment for
help in designing an approach that could both capture the complexity of an academic
organization and present a coherent image of the school’s performance. The
committee found a promising framework in Robert Kaplan and David Norton’s

76
“balanced scorecard” approach. Although the balanced scorecard was developed
with business organizations in mind, the framework was adaptable to the unique
characteristics of academic organizations (O’Neil, Bensimon, Diamond & Moore,
1999).
What was created was defined as an academic scorecard to fit the higher
education setting, which was later named the Productivity Index (PI) in School of
Education at a private university, and it consisted of goals and corresponding
measures which were not fixed as the environment changed. Each activity was given
points (quantitative measurement). For example, writing a book, publication for
journals, teaching a course, participating in committees, etc., all had assigned points
according to the three main domains of faculty responsibilities: teaching, research
and service. The assignment of points was determined by the faculty committee with
consultation from the Dean. The faculty productivity profile (40-40-20) would also
depend on the type of position, rank, years of service, etc.
Conclusion
Unlike many employees, faculty are not directly supervised in the traditional
sense (clock-in and clock-out), but may pursue goals and projects somewhat
independently and with little or no supervision. This autonomy has undoubtedly led
to some abuses, and the lack of supervision has led some to believe that professors
are not held accountable for their time and the measurement of their achievements
and productivity (AAUP, 1994). Although the areas of faculty work may be
consistent over time (teaching, research and service), individuals experience change

77
in their distribution of effort and duties across each of these three main
responsibilities.
One might expect to see changes in the balance between teaching and
research from one year to the next, or a growth in confidence with teaching that
might lead to more time spent in instruction, or success in getting grants that might
mean more time spent in research. These changes may be affected by adjustments in
student enrollment, the needs of the discipline, different stages in the faculty career,
and / or individual preference (Gullatt & Weaver, 1995). Ideally, individuals mature
and improve their skills and increase their expertise within the discipline. They may
earn the respect of their peers and be asked to take on other responsibility such as
chairing a committee, redesigning a curriculum, or serving in a professional
organization (AAUP, 1994).
Understanding what contributes to the productivity of faculty is essential to
the academic and financial health of an institution of higher education (Tierney,
1999). However, measuring faculty productivity is often difficult, leaving
administrators with subjective information (Middaugh, 2001). The development of
reliable and valid instruments needed to measure faculty productivity, in turn is
greatly hindered by the problems in defining faculty productivity (Middaugh, 2001).
The theories on faculty work and motivation theories provide a framework for
understanding faculty behavior and what motivates faculty to be productive. In
another words, how faculty spend their time will differ depending on what factors
motivate the behavior and how productivity is defined.

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Defining and measuring faculty productivity are among the most central
issues for quality and accountability in higher education today. As the literature
states, the factors influencing the various definitions of faculty productivity are
faculty rank, institutional mission, academic discipline and type of faculty position.
The influence of each factor varies, especially as faculty productivity is
differentiated among teaching, research and service responsibilities. Faculty
productivity can vary among different institutions; for example, the expectations for
faculty at research universities and community colleges are different. Even among
different disciplines at the same institution, faculty have different expectations. For
example, faculty in the School of Business will have a different set of expectations
than faculty in the School of Theatre. Currently, the definition of faculty productivity
is rather institution-specific and particular to each field of study.
In Chapter 3, the methodological strategy will be described to address the
research questions. How do age, gender, ethnicity, and faculty rank influence faculty
productivity? Do these demographics play any part in influencing faculty
productivity? This paper aims to answer these questions by looking at the data
provided from a Productivity Index (PI) of a professional school at a private research
university. In addition, the statistical factors will be identified and then statistical
techniques will be used to test whether there is a statistical significant difference or
association between the factors.

79
CHAPTER THREE
METHODOLOGY
The objective of this study is to use the Productivity Index (PI) developed by
a School of Education in a private university to examine how faculty report spending
their time. The PI is an annual measure which was used by faculty reporting on how
they spent their time. It was also used to determine raises and promotion, one
outcome of faculty productivity evaluation. Data from the PI collected over a six
year period is used to examine how faculty report spending their time, to determine
which common characteristics, if any, exist among productive faculty, and to detect
what percentage of faculty participate in activities considered highly productive
according to the PI. This chapter includes the study’s research questions and a
description of the research methodology. The latter includes the sampling procedure
and population, instrumentation, and procedures for data collection and analysis.
This study is a secondary data analysis.
Unlike many employees, faculty are not directly supervised in the traditional
sense (clock-in and clock-out), but may pursue goals and projects somewhat
independently and with little or no supervision (AAUP, 1994). This autonomy has
undoubtedly led to some abuses, and the lack of supervision has led some to believe
that professors are not held accountable for their time through measurement of their
achievements and productivity (AAUP, 1994). The purpose of this study is to
examine patterns of faculty productivity in one professional school at a research
university.

80
Research Questions
For the purposes of this study, faculty workload, or how faculty spend their
time will be used as a measure of productivity using a weighted index (the
Productivity Index) of specific tasks and outputs related to the three domains of
teaching, research, and service. This study will analyze information from the PI in
order to establish the extent to which faculty spend their time in accordance with the
school’s expected 40-40-20 (40 percent of their time is spent on teaching activities,
40 percent on research activities, and the remaining 20 percent on service activities)
faculty workload for tenured and tenure-track faculty versus 80-20 (80 percent of
their time is spent on teaching activities and 20 percent on service activities) for
special faculty and to see how the reported activities align with what is considered
productive by the organization. This study will attempt to answer the following
questions:
1. How do faculty in the School of education spend their time on an annual
basis?
a. How does productivity vary according to different faculty
characteristics (gender, ethnicity and rank)?
b. How does each domain of productivity (research, teaching and
service) vary according to rank?
2. What are the highly valued activities of the faculty at this organization
according to the PI?

81
a. Which category among teaching, research and service did the
faculty spend most of their time?
3. What percentage of faculty participate in highly valued activities
according to the PI? What proportion of faculty time is spent on these
highly valued activities?
a. To what extent do ratios align with the standard 40-40-20 faculty
workload for Tenured and Tenure-Track faculty and 80-20 for
Special faculty? Is any category "over represented" or "under
represented" based on what we'd expect for that group (i.e.,
compared to the standard)?
Research Design
This study’s purpose is to evaluate the differences in productivity among
faculty of the School of Education at a large private research university. This study
will evaluate whether or not certain factors contribute to any differences in faculty
productivity across the three areas of faculty responsibility – teaching, research, and
service. This study examines the data collected each January from 2000 to 2005,
representing six years of productivity information for the years 1999 through 2004.
This study will be referring to the actual collection dates of the PI.
This study also examines the relationship between faculty demographic
characteristics (ethnicity, gender, and rank) which may have influenced the PI total
and PI average over six years. The PI total would be the total sum of scores for all
activities over six years. PI average is PI total divided by the number of years (6)

82
participated. This study is a secondary analysis of longitudinal data collected by the
PI. As the research questions being considered are largely comparing groups, a
quantitative approach will be used.
Quantitative analysis can test statistical significance and compare means
between two or more groups using the numerical data. The research questions in this
study focus on the evaluation of relationships among quantifiable variables rather
than more subjective concepts, such as faculty motivation, which do not warrant a
qualitative approach. The data produced from the Productivity Index are primarily
categorical. Due to such an approach, the PI outlines the definition of faculty
production by assigning points to various observable outcomes in the areas of
research, teaching, and service. To provide a brief reference for this point system,
Table 1 gives the highest and lowest assigned values in each of the three categories:
research, teaching and service.
In determining relationships between faculty characteristics and productivity
levels as measured by the Productivity Index, a quantitative design is the best
approach. It will allow us to draw specific conclusions about the data in the study.
The quantitative measures of performance, the PI, at a School of Education in a
private university are adapted from a model originally developed for business firms
to satisfy the central administration’s need to know how the company is doing and
how the company measures up to others (O’Neil, Bensimon, Diamond & Moore,
1999).

83
Table 1
Research, Teaching and Service Activity Points
Research, Teaching or
Service (R,T or S) Point Activity
Research highest point 268 Total amount per year as PI/Co-PI
Research lowest point 0 Conference presentation or discussant other
than AERA/APA
Teaching highest point 312 Number of Quals completed as chair
Teaching lowest point 1 Undergraduate Advisement
Service highest point 96 Journal Referee (1paper-4pt); Conference
Program Referee (3 papers-4pt)
Service lowest point 0 Recruiting Activities

The PI was named and defined as an academic scorecard to fit the higher
education setting in a School of Education at a private university, and it consisted of
goals and corresponding measures which were not fixed as the environment changed.
Each activity was given points (quantitative measurement). For example, writing a
book, publication for journals, teaching a course, participating in committees, etc., all
had assigned points according to the three main domains of faculty responsibilities-
teaching, research and service. The assignment of points was determined by the
faculty committee with consultation from the Dean. The faculty productivity profile
would also depend on the type of position, rank, years of service, etc. O’Neil (1999)
indicated that the PI was created as the quantitative measuring tool for activities

84
(assigned points per activity) that faculty spend their time on to determine annual
performance and productivity.
Population and Sample
All participants are from the School of Education at a private research
university and are faculty of varying ranks. The number of faculty varies from year
to year, due to hire dates, with the first survey being conducted in January 2000
representing activities completed in the 1999 calendar year. Since the PI is
completed by 100% of the school’s faculty, the sample represents the total
population of faculty at School of Education at a private research university. In this
study, the subjects completed the PI from January 2000 to January 2005. A total of
54 participants (tenured, tenure-track and special) were included in the study.
Instrumentation
The Productivity Index (PI) from School of Education at a private research
university was used as an instrument to assess faculty achievement or workload
profile information for the years 1999-2004. The purpose of the PI data collection
was to quantify the work each faculty member successfully accomplished each year.
The PI was developed by the Salary, Promotion and Tenure (SPT) Committee.
Despite the strong reservations from the faculty committee regarding the value of
quantitative measures of performance, the PI was implemented in January 2000
(O’Neil, Bensimon, Diamond & Moore, 1999). The committee found a promising
framework in Robert Kaplan and David Norton’s “balanced scorecard” approach.
Although the balanced scorecard was developed with business organizations in mind,

85
because business firms to satisfy the central administration’s need to know how the
company is doing and how the company measures up to others, the framework was
adaptable to the unique characteristics of academic organizations (O’Neil, Bensimon,
Diamond & Moore, 1999).
For example, self-reported data from the previous calendar year was entered
by the individual faculty member each January. PI is a self-reporting system of
faculty activities followed by verification from the Salary, Promotion and Tenure
(SPT) Committee. The PI data were collected based on an annual calendar from
January to December. Each January, faculty were required to go online and complete
the PI. The scores were then used by the school’s Dean to determine salary increases
for each faculty member. Therefore, the incentives for completion are clear and the
participation rate was 100%. Appendix I contains the instrument that was used
during the Academic Year 2005 the last year the PI was used. The PI is no longer
used and a new evaluation system is in place.
The purpose of the PI data collection was to quantify the work each faculty
successfully accomplished each year. Each activity was assigned points. For
example, the highest point earning activity in PI (teaching category) was 312 points
per year for number of qualifying exams completed as chair. Equivalent effort is
recognized by assigning points for activities that align with school’s strategic goals.
Self-reported points are aligned to increase the US News Rankings, to increase the
grant funding and to have visibility in conferences such as the American Educational
Research Association (AERA). Small modifications were done on the PI every year;

86
the SPT Committee made recommendations and the Faculty Council votes to decide
if the recommendations of the committee would be used to modify the PI for the next
year. For example, the SPT Committee suggested adding service points to account
for role of the Associate Deans, and the faculty voted to implement that change. The
faculty also voted to add training grants for non-tenure track faculty as a service
component.
Dependent Variables
Dependent variables are those values that change as a consequence of
changes in other values in the system. The dependent variables in this study are
explained in detail below.
Overall Productivity. In examining the PI as it was used, points are assigned
for reported instances of activities in the PI. Overall Productivity is be the sum of
total points assigned for a given individual or year. For each of the productivity
items, the PI includes an Index Code, Index Caption, Main Category Caption, Item
Year, and Item Submitted Points. The Index Code is a shorthand reference to the
item such as “R1”. Research items begin with an “R”, Teaching items begin with a
“T”, and Service items begin with an “S”. The accompanying numbers refer to the
specific item activity. The Index Caption is a short description of the specific item
activity, such as “Development of a new course”. The Main Category Caption
indicates which area the item falls under with values of “Research”, “Teaching”, or
“Service”. The Item Year indicates which calendar year the item was performed
with values from 2000 to 2005. Finally, the Item Submitted Points indicates the

87
relative value of the item with values ranging from 0 to 312. The maximum points
per activities are R (research) = 268, T (teaching) = 312, S (service) = 96 and the
maximum Total = 312.
Average PI. The Average PI is determined by dividing the Overall
Productivity by the number of reported participation years from 2000 to 2005. This
will vary depending on the number of years a given faculty member worked for the
department.
Research Productivity. The score for Research Productivity will be a
composite of PI points for items with a Main Category Caption value of “Research”.
Research items are related to various publications, conference presentations, and
other recognitions of scholarly work, as well as the awarding of contracts and grants
related to research study. Research Productivity values are calculated based on
summing the points for all research items. There are 45 distinct Index Codes for the
research variable, as listed in Table 2.

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Table 2
Index Captions for Research Category
Index
Code Index Caption
R1 Sole author of a book
R2 Co-author of a book
R2a First co-author of a book
R3 Sole editor of a book
R4 Co-editor of a book
R4a First co-editor of a book
R4b Author of standardized test manual
R5 Sole author of a book
R6 Co-author of a book
R7 Sole editor of a book
R8a First co-editor of a book
R9 Sole author of a book chapter or monograph
R10 Co-author of a book chapter or monograph
R10a First co-author of a book chapter or monograph
R11 Sole author of a major article
R11a First co-author of a major article
R12 Co-author of a major article; sole author of an invited paper
R13 Sole author of a book review or minor article
R14 Co-author of a minor article or book review
R15 Author or co-author of unrefereed technical report; article in state journal or newsletter
R16 Conference presentation or discussant at AERA/APA
R16a Conference presentation or discussant other than AERA/APA but at the single prestigious conference of the
discipline
R17 Conference presentation or discussant other than AERA/APA
R18 Recognition by national professional organization; recognition and/or honors by USC
R19 Fellow in national organization
R20 Distinguished appointment
R21 Other distinguished appointment
R22 Invited or keynote prestigious address
R23 Lesser professional recognition
R23c Presentation at non-education conference
R23d Publication in non-education journal
R24 Other
R25 Total amount per year as PI/co-PI
R26 Indirect cost recovery per year as PI/co-PI
R26a Equivalent contribution to SCHOOL OF EDUCATION operations from contracts/grants without overhead
R27 Written contribution on a funded research contract/grant proposal
R28 Proposal is focused on University/SCHOLL OF EDUCATION mission statement
R28a Co-Principal Investigator, outside SCHOOL OF EDUCATION or University
R28b Working on a contract/grant, outside SCHOOL OF EDUCATION or University
R28c Written contribution to funded research contract/grant proposal with outside SCHOOL OF EDUCATION or
University members
R29 Other
R30 Major contributor
R30a Co-Principal Investigator, outside SCHOOL OF EDUCATION or University
R30b Team member on a contract/grant, outside SCHOOL OF EDUCATION or University
R30c Written contribution to research grant proposal with outside SCHOOL OF EDUCATION or University members

89
Teaching Productivity. The score for Teaching Productivity is a composite
of PI points for items with a Main Category Caption value of “Teaching”. Teaching
items are related to teaching courses, development of programs and curricula, and
academic advisements, as well as field supervision of students. Teaching
Productivity values are calculated based on summing the points for all teaching
items. There are 44 distinct Index Codes for the teaching variable, as listed in Table
3.

90
Table 3
Index Captions for Teaching Category
Index
Code Index Caption
T1 Public honor for teaching
T1a University academic appointment outside SCHOOL OF EDUCATION
T2 Excellence in teaching
T3 Taught a course that was rated >4.50 and <4.70 on item 11 of the student evaluation form
T4 Taught a course that was rated >3.00 on item 11 of the student evaluation form
T4a Supervisor of TAs for large classes
T4b Had a total of at least 25 student enrollments per class
T4c Use of educational software in course
T4d Team teach outside SCHOOL OF EDUCATION
T5 Chair or co-chair a committee to develop a new program or major program revision
T5a Member of committee to develop new program
T6 Receipt of instructional improvement grant
T7 Development of a new course
T8 Creation of educational software
T8a Creation of cooperative degree with outside SCHOOL OF EDUCATION school
T8c Cross-listed course
T9 Number of dissertations completed as chair
T9a Thesis chair master's theses
T10 593b or 690 load
T12 Undergraduate advisement
T14 M.A. advisement
T14a Ed.D. or Ph.D. advisement prior to assignment of dissertation supervisor
T16 Best Dissertation Award for student under supervision
T17 Recommendation by Program for Best Dissertation Award
T18 Number of quals completed as member
T18a Number of quals completed as chair
T19 Mentoring (formally appointed) a junior faculty member
T20 Number of dissertations completed as member
T20a Committee service on SOE Ph.D./Ed.D. committees
T21 Serving on non-SOE Ph.D./Ed.D. committees
T22 Inclusion of students as co-authors of a published work
T23 Fund a post-doctoral student
T24 Had a total of at least 80 student enrollments per year
T25 Inclusion of student on conference paper
T25a Supervisor of students in field components, one per student
T26 Other
T27 Total amount per year as PI/co-PI
T28 Indirect cost recovery per year as PI/co-PI
T28a Equivalent contribution from grants without overhead
T28c Working on a contract/grant, outside SCHOOL OF EDUCATION or University
T29 Written contribution on a funded training grant proposal
T30 Major contributor
T30a Co-Principal Investigator, outside SCHOOL OF EDUCATION or University
T30c Written contribution to research grant proposal with outside SCHOOL OF EDUCATION or University members

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Service Productivity. The score for Service Productivity is a composite of PI
points for items with a Main Category Caption value of “Service”. Service items are
related to participation in various committees, journal editing, supporting recruiting
efforts, and being a guest lecturer, as well as providing consulting assistance to
relevant organizations. Service Productivity values are calculated based on summing
the points for all service items. There are 48 distinct Index Codes for the service
variable, as listed in Table 4.

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Table 4
Index Captions for Service Category
Index
Code Index Caption
S1 Chair, School committee or School search committee
S2 Serve as one of four SCHOOL OF EDUCATION program heads
S3 Chaired the SCHOOL OF EDUCATION faculty council
S3a Member of faculty council
S4 Serve as an SCHOOL OF EDUCATION center mentor
S5 Directed a funded institute (>50K) or large-scale (>50K) SCHOOL OF EDUCATION project
S6 Member, School committee(s)
S7 Chaired an SCHOOL OF EDUCATION ad hoc committee or task force
S8 Served as an SCHOOL OF EDUCATION specialty chair
S9 Recruitment activities
S9a Attend SCHOOL OF EDUCATION recruitment meeting
S9b Attend off-site recruitment meeting
S9c Host prospective students
S9d Exceptional credential-related activities
S10 Other
S11 Chair, major University committee, including search committee
S12 Member of one or more University committees
S12a USC Senate Representative
S13 Other
S14 Editor-in-chief of national or international journal
S15 President of national or international professional organization
S16 Officer or board member - national or international organization; president - regional organization
S17 Committee chair of international, national, or regional organization
S18 Associate editor of national journal; newsletter editor
S19 Appointment to prestigious panel
S20 Committee member of national or regional organization, editorial board member
S21 Journal referee (1 paper - 4 points); conference program referee (3 papers - 4 points)
S22 Workshop organizer or presenter; consultant activity
S23 Organized national/international professional conference
S24 Invited lecturer at another university
S25 Invited lecturer on sabbatical
S26 Continuing education course
S26a Tenure/promotion review for other universities
S27 Other
S28 Consultant to public schools and colleges
S29 Consultant to state or federal government
S30 Consultant to nonprofit organization
S31 Consultant to for-profit organization
S32 Consultant to international organization
S33 Other
S34 Public honors
S35
Testimony provided by faculty to policy-making bodies including local, state and federal legislators and executive branch policy
makers
S36 Letters to the Editor and Op Ed pieces to newspapers
S37 Media appearances as an expert witness on news programs, in newspaper, etc.
S38 Appointment to policy-making commissions, committees, boards, etc.
S39 Request to author or assist in RFPs from foundations and government agencies based on faculty research
S40 School district, college trustee
S41 Other

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Independent Variables
Independent variables are inputs to a statistical analysis which are then used
to predict the behavior of the dependent variables. For this reason they are also
known as "predictor variables" or "explanatory variables." The following are the
independent variables in the PI.
Gender. While much of the research related to gender and faculty has
focused on the relative numbers of faculty appointments and compensation, previous
studies have concluded that men are generally more productive than women,
particularly at the highest levels of productivity in research publications (Middaugh,
2001). Gender is included in the PI.
Ethnicity. The majority of research related to ethnicity does not focus on
faculty productivity, but rather relative number of faculty appointments,
compensation, etc. (D’Souza, 1991). Ethnicity is included in the PI and collected as
values of “African American” (3 respondents), “Asian” (2 respondents), “Caucasian”
(40 respondents), “Hispanic” (8 respondents), or “Unknown” (1 respondents). The
faculty with unknown values are being excluded from the analysis of ethnicity.
Rank. Academic Rank is often tied to age, with a few studies indicating that
faculty in higher ranks are less productive than their junior counterparts, while others
suggest that faculty in higher ranks may merely be more efficient in their work
(Sutton & Bergerson, 2001). Rank is included in the PI and collected as values of
“Assistant”, “Associate”, “Professor”, and blank. The faculty with blank values are
excluded from the analysis of academic rank.

94
Time. While studies have examined the differences in productivity of
separate groups related to age or academic rank, it is difficult to find those which
observe the changes in productivity for an individual over time (Layzell, 1999;
Middaugh, 2001). The data collected in the PI represents up to six years of
productivity information for each individual faculty. Time is captured in the PI as
Item Year with productivity points assigned to each year from 2000 to 2005. The
data used represent six years of productivity information for the years 1999 through
2004. This study will be referring to the actual collection dates of the PI.
Years of Service. Much of the research related to faculty productivity
comparisons over time focuses on age as well as academic rank or tenure status
rather than actual years of service within an institution (Green, 1998). This study
attempts to provide data related to this variable. The PI includes items for both Hire
Date and Term. The Hire Date indicates when the faculty member became affiliated
with the institution. The Term indicates the number of academic years the faculty
member has been active at the institution. The Hire date and Term is used to
determine a faculty member’s Years of Service.
Administrative Role. The literature review did not reveal any studies which
analyzed the nature of a faculty member in an administrative role at the institution
with relation to productivity. Faculty who have the service items of “Serve as one of
four School of Education program heads” or “Served as an School of Education
specialty chair” for a given year are considered to have an administrative role for that
year.

95
Data Collection
The data were collected online each January from all faculty in the school.
The faculty were given directions regarding the website where they must login and
enter their information. Each faculty individually enters his or her own information
regarding number of doctoral students mentored, number and type of publications
produced, course evaluation scores, total grant dollars, service on committees,
community services, roles in administration of the school, and other activities in the
teaching, research, and service areas. The information entered through the web-based
forms was then stored in a database maintained by the school.
For this study, the data collected has been coded and input using the
Statistical Package for the Social Sciences (SPSS) Version 19 (SPSS Inc., 2010). It
was expected that the majority of the PI data will produce discrete data, while there
may be areas that contain continuous data. For example, faculty research activities
might be discrete data. However, faculty teaching activities may stay the same from
the previous year making it continuing data. This study examines the data collected
each January from 2000 to 2005. The data used represent six years of productivity
information for the years 1999 through 2004. As previously stated, records do not
exist for all faculty in all years due to changes in employment.
Data Analysis
Data analysis is conducted using the Statistical Package for Social Sciences
(SPSS). This analysis and an accompanying descriptive analysis is used to answer
the research questions stated above. The sample analyzed in this study is based on

96
data from the Productivity Index for the years 2000-2005. This data represents 6,908
discrete productivity items for 54 faculty members. The data has been examined
with information reported for the independent variables of interest. This information
includes the total number of faculty participants (54) as well as yearly totals of
faculty participants; the frequencies and percentages of gender, ethnicity, rank, and
tenure status; averages and ranges for age and years of service based on date of birth
and hire date; and total and yearly averages and ranges for productivity points
assigned in teaching, research, and service.
For the first research question, the distribution of faculty’s time is examined
to find out how faculty spend their time on an annual basis. Several independent
variables have been examined to determine which characteristics influence faculty
productivity. The categorical independent variables examined are gender, ethnicity,
and faculty rank. To determine if any productivity differences exist based on gender,
a t-test will be performed for the Overall Productivity scores of men and women, as
well as for the separate scores of Research Productivity, Teaching Productivity, and
Service Productivity.
To determine if any productivity differences exist based on ethnicity, an
analysis of variance (ANOVA) was performed for the Overall Productivity scores of
faculty with a reported ethnicity value, as well as for the separate scores of Research
Productivity, Teaching Productivity, and Service Productivity. Similarly, to
determine if any productivity differences exist based on faculty rank, an ANOVA
was performed for the Overall Productivity scores of faculty who are tenured or

97
tenure-track, as well as for the separate scores of Research Productivity, Teaching
Productivity, and Service Productivity.
To examine the effects of quantitative rather than categorical variables in the
first research question, a t-test and ANOVA was conducted to compare the total PI
score among gender, ethnicity and rank. A t-test was used to compare the overall
means of productivity for each group as well as the means in each of the three
individual areas. I examined how faculty distribute and spend their time among
those categories. For the first research question, regression was used to examine the
relationship between total productivity and the independent variables. The
regression analysis helps explain how much of the variance in the dependent
variables (total PI score and average PI score) can be explained by each of the
independent variables (ethnicity, gender and rank). Table 5 lists the statistical
methodologies that will be used in each of the research questions.
To examine the second research question, a descriptive analysis and
correlation is used. The total PI score and total scores of research, teaching and
service are compared in order to examine which category earned most points. Each
category will be summed and the total points earned in each category will be
compared with each other. The dependent variable is total productivity (measured by
the total PI score). The independent variables are the Teaching, Research and
Service components of the total score. The second research question examines how
total productivity is influenced by teaching, research and service.

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Table 5
Research Questions Methodology
RQ# Methodology
RQ1 Gender: t-test
Ethnicity: ANOVA
Rank: ANOVA
Tenured &Tenure-Track vs. Special
Research: t-test
Teaching: t-test
Service: t-test
Tenured & Tenure-Track
Among rank: regression
RQ2 Descriptive Analysis
Teaching vs. Research: correlation
RQ3 Descriptive Analysis
Faculty workload profile: Chi-square

To investigate the third research question, descriptive analysis and Chi-
square tests are used. The third research question is investigated using simple
percentage calculations to see what percentage of faculty are spending the majority
of their time in the highly productive activities as identified by the statistical
analysis. The analysis will be completed for Overall Productivity, as well as with the
separate scores of Research Productivity, Teaching Productivity, and Service
Productivity.

99
This chapter includes the research questions and research design. This study
is a secondary data analysis of the Productivity Index, quantification of faculty
achievement. The research design includes the sampling procedure and population,
instrumentation, and procedures for data collection and analysis. Results of the
analyses described above are discussed in Chapter 4.

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CHAPTER FOUR
RESULTS
In the pursuit of objective of this study, the Productivity Index (PI) developed
by a School of Education in a private university was used to examine how faculty
report spending their time. The PI was an annual measure of how faculty reported
spending their time which was used to determine raises, and as such was a means of
determining how faculty distribute their time. Data from the PI collected over a six
year period was used to examine how faculty report spending their time, to
determine which common characteristics, if any, exist among productive faculty, and
to detect what percentage of faculty participate in activities considered highly
productive according to the PI.
For the purpose of this study, faculty workload (how faculty spend their time)
was used as a measure of productivity using a weighted index, the PI, of specific
tasks and outputs related to the three domains of teaching, research, and service. This
study analyzed information from the PI in order to establish the extent to which
faculty spend their time in accordance with the school’s expected 40-40-20 faculty
workload for tenured and tenure-track faculty (40 percent of their time is spent on
teaching activities, 40 percent on research activities, and the remaining 20 percent on
service activities), and to see how the reported activities align with what is
considered productive by the organization.
This study used the secondary data analysis and descriptive analysis. The
data collected was coded and input using the Statistical Package for the Social

101
Sciences (SPSS) Version 19 (SPSS Inc., 2010). For the first research question, the
PI total (total points of teaching, research and service) and PI average (total points of
teaching, research, and service divided by the data entry of number of years) was the
dependent variable. The independent variables were ethnicity, gender and rank. A t-
test was used to determine if there were significant differences between the males
and females in terms of how they spent their time. An ANOVA was used to
determine if faculty vary their time spending on average according to rank
(Unknown, Associate and Full rank). Regression was also used to see if rank had any
significance in predicting scores. An ANOVA was used to analyze whether faculty
vary their time spending according to their ethnicity. Descriptive statistics were also
used to analyze the ethnicity. It is important to mention that a substantial majority of
the faculty, 74%, reported as Caucasian. Due to the data being skewed, the statistical
analysis might be biased, therefore, it is helpful to examine the descriptive statistics.
For the first research question, how faculty distribute and spend their time
among the research, teaching and service categories were examined by comparing
the total sum of scores in each category. The highest score out of the three
categories is to be considered to be the most highly valued of activities. Most faculty
time is spent on the activities considered most valuable at the organization. For the
second research question, mainly descriptive statistics are used. Basic correlation
was also used between teaching and research scores (overall group, tenured and
tenure-track faculty, and special faculty) to see if any relationship between the two
categories exist. Questioned to be examined: what are the activities that received the

102
highest scores? Is there a trend in faculty who received the high score activities or
are there some outliers? The result of any positive correlation between teaching and
research scores means that the two categories are aligned.
The third research question also uses mainly descriptive statistics to
investigate what percentage of faculty are spending the majority of their time in the
highly valued activities. Is there any “over-represented” activity versus “under-
represented” activity? A combination of descriptive statistics and Chi-square
analyses was used to see if the 40-40-20 (tenured and tenure-track faculty) and 80-20
(special faculty) expected faculty workload is met.
The following codes were entered into SPSS for data analysis:

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Table 6
Demographic Codes
Item Coding N Percentage
Faculty 1 to 54 54 100%
Gender 0 = Male 35 65
1 = Female 19 35%
Ethnicity 1 = African American 3 5%
2 = Asian 2 4%
3 = Caucasian 40 74%
4 = Hispanic 8 15%
5 = Unknown 1 2%
Rank 0 = Unknown TT 5 16%
1 = Assistant TT 0 0%
2 = Associate TT 9 29%
3 = Full TT 17 55%
4 = Unknown NTT 2 9%
5 = Assistant NTT 9 39%
6 = Associate NTT 3 13%
7 = Full NTT 9 39%
Tenure Status 0 = Special 23 43%
1 = Tenure/ Tenure-Track 31 57%

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Descriptive Statistics
In this study, a total of 54 participants completed the Productivity Index (PI)
in January between 2000 and 2005. All participants were from the School of
Education at a private research university and were faculty of varying ranks and
types. The sample size (number of faculty) is 54. The first survey was conducted in
January 2000, collecting information for the activities completed in the 1999
calendar year. Since the PI was completed by 100% by the school’s faculty, the
sample represents the total population of faculty at School of Education at a private
research university each year.
The average age of faculty members was 56 years old. The range of age was
from 29 to 76 years old. The gender distribution was 35% female and 65% male. The
ethnicity was distributed among African American (5%), Asian (4%) and Unknown
(2%) with little significance. However, at 74%, Caucasian has a significantly higher
percentage than any other ethnicity. Hispanic makes up second largest ethnicity with
15%. The rank has the following breakdown: the highest percentage of Full
Professor (48%) to Associate Professor (22%) to Assistant Professor (17%) to
Unknown (13%) respectively. For tenured and tenure-track faculty, there are no
designation of Assistant rank but five of Unknown rank. Unknown rank may be the
Assistant rank but this cannot be assumed to be true since it cannot be confirmed.
For the special faculty, there are two of Unknown rank. Lastly, 57% of the faculty
were in tenured or tenure-track positions; 43% of faculty were in non-tenure / special
track positions. Table 7 lists the demographic information of the total population.

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Table 7

Demographic Information (Total N=54; Year 2000-2005; Average Age=56)
Item N Percentage
Total Sample 54 100%
Total TT = 31 57%
Total NTT = 23 43%
Gender Male = 35 65%
Female = 19 35%
Ethnicity African American = 3 5%
Asian = 2 4%
Caucasian = 40 74%
Hispanic = 8 15%
Unknown = 1 2%
Rank Unknown = 7 13%
Assistant = 9 17%
Associate = 12 22%
Tenured/Tenure-Track Full = 26 48%
TT Unknown = 5 16%
TT Associate = 9 29%
TT Full = 17 55%
Non-Tenure Track/ Special NTT Unknown = 2 9%
NTT Assistant = 9 39%
NTT Associate = 3 13%
NTT Full = 9 39%
Tenure Status Non-Tenure Track/Special = 23 43%
Tenure/ Tenure-Track = 31 57%

106
The total number of PI points earned among the 54 faculty members was
60,023. There was no total maximum points since each faculty member could list as
many activities as he / she wanted and earn points for those activities. Table 1 in
Chapter 3 lists the highest and lowest points earned in each of the categories
(research, teaching and service) in the PI data. Each activity in the research, teaching
and service categories have pre-assigned points. The total research score was 17,581;
the total service score was 17,865; and the total teaching score was 24,577
respectively. The average score (total PI score / 54 participants) was 1,112. Out of
the 54 total observations, 24 (44%) had the total PI score greater than the overall
average. Among those 24, 8 (33%) spent the most time on research, 6 (25%) spent
most time on service and 10 (42%) spent most time on teaching. Table 8 lists the
breakdown of the PI scores.

107
Table 8
PI Scores
Item PI Score N Percentage
Total 60,023 54 100%
TT Total 43,743 31 73%
NTT Total 16,280 23 27%
Research 17,581 54 100%
TT Research 14,305 31 81%
NTT Research 3,276 23 19%
Service 17,865 54 100%
TT Service 13,748 31 77%
NTT Service 4,117 23 23%
Teaching 24,577 54 100%
TT Teaching 15,690 31 64%
NTT Teaching 8,887 23 36%
Overall Average 1,112 54 100%
TT Average 1,411 31 100%
NTT Average 273 23 100%
> Overall Average >1,112 24 44%

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Among the 31 tenured and tenure-track faculty, 13 (42%) spent most of their
time on research, 8 (26%) spent most of their time on service, and 10 (32%) spent
most of their time on teaching. Among the 23 special faculty, 4 (17%) spent most of
their time on research, 4 (17%) spent most of their time on service, and 15 (66%)
spent most of their time on teaching. The disaggregated total PI score of tenured and
tenure-track faculty was 43,743. The total score can be divided into the research
score of 14,305 (33%), service score of 13,748 (31%), and teaching score of 15,690
(36%). Therefore, even with higher number of faculty spending most of their time on
research, teaching had the higher PI score among tenure and tenure-track faculty. For
special faculty, total PI score was 16,280; research score of 3,276 (20%), teaching
score of 8,887 (55%) and service score of 4,117 (25%).
In examining the tenured and tenure-track faculty more closely, Unknown
rank had total PI score of 1,798, research score of 345 (19%), teaching score of 775
(43%) and service score of 678 (38%). Associate Professor rank had a total PI score
of 11,224, a research score of 3,946 (35%), a teaching score of 4,033 (36%) and a
service score of 3,245 (29%). Full Professor rank had total PI score of 30,721,
research score of 10,014 (33%), teaching score of 10,882 (35%) and service score of
9,825 (32%). Out of the 31 tenured and tenure-track faculty, 5 were Unknown rank.
Among the Unknown rank, 2 faculty members spent most of their time on research,
2 faculty spent most of their time on teaching and 1 faculty member spent most of
their time on service. Among the 9 of Associate Professor rank, 5 faculty spent most
of their time on research, 2 faculty sent most of their time on teaching and 2 faculty

109
spent most of their time on service. Even though more number of faculty spent most
of their time on research, the total teaching score was higher than the total research
score. Among 17 of the Full Professor rank, 6 faculty spent most of their time on
research, 6 faculty spent most of their time on teaching and 5 spent most of their time
on service.
Reporting of Results and Analysis of Data
This study’s purpose is to evaluate the differences in productivity among
faculty of the School of Education at a large private research university. This study
evaluated whether or not certain factors contribute to any difference in faculty
productivity across the three areas of faculty responsibility – teaching, research, and
service. This study also examined the relationship between faculty demographic
characteristics (gender, ethnicity and rank) and the PI total and PI average over the
years the measure was used. This study is a secondary analysis of longitudinal data
produced by the PI.
Results for Research Question #1
The first research question examines the faculty characteristics (gender,
ethnicity and rank) to see if any significant difference is detected in how faculty
spend their time. A T-test was run to see if there was any significant difference
between males and females in total and average PI scores. The results indicated that
there was no significant difference between males and females on the total PI scores.
The results indicated that there was also no significant difference between males and

110
females on the average PI scores (male PI total = 39,235, male PI average mean =
1,121, female PI total = 20,786, female PI average mean = 1,094).
ANOVA was run to see if any significance existed in ethnicity (as coded
above) with total PI and average PI score. The results indicated that there was no
significant difference among ethnicity. The sample was not very diverse and
substantial majority of the faculty, 74%, reported being Caucasian.
ANOVA was run to see if any significance existed in rank (as coded above)
with total PI and average PI score. The ANOVA test revealed that the rank had a
significant impact on the total PI score (F (3,53) = (6.056), p = .001). When the
specific comparisons were examined, the ANOVA results in Table 4 indicated that
there was a significant difference between the full professor and the unknown rank
group with (F (3,53) = (4.720), p = .006). This demonstrates that the total PI score is
affected by the faculty rank. From the descriptive statistics, the full rank professors
did more activities to contribute to a higher overall PI score. Out of 17 Full rank
professors, 6 spent most of their time on research, 6 spent most of their time on
teaching and 5 spent most of their time on service.

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Table 9
ANOVA Tables: The mean difference is significant at the 0.05 level
PI Total Sum of Squares df Mean Square F Sig.
Between Groups 11507714.232 3 3835904.744 6.056 .001
Within Groups 31667983.616 50 633359.672
Total 43175697.848 53

PI Total Sum of Squares df Mean Square F Sig.
Between Groups 270608.949 3 90202.983 4.720 .006
Within Groups 955452.170 50 19109.043
Total 1226061.119 53

Within the tenured and tenure-track subsample, regression was used to
examine whether rank predicts the scores on research, teaching, service, and the total
PI score. The results showed that rank explained 15% of variance in faculty’s
research scores (R
2
= .15, F(1,29) = 5.16, p < .05), 25% of variance in faculty’s
teaching scores (R
2
= .25, F(1,29) = 9.77, p < .05), 28% of variance in faculty’s
service scores (R
2
= .28, F(1,29) = 11.41, p < .05), and 28% of variance in the total
PI scores (R
2
= .28, F(1,29) = 11.45, p < .05). Rank was a positive and significant
predictor of the research score (β = .38, p < .05), the teaching score (β = .50, p < .05),
the service score (β = .53, p < .05), as well as the total PI score (β = .52, p < .05).

112
This indicates that there are positive relationships among increase in rank and
increase in research, teaching, service and total scores. Therefore, higher rank means
higher research, teaching, service and total scores.
After disaggregating the research, teaching and service scores by faculty
type (tenured and tenure-track versus special), T-tests were run to see if difference
existed between the two groups. A series of T-tests revealed that, compared to
special faculty, tenured and tenure-track faculty scored higher on research (M
Tenure
=
461.45, M
Special
= 142.43, t(52) = 2.89, p < .05) and also on service (M
Tenure
=
443.48, M
Special
= 179.00, t(52) = 3.79, p < .000). However, there was no significant
difference between the two groups when it comes to teaching (M
Tenure
= 506.13,
M
Special
= 386.39, t(52) = 1.19, p > .10).
Results for Research Question #2
The most highly valued category of activities among faculty may be
determined by examining the total number of points earned. Highest points earned in
a category would be considered highly valued, therefore, the most time was spent on
that category. The total number of PI points among 54 faculty was 60,023. Among
the total PI points, the total research points were 17,581 (29%); total service points
were 17,865 (30%); and total teaching points were 24,577 (41%). Teaching was the
most highly valued activity and the activity on which faculty spent most of their time
among research, service and teaching.
The total number of PI points among 31 tenured and tenure track faculty was
43,743. Among the total PI points of tenured and tenure-track faculty, the total

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research points were 14,305 (32%); total service points were 13,748 (31%); and total
teaching points were 15,690 (35%). Teaching was the most highly valued activity
among the tenured and tenure-track faculty, and the activity on which faculty spent
most of their time among research, service and teaching.
The total number of PI points among 23 non-tenure-track or special faculty
was 16,280. Among the total PI points of non-tenure-track or special faculty, the
total research points were 3,276 (20%); total service points were 4,117 (25%); and
total teaching points were 8,887 (55%). Again, teaching was the most highly valued
activity among Special faculty, and the activity on which faculty spent most of their
time among research, service and teaching.
When the whole sample is considered, the correlation between research and
teaching scores is positive (r = .37, p = .006), which means that the two are aligned.
However, when the sample is split up into tenured and tenure-track versus special
faculty, different result emerges. Specifically, for tenured and tenure-track faculty,
the correlation between research and teaching still remains positive and significant (r
= .52, p < .05). In contrast, for special faculty, the correlation becomes non-
significant (r = -.04, p > .10).
These results suggest that for tenured and tenure-track faculty, the higher the
teaching productivity, the higher the research productivity, and vice versa, whereas
for special faculty there is no relationship between teaching and research. The results
further suggest that since the productivity patterns differ between tenured and tenure-
track versus special faculty, putting the two faculty subsamples into one can produce

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diluted results. Hence, it is important that two subsamples are explored
independently as well.
Results for Research Question #3
The average total PI score (total PI score / 54 participants) was 1,112. Out of
the total sample size of 54, 24 (44%) had the total PI score above the overall average.
Those faculty members with average PI score above the average total PI score
(1,112) are categorized as highly productive faculty. Among those 24 highly
productive faculty, 10 (42%) spent most time on teaching, 5 (21%) spent most time
on service and 9 (37%) spent most time on research. Teaching was again the most
highly valued activity among the highly productive faculty. Even though the tenured
and tenure-track faculty had the highest score in teaching, 13 faculty spent most of
their time on research; 8 on service; 10 on teaching. For special faculty, teaching had
the highest score: 4 spent most of their time on research, 4 spent most of their time
on service and 15 spent most of their time on teaching.
Using the disaggregated scores, some Chi-square tests were run to see how
tenured and tenure-track faculty and special faculty are distributing their time and
also to see if they are meeting the expectations of 40-40-20 or 80-20 faculty work
load respectively. For tenured and tenure-track faculty, the ratio between research,
teaching, and service is 33-36-31. A Chi-square test rejected the null hypothesis that
this ratio is equal to the standard distribution of 40-40-20 (χ2(2) = 3,625.95, p <
.001). It appears that service is strongly overrepresented, while research and teaching
are somewhat underrepresented among tenured and tenure-track faculty. For Special

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faculty, the ratio between teaching, research, and service is 55-20-25. A Chi-square
test rejected the null hypothesis that this ratio is equal to a distribution of 80-0-20
(χ2(2) = 7,351.23, p < .001). It appears that teaching is strongly underrepresented,
while research and service are both somewhat overrepresented among non-tenure-
track faculty.
Results from Interviews: How did PI evolve?
In an interview with a faculty member who was one of the original members
of the faculty committee in charge of creating the academic scorecard, he indicated
that the Productivity Index (PI) was created as the quantitative measuring tool for
activities (assigned points per activity) that faculty spend their time on to determine
annual performance and productivity.
During calendar year 2009, the Productivity Index (PI) was changed to
Faculty Annual Performance Review (FAPR). The Vice Dean of Research in a
School of Education at a private university, was instrumental in changing the PI
(quantitative measurement) to the current FARP (qualitative measurement). When
the Vice Dean was appointed to the Faculty Council Chair in 2006, he had the
opportunity to examine the PI and observed the gaming behaviors of faculty. He
described the “gaming behaviors” as some faculty taking advantage of the
quantitative system to add activities and points which would work to some faculty’s
advantage. Although the PI was created to attempt to standardize the faculty
assessment process, some faculty used the system for personal gains.

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In an interview, the Vice Dean (2012) stated when he first examined the PI in
2006 “the review was a nice concept but the PI at the time was assigning points to
any activities that did not look substantial”. One of the benefits of having goals and
corresponding measures unfixed was in consideration of the changing environment,
however, now the unfixed set of goals became a flaw in the system. With the
“gaming behaviors”, faculty added any activities they saw deemed important and
took their time away instead of taking routine responsibility as a faculty member.
The Vice Dean explained that after each annual review, faculty were able to suggest
additional activities to add to the PI for the following year. For example, some
faculty suggested to add attending commencement ceremony as one of the activities
and wanted to assign points for it. Others suggested individual meetings (office
hours, etc.) with students as additional activities and wanted to assign points for it.
There was no consistency and lacked clear reasons for adding the activities.
According to the Vice Dean, “Conceptually, PI was a nice idea but what
happened over time is that every daily routine activity became points on the PI”. It
especially became a concern when it was determined that the system put new faculty
at a disadvantage for new. It was hard to categorize what counted where, etc. For
example, if a faculty member forgot or put an activity in the incorrect place, that
activity did not receive any points. However, the faculty who put it on the correct
place received points. The biggest issue was that the faculty council did not identify
and inform the faculty of these errors.

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Another faculty committee was created to examine the PI and looked at the
alternatives. The new system came about after an examination of the School of
Social Work’s system. The new system is not a quantitative report of activities but
rather, the new system is to report quality of activities with narrative from the
faculty. The faculty tend to have high expectations of themselves. As the vice Dean
said, “Faculty all think that they are above the average”. The new system may be less
precise than the PI and does not use any quantitative measures but the “gaming”
cannot happen.
The new system in place now is called the Faculty Annual Performance
Review (FAPR) and it is pre-populated with most activities such as courses taught,
committees serviced, etc. The faculty must also answer some serious questions for
themselves. For example, reflective questions such as ‘Why do you deserve merit?’
are asked and the faculty must provide qualitative answers. The reality is, from an
administrative point of view, peer review is limited. As the Vice Dean put it,
“Faculty are autonomous, biased and subjective”. However, the new system is based
on merit and cost of living, rather than on automatic raises or salary schedule.
According to the Vice Dean, the new system tends to favor senior faculty as
well. It overvalues some activities and de-values others. It is very hard to weigh the
importance with diverse faculty population. However, it is more holistic and
reflective than the PI system. Faculty will take time and reflect on their past year’s
activities and determine how they may have spent their time. In the near future, the
new system would ideally provide formative feedback to individual faculty. That has

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not been put into place yet but the consideration is in the pipeline according to the
Vice Dean. In the new system, the activities are listed by the faculty in the categories
of teaching, research and service. However, the judgment of what counts is up to the
faculty committee. Some senior faculty may feel that formal feedback would not be
necessary but it does not make any sense that anyone would go through this much
trouble and not share the findings.
The Vice Dean feels that there should be a hybrid between the old and the
new system. He guesses that the new system will be in place for few years and it will
degrade as well. However, he has yet to find other alternatives that might be better.
As an economist, he can easily see the advantage of a quantitative system. However,
after seeing the implementation of the PI and integrity of the implementation, he
quickly worked to find an alternative system. The full faculty body voted with 75%
voting to implement the new system. One group of faculty, some of whom were
involved with the creation of the PI, did try to stop the implementation of the new
system, but failed. The new system was implemented in 2009. The Dean ultimately
makes the recommendation to the provost based on results of the FARP and the
provost will make the final decision on faculty annual reviews.
Conclusion
The objective of this study is to use the Productivity Index (PI) developed by
a School of Education in a private university to examine how faculty report spending
their time. For the purpose of this study, faculty workload, or how faculty spend their
time, was used as a measure of productivity using a weighted index (PI) of specific

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tasks and outputs related to the three domains of teaching, research, and service. This
is a secondary data analysis using the descriptive statistics. The data collected was
coded and input using the Statistical Package for the Social Sciences (SPSS) Version
19 (SPSS Inc., 2010).
In this study a total of 54 participants completed the PI from January 2000 to
January 2005. During the academic year 2006-2007, the Productivity Index (PI) was
changed to Faculty Annual Performance Review (FAPR). The new FAPR system is
pre-populated with most activities such as courses taught, committees serviced, etc.
The faculty must also answer some serious and reflective questions for themselves.
Instead of using the quantitative measure of activities, the new system is rather a
report of quality of activities with narrative from the faculty.
Among the statistical analysis, ANOVA was run to see if any significance
exited in rank with total PI and average PI score. The ANOVA results indicated that
there was a significant difference between the full professor and the unknown rank
group. The faculty rank had a significant impact on the total PI score and it did
predict PI score well. Faculty spent most of their time on teaching. Teaching was the
most highly valued activity among research, service and teaching. This was true for
both the tenured and tenure-track faculty and special faculty. Among the 54 faculty
in the sample, 24 (44%) had total score above the average and they were identified as
the highly productive faculty. Among the highly productive faculty, most spent their
time on teaching. Between tenured and tenure-track faculty and special Faculty,
neither is quite on target for the expected distribution. The tenured and tenure-track

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faculty spent much more time on service than expected. The special faculty spent
less time on teaching than expected. Discussion, implications and future research
suggestions will be discussed in Chapter 5.

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CHAPTER FIVE
CONCLUSIONS AND IMPLICATIONS
The primary purpose of this study was to explore how faculty spend their
time in the School of Education at a private university by examining the measure of
productivity using a weighted index (the Productivity Index) of specific tasks and
outputs related to the three categories of teaching, research, and service. This study
analyzed information from the PI in order to establish the extent to which faculty
spend their time in accordance with the school’s expected faculty workload and to
see how the reported activities align with what is considered productive by the
organization. In this chapter, an overview of the key findings will be presented along
with discussion about how these findings are in line with the literature. The findings
will then be examined in the context of the research questions. Theoretical and
practical implications will also be addressed. Lastly, this chapter will conclude with
suggestions for future research.
In conducting the review of the literature, it is apparent that prior to the late
1960s the productivity of individual faculty was not a widely studied research topic.
The increase of government funding applied to education and the growing number of
institutions under public governance prompted closer scrutiny of the internal
workings of higher education, particularly the work of faculty (Cage, 1995). Also,
the literature review revealed that given the varied tasks and outcomes in each
academic area, it would be impractical to rely on a single measure to determine the
productivity of a faculty member, department, or institution. While various

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approaches are reviewed, it is clear that multiple measures are needed to gain a
complete and accurate representation of faculty productivity.
Several perspectives from the literature guide this study. First, theories on
faculty work helped us better understand faculty behavior. Second, motivation
theories provided a framework for what motivated faculty to be productive- that is,
spend time on certain activities versus other activities. The Productivity Index (PI)
was developed from the School of Education at a private university; it was adapted a
model originally developed for business firms to satisfy the central administration’s
need to know how the company is doing and how the company measures up to others
(O’Neil, Bensimon, Diamond & Moore, 1999).
The faculty committee turned to literature on organizational performance and
assessment for help in designing an approach that could both capture the complexity
of an academic organization and present a coherent image of the school’s
performance. The committee found a promising framework in Robert Kaplan and
David Norton’s “balanced scorecard” approach. Although the balanced scorecard
was developed with business organizations in mind, the framework was adaptable to
the unique characteristics of academic organizations (O’Neil, Bensimon, Diamond &
Moore, 1999).
Research Findings and Discussion
Why is this study interesting?
Education is important for the knowledge gain of the world. It enables
individuals to develop the perspective of global life. It helps develop opinions and

123
points of view for different generations. Education is also important because it
equips young minds with all that is needed to pursue some of their dreams. It opens
up doors to brilliant career opportunities. This is one of the reasons why higher
education is a controversial and emphasized topic for students, parents and the public
at large. Graduating from a university or college provides the necessary expertise to
be valuable in the workforce. Higher education becomes an eligibility criterion for
employment in many sectors of the workforce. With such criterion, higher education
and higher education institutions have been scrutinized for their products: graduates.
Are they learning the necessary tools with enough knowledge to survive in the “real
world”? To answer that question, faculty involvement, accountability and
productivity are consistently questioned.
This study is interesting because it attempts to answer some of the questions
about faculty productivity by analyzing the data set collected from an instrument
called the "balanced scorecard," which had the framework of a business model. The
“balanced scorecard” was refined into an “academic scorecard” to fit the educational
setting. Then later it was named the Productivity Index (PI) of the School of
Education at a private university. The PI quantified activities in three categories:
research, teaching and service. Points were assigned to activities in each category,
and faculty reported accomplished activities from the categories. In other words,
even though the expected faculty workload was assigned, the faculty had the option
of choosing which activities to spend time on.

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The research questions that were posed for this study were:
1. How do faculty in the School of education spend their time on an annual
basis?
a. How does productivity vary according to different faculty
characteristics (gender, ethnicity and rank)?
b. How does the total productivity vary according to faculty rank?
2. What are the highly valued activities of a faculty at this organization
according to the PI?
a. Which category among teaching, research and service did the
faculty spend most of their time?
3. What percentage of faculty participate in highly valued activities
according to the PI? What proportion of faculty time is spent on these
highly valued activities?
a. To what extent do ratios align with the standard 40-40-20 faculty
workload for Tenured and Tenure-Track faculty and 80-20 for
Special faculty? Is any category "over represented" or "under
represented" based on what we'd expect for that group (i.e.,
compared to the standard)?
Findings for Research Questions
This study evaluated whether or not certain factors contributed to any
significant difference in faculty productivity across the three areas of faculty
responsibility – teaching, research, and service. Data was collected each January

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from 2000 to 2005. The data used represent six years of productivity information for
the years 1999 through 2004. The relationship between faculty demographic
characteristics (ethnicity, gender, and rank), Productivity Index total and
Productivity Index average over six years was examined in this study.
The PI total was the total sum of scores for all activities over six years. PI
average is PI total divided by the number of years (6) conducted. This study is a
secondary analysis of longitudinal data collected by the PI. As the research
questions above were largely comparing groups, a quantitative approach was used.
The population included a total of 54 participants (tenured and tenure-track (31);
special (23)). The data collected was coded and entered using the Statistical Package
for the Social Sciences (SPSS) Version 19 (SPSS Inc., 2010). To answer the first
research question, t-test, ANOVA and regression procedures were used. To answer
the second, descriptive analysis and correlation was used. To answer the third
questions, descriptive analysis and Chi-square tests were used.
In an interview with a faculty member (2011) who was involved in the
creation of the PI, he indicated that the PI was created as the quantitative measuring
tool for activities (assigned points per activity) that faculty spend their time on to
determine annual performance and productivity. However, during the calendar year
2009, the PI was changed to the Faculty Annual Performance Review (FAPR). Vice
Dean of Research in the School of Education at a private university was instrumental
in changing the PI (quantitative measurement) to the current FAPR (qualitative

126
measurement). When the Vice Dean was appointed to the Faculty Council Chair in
2006, he had the opportunity to examine the PI and saw the flaws in the system.
For the first research question, the statistical analysis revealed that the total
productivity did not vary between gender and ethnicity. However, there was
significance detected in the area of faculty rank with total PI score and PI average
score. ANOVA was run to see if any differences existed in rank with total PI and
average PI score. The overall ANOVA test revealed that the rank had a statistically
significant relationship with the total PI and average PI scores. When the specific
comparisons were examined, the ANOVA results indicated that there was a
significant difference between the Full Professor and the Unknown rank groups for
total PI ( F (3,53) = (6.056), p = .001) and average PI ( F (3,53) = (4.720), p = .006).
A series of t-tests were run when the scores were disaggregated according to
faculty type (tenured and tenure-track faculty vs. special faculty). The results
revealed that compared to the special faculty, the tenured and tenure-Track faculty
scored significantly higher on research and service. However, there was no
significant difference between the two groups for teaching. This can be interpreted as
some diffusion of role for the Special faculty. They are doing significantly more
teaching but not close to 80%. Also, the Tenured and Tenure-Track faculty are
spending too much time on service and less than expected on research and teaching.
Within the tenured and tenure-track subsample, regression was used to
examine whether rank predicts the scores on research, teaching, service, and the total
PI score. Rank was a positive and significant predictor of the research score (β = .38,

127
p < .05), the teaching score (β = .50, p < .05), and the service score (β = .53, p < .05),
as well as the total PI score (β = .52, p < .05).The findings also revealed that the
overall faculty rank is the only characteristic that appears to influence the faculty
total PI score. The descriptive statistical findings indicate that the full rank professors
are doing more total activities to contribute to a higher overall PI score.
For the second research question, teaching was the most highly valued
activity with a total teaching score of 24,577 (41%); therefore, it was the most highly
valued activity between research, service and teaching. This finding suggests that
the School of Education at a private university values teaching activities more than
research activities overall; therefore, the faculty are allocating most of their time to
teaching. This finding is in contrast to the norm of other research universities
revealed in the literature. Melguizo and Strober (2007) stated that producing research
outputs (articles, books, patents, etc.) bring prestige to faculty as well as their
employing institutions. Faculty are then rewarded (increased salary) for bringing
prestige to their institutions. It is also expected that faculty spend a minimum amount
of time teaching, and a ‘buy out’ teaching option is available so that the faculty can
spend more time on research (Melguizo & Strober, 2007).
Examining the disaggregated scores among faculty type, both tenured and
tenure-track faculty and special faculty had their highest scores in teaching: 15,690
(36%) and 8,887 (55%), respectively. This confirms that the most valuable activity is
teaching overall. Even though the tenured and tenure-track faculty had the highest
score in teaching, 13 (42%) faculty spent most of their time on research, 8 (29%)

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faculty on service, and 10 (32%) faculty on teaching. This indicates that while only
10 faculty spent the majority of their time on teaching, teaching still accounted for
largest portion of the faculty time and faculty primarily allocated their time in this
category. For the special faculty, teaching had the highest score, 4 spent most of their
time on research, 4 spent most of their time on service and 15 spent most of their
time on teaching.
For the third research questions, out of the sample size of 54, 24 (44%) had
the PI score above the average total PI score (1,112). Those faculty members with
average PI score above the average total PI score (1,112) are categorized as highly
productive faculty. Among those 24 highly productive faculty, 8 (33%) spent most
time on research, 6 (25%) spent most time on service and 10 (42%) spent most time
on teaching. This finding is worrisome as the institution observed in this study is a
private research university. The finding is even more worrisome since out of the 24
highly productive faculty, 18 (75%) are tenured or tenure-track faculty members.
One would expect to see that the highly productive faculty would spend most of their
time on research. Teaching was again the most highly valued activity even among
the faculty members considered highly productive.
Among the 31 tenured and tenure-track faculty, 13 (42%) spent most of their
time on research, 8 (26%) spent most of their time on service, and 10 (32%) spent
most of their time on teaching. Only 42% of tenured and tenure-track faculty are
spending the most time on research whereas one would expect that a higher number
of tenured and tenure-track faculty would spend the majority of their time on

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research. Also, in contrast to the number of faculty spending most of their time on
research, the total PI score for teaching was the highest among research, teaching
and service: 14305 (33%), 15690(36%), 13748 (31%), respectively. When the scores
of tenured and tenure-track faculty in research, teaching and service were
disaggregated into ranks, the same result emerged. Unknown 775 (43%), Associate
4033 (36%), and Full 10882 (35%) rank all spent most of their time on teaching.
Among the special faculty, 4 (17%) spent most of their time on research, 4 (17%)
spent most of their time on service, and 15 (66%) spent most of their time on
teaching. The Special faculty are spending most of their time on teaching as
expected. However, if 17% of Special faculty are spending most of their time on
service, this suggests that the service responsibilities have been outsourced and only
a few individuals have concentrated on them.
For the Tenured and Tenure-Track faculty, the ratio between research,
teaching, and service was 33-36-31. The ratio does not align with the 40-40-20
expected faculty workload. For the Special faculty, the ratio between teaching,
research, and service was 55-20-25. Again, the ratio does not align with the 80-20
teaching-service expected faculty workload. The Chi-square tests confirmed these
findings A Chi-square test rejected the null hypothesis that this ratio is equal to the
standard distribution of 40-40-20 (χ
2
(2) = 3,625.95, p < .001) for the tenured and
tenure-track faculty. A Chi-square test also rejected the null hypothesis that this ratio
is equal to a distribution of 80-0-20 (χ
2
(2) = 7,351.23, p < .001). The findings
revealed that the teaching was the most highly valued activity overall and the School

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of Education faculty spent most of their time on teaching on average. In other
words, the tenured and tenure-track faculty spent most of their time on teaching, and
the special faculty spent most of their time on teaching as well. This finding is in line
with the theoretical framework that the individual faculty characteristics and the
environment are the two main factors that impact faculty behavior and productivity.
Possible Reasons for Findings: Teaching as the Most Valuable Category
To explore the reasons why faculty are spending most of their time on
teaching (behavior), we have to consider the motivation behind the behavior. It may
be that faculty are intrinsically motivated to dedicate more time to teaching. Faculty
may just simply enjoy teaching more than any other activity. Even though raises are
heavily dependent on research and scholarly work for the tenured and tenure-track
faculty, the nature and tradition of the School of Education is to disseminate
knowledge to produce educators. Further, research can be a grueling process.
Research activities require focus, autonomy, independence and collaboration
combined. However, through teaching, faculty can build relationships, disseminate
the knowledge they create (research) and build more foundations for their current
and future research.
Another reason may be that faculty were trained more thoroughly in research
during their doctoral studies (Ph.D., Ed.D., etc.) programs whereas they were less
well equipped and less trained in teaching. Typically, doctoral programs focus
heavily on research. Such programs want their graduates to publish in scholarly
journals to achieve the best job placements after graduation – those being top-tier

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research university tenure-track faculty positions. Placements can also effect the
reputation and ranking of the doctorate programs. While in the doctoral programs,
doctoral students must produce research and thus spend years training in that
domain. However, the reality is that once they complete their studies, they must also
teach and devote time to teaching after they graduate and become faculty. The lack
of training in the teaching area may be contributing to faculty spending more time in
teaching.
Using Instruments such as Productivity Index (PI)
As the findings revealed, teaching was the most valued activity among all
faculty whether they were tenured and tenure-track faculty or special faculty. The
advantage of having such an instrument as the Productivity Index (PI) is that the data
can be quantified to calculate clear means, totals and sub-totals. However, the flaw in
the PI was that there was no limit in the total number of scores. The no limit in
scores might have encouraged “gaming behaviors.” “Gaming behaviors” is defined
as faculty adding any activities they deemed important instead of considering them
to be routine responsibilities as a faculty member. For example, some faculty
suggested additional activities to add to the PI for the following year such as
attending commencement and office hours. There was no consistency and they
lacked clear reasons for adding the activities. The lack of consistency caused
confusion. It also degraded the instrument and the whole evaluation system.
The PI instrument also lacked clear vision of what to measure. This drove
gaming behaviors, as well as offering fluidity to faculty as far as what activities they

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were spending time on. The motivation also had no boundaries. For example, if the
instrument had clear vision of measuring allotted points in research, teaching and
service, the effort in service areas could have been minimized whereas effort in
research and teaching could have been emphasized. The motivation would have to
have been clearly defined to meet the expectations. The lack of clear vision in what
to measure also resulted in under-utilization of the instrument.
For an example of this under-utilization, consider that the PI scores were not
used to provide feedback to faculty. They also were not used to mentor junior
faculty. The results could have provided valuable information for the junior faculty
in the salary raise, promotion and even tenure processes. As was demonstrated in the
literature review, mentoring is the most prevalent method of support in preparing
junior faculty for the promotion and tenure review processes. Mentoring typically
involves tenured senior faculty guiding tenure-track junior faculty to a career path
that will help them earn tenure. In addition, the literature review demonstrated that
senior faculty also benefit from mentoring, creating reciprocal synergy. For this
reason alone, the PI was under-utilized and the results did not reach the potential
maximized usage.
Career Stage: Faculty Workload
During the merit review process, it became clear that the PI instrument was
problematic for assistant professors. As mentioned by the Vice Dean of Research
during the interview, the PI instrument was not fair to the lower ranked faculty
members. The assistant professors tend to have low scores because they were not

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able to participate in some activities listed on the PI. For example, they were not able
to chair dissertation committees. Also, assistant professors were in a career stage
where they had not yet flourished in some research activities and therefore did not
earn any points on the PI. For example, they may have submitted research papers to
journals that were still at the reviewing stage. They may have had multiple research
papers in the reviewing stages. Yet they were not rewarded points for those activities
on the PI.
There were some issues with the instrument itself. The PI had the Index
Codes and Index Captions in research, teaching and service. There were 45 distinct
Index Codes in research, 44 in teaching, and 48 in service. Since there was no limit
on the maximum points that could be earned, there was no boundary and direction of
the expectations. If the 40-40-20 was the expected allocation of faculty workload,
then the instrument should have been designed to reflect that. As we learned from the
literature review on motivation, the expectancy theory explains that motivation is a
product of the individual’s expectancy that a certain effort will lead to the intended
performance, the instrumentality of this performance to achieving a certain result,
and the desirability of this result for the individual (Vroom, 1964).
For instance, if the expectations are set to accomplish more research and
teaching activities, the PI should have had twice as many Index Codes in research
and teaching categories to reflect such expectations in the instrument. However, this
was not the case. The point assignments were also questionable. As shown in Table
1, the highest individual-activity points earned in the data were 312 given in the

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teaching category. As the School of Education is at a private research university with
high priority to research (RU/VH), it seems as though the priority and the
expectations for research should have been set higher. This also brings up a point
about the 40-40-20 faculty workload profile.
Is it appropriate to assign 40-40-20 ratio for all ranks of tenured and tenure-
track faculty? As the literature revealed that more and more emphasis is put on
research during the promotion and tenure process, especially at research universities
defined by the Carnegie Foundation, the highest priority of faculty is often research
activities. By definition, the creation of new knowledge through research is a
primary goal of the RU/VH institutions (Middaugh, 2001). Research publications
and scholarly productivity have become the top ranked items in determining
promotion and tenure (Green, 1998). It is also common to hear that new assistant
professors have been told that publications are the only criterion for promotion and
tenure, and they must “publish or perish” (Boyer, 1990).
With that said, it would be logical to assign different faculty workload
profiles than 40-40-20 to assistant professors. Perhaps the faculty workload profile
should reflect the career stages of the tenured and tenure-track faculty rather than
standardizing it to a fixed ratio. Given the varied activities and motivation in each
rank, it would be impractical to rely on a single measure to determine the
productivity of the faculty members in different career stages. While various
approaches have been reviewed, it is clear that multiple measures are needed to gain
a complete and accurate representation of faculty productivity. With differing

135
expectations among tenured faculty who face a multitude of responsibilities in
teaching, research and service versus specific contract-driven requirements for non-
tenure track faculty, a one-size-fits-all approach of PI to productivity assessment is
impractical.
Implications and Future Research
Perhaps the value of this study on an effective faculty evaluation system is
best used by university administrators who bear the daunting task of deciding what
approaches and tools to implement in future evaluations. It is not likely that the
external pressure and demands on colleges and universities for full disclosure about
what faculty do and how productive they are will disappear any time soon. Many
state governments are requesting this information as part of a performance indicator
movement, typically related to state appropriations and funding for higher education
(Middaugh, 2002). The public is also focusing on faculty activity, in particular how
it relates to instruction of undergraduates. Higher education will be better served by
taking a proactive rather than a reactive position, with the collaborative development
of a nationally usable, credible and understandable metric system for describing and
evaluating faculty activities and productivity across institution types and disciplines.
It is apparent that there is no one perfect system for evaluating faculty
productivity and performance. As the literature revealed, the autonomous nature of
faculty work makes it hard to create a standardized system. Blackburn and Lawrence
(1995) theorized and their empirical work proved that the main impact on faculty
behavior and productivity comes from social knowledge, how faculty perceive their

136
environment with regard to their work. In other words, how faculty spend their time
is dependent on support and the effort faculty believed their institution desired. The
finding in this study also revealed that the faculty behavior (how they spend their
time) is motivated by the activities considered valuable at the institution (spent most
time on teaching activities). Despite the perception and reality that research is
rewarded over teaching, significant faculty time goes into teaching related activities
by the tenured and tenure-track faculty.
As the School of Education is widely known for producing educators,
administrators and teachers mainly through an innovative and collaborative teaching
environment, the findings reveal that teaching is the most highly valued category
among research, teaching and service in this study. However, the findings also
revealed that the faculty type (tenured and tenure-track versus special track) did not
determine how faculty distribute their time and effort. Research active (tenured and
tenure-track) faculty spent most of their time on teaching while special (non-tenure-
track) faculty also spent most of their time on teaching.
For teaching, studies have revolved around teaching hours, number of
courses taught, with some analysis of student evaluations. However, few if any have
looked at quality of teaching over time or identified characteristics of teaching-
productive faculty. As with any evaluation, there are a number of ways to look at
faculty work and productivity with continuing debate about which items most
accurately measure and quantify productivity. What remains to be learned is a way

137
to quantify and measure merit in faculty work and productivity across all research
universities.
The literature revealed that given the public and institutional demands to
provide teaching and research, faculty at research universities are likely to have
much less of their time and effort left to devote to service activities. Few studies
have focused on service when discussing faculty productivity. However, the results
of this study revealed that all faculty types (tenured and tenure-track and special
faculty) spent more time on service activities than expected. Such results could have
been a result of the flaw in the instrument. Again, it is inappropriate to use a same
assessment approach with differing expectations among tenured and tenure-track
faculty who face a multitude of responsibilities in teaching, research and service
versus specific contract-driven requirements for non-tenure track faculty
Tenure is a topic that has produced much heated discussions in recent years
due to the controversial discussions about the workload distribution among
categories of research, teaching and service. Tenure at many universities depends
solely on research publications and research grants, although the universities' official
policies are that tenure depends on research, teaching and service (Boyer, 1990).
Some dispute that tenure is an outmoded concept, and, if institutions are going to
remain competitive, they need to be able to have more flexibility to hire and fire
faculty as student needs change.
As with any study that has theoretical framework, the selection of variables is
of utmost importance in the significance of the findings. This study selected a limited

138
number of variables that were assumed to best represent faculty demographics.
Future studies in faculty productivity research should look at the effects of such
variables as emotions towards previous evaluation system, future promotion and
raise expectancies, personal goals, and so on. The negative influence of previous
evaluation and expectancies of faculty is one worth pursuing in further research,
possibly by examining the role of fear of failure and self-efficacy in literature.
While this study did not examine raise and promotion outcomes, future
research can compare the faculty time distribution and how it affected raise and
promotion. It would particularly be interesting to do a quantitative study on recently
tenured faculty and how their time distribution ratio compared to the 40-40-20
expected faculty workload profile. Qualitative studies that use in-depth interviewing
would also help us further understand the reasons why and what factors influence
faculty to spend their time (behavior) on research, teaching or service. Longitudinal
studies could examine not only how behavior and motivation change over time, but
also how particular aspects of motivation, such as perceptions of job satisfaction and
expectancies, change. These suggestions all have the potential to expand the field of
behavior and motivation research as it pertains to faculty productivity.
Conclusion
This study expanded on current literature by examining the theories of faculty
work and motivation to explain how faculty spend their time. It showed that the most
interesting finding was the amount of time that was spent on teaching among the
faculty members of the School of Education at a private university. As illustrated in

139
the findings, teaching was the most highly valued activity. By examining the faculty
behavior (how they spend their time) and what motivated faculty to be productive
(what activities they spend their time), we have deeper understanding of how the
faculty characteristics and the environment impact the productivity. Research,
teaching and service work hand-in-hand. The “research” is creation of new
knowledge. However, the “teaching” is the act of disseminating the creation
(research) to the curious and creative minds. As teachers, faculty members are to
create, to educate and to serve our future leaders. It is crucial to find the right balance
among research, teaching and service for the success of future higher education
institutions.

140
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153
APPENDIX
UNIVERSITY OF SOUTHERN CALIFORNIA SCHOOL OF EDUCATION
PRODUCTIVITY INDEX (VERSION 16b)
Documentation by year (e.g., titles of articles, names of accomplishments, etc.) for
awarded points needs to accompany these sheets. See attached example
documentation.

Research
Points per
activity
Calendar
Year
2003
Calendar
Year
2004
Calendar
Year
2005
A. Scholarly Work (Include
only works that have been
published by journals or
publishers that have review
or referee procedures.)

R1. Sole author of a book (first
edition)
36 ____ ____ ____
R2. Co-author of a book (first
edition)
26 ____ ____ ____
R2a. First co-author of a book
(first edition)
32 ____ ____ ____
R3. Sole editor of a book (first
edition)
18 ____ ____ ____
R4. Co-editor of a book (first
edition)
12 ____ ____ ____
R4a. First co-editor of a book
(first edition)
15 ____ ____ ____
R4b. Author of a standardized
test manual
20 ____ ____ ____
R5. Sole author of a book
(revised edition)
18 ____ ____ ____
R6. Co-author of a book
(revised edition)
14 ____ ____ ____

154
Research
Points per
activity
Calendar
Year
2003
Calendar
Year
2004
Calendar
Year
2005
R6a. First co-author of a book
(revised edition)
16 ____ ____ ____
R7. Sole editor of a book
(revised edition)
9 ____ ____ ____
R8. Co-editor of a book
(revised edition)
5 ____ ____ ____
R8a. First co-editor of a book
(revised edition)
7 ____ ____ ____
R9. Sole author of a book
chapter or monograph
10 ____ ____ ____
R10. Co-author of a book chapter
or monograph
6 ____ ____ ____
R10a. First co-author of a book
chapter or monograph
8 ____ ____ ____
R11. Sole author of a major
article (6 or more journal
pages
1
; data-based or
theoretical; refereed
national journal
2
)
12 ____ ____ ____
R11a. First co-author of a major
article (6 or more journal
pages
1
; data-based or
theoretical; refereed
national journal
2
)
10 ____ ____ ____
R12. Co-author of a major
article; sole author of an
invited paper
8 ____ ____ ____
R13. Sole author of a book
review or minor article (1-5
pages
1
; secondary-level
journal
2
; position paper;
wisdom of experts)
6 ____ ____ ____

155
Research
Points per
activity
Calendar
Year
2003
Calendar
Year
2004
Calendar
Year
2005
R14. Co-author of a minor article
or book review
4 ____ ____ ____
R15. Author or co-author of
unrefereed technical report;
article in state journal or
newsletter (including
producing a CD or
authoring RSOE Urban Ed
article)
4 ____ ____ ____
R16. Conference presentation or
discussant at AERA/APA
(4 per paper, if invited, add
1 point; no substitutions for
AERA/APA permitted)
4 ____ ____ ____
R16a. Conference presentation or
discussant other than
AERA/APA but at the
single prestigious
conference of the discipline
(faculty documents why
prestigious)
3 ____ ____ ____
R17. Conference presentation or
discussant other than
AERA/APA (2 per paper)
2 ____ ____ ____
R18. Recognition by national
professional organizations;
recognition and/or honors
by USC
10 ____ ____ ____
R19. Fellow in national
organization (points
awarded only in year
awarded)
36 ____ ____ ____
R20. Distinguished appointment
(e.g., Guggenheim,
traditional Fulbright)
6 ____ ____ ____

156
Research
Points per
activity
Calendar
Year
2003
Calendar
Year
2004
Calendar
Year
2005
R21. Other distinguished
appointment (e.g., Senior
Specialist Fulbright)
4 ____ ____ ____
R22. Invited or keynote
prestigious address
6 ____ ____ ____
R23. Lesser professional
recognition (best research
article)
4 ____ ____ ____
R23a. Publications with co-
author(s) outside education
b

2 ____ ____ ____
R23b. Conference paper with co-
author(s) outside education
b

1 ____ ____ ____
R23c. Presentation at non-
education conference
b

1 ____ ____ ____
R23d. Publication in non-
education journal
b

2 ____ ____ ____
R24. Other
4
: ________________ (max 12) ____ ____ ____
B. Funded USC Research
Grants/Contracts and
USC External Financial
Support (Business office
will compute R25 and
R26) PI/co PI roles are
formal and are reflected in
the contract/grant.

R25. Total amount per year as
PI/co-PI (1 point per $3000
expended)
____ ____ ____
R26. Indirect cost recovery per
year as PI/co-PI (1 point
per $2000)
____ ____ ____

157
Research
Points per
activity
Calendar
Year
2003
Calendar
Year
2004
Calendar
Year
2005
R26a. Equivalent contribution to
RSOE operations from
contracts/grants without
overhead (e.g., tuition
remission) (1 point per
$2000)
____ ____ ____
R27. Written contribution on a
funded research
contract/grant proposal
8 ____ ____ ____
R28. Proposal is focused on
USC/RSOE mission
statement
4 XXX XXX XXX
R28a. Co-Principal Investigator,
outside RSOE or
University
b

8 ____ ____ ____
R28b. Working on a
contract/grant, outside
RSOE or University
b

4 ____ ____ ____
R28c. Written contribution to
funded research
contract/grant proposal with
outside RSOE or University
members
b

4 ____ ____ ____
R29. Other
4
:
__________________
____ ____ ____
C. Unfunded
Grants/Contracts, etc.

R30. Major contributor 6 ____ ____ ____
R30a. Co-Principal Investigator,
outside RSOE or
University
b

6 ____ ____ ____

158
Research
Points per
activity
Calendar
Year
2003
Calendar
Year
2004
Calendar
Year
2005
R30b. Team member on a
contract/grant, outside
RSOE or University
b

3 ____ ____ ____
R30c. Written contribution to
research grant proposal
with outside RSOE or
University members
b

3 ____ ____ ____
TOTAL RESEARCH
POINTS PER YEAR
____ ____ ____
AVERAGE RESEARCH
POINTS FOR 3-YEAR
PERIOD (divide total by
3)
____
D. Experimental Items
(Dropped)

159
Teaching
Points
per
activity 2003 2004 2005
A. Classroom Teaching
T1. Public honor for teaching (students or
peers – e.g., Socrates Award or Rose
Award)
36 ____ ____ ____
T1a. USC academic appointment outside
RSOE
b

9 ____ ____ ____
T2. Excellence in teaching
a

Taught a course where instructor rating
(item 11) was > 4.70 on the student
evaluation form
3
(9 points per course)
9 ____ ____ ____
T3. Taught a course that was rated > 4.50
and < 4.70 on item 11 of the student
evaluation form
3
(6 points per course)
6 ____ ____ ____
T4. Taught a course that was rated > 3.00 on
item 11 of the student evaluation form
3

(3 points per course)
3 ____ ____ ____
T4a. Supervisor of TAs for large classes (4
points per TA)
4 ____ ____ ____
T4b. Had a total of at least 25 student
enrollments per class (used to be T24;
modified to be at class level, not year
level)
6 ____ ____ ____
T4c. Use of educational software in course
(e.g., Blackboard; email alone doesn’t
count)
1 ____ ____ ____
T4d. Team teach outside RSOE
b
1 ____ ____ ____
B. Curriculum Development
T5. Chair or co-chair a committee to develop
a new program or major program
revision (e.g., undergraduate minor, a
new master’s program, a new Ed.D.
program)
36 ____ ____ ____

160
Teaching
Points
per
activity 2003 2004 2005
T5a. Member of committee to develop new
program
4 ____ ____ ____
T6. Receipt of instructional improvement
grant
8 ____ ____ ____
T7. Development of a new course (awarded
when approved by GPSC/UPSC)
12 ____ ____ ____
T8. Creation of educational software 12 ____ ____ ____
T8a. Creation of cooperative degree with
outside RSOE school (awarded when
approved by GPSC/UPSC)
b

36 ____ ____ ____
T8b. Creation of course cross-listed with
outside RSOE school (awarded when
approved by GPSC/UPSC)
b

12 ____ ____ ____
T8c. Cross-listed course
b
4 ____ ____ ____
T8d. Instructional CD (such as How to Write
a Dissertation)
4 ____ ____ ____
C. Advisement
T9. Number of dissertations completed as
chair (12 points per completed
dissertation; if co-supervisors, then
divide the points)
max
108
____ ____ ____
T9a. Chair master’s theses (6 points per
completed thesis)
6 ____ ____ ____
T10. 490, 590, 593b or 690 load (2 per
student)
2 ____ ____ ____
T11. Acceptable 593b load (minimum 2
students)
XXX XXX XXX XXX
T12. Undergraduate advisement (1 point per
student)
1 ____ ____ ____

161
Teaching
Points
per
activity 2003 2004 2005
T13. Acceptable undergraduate advisement
(minimum 7 students)
XXX XXX XXX XXX
T14. M.A. advisement (2 points per student) 2 ____ ____ ____
T14a. Ed.D. or Ph.D. advisement prior to
assignment of dissertation supervisor (2
points per student)
2 XXX XXX ____
T15. Acceptable M.A. advisement (minimum
7 students)
XXX XXX XXX XXX
T16. Best Dissertation Award for student
under supervision
12 ____ ____ ____
T17. Recommendation by Program for Best
Dissertation Award
6 ____ ____ ____
T18. Number of quals completed as member
(2 points per committee)
2 ____ ____ ____
T18a. Number of quals completed as chair (8
points per committee)
8 ____ ____ ____
T19. Mentoring (formally appointed) a junior
faculty member
12 ____ ____ ____
T20. Number of dissertations completed as
member (4 points per committee)
4 ____ ____ ____
T20a. Committee service on SOE Ph.D./Ed.D.
committees (2 per committee, excluding
chairs)
2 ____ ____ ____
T21. Serving on non-SOE Ph.D./Ed.D.
committees (2 per committee)
2 ____ ____ ____
T22. Inclusion of students as co-authors of a
published work
8 ____ ____ ____
T23. Fund a post-doctoral student 6 ____ ____ ____

162
Teaching
Points
per
activity 2003 2004 2005
T24. Had a total of at least 80 student
enrollments per year
6 XXX XXX XXX
T25. Inclusion of student on conference paper 4 ____ ____ ____
T25a. Supervisor of students in field
components, one per student (e.g., 2 per
student field placement)
2 ____ ____ ____
T25b. Thematic dissertation: summer
conference
2 ____ ____ ____
T26. Other
4
: __________________________ (max
36)
____ ____ ____
D. Funded USC training
Grants/Contracts and USC External
Financial Support (Business office will
compute T27 and T28) PI/co-PI roles
are formal and are reflected in the
contract/grant.

T27. Total amount per year as PI/co-PI (1
point per $3000 expended)
____ ____ ____
T28. Indirect cost recovery per year as PI/co-
PI (1 point per $2000)
____ ____ ____
T28a. Equivalent contribution from grants
without overhead (e.g., tuition remission
(1 point per $2000)
____ ____ ____
T28b. Co-Principal Investigator, outside RSOE
or University
b

8 ____ ____ ____
T28c. Working on a contract/grant, outside
RSOE or University
b

4 ____ ____ ____
T28d. Written contribution to funded research
contract/grant proposal with outside
RSOE or University members
b

4 ____ ____ ____

163
Teaching
Points
per
activity 2003 2004 2005
T29. Written contribution on a funded training
grant proposal
8 ____ ____ ____
E. Unfunded USC training
Grants/Contracts and USC External
Financial Support T30. Major
contributor
6 ____ ____ ____
T30a. Co-Principal Investigator, outside RSOE
or University
b

3 ____ ____ ____
T30b. Team member on a contract/grant,
outside RSOE or University
b

3 ____ ____ ____
T30c. Written contribution to research grant
proposal with outside RSOE or
University members
b

3 ____ ____ ____
TOTAL TEACHING POINTS PER
YEAR
____ ____ ____
AVERAGE TEACHING POINTS
FOR 3-YEAR PERIOD (divide total by
3)
____

164
Service
Points per
activity 2003 2004 2005
A. Service—School of Education
S1. Chair, School committee or
School search committee
36 ____ ____ ____
S2. Serve as one of four RSOE
program heads (i.e.,
undergraduate, masters, Ed.D.,
Ph.D.) or as a staff director
(career placement)
36 ____ ____ ____
S2a. Serve as one of three RSOE
associate deans (i.e., academic
programs, external relations,
faculty.)
54 ____ ____ ____
S3. Chaired the RSOE faculty council 36 ____ ____ ____
S3a. Member of faculty council 12 ____ ____ ____
S4. Serve as an RSOE center mentor 16 ____ ____ ____
S5. Directed a funded institute
(>50K) or large-scale (>50K)
RSOE project
16 ____ ____ ____
S6. Member, School committee(s) (4
points for each committee)
4 ____ ____ ____
S7. Chaired an RSOE ad hoc
committee or task force
8 ____ ____ ____
S8. Served as an RSOE specialty
chair
16 ____ ____ ____
S9. Recruitment activities 2
max 16
XXX XXX XXX
S9a. Attend RSOE recruitment
meeting (2 points per meeting)
2
max 16
____ ____ ____
S9b. Attend off-site recruitment
meeting (2 points per meeting)
2
max 16
____ ____ ____

165
Service
Points per
activity 2003 2004 2005
S9c. Host prospective students (e.g., in
class) (2 points per meeting)
2
max 16
____ ____ ____
S9d. Exceptional credential-related
activities (please describe)
max 16 ____ ____ ____
S9e. Program or center web page
development/maintenance
4 ____ ____ ____
S10. Other
4
: _____________________ (max 36) ____ ____ ____
B. Service—University
S11. Chair, major University
committee, including search
committee
8 ____ ____ ____
S12. Member of one or more
University committees
4 ____ ____ ____
S12a. USC Senate Representative 8 ____ ____ ____
S13. Other
4
: _____________________ (max 8) ____ ____ ____
C. Service—External
S14. Editor-in-chief of national or
international journal
16 ____ ____ ____
S15. President of national or
international professional
organization
16 ____ ____ ____
S16. Officer or board member—
national or international
organization; president—regional
organization
8 ____ ____ ____
S17. Committee chair of international,
national, or regional organization
8 ____ ____ ____
S18. Associate editor of national
journal; newsletter editor (4
points)
4 ____ ____ ____

166
Service
Points per
activity 2003 2004 2005
S19. Appointment to prestigious panel
(e.g., National Research Council)
8 ____ ____ ____
S20. Committee member of national or
regional organization, editorial
board member
2 ____ ____ ____
S21. Journal referee (1 paper—4
points); conference program
referee (3 papers—4 points)
4 ____ ____ ____
S21a. Book manuscript review 8 ____ ____ ____
S22. Workshop organizer or presenter;
consultant activity (2 per activity)
2 ____ ____ ____
S23. Organized national/international
professional conference (Program
Chair)
10 ____ ____ ____
S24. Invited lecturer at another
university
2 ____ ____ ____
S25. Invited lecturer on sabbatical 2 ____ ____ ____
S26. Continuing education course
(Extension)
2 ____ ____ ____
S26a. Tenure/promotion review for
other universities
2 ____ ____ ____
S27. Other
4
: _____________________ (max 16) ____ ____ ____
D. Service—Community
S28. Consultant to public schools and
colleges
4 ____ ____ ____
S29. Consultant to state or federal
government
4 ____ ____ ____
S30. Consultant to nonprofit
organization
4 ____ ____ ____

167
Service
Points per
activity 2003 2004 2005
S31. Consultant to for-profit
organization
4 ____ ____ ____
S32. Consultant to international
organization
4 ____ ____ ____
S33. Other
4
: _____________________ (max 4) ____ ____ ____
E. Other Indicators
S34. Public honors (e.g., endowed
chair or lifetime achievement
award) (Points awarded only in
year of award.)
36 ____ ____ ____
S35. Testimony provided by faculty to
policy-making bodies including
local, state and federal legislators
and executive branch policy
makers
4 ____ ____ ____
S36. Letters to the Editor and Op Ed
pieces to newspapers
4 ____ ____ ____
S37. Media appearances as an expert
witness on news programs, in
newspaper, etc.
4 ____ ____ ____
S38. Appointments to policy-making
commissions, committees,
boards, etc.
4 ____ ____ ____
S39. Requests to author or assist in
RFPs from foundations and
government agencies based on
faculty research
4 ____ ____ ____
S40. School district, college trustee 4 ____ ____ ____
S41. Other
4
: _____________________ (max 4) ____ ____ ____
TOTAL SERVICE POINTS
PER YEAR
____ ____ ____

168
Service
Points per
activity 2003 2004 2005
AVERAGE SERVICE POINTS
FOR 3-YEAR PERIOD (divide
total by 3)
____

169
This page is for SPT use: Faculty, please do not fill out.

Points
per
activity 2003 2004 2005
TOTAL POINTS PER YEAR Research: ____ ____ ____
Teaching: ____ ____ ____
Service: ____ ____ ____
AVERAGE FOR 3-YEAR
PERIOD (divide total by 3)
Research:
____
AVERAGE FOR 3-YEAR
PERIOD (divide total by 3)
Teaching:
____
AVERAGE FOR 3-YEAR
PERIOD (divide total by 3)
Service:
____
WORK LOAD PROFILE (e.g.,
40/40/20). E.g., multiple for 3-
year average X 40% for
research.

Work
load
Research
____

Work
load
Teaching
____

Work
load
Service
____
TOTAL POINTS ____

170
APPENDIX ENDNOTES
1
Pages equivalent to double-columned, 8.5 by 11 inch page (for example, American
Psychologist and Educational Researcher).

2
“Refereed national journal” and “secondary level journal” defined by SPT. Faculty
member should submit information to SPT supporting claim that publication is in a
refereed national journal or secondary level journal.

3
“Overall, how would you rate this instructor: Poor (1), Below average (2), Average
(3), Above average (4), Excellent (5)?”

4
List contributions included in this item. Points proposed in this item are to be
approved by SPT.

a
If normal teaching load of 4 courses, then excellence in teaching would be 36
points, or equivalent to excellence in research or service.

b
Indicator to acknowledge interdisciplinary values of the University.

Abstract (if available)

Abstract Defining and measuring faculty productivity are among the most central issues for quality and accountability in higher education today, and it is the subject this study seeks to illuminate. This study first examines how the productivity of faculty in the School of Education at a private university differ according to different faculty characteristics and according to faculty rank. It then examines which activities are highly valued at this organization by determining the relative value of each activity, providing a benchmark to evaluate whether faculty are indeed spending their time on the activities that enhance the mission of the school and the institution. Finally, the study examines the percentage of faculty participating in activities that are considered highly productive and what proportion of faculty time is spent on these activities. ❧ I examine the Productivity Index (PI) of faculty from the School of Education at a private research university to answer these questions. Data from the PI were collected over the six years the measure was in use. This study will contribute to the existing body of research on faculty work by examining the use of this measurement tool and the resulting data collected by that tool to further understand ways in which faculty work may be reported and valued, and to identify variances in faculty work and time spent which may be influenced by various faculty characteristics. This study is a secondary analysis of longitudinal data collected by the PI. As the research questions above were largely comparing groups, the study uses a quantitative approach. ❧ This study is significant in adding to the understanding of the factors and characteristics of productive faculty which will in turn provide insight to the facts and myths regarding faculty productivity. This study examines how faculty spend their time (behavior) and how they are motivated to spend time on certain activities versus other activities (motivation). This study also identifies which characteristics effect behavior and motivation, and in turn, how those characteristics influenced productivity. Significantly, what was found, using statistical analysis, was that teaching was the most highly valued activity among research, service and teaching. This was true for both the tenured and tenure-track faculty and special faculty.

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Asset Metadata

Creator Lee, Michelle Silver (author)

Core Title Evaluating how education faculty spend their time at a private research university

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 11/19/2012

Defense Date 10/10/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag evaluation,OAI-PMH Harvest

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sundt, Melora A. (committee chair), Cerling, Lee (committee member), Melguizo, Tatiana (committee member)

Creator Email michelll@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-115286

Unique identifier UC11289305

Identifier usctheses-c3-115286 (legacy record id)

Legacy Identifier etd-LeeMichell-1306.pdf

Dmrecord 115286

Document Type Dissertation

Rights Lee, Michelle Silver

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA