An Essay for Our Students on the Use of the Canons of the Scientific Method:

A Lens for Understanding Organizational Studies

Jon L. Pierce

Geoffrey G. Bell

Department of Management StudiesLabovitz School of Business and Economics

University of Minnesota Duluth10 University DriveDuluth, MN 55812

(218) 726-7929(218) 726-7640

(218)726-7578 (fax)jpierce@d.umn.eduggbell@d.umn.edu

May 22, 2006

We extend our appreciation to Anne Cummings and John Newstrom for their substantive comments on an earlier draft of this essay.

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Abstract

Students are bombarded daily with a myriad of assertions and claims made by many people, and

in the process they acquire many beliefs and reject many insights. This essay is directed primarily to

undergraduate students of organizational studies. We examine the different ways by which we come to

‘know that which we know,’ first by reviewing those methods with which most students are familiar –

experience, expert opinion, faith, reason, and intuition. We then turn our attention to the scientific

method and the language of science. We suggest that students can enhance their sophistication as

consumers of information by applying the canons of the scientific method as a lens to inform their level

of understanding. We highlight the nature of the scientific process and conclude with a set of insights

that can be used to enhance students understanding of the manifest world.

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An Essay for Our Students on the use of the Canons of the Scientific Method: A Lens for Understanding Organizational Studies

Listen in on almost any conversation and it becomes readily apparent that people ‘know’ many

things. It is also evident that we take many of our beliefs (opinions) for granted, and only rarely do we

ask ourselves, “How do I know that to be true?” Moreover, we rarely ask that question of others. Be it

from class, conversations with fellow students, stories appearing on the evening news, or statements

appearing on the internet, we are inundated with information – medical studies warning patients taking

cholesterol lowering medications to avoid eating grapefruit, assertions prior to the 2003 invasion of Iraq

that that country possessed weapons of mass destruction, studies associating daily dental flossing and red

wine with longevity, and claims that a satisfied worker is a productive worker. Assertions of this nature

should leave us questioning “How do we know this?” and “From where does that belief stem?”

These questions form the core of this essay. We review the different ways by which we come to

that which we know, from the more familiar (e.g., personal experience) to the less familiar (i.e., science),

and we suggest use of the canons of scientific method as a path/route through which our ways of

knowing can be broadened and enhanced. In this essay, our primary audience is undergraduate students

of organizational studies. As a natural part of their university experience, students daily come face-to-

face with a variety of assertions. We hope that they will consider seriously the veracity of the claims

they hear, and, in so doing, become more sophisticated consumers of information.

James (1890 (1918)p. 480), observed that we live in a world of “blooming, buzzing confusion,”

and each of us works to make personal sense of that which surrounds us. In the process it is important

that each of us come to understand the ways that we come to know, and the means by which we can

improve upon the processes of adding to our storehouse of knowledge. Our focus on the use of the

canons of the scientific method is intended to encourage and provide an additional tool to assist in this

process.

While the publication of knowledge created through the scientific process must normally pass

peer scrutiny before it is published, no similar structure exists to screen the vast majority of the

information to which the students and the general public is exposed. As a consequence, it is important

that we become more sophisticated information processors. All of us should be able to determine when

someone is being intellectually honest; to be able to differentiate between assertions of fact (e.g., “I

know ____”) from mere expressions of one’s personal belief (e.g., “I believe/I think ____”). One should

respond with caution when someone talks about the future in terms of what ‘will happen’ as opposed to

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framing their assertion in terms of “I predict” or “I believe that ____ will happen.” The ability to discern

such differences is critically important as we organize and store information in our warehouse of

knowing for later use.

As they sift and winnow in search of truth and understanding, sophisticated consumers of

information recognize the different types of relationships that connect phenomena to one another, they

can comprehend both single and multiple causality, and they understand the limitations that stem from a

sample size of one. They instinctively think in terms of variance, reliability, validity, and they recognize

and can differentiate causality from mere correlation. While they employ multiple ways of knowing,

they acknowledge the appropriate role and limitations of each.

In this essay, we discuss some of these issues. We begin by over viewing some of the more

familiar ways by which we acquire our beliefs – personal experience, faith, and so forth. We then

introduce our primary focus – science as a way of knowing. This is not to suggest that science is more

important or “better” than the other ways of knowing – each has its place. However, undergraduate

students are less likely to be familiar with science as a way of knowing, and it provides a rigorous way to

test assertions to which students are exposed. Because of these factors, we provide a detailed

examination of the scientific method from a user’s perspective.

The Genesis of Our Beliefs

We come to our beliefs and knowledge via many different means. In this section of our essay,

and as a prelude to our primary focus on science as a way of knowing, we briefly review some of the

many alternative ways that we develop the beliefs and opinions that we hold. The processes associated

with acquiring knowledge are quite complex, and for the most part beyond the scope of this essay. Each

is founded on a different set of understandings and provides somewhat different insights into the world.

We conclude this section with some observations on subjectivity and distortions associated with the

genesis of our beliefs. In the remainder of our essay we focus on and provide a users’ guide to the use of

the canons of the scientific method as a way of knowing in the manifest (evident, apparent to the senses)

world.

Direct personal experiences. We have multiple senses (sight, taste, hearing, touch, and smell)

through which we directly and personally experience the world. In an attempt to come to know,

understand, and predict the world that surrounds them, people take small samples from the array of

stimuli to which they are exposed. It is from this small snapshot of their experienced world that people

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develop beliefs, routines, and ‘rules of thumb’ that enable them to navigate through life (Albert & Bell,

2002).

Because we have to navigate through life’s experiences, it is quite natural that people will rely

upon their direct and personal experiences as a way of learning (Kolb, 1976). As many of our

experiences are repeated we hopefully will travel from novice to expert, from a thin and superficial vail

of understanding to one of intimate knowing, a journey more likely to occur if experience is consciously

and explicitly reflected on for the lessons it may teach us. As a consequence, personal experience is a

powerful teacher. Much of this power comes from the fact that experiences are direct, personal, often

impacting multiple senses, and for which the consequent reinforcement is also direct and personal.

Because experiences are personal and direct, they play a powerful role in shaping our beliefs.

Bandura (1982; 1989) has suggested that experience possibly plays the most influential role affecting the

development of our beliefs. While a powerful teacher, beliefs that stem from direct and personal

experiences often suffer from many biases and limitations. It is often said, for example, that people do a

very good job of ‘learning their experiences,’ yet they often fail miserably at ‘learning from their

experiences,’ partially because we are biased observers. In addition, people all too often treat their

experiences as though they are truly reflective of the norm, rarely asking how similar or dissimilar they

are from the experiences of others. According to Morgan (1998), ways of seeing are often ways of not

seeing, and our experience base is much more limited than the vast array of possible experiences in the

world (Levitt & March, 1988; March, Sproull, & Tamuz, 1991).

Vicarious experiences. Bandura’s (1982; 1989) work reveals that people often develop beliefs

about their ability to organize and execute a course of action from the informational cues stemming from

vicarious experiences. Vicarious experiences are indirect experiences, experiences that a person has that

emanate from observations of the direct experiences of another. They represent the person’s imagined

participation in another’s experiences.

Many of the beliefs that people hold stem from their observation of the stimulus, response, and

consequent connections that are associated with the experiences of others. There has been more than one

occasion when a bystander has observed someone ‘treading on thin ice’ only to fall through (literally or

figuratively). Cognitively, people are capable of forming images of themselves in a similar situation.

Concluding that the experience will not be a positive and pleasurable one, a belief gets formed

suggesting that it is best to avoid walking in that person’s footsteps.

The use of vicarious experiences as a way of knowing can be both a powerfully positive and

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deceptive teacher. On the positive side many beliefs and opinions can be formed without incurring the

costs that are associated with direct and personal experience (e.g., near drowning that accompanied

falling through the thin ice). On the negative side, people all too often fail to fully observe an event and

its context which can easily lead to the formation of erroneous cause and effect beliefs (March & Olsen,

1988).

As with perceptions of one’s direct and personal experiences, the informational cues transmitted

from observations of other people’s experiences are filtered through the perceiver’s needs and values

often resulting in a perception of that which one wants or needs to see. Moreover, the efficacy of direct

and vicarious experience as a teacher is strongly related to the representativeness of that experience.

There is variance (variability) in the world and a given experience may not be representative of the

underlying population of similar experiences.

Social information processing (verbal persuasion). In addition to the direct observation of the

experience of others, it is also possible for people to acquire their attitudes, beliefs, or opinions through

the recountings (i.e., stories, statements of opinion) expressed by others. According to Salancik and

Pfeffer (1978), many of the beliefs and opinions that we carry around with us are simply the

constructions of others, arrived at through the information provided by others and ‘verbal persuasion’

(Bandura, 1982, 1989).

Other (socially) constructed beliefs generally stem from the views expressed by people whom

we find attractive, people who are friends, others whom we believe have our best interest at heart,

authority figures, and ‘experts’ (i.e., people who by their formal education and training or other means

have developed deep insight into a particular phenomenon). For many of us, physicians, dentists,

lawyers, clergy, professors, and automobile mechanics fall into this category. Many of the beliefs and

opinions they pass along to us become internalized and part of our ‘knowing and understanding’ of the

world. This can be a positive thing because becoming an expert often requires a large investment of time

and energy. Hence, a student can acquire a vast understanding at a much smaller cost by listening to that

which a true expert professesa. There can, however, be a negative side to this way of knowing when the

learner relies on expertise without assessing the claims made. For example, in the lead-up to the Iraq

war of 2003, many American citizens came to believe that Saddam Hussein had weapons of mass

destruction. This belief was largely the result of verbal persuasion and the socially provided information

made in highly visible and publicly accepted bodies (e.g., the UN Security Council) by authority figures

such as the President, Vice President and Secretary of State. This belief has largely been discredited by a This is one reason why much knowledge transmission in universities occurs via lecture.

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evidence now available, and which indeed may have been available at that time.

We frequently seek to confirm our beliefs by turning to our peers and friends. This may give

rise to what Kahneman and Tversky (1982) refer to as a representative bias. Similarity attraction (birds

of a feather flock together) often means that the sample of people we use to confirm our beliefs are

people just like us and frequently they are unrepresentative of the population as a whole (Byrne, 1961,

1971).

Faith. There are times when people find themselves in that state where they exhibit confidence,

complete trust, and an unquestioning belief in the notion that something is true. Such beliefs become

articles of faith for those who hold them. Faith can be seen as a belief which is not based on proof;

instead, it stems from trust or a strong and underlying desire that something is true. For example,

children commonly have faith in their parents, a confidence and an unquestioning belief that mom and/or

dad will protect and take care of them. Many citizens hold an unquestioning belief in the rightfulness of

their nation’s acts, as illustrated by the observation that many American citizens are unquestioningly

confident and comfortable in the belief that the United States could never have done anything that might

have contributed to the tragic events of September 11, 2001. Such articles of faith simply represent

beliefs that people accept and without question hold as true.

Drawing from a religious context, “faith is the evidence of things hoped for [emphasis added]

…” (Hebrews 11:1). While direct personal experience represents the claim “I see it, so I believe it,”

faith often claims, “I believe it, so I see it!” (Wallis, 2005).

Intuition. There are those times when we just seem to know – what is right or wrong, what is

happening or what is going to happen – without conscious prior thought. There are those times when

‘some things just appear to be self-evident.’ When asked why or how we know, a typical response is ‘I

just know’ or I can’t explain it.’ The word intuition is frequently employed within this context, as an

explanation for the phenomenon at work.

Intuition refers to that psychological process whereby one comes to a state of awareness without

any consciousness of having gone through the process of searching for information, processing it,

weighing the evidence, critiquing, comparing it with alternatives, and arriving at a conclusion. Instead

intuition reflects the sudden appearance of a belief in one’s consciousness, often referred to as

instantaneous apprehension (Haidt, 2001).

Haidt (2001) contrasted intuition with reasoning and indicated that they are similar in that both

are a form of cognition, yet two different kinds of cognition. According to Haidt (2001, p 818), intuition

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“occurs quickly, effortlessly, and automatically, such that the outcome, but not the process is accessible

to consciousness, whereas reasoning occurs more slowly, requires some effort, and involves at least

some steps that are accessible to consciousness.” As such, intuition may be thought of as a state of

awareness arrived at by feeling, rather than through logical and conscious analyses.

Logical verification (reasoning). As a part of his presentation of social learning and social

cognition theories, Bandura (1977; 1986; 1997) notes that individuals derive new knowledge from things

that are already known. The application, for example, of integrative thinking represents a process

through which people arrive at many of the beliefs (opinions) that they hold. Logical analyses, and

inductive and deductive reasoning provide the individual with the tools through which they can weave

observations, existing facts and beliefs together to arrive at a new state of understanding. For example,

many individuals developed their opposition to the Vietnam War by weaving together messages from

their childhood religious socialization that emphasized ‘peace and love,’ coupled with a firm belief in the

commandment “Thou shall not kill” (which rarely appears with a footnote highlighting exceptions).

Science. Science is yet another way through which we come to know. The word science gets

employed in at least three different ways, and possibly in different contexts. First, there is a dynamic

view of science which envisions it as an ‘activity or process.’ As a process, science consists of an

application of the scientific method (i.e., the application of theory to guide inquiry, the use of

measurement or manipulation, and an assessment of relationships under controlled, objective, and

systematic conditions)b. According to McMullin (1987), it is “the ensemble of activities of the scientist

in pursuit of his [her] goal of scientific observation and understanding” (p. 3). Second, there is a static

view of science. In this context, the word science refers to a ‘body of knowledge,’ a collection of

propositions (McMullin, 1987: 3) that has been created through the application of the scientific method.

The third view of science is heuristic. The heuristic view is focused on the purpose of science.

According to the heuristic perspective, the purpose underlying science is the ‘creation of a body of

knowledge’ to explain what is, to understand why, to comprehend how things got that way, and to

predict what will happen in the future. The main goals of science are explanation and prediction –

understanding what happens and why it happens (Salmon, 1987).

From a contextual perspective, it is important to note that science (e.g., the dynamic search to

b In part and in this context, ‘controlled’ means that the researcher attempts to rule out those forces that would render the results of their study non-credible; ‘objective’ means that there is a reliance upon measurement and the use of data for one’s observations and upon which to render one’s conclusions, as opposed to personal observation and judgment; and ‘systematic’ means that there is a logical progression from the beginning to the end of the process of knowledge creation.

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distinguish ‘what is’ from ‘what seems’ to be) in the natural sciences is seen as somewhat distinct from

science in the social sciences. Flyvbjerg (2001, p. 5) observes that the “social sciences have not had the

type of theoretical and methodological success that the natural sciences have,” while MacIntyre (1981)

notes that unlike achievements in the natural sciences the social sciences are completely devoid of the

discovery of law-like generalizations. Whether it is the natural or social sciences, the goal of science is

to discover or reveal the unknown through objective, controlled, and systematic research. Science

ultimately seeks to make available a body of knowledge arrived at through a means other than an

expression of personal opinion while guided, to varying degrees, by the canons of the scientific method.

Subjectivity and Distortions to Ways of Knowing

There are limitations to and learned cognitive tendencies that impair each of the ways through

which people come to the beliefs and opinions that they hold. The first six ways of knowing can be

referred to as ‘non-science.’ While each of the non-science ways of knowing have a role to play as

people come to know the world in which they live, each has its own unique strengths and limitations,

and each is subject to contamination caused by ‘subjectivity.’ Additionally, all ways of knowing – both

science and non-science, fundamentally rely on people acting with integrity, and not knowingly

misleading themselves or others.c

It is generally agreed that the perceptual process involves four distinct stages: sensation,

selection, organization, and translation. Each stage acts, in sequence, as both filter and organizer of the

stimuli that impacts individuals from the world that surrounds them. Together the stimuli determine how

an individual makes sense of his/her experiences. In addition, each stage is influenced by forces that

potentially contaminates the degree to which perceived and objective reality converge (Anderson,

Klatzky, & Murray, 1990; Berlyne, 1960). At the translation stage, for example, the perceptual process

is potentially contaminated by numerous forces such as first impression bias, recency effects, primacy

effects, attractiveness of the source effect, stereotyping, halo effects, projection, selective perception,

expectancy effect, and the Pygmalion (self-fulfilling prophecy) effect. For example, and as previously

noted, the ‘attractiveness of the source effect’ simply means that we are much more likely to accept as

true a statement or an assertion made by someone who is attractive to us than the same statement coming

c We acknowledge that “fudging” data and “scientific” findings has, unfortunately, occurred on more than one occasion. Throughout our essay we assume that when people seek to communicate their knowledge with others, they do so with ‘good intentions’ and ‘good will.’ In any way of knowing, failure to exhibit good will renders the knowing suspect. One need only think about Jim Jones or the current debate about the role of violence in Islam (Nomani, 2006) to realize that faith too is subject to potential problems of lack of good will.

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from a complete stranger. Science, on the other hand, has as its aim the use of a method for the creation

of a body of knowledge that strives to achieve control to thereby overcome the subjective interpretation

of events. The scientific method relies on the cumulative development of a body of knowledge, such

that one study builds on its predecessors as narrow theories slowly expand. While the goals of and

means to science are laudable, it is important to note that not all science is ‘good science.’ We will

examine the characteristics of good theory and science in more detail later.

It is generally recognized that the human species, like many others, is programmed to learn. As

consumers of a variety of life’s experiences it is not at all uncommon for us to develop a familiarity with

and confidence in one ‘teacher’ more than others. Such cognitive tendencies are learned and over a

period of time they become a habit of the mind.

A habit of the mind suggests a recurrent, often unconscious pattern of behavior that reflects an

inclination (tendency) of the mind’s operation. Some individuals, early in life, learn to become critical

thinkers, or exaggerators, or habitual liars, or optimistic or pessimistic in nature. The ability to change

one’s mind and to delay or suspend judgment, cynicism, indecision, fantasy, the use of emotion as

opposed to reason in the decision making process are illustrations of habits of the mind. Some people

learn to place trust in their intuition, others seek out expert opinion, while many others come to believe

that personal experience is the ‘best’ teacher. Each of these reflect a habit of the mind, that state

whereby an individual develops a cognitive tendency which ultimately informs their thinking, shaping

their beliefs and opinions, forming that which they know.

Some of the assertions made by Jesse Ventura, former Governor of the State of Minnesota,

illustrate habits of the mind based on something other than fact. In January of 2001, Governor Ventura

chose the University of Minnesota Duluth as the site to comment on his new tax and spending proposals

for higher education. During his visit the governor said to his largely student audience, “most of your

professors get paid more than I do and I’m the governor” (Holwerk, 2001). According to the Duluth

News-Tribune “the governor’s statement is demonstrably false” –his salary at the time was just over

$120,000, while the average assistant professor earned $44,000, associate professors earned $53,000,

and full professors were at $67,000. An editorial appearing in the Duluth paper claimed that his

statements revealed the Governor’s “underlying animus toward higher education.” The editorial went on

to say “What do you expect from a guy who made his career in the ‘fictional’ world of professional

wrestling and ‘extreme’ football? … Ventura’s unshakable, fictional attitudes inform his policies

regarding higher education.” (Holwerk, 2001). Fiction (make believe, non-truths, play-acting) was part

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of Ventura’s habit of the mind.

In the remainder of this essay we hope to provide an understanding of and a road map (i.e., a

users’ guide) for the employment of science as a way of knowing. Within this context we encourage all

students and consumers of information to ask themselves –Where, when, and how can science play a

role in assisting me in my coming to know? The answer to this question is simple –with more frequency

than is generally the case.

Science and the Levels of Science

In this discussion of science, we intend to expose the student to both the language of science,

and to the basic elements of the scientific paradigm. We seek to provide a framework which can be used

to aid the student in their understanding of information that is presented to them as stemming from

and/or that can be interpreted through the canons of the scientific method.

Science unfolds at two levels – conceptual and operational. There exists a tight and reciprocal

relationship between theory operating at the conceptual level and research (scientific inquiry) operating

at the operational level. Theory is constructed from research (observations from the ‘real’ world) and

serves to guide future and further scientific inquiry. It is through this interactive process that a body of

knowledge is built, much like the construction of a brick wall –one brick at a time, each brick building

upon and adding to that which has been laid before. We now examine in turn the conceptual and

operational levels of science.

The Conceptual Level of Science

The conceptual level of science works with constructs, conceptual definitions, conceptual

relationships and theory. A construct refers to any concept employed in science which has properties

that vary. For example, job satisfaction is a construct that is frequently employed as part of the micro-

organizational behavior literature. People display varying degrees (levels) of job satisfaction –some

people are job dissatisfied, some display low levels of job satisfaction, others are moderately satisfied,

while some are extremely job satisfied.

A conceptual definition is the formal meaning that is given to each construct. A parallel notion

is reflected by the dictionary in that it employs words to express the meaning of each word contained

therein. Conceptual definitions are useful because they allow us to converse in a standard way about

constructs. For example, Locke (1976) defined job satisfaction as a positive pleasurable emotional state

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that stems from a person’s evaluation of his/her job’s facilitating the attainment of one’s job values.

A conceptual relationship represents the articulation of the type of relationship (e.g., causal or

covariational, and direction) that is believed to exist between two or more constructs. For example, if we

were to tell you that there is a positive and causal relationship between job satisfaction and the

frequency of a person’s acts of good organizational citizenship this would be an illustration of a

conceptual relationship. The two constructs employed here are job satisfaction and acts of good

organizational citizenship. The type of relationship that is expressed is a positive one –meaning that as

the strength of job satisfaction increases so does the frequency of that person’s acts of good

organizational citizenship. The relationship is also said to be causal in nature, whereby job satisfaction

is positioned as a cause of those acts of citizenship.

Finally, theory plays a critical role at the conceptual level of science. Theory is a generalized

explanation of the nature and character of the relations among a set of interrelated constructs. A theory

consists of the definitions and propositions that present a systematic view of some phenomenon by

specifying relations among constructs with the purpose of explaining and predicting the phenomenon.

More specifically, a theory seeks to explain what a particular phenomenon is, and it also seeks to

answer the questions ‘how’ and/or ‘why’ something is. A theory may be as simple (i.e., a narrow

theory) as an explanation that discusses the nature and character of the relationship between two

constructs (i.e., X and Y), accompanied by an explanation that provides insight into the relationship

between two constructs. A theory may also be much more complex. There are many mid-range theories

that connect several constructs (e.g., X, Y, Z, U, V, W) and/or join several narrow theories together,

accompanied by the ‘story’ that explains the nature and character (i.e., the who, what, when, where, how

and why) of their relationship. Finally, there is grand theory which seeks to explain the totality of some

phenomenon. A grand theory of work motivation would explain what it is, when and for whom it exists,

while providing insight into how it comes into being, its causes, and its consequences. As of today, there

are many narrow and mid-range, yet virtually no grand theories in organizational studies.

By way of illustration, Homans’ (1961) theory of distributive justice, a theory of motivation,

provides us with partial insight into both the cause and consequences of job satisfaction (dissatisfaction).

His theory states that people are motivated to achieve ‘fairness’ when they are engaged in exchange

relationships. Fairness is defined as the perception that there is a balance in the exchange that takes

place. Fairness/unfairness is experienced when an individual looks at the outcomes received and the

inputs given in an exchange. When the Outcome-to-Input ratio is perceived to be equal to one, the

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exchange relationship is judged as fair, satisfaction in the exchange relationship is experienced, and the

individual will be motivated to repeat that exchange again in the future –a way that hedonistic people

seek to maintain satisfaction. When the Outcome-to-Input ratio is greater or less than one, unfairness is

perceived to exist. The lack of fairness is said to produce dissonance, feelings of dissatisfaction with the

exchange, and a motivation to take corrective action by modifying the outcomes and/or inputs to future

exchanges. Note that this illustration is an attempt to explain the interrelationship between a limited set

of constructs (e.g., outcomes from an exchange, inputs into an exchange, positive/negative feelings --

satisfaction/dissatisfaction-- with the exchange, dissonance/tension, motivation, and corrective

behavior).

“Theory,” according to Dubin (1976, p. 26), “tries to make sense out of the observable world by

ordering the relationships among elements that constitute the theorist’s focus of attention in the real

world.” Theory has an important role to play in our understanding of that world. Social psychologist

Kurt Lewin expressed it well when he stated that “nothing is so practical as a good theory” (Lewin,

1945, p. 129). Building upon Lewin’s (1945) statement, Van de Ven (1989) writes “good theory is

practical precisely because it advances knowledge in a scientific discipline, guides research toward

crucial questions, and enlightens ....” (Van de Ven, 1989, p. 486). Several scholars (e.g., Klein and

Zedeck, 2004; Kuhn, 1987) provide us with insight into what it is that constitutes a ‘good theory.’ Good

theory, according to Klein and Zedeck (2004) (1) offers novel insights –it provides a “sense of discovery

and illumination;” (2) is interesting –it is more than a “ho-hum” documentation of the obvious; (3) is

focused and cohesive – “good theory illuminates and clarifies, often by organizing, and thus simplifying,

a set of previously unorganized and scattered observations” ... it “renders real-world processes and

phenomena clear and coherent by simplifying and structuring our inchoate understanding of them. This

is only possible if the theory itself is clear and coherent” (p.932). They go on to note that good theory:

(4) is grounded in the relevant literature, but offers more than a review or integration of this literature;

and (5) presents clearly-defined constructs and offers clear, thorough, and thoughtful explanations of

how and why the constructs in the model are linked –“If clearly defined constructs are the building

blocks of good theory, then thorough and thoughtful propositions linking the constructs –explaining

what constructs lead to what, when, how, and why –provide the mortar” (p.932). Finally, they indicate

that good theory (6) is testable – the constructs are clear and precise; how the constructs are to be

measured and how key ideas are to be tested is clearly articulated; (7) in many fields, the theory has

practical implications [for example, good organization theory is theory that can be used to address

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organizational problems (e.g., the causes and consequences of job satisfaction)]; and (8) it is well-written

–the work presents a clear and logical flow, while it is simultaneously “clear, focused and interesting” (p.

933).

Application of the criteria presented by Klein and Zedeck (2004) reveals that not all theory (or

espoused theory) is good theory. Many purported theories, for example, are not testable, they are not

well grounded in the relevant literature, nor do they offer a clear explanation for what constructs lead to

what, when, how and why. It also is apparent that many people use the term ‘theory’ loosely (e.g.,

“What is your theory on why so many hurricanes have hit the gulf states over the past two years?”), as

they make reference to a personal opinion rather than offering observations that are grounded in sound

scientific inquiry.

Maslow’s (1943) popular theory of human motivation may fail the test of good theory (Hall &

Nougaim, 1968; Lawler & Suttle, 1972). While it offers an appealing, intuitively logical, interesting

explanation of human motivated behavior, the ‘theory,’ as articulated does not appear to be testable. For

example, Maslow failed to provide sufficient insight into many different facets of his theory, such as

when and where food and water satisfies a physiological as opposed to a safety need, when growth and

development fulfill the esteem as opposed to the self-actualization need, where is the divide between the

social and self-esteem needs, and he failed to provide an exact conceptual definition of self-actualization.

These are only a few of the questions, challenges and controversies that have surrounded this popular

‘theory’ of human motivation, leading to the question –Is the need hierarchy theory a ‘good theory,’ or is

it Maslow’s informed, insightful, and philosophical views on human motivation?

Theories provide the basis for research questions and hypotheses. Research questions inquire

about the relationship between two or more constructs (e.g., Is there a positive relationship between pay

level and subsequent levels of performance?). Researchers routinely seek to answer such questions by

testing research hypotheses. A hypothesis provides the ‘best possible’ answer to the question at the time

that it is posed. Thus, a hypothesis is a statement that expresses what the nature and direction of the

relationship is between two or more variables (e.g., There is a positive relationship between pay level

and subsequent levels of performance.).

The Operational Level of Science

The second level at which science unfolds is the operational level. The operational level is

where research is conducted. At this level the researcher seeks to explore, provide evidence in support

14

of (or to refute) the conceptual relationship (or theory) that s/he is interested in understanding. For the

purpose of differentiating the substantive level at which a conversation is unfolding, the language

employed at this level of science is different from that employed at the conceptual level (which employs

such words as constructs, conceptual definitions, conceptual relationships and theory). At the

operational level in science we deal with such terms as empiricism, variables, operational definitions,

measurement and manipulation, and empirical relationships.

Empiricism/empirical research is a term frequently employed within the context of the ‘conduct

of research.’ Schwab (1999) observes that “Empirical research can help obtain evidence of the veracity

of expected causal relationships ...” (p. 4) as they are expressed at the conceptual level. He goes on to

state that “empirical research addresses expected relationships through the systematic study of

relationships between scores obtained from cases on measures” (p. 4). As suggested by these comments,

empirical research consists of an examination of ‘expected’ relationships, through the systematic

observation (i.e., measurement and/or manipulation) of the constructs embedded in the hypothesis being

tested.

At the operational level of science the term variable is substituted for the term construct. Thus,

a variable is simply a construct manifested at the operational level of science, and as such it too has

properties that vary. There are several different properties that can characterize a variable --it can be

dichotomous, discrete, continuous, ordinal, or ratio in nature.

An operational definition refers to how a variable is actually measured (observed) or

manipulated (experimented with) when research is being conducted. The operational definition for a

variable should reflect the ‘conceptual definition’ that has been given to the construct. For example,

Kunin (1955) developed, and Dunham and Herman (1975) expanded, the Faces Scale (see Figure 1) as

an operational definition for the measurement of job satisfaction, which has been conceptually defined as

the positive emotional feelings (i.e., positive affect) that are produced as a result of one’s appraisal of

one’s job.

-------------- Insert Figure 1 here

--------------- Flexible working hours have been conceptually defined as a work schedule arrangement that

permits an employee to exercise an element of discretion in choosing their time of work (Pierce &

Newstrom, 1982). A researcher studying the job satisfaction effects of work scheduling might propose a

positive and causal relationship between the amount of work scheduling discretion and the level of job

15

satisfaction (a conceptual relationship). To test this hypothesized (conceptual) relationship the

researcher might take a group of employees and move them from a fixed work schedule (e.g., 8am to

5pm Monday through Friday) and put them on a flexible working hour arrangement, where the employee

can choose to start each and every working day anytime between 6am and 9am. The implemented work

schedule (a manipulation) would be the operational definition given to the conceptual definition for

flexible working hours. The Faces Scale, illustrated earlier, could be employed to assess (measure) job

satisfaction while the employees are working under each of the two schedules.

Like many words in the English language, not everyone employs the same conceptual definition

for a particular construct. Job satisfaction, for example, has been conceptually defined as the affective

(feeling) component of an attitude reflecting how one’s job makes one feel (Kunin, 1955; Weiss, Dawis,

England, & Loftquist, 1967). It has also been conceptually defined as simply “the overall evaluation one

has towards one’s job (Weiss & Cropanzano, 1996, p. 65). In addition, people doing research (e.g.,

students, managers, scientists) often create their own variable measures.d The use of different measures

to capture an underlying construct may generate a very different perspective on that construct. For

example, periodic national surveys of worker job satisfaction have frequently employed the following

operational definition –“If you were to do it all over again, would you have the same job?” (Leatherman,

2000). This assumes, correctly or incorrectly, that an answer of ‘yes’ implies job satisfaction and ‘no’

signals job dissatisfaction. The operational definition employed in the national survey is very different

(e.g., in terms of meaning, and possibly in terms of results generated) than that which is revealed by the

Faces Scales that we presented earlier. Because of this, it is important for students to understand both

the conceptual and operational definitions, if they truly want to understand what is being communicated.

Finally, an empirical relationship refers to a relationship that has been identified through

research. As previously noted, Schwab (1999) suggested that an empirical relationship is revealed by

measurement taken on the variables being studied and the interconnection between them. If a

researcher’s data revealed a correlation of .45 between job satisfaction and acts of good organizational

citizenship, this observation is an example of an empirical relationship. In the flexible working hour

study, if the researcher were to observe a significant increase in the level of job satisfaction (e.g., mean

satisfaction on the Faces Scale changing from 3.1 to 5.0) as workers were moved from a schedule with

no discretion to a schedule with discretion this observation would reflect an empirical relationship.

d It is extremely difficult to develop and validate a research tool. Consequently, we strongly encourage students and anyone else conducting research to employ ‘existing’ research measures, as opposed to developing their own. The use of existing instruments also makes available normative data with which one can compare their findings.

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Assuming that work scheduling flexibility is one cause of job satisfaction, this empirical relationship

would provide evidence in support of, yet not proof of a causal connection (i.e., a cause and effect

relationship) between the two variables (flexibility as the cause and job satisfaction as the effect).

There are many different ways through which we can learn about organizations, management,

and organizational behavior. Among some of the different types of research there are: (1) setting-based

strategies where research is carried out either in the field or in the laboratory; (2) time-based strategies

where each variable is measured at a single-point in time (i.e., cross-sectional studies), or where the

research is carried out across-time (i.e., longitudinal studies); (3) purpose-based strategies wherein

research that is conducted with the specialized purpose of validating a research instrument (i.e.,

validation research), or to address real relationships (i.e., substantive research); (4) data-based strategies

are characterized by the production of quantitative (e.g., as in the measurement of the job satisfaction

with the Faces scale), or qualitative data (e.g., illustrated by an employee’s recounting of work incidents

that resulted in feelings of job dissatisfaction that persisted over a long period of time); (5) source of

data-based strategies where researchers measure or manipulate the variables under investigation (i.e.,

primary data) or where the data to be worked with is collected from existing (archival) sources (e.g., last

year’s employee pay and performance information) commonly referred to as secondary data; and (6) the

technique-based strategies such as case studies, surveys, field studies, and experimental studies. The

seventh and final strategy is one that is based upon ‘researcher control.’ Typically this research method

is characterized by three different designs, namely the (a) correlational (non- or pre-experimental) design

where the researcher measures the variables under consideration, (b) quasi-experimental design where

the researcher studies a natural occurring event (e.g., organizational downsizing) or where s/he

manipulates the independent variable (e.g., introduces a compressed work week schedule) with virtually

no control over ‘who’ is involved in the study and without random assignment of the study participants

to either the control or experimental groups, and (c) the true (pure) experimental design where the

researcher exercises control over who is studied, who is exposed to the manipulation, and the

independent variable.

The Development of the Scientific Body of Knowledge –Theory and Empiricism

As previously noted, theory has an important role to play in our understanding of the world.

First, a good theory should summarize what it is known about the phenomenon in question. A good

theory is a generalized explanation that integrates what has been previously learned through empirical

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research that has been guided by the canons of the scientific method. Second, theory is intended to guide

further inquiry into a particular phenomenon, so as to advance, deepen and strengthen our understanding

of that phenomenon. Klein and Zedeck (2004) offer the following comment on the linkage between the

conceptual (theory) and operational (empirical) levels of science. “Theories provide meaning. They

allow us to understand and interpret data. Theories specify which variables are important and for what

reason, describe and explain the relationships that link the variables, and identify the boundary

conditions under which variables should or should not be related (Campbell, 1990). Theories help

identify and define problems, prescribe a means for evaluating or solving the problems, and facilitate

responses to new problems. They permit generalizations beyond the immediate sample and provide a

basis for making predictions. Theory tells us why [emphasis added] something occurs, not simply what

occurs. Research in the absence of theory is often trivial –a technical feat more likely to yield confusion

and boredom than insight. In contrast, research that is guided by theory, or that develops theory,

generates understanding and excitement” (p. 931).

Systematic observations of phenomena (events) provide the grist out of which theory is

constructed. Commonly theory in the behavioral and social sciences derives from authors combining

observations from the existing literature, experience, and rational and creative thought. Employing

deduction, general ideas and observations are honed, resulting in the articulation of very specific stories

of cause and effect relationships. Alternatively, there are scholars in the social and behavioral sciences

who employ induction, as they work from a specific, in-depth case (cf. Eisenhardt, 1989) and/or personal

observations (cf. Gersick, 1992) as they build a theory. Without regard for ‘how’ (i.e., the process

employed) a theory was constructed, theory is intended to provide us with hypotheses that are to be

tested through empirical (i.e., controlled, objective, systematic) research, the results of which are

intended for theory modification. This modified theory once again becomes the guiding light for further

testing giving rise to subsequent theory modification. It is through this iterative (repetitious) process that

a body of knowledge, insight into and an understanding of phenomenon emerges. Since there are few , if

any, grand theories that explain one hundred percent of the variance in some phenomena (e.g., the

totality of work motivation), too many people show a tendency to judge a theory as incomplete

(deficient) and dismiss it too readily. The sophisticated consumer of information, on the other hand,

recognizes that there are good theories that can aid our understanding of a small part of complex and

multi-dimensional phenomenon. For example, Homans’ (1961) theory of distributive justice provides

good insight into a small, but significant part of human motivation.

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It is this dynamic interplay between empiricism conducted at the operational level and theory

construction/refinement at the conceptual level that generates advances in our understanding. At any

point in time a theory, assuming that it is good theory, serves to inform the most current state of

understanding of a particular phenomenon.

How then does scientific knowledge develop and advance? Scholars commonly start with a

research question, formulate a hypothesis, and then seek to find evidence that refutes (disproves,

falsifies) the hypothesis. This basic process has not always been the norm as the philosophy of science

has been characterized by two distinct traditions. Initially scientists worked to verify a theory; assuming

that a statement must, in principle, be empirically verifiable in order for it to be both meaningful and

scientific. Subsequently, verification was replaced by Popper’s (1987) arguments favoring the use of

falsification as the preferred means to determine if a theory is scientific. Essentially the Popperian view

argues that unless a theory is falsifiable –that is, capable of being proven false-- it is not science.

Falisification is seen as both a more rigorous and at the same time a more realistic way to advance a

body of knowledge, because disproving a hypothesis may be easier than attempting to prove a

hypothesis to be true. For example, if one were to hypothesize that ‘all swans are white,’ it would be

much more efficient to look for a non-white swan (thus, disproving the hypothesis upon the location of

one black swan) than to attempt to find all of the swans in the world and to demonstrate that each of

them is white. In a recent Arkansas court case Judge William Overton employed falsification as one

criteria to determine that ‘creation science’ was not scientific and therefore should not be taught in

Arkansas’ public schools. It is important to note that Popper acknowledges that a theory which is not

falsifiable may, however, be both interesting and meaningful without it being scientific. Thereby,

suggesting the existence of scientific and non-scientific theories, while offering the observation that

many non-scientific theories may, for example, find their origin in creative and inductive reasoning.

Continuing with the question ‘how does knowledge within a scientific field develop,’

philosophers of science have advocated four different motors of change –the cumulative, evolutionary,

revolutionary, and gradualist models of development (Kourany, 1987). Each model takes a different

position on the question –“When does a scientific field advance?” Kourany (1987) notes that the

cumulative model is the one most frequently promoted in most science texts, and it simply proposes that

a scientific field progresses when new knowledge (i.e., concepts, observations, theories) gets added on

top of old knowledge. According to Popper (1987b), the evolutionary model proposes that new theories

in a field of scientific endeavor replace, rather than build upon, earlier theories (Kourany, 1987). More

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specifically, the evolutionary model proposes that a field progresses when an existing theory is tested

and negative results emerge refuting the theory, new tentative theories emerge from that which was

found coming to replace the current theory. Thus, a scientific field progresses when there is a succession

of replacements of existing theories, with new and more successful theories. Thomas Kuhn is seen as

one of the leading proponents of the theory of scientific revolution (Kourany, 1987; Kuhn, 1987; Kuhn,

1970). According to revolutionary model a body of knowledge rarely develops unless there is a

succession of radical shifts –radical shifts in its theories and associated facts, methods, and goals.

Finally, there is the gradualist model of change as espoused by Larry Laudan (1987). Gradualists argue

that a scientific field advances when it is characterized by a series of limited changes in its dominant

theories, facts, methods, and goals. Each changing distinctly in turn. Sometimes theories change,

sometimes goals change, and sometimes the methods employed in the conduct of science change.

Rarely do all three change at the same point in time. Thus, the development of a field comes amidst a

succession of separate changes (Kourany, 1987).

Types of Relationships

Science at both the conceptual and operational level is largely about the development of an

understanding of different phenomenon (e.g., the meaning of job satisfaction, the causes of and

consequences of this state). As such, science involves the development of an understanding of

relationships among phenomena. In this section, we intend to explore the word ‘relationship,’ as it is an

important part of science.

Relationship simply means that two or more things are associated, connected, or tied to one

another. Each of us has a relationship with our parents, in that we are connected or tied together. As is

readily apparent, there are many different types of relationship (e.g., friendships, parental, contractual,

causal). As should be evident though often confused, two phenomenon can be similar in appearance and

strongly related (e.g., identical twins) and still not be the same phenomenon. In science we also deal

with different types of relationships; as previously noted conceptual relationships are expressed in our

theories and empirical relationships are revealed in our research.

A relationship between two variables can be either covariational or causal in nature. In a related

manner, there are two different types of predictive relationships. At the outset a predictive relationship

is simply one where the appearance of one variable (e.g., X at time-one), with varying degrees of

certainty, expresses what will happen to another variable (e.g., Y at time-two). For example, farmers can

20

predict the rising of the sun with the crow of the rooster. In this instance, the presence of ‘X’ (rooster

crowing) enables the prediction of ‘Y’ (rising sun) under conditions where ‘X’ is not the cause of ‘Y.’

(While this example illustrates a co-variational relationship across time, such a relationship may also

occur at a single point in time.) The two variables have a relationship in that they appear or co-vary with

one another. It is also possible for ‘X’ to be the cause (i.e., determinant) of ‘Y.’ In this case ‘X’ would

precede the emergence of ‘Y,’ and ‘X’ is capable of predicting the emergence of ‘Y.’

From the perspective of direction (sign), we have positive and negative relationships. A positive

relationship simply means that as the value of one variable increases so too does the value of the second.

A negative relationship, on the other hand, means that as the value of one variable increases the value of

the second variable declines. It is also possible that there is no systematic (or predictable) relationship

between the value of one variable relative to the value of a second. A positive and a negative

relationship between two variables (e.g., X and Y) can occur under conditions where ‘X’ is the cause of

‘Y,’ and when ‘X’ is merely a covariate (not cause) of ‘Y.’

Relationships between two or more variables are further revealed by the strength of that

relationship. In the simplest of terms, the strength of a relationship can be revealed by the size of the

correlation coefficient between two variables, or the percent of variance in one variable that can be

predicted by (accounted for) knowledge of a second variable. Correlationally the strength of relationship

ranges from .00 to 1.00 (with a plus or minus revealing the direction/sign of the relationship between the

two variables). Two variables with a correlated relationship of .00 means no systematic relationship

and/or zero percent of the variance in one variable can be predicted (explained; accounted for) at this

time by the second variable. A correlated relationship of 1.00 means that there is a systematic

relationship between the two variables and that one hundred percent of the variance in one variable can

be explained by knowledge of the second variable. This conversion from the correlated value to the

percent of variance is found by taking the squared value of the correlations coefficient. A correlation

coefficient of .60 between two variables (e.g., job satisfaction and acts of organizational citizenship)

means that 36 percent of the variance of one variable (e.g., acts of citizenship) can be accounted for by

the second (e.g., job satisfaction).

The shape of the relationship reveals the pattern of change in one variable relative to the pattern

(amount) of change in the second variable. Relationships, therefore, can be linear or curvilinear in

nature. A linear relationship (i.e., one characterized by a straight line) is one for which there is a

constant and proportionate increase or decrease in the value between two variables. For example, there

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may be a linear relationship between education and salary, such that every year of education after high

school increases mean salary of an employee by five percent. That would indicate that the employee

with a typical four-year undergraduate degree could expect to earn about twenty percent more than an

‘average’ high-school graduate. A curvilinear relationship means that the relationship between two

variables takes the shape of a curve (e.g., monotonic, U-shaped, inverted-U-shape) rather than a straight

line. For example, it is often assumed that employee performance decreases as stress increases. This

assumption is only partially correct as the relationship is best represented by an inverted U-shaped

(curvilinear) relationship. Research evidence reveals that stress initially raises an individual’s arousal

level, causing a person to be more attentive to job demands and thus perform more effectively. At a

critical point, however, additional increases in stress and thus one’s arousal level makes the person

incapable of coping and their performance subsequently drops (McGrath, 1976).

Finally, there are variable-based relationships. In order to understand the different variable-

based relationships, it will be helpful to first understand the different types of variables. Among the

most common variables employed in science are the independent variable (i.e., the cause, antecedent, or

predictor), the dependent variable (i.e., the effect, consequence, criterion, outcome, the unit of analysis,

criterion), the moderating variable (i.e., the situation, contingency; the ‘yes-but’ or ‘I can think of the

exception’ variable), the intervening (also known as the mediating) variable (i.e., the one that comes in

between two other variables, such as that which comes in between the independent and dependent

variables), and the extraneous variable (i.e., that variable which lies outside of the specified conceptual

relationship, yet one that potentially plays a meaningful, though an unrecognized role). While there are

other types of variables (e.g., control, nuisance and suppressor) the primary ones have been identified

and defined.

We cannot provide a listing of variables as independent, dependent, moderating, intervening,

and extraneous within a discipline. The only way to ‘type’ a variable is by its placement within the

causal chain, and this can be revealed by the placement of the corresponding construct in the conceptual

relationship that is being explored. Consider the following two ‘research questions’: (1) Is there a

positive and causal relationship between pay level and subsequent levels of performance? and (2) Is

there a positive and causal relationship between the level of an individual’s performance and his/her

subsequent level of pay? In the first question ‘pay level’ is positioned as the independent variable (i.e.,

the cause, predictor, or antecedent) and ‘performance’ the dependent variable (i.e., the effect,

consequence, criterion, outcome). The second research question reverses their order. Thus, the

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variable’s type is determined by the research question that is being asked, or the placement of its parallel

construct in the conceptual relationship.

The first type of variable-based relationship is the main effect relationship, which is frequently

referred to as the independent-dependent variable relationship. In causal language it depicts the effect

(i.e., main effect) on the dependent variable produced by the independent variable. In non-causal (i.e.,

co-variation or correlational) language it means that the independent variable is a predictor of the

dependent variable. Visually this relationship when modeled looks like Figure 2-a, where X (control) is

the independent and Y (feelings of ownership) the dependent variable. Independent-dependent variable

relationships may have one or more independent (predictor) variables (see Figure 2-b). For example,

Pierce, Kostova, and Dirks (2003) proposed that feelings of organizational ownership (Y) finds its origin

in control over that target of ownership (X1), intimate knowing of the target of ownership (X2), and an

investment of the self (X3) into the target of ownership.

--------------- Insert Figure 2 here

---------------

A reciprocal relationship is one where two variables influence one another, in what might be

considered a back-and-forth influence relationship. For example, it is possible that the amount that one

is paid positively affects the level of that employee’s performance, which in turn positively impacts the

amount that they are subsequently paid.

Next we have the mediated relationship (also referred to as an ‘intervening’ variable

relationship). As noted above an intervening variable is one that comes in between two other variables

of interest. Figure 3 illustrates the mediated relationship, such that X is the independent variable, Y is

the dependent variable, and Z is the intervening (mediating) variable. Note that in the model illustrated

here the variable Z is positioned in between variables X and Y. Interpreting this relationship, we note

the following: (a) there is a significant relationship between X and Y, (b) there is a significant

relationship between X and Z, (c) there is a significant relationship between Z and Y, (d) X affects Y by

working through the effect that X has on Z, and (e) when the effects of Z are accounted for in Y there is

no longer any meaningful relationship between X and Y, as all of X’s effects on Y have been accounted

for by the fact that X works on (affects) Y through its effects on Z. In other words, the path between X

and Y is through Z; thus, X can only have a relationship with Y through its (i.e., X’s) relationship with Z

and Z’s relationship with Y. Under this condition we have full mediation. Consider the following

statement –“It is currently thought that formal employee ownership as an organizational arrangement

23

impacts employee organizational commitment by working through the employee’s psychological

experiences of ownership” (Pierce, Rubenfeld, & Morgan, 1991). This statement suggests that the

relationship between employee ownership as an organizational arrangement and organizational

commitment is mediated by psychological ownership (see Figure 3). As illustrated in Figure 3,

psychological ownership lies in the path between employee ownership as an organizational arrangement

and organizational commitment.

------------

Insert Figure 3 here

--------------

Figure 4 illustrates partial (part) mediation. If and when X (i.e., the independent variable)

works on Y (i.e., the dependent variable) both directly and in part (indirectly) through Z (i.e., the

intervening variable), it is said that we have partial mediation –only part of the relationship between the

independent and dependent variable stems from the influence that the independent variable has upon the

dependent variable by working through the intervening variable (an indirect effect), and part of its effect

is direct (i.e., a main effect). For example, work environment control affects organizational citizenship

behavior both directly and through its effects on feelings of ownership which, in part, stems from work

environment control (see Figure 4).

---------------

Insert Figure 4 here

----------------------

Next, we have the moderated relationship (also referred to as the moderating variable

relationship). Recall that the moderating variable is a situational or conditional variable. Quite simply

the moderating variable changes the nature (e.g., direction, strength) of the relationship between two

other variables. If someone were to say that job complexity causes job satisfaction for some employees

and dissatisfaction for others, we have an expression of a moderated relationship. The relationship

between job complexity (X) and job satisfaction (Y) is positive for some employees and negative for

others --note the relationship between X and Y went from positive to negative (a change in the sign of

the relationship occurred). The relationship could have also gone from strong positive (or strong

negative) to weak positive (or weak negative) with only its strength changing. What has not been

revealed in this statement is exactly what individual difference variable (i.e., a personal characteristic,

such as age, education, and need motivation, that can be used to differentiate people) gave rise to the

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change in relationship depicted. Until that variable is known (identified) we have an extraneous variable

at work (i.e., one not yet identified in the expressed conceptual relationship). When further research

reveals that the strength of the employee’s growth needs (W) is the ‘culprit,’ we will have identified the

moderating variable. The moderating variable relationship is modeled in Figure 5, and revealed by the

following expression –Job complexity leads to job satisfaction for employees who are motivated by their

growth needs, and much less so for those employees with weak growth need motivation.

-------------------------

Insert Figure 5 here

------------------------

Note that the moderating variable is working on the relationship between X and Y, and not

directly on either variable X or Y. This reveals that there is an interaction effect between X and W. The

two working in combination account for the impact (effect) on Y. (An interactive or joint effect means

that there is an outcome that is created when two or more things are simultaneously present, that would

not be there otherwise.)

Finally, there is the spurious relationship. The spurious relationship is a false, non-genuine, or

illegitimate relationship –a causal relationship that appears to exist between two variables, but one that

does not exist in reality. Consider the following hypothetical example (cf. Stone, 1978). A researcher is

interested in the relationship between coffee consumption and heart disease. Research gets conducted in

an organization involving employees from different levels in the organizational hierarchy (i.e., shop-

floor and lower-level supervisory personnel, middle and upper-level managers). A significant empirical

relationship (r = .46, p < .01) is observed between the two variables, leading the author to argue that

coffee consumption causes heart disease. Upon closer inspection of the study from which those findings

were reported it was observed that job level is positively related to (a) coffee availability and coffee

consumption, and simultaneously to (b) job induced stress and heart disease. Thus, the researcher’s

observation of a positive relationship between coffee consumption and heart disease, and the suggestion

that there is a causal relationship between the two variables. Instead, coffee consumption and heart

disease are spuriously related, since both variables were caused by job level. There are many interesting

examples of the spurious relationship. A few years ago, a Wall Street ‘theory’ arose suggesting that the

length of women’s skirts was related to the emergence of a bullish/bearish market

(www.investopedia.com/s/skirtlength theory.asp). More specifically, there was the observation that

when women’s skit length shortened (lengthened) the stock market became bullish (bearish). While the

25

two variables may be correlated with one another, rising and falling skirt lengths does not cause the

market to rise and fall. The relationship is a false relationship, one that may exist because both variables

have a common driver (e.g., when consumer confidence and excitement is high skirt may length may

shorten and become investors active, and when there is fear and gloom in the economy skirt length may

shorten and investors become bearish).

There are many other types of relationships, such as, the biased relationship, noise relationship,

multiple main-effect relationship, combined moderated and mediated relationship, and suppressor

variable relationship. Many of them build upon and integrate the variables that were previously

discussed, as well as, three basic relationships, namely, the independent-dependent, mediated and

moderated relationships.

Threats and Limitations to Science as a Way of Knowing

As previously noted, there are threats and limitations to the beliefs that we hold (i.e., our

knowing) arrived at through non-science. Knowing through science is also vulnerable to a variety of

threats. In this section we briefly comment on the role of the participants in the scientific process (the

passionately interested, disinterested participants), the role that orthodoxy can play as both a guiding

light to and a restriction on the scientific process, and two issues that pertain to scientific validity –

flawed data and flawed conclusions.

Interested / Disinterested Participants

As with other ways of knowing, subjectivity may influence the scientific method. Researchers,

for example, are not always ‘disinterested’ parties. Many of them will try to ‘sell you’ on the correctness

or soundness of their ideas, hypotheses, and theories. There is always a story to tell, one that details the

relationships under investigation, and the second provides the consumer of information with an

interpretation of the research findings –a story that interprets observed support for one’s hypotheses or

one that offers speculation as to why the observations were other than what was expected. Consumer

beware!

There may even be those times when the researcher’s reputation is at stake. Failure to

demonstrate empirical support for a popular theory into which one has invested them self may lead some

to over or under report (or fail to report) the results of their research. For example, there have been

allegations that Peters and Waterman (1984) faked some of the data for their bestseller In Search of

26

Excellence (Byrne, 2001). In addition, there may be those instances where the researcher stands to

financially profit from failing to report the findings from a scientific investigation. For example, it is

widely believed that capital markets are efficient, in the sense that all the published studies show an

inability of an individual investor to profit from inefficiencies in the capital markets. There remains,

however, a suspicion in some quarters that the possible cause of this is not that the markets are efficient,

but rather that researchers who find evidence of inefficiencies choose to trade on their findings rather

than publish them.

Increasingly the funding for research coming out of the nation’s universities is coming ‘private’

interests (e.g., corporations, politically-leaning think-tanks), as opposed to impartial agencies who are

interested in basic research and the advancement of the frontiers of knowledge for the benefit of society.

Organizations such as these are now playing an increasing role in defining the research questions

pursued, and simultaneously they are picking up the tab for the research being conducted. As a

consequence it is unlikely that they are disinterested parties, prompting many of them to act as

‘cheerleaders’ for the research that they are funding, by ‘overselling’ research findings if it will promote

the sale of a product, or failing to report on (or tainting) those findings that are likely to adversely affect

sales and corporate profits, or a special interest group’s cause.

Orthodoxy as a guiding light or constraining force

Another threat to sciences comes from the presence of ‘orthodoxy.’ In some disciplines (fields)

there exists a relatively well-entrenched accepted world-view. This orthodoxy both serves to guide

future empirical inquiry, while at the same time is stifles research and the publication of research

findings which challenge that orthodox perspective. Economist Brian Arthur, for example, related the

difficulty which he encountered when seeking to publish his work on positive returns economics. He

writes: “Ideas that involve some form of increasing returns are now acceptable in economics –indeed

they have become highly fashionable. But this was not always so. As recently as the mid-1980s, many

economists still regarded increasing returns with skepticism …. In a sense, ideas that made use of

increasing returns have always been part of the literature in economics. But in the past, they were only

partially articulated and were difficult to bring under mathematical control. And they tended to have

disturbing implications. As a result many in our profession chose to disregard or dismiss them. This

distaste reached its peak in the 1970s … in the main they were treated like the pathological specimens in

labeled jars to be paraded to medical students – anomalies, freaks, malformations that were rare, but that

27

nevertheless could serve as object lessons against interference in the natural workings of the economy”

(Arthur, 1994, xi-xii, italics added).

The scientific process

In terms of the science itself, there are two major threats to science as a way of knowing that we

focus on in this section. These threats stem from (a) flawed measurement and/or manipulation, and (b)

contaminated findings. The measures employed by a researcher must measure the intended variables,

otherwise we will be unable to draw any meaningful conclusions that pertain to those variables and their

underlying constructs. In addition, the researcher needs to exercise sufficient control in the conduct of

his/her study so that competing explanations for what was found can be eliminated. Without such

control it would be virtually impossible to address the true relationship between constructs.

Understanding flawed measurement (manipulation) and contaminated findings requires that

students first understand the meaning of the word validity and its different uses. The words validity and

reliability differ from their common usage, and within the realm of science there are two different uses

of the term validity –valid data and valid findings.

Flawed observations (bad data). To have credible (valid) findings, the data (i.e., scores,

observations) that derive from measurement must be credible (valid). The instrument that is employed

to measure a variable must measure that variable and nothing else. That is, the instrument should be

neither contaminated nor deficient. A contaminated measure is one that purports to measure ‘X,’ while

it simultaneously measures, in part, something else. A deficient measure is one that measures only part

of what it is that it purports to measure, leaving part of the construct unassessed. A measurement

instrument is said to be construct valid when it measures what it ‘purports to measure’ –nothing more

and nothing less. If a measure is construct valid the operational definition and the conceptual definition

reflect and correspond with one another.

While we may never be able to absolutely prove that our measure is measuring what it purports

to measure, we can build evidence (confidence or trust) in support of its construct validity. Among some

of these tests that the consumer can easily perform are tests for: face validity, content validity,

reliability, and behavioral validity. The test of face validity is as straight forward as the name of the test

implies –Does the measure (i.e., the operational definition for our variable) look like it measures

(assesses) the construct as defined by its conceptual definition? Do independent judges come to the

same opinion, giving us consistency in the judgment rendered? While the answer ‘yes’ does not

28

demonstrate construct validity, the evidence (i.e., the judgments rendered) is somewhat suggestive.

Content validity simply asks us to think about the construct’s domain –what does it include and what

does it not? How reflective of this domain is the actual measure itself? If the measure is said to be

neither deficient (i.e., not missing a major portion of the construct’s domain) nor contaminated (i.e., not

measuring something that is outside of the construct) it is judged as content valid. Once again, this does

not prove validity, but increases our confidence level. We encourage the reader to reflect upon the

conceptual definition of job satisfaction defined in terms of how one’s job makes them ‘feel,’ and ask

themselves about the construct validity of the Faces Scale and the national survey question.

Reliability is often confused with the words valid and validity. Reliability means consistent and

predictable. A measure needs to be reliable for it to be valid, and for it to give us valid data. Thus,

reliability becomes a necessary condition for validity even though it is not a sufficient condition. More

specifically, it is possible for a measure to be reliable yet not valid, while all valid measures are reliable.

Consider the following illustration --Get on and off of your bathroom scale several times and without

adding or subtracting what you are carrying onto and off of the scale. Do the readings match? If each

reading is 125 pounds we would conclude that the scale is reliable, that is, it gives you a consistent

(predictable) reading. If you actually weigh 125 pounds the reliable assessment is an accurate

assessment providing evidence in support of its measurement validity; if, however, you weigh more than

125 pounds the assessment is reliable, deficient, and lacking validity.

Finally, we can gain some additional insight into the validity of our measure by examining its

behavioral validity. The question posed by behavioral validity asks, how does the measure ‘behave’ in

its prediction and association with other variables? Does it relate to other variables consistently with

what theory tells us? When we are dealing with construct validity the relationships expressed in the

theory or conceptual model that we are working with is referred to as the construct’s nomological

network (Schwab, 1999). If we have a good theory which suggests that experiences of organizational

justice are positively correlated with job satisfaction, the observation of a positive relationship between

the two variables should increase our confidence that our measure of job satisfaction is actually

measuring what it purports to measure. Once again, this observation does not prove that we have a

construct valid measure of job satisfaction, it merely adds to the ‘stockpile’ of evidence –especially if it

has been judged to have face and content validity, coupled with demonstrated reliability.

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Flawed conclusions. To have valid research findings we must start with construct valid

measures on all of the variables that are specified in the conceptual relationship (or research question).

Thus, construct validity is a necessary, but not a sufficient condition to having valid empirical findings

(i.e., a valid observation). The two primary threats/challenges to valid research findings are internal and

external validity.

Internal validity simply asks whether there are rival explanations for the observed (empirical)

relationship. In the study of flexible working hours mentioned above, the researcher observed a decrease

in symptoms of physical and psychological stress following the conversion from the fixed working hour

arrangement to the flexible working hour arrangement. This study was conducted over a six month

period of time between the first and last assessment of stress symptoms and a five month period of time

elapsed after the implementation of the work schedule change and the second assessment of the stress

symptoms. The claim stemming from this study was that the implementation of the flexible working

hours resulted in a decrease in stress symptoms. The study’s author reasoned that the employees had

more flexibility in the morning enabling them to deal with delays in arriving at work due to inclement

weather, over sleeping, dealing with sick children, etc., which resulted in lower levels of stress when

they arrive and while at work. When considering the internal validity of this study, we ask –Are there

rival explanations for the decrease in stress? The presence of viable alternative explanations [e.g.,

environmental threats --the researcher’s control over the research environment could affect the observed

outcomes; between group effects --selection of which individuals/groups are to be studied and their pre-

existing condition could influence the observed outcome; across-time effects --historical (outside events)

that simultaneously occurred with the manipulation of the flexible hours could affect the observed

outcomes (Schwab, 1999)] for the observed change in the level of stress would call into question claims

of internal validity. Without any viable alternative explanation for the observed effect the researcher

would likely claim that the study’s findings appear to be internally valid.

The external validity question inquires about the ‘generalizability’ of the research findings. Are

the findings unique to the study that was conducted? If the research findings are generalizable

(extendable, applicable) to other settings, to another time and place, to a different sample of study

participants, then the test of external validity has been achieved. A study which finds no adverse stress-

related effects of a 12-hour work day conducted on telemarketers is unlikely to yield results that can be

applied to people who are air-traffic controllers at the world’s busiest airports as these two jobs may be

vastly different in terms of the burden of responsibility associated with the lives of others. In this case,

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the study is said to have constrained (limited) external validity (generalizability). Uniqueness in terms of

the cases studied, when the study was conducted, and/or where the study was conducted can constrain a

study’s external validity.

Study design. Not all research investigations are equally efficacious. As previously noted,

some research is conducted in the laboratory and some in the field (i.e., the ‘real world’). Field studies

generally possess greater external validity, while laboratory studies allow the researcher to exercise more

of the control needed for internal validity. Some research is experimental (i.e., conducted under

conditions where the independent variable is manipulated and subjects are randomly assigned to the

different study conditions), while other studies are simply correlational in nature (e.g., performed such

that both the independent and dependent variable are measured). Only in true experimentally designed

studies are we capable of ‘observing’ (and proving) causality and ascertaining the direction of the causal

arrow. The results stemming from correlational studies (e.g., an examination of the relationship between

job satisfaction and acts of good organizational citizenship) can be employed to ‘infer’ causality, but

‘never prove’ a cause and effect relationship. The strength and value of findings produced by the

popular correlational field study stem from: (a) the soundness of the theory being tested, (b) the validity

of the data collected, (c) their real world setting, (d) observations that confirm the hypothesized

relationships, and (e) replication, replication, replication –observing the same relationships time and

again, and in multiple settings.

Discussion

At the outset of this essay, we highlighted a common problem that people face – all too often,

they take information presented to them at face value, especially when it comes from an apparent

“authority figure.” Only rarely do people stop to ask themselves, “But how do they (or we) know this?”

Our primary goal in this essay is to help students become more sensitive to the ways by which they come

to know. We highlighted both science as a way of knowing, as well as, several non-science ways of

knowing. We suggested that there are limitations to science and non-science as ways of knowing, and

we acknowledged that not all science is ‘good science.’ To a large extent the limitations (shortcomings)

associated with each differ greatly, yet both are susceptible to personal biases that can ‘cloud’ our

knowing. Finally, we note that science can only generate answers to some questions, and a poor

application of the scientific method is likely to produce flawed observations and/or flawed conclusions.

31

We focused extensively on science because it is that method which is least understood and

possibly the most feared by many undergraduate students. We hope to encourage students to employ the

canons of science to aid their thinking about and organizing information that is presented to them. In

this final section, we present some concluding thoughts that are intended to help students become better,

more sophisticated consumers of information.

First, we hope that by reading this, students will become more sensitized both to the rhetoric

they hear employed in everyday speech, as well as, the bases underlying the assertions they hear in

conversation. When students become more cognizant of the ways of knowing underlying the assertions

made by potential sources of influence, they should be better able to dissect those assertions and,

hopefully, make better judgments about whether to integrate those assertions into their knowledge base

or to rebut them.

Second, we encourage students to apply the canons of science – constructs, relationships,

sources of variance, etc. – to issues presented to them by others, regardless of whether those others used

the scientific method in developing their beliefs. Applying these canons will help students evaluate

whether the assertions they are hearing make sense, and whether the posited relationships are likely to

hold in situations they themselves are likely to encounter. In terms of the first part, students need to ask,

“What is the nature of the relationship presented? What are the data? How was the assertion verified?”

Answering these questions will help listeners assess the veracity of the information presented to them.

In terms of the second part, by evaluating the basis of the assertion, the listener will be able to consider,

“Will this assertion, even if true elsewhere, hold in my circumstance?” In other words, students will be

able to contemplate the generalizability of the information.

Third, because there is no ‘official agency’ to screen the veracity of assertions – and indeed, if

there were, we would be wary of such an Orwellian “Ministry of Truth” (Orwell, 1950) – students need

to understand the credibility both of the assertions made and of the maker. This is particularly important

because students and practicing managers often take the words of “experts” as “truth,” and find it

exceedingly hard to question the veracity of authority figures.

Fourth, even when reading a published scientific study, students need to develop expertise in

questioning the study. They need to think carefully about the validity and reliability issues discussed

earlier in this essay. They need to consider whether the study conducted was experimental or merely

correlational in nature, and how the findings are being framed (e.g., as causal or correlational in nature).

32

Fifth, students need to consider what their default way of knowing is as fostered by their own

habits of the mind. Do you tend to rely on personal experience, experiences of others, faith, etc.? By

recognizing your default way of knowing, you may be able to alert yourself to some of the biases

inherent in it.

While we have focused on science as a way of knowing, and we may have created the

impression as to its general superiority as a way of knowing, it is important to recognize that science also

has limits and limitations. Science as a way of knowing has limits which are both ethereal (not earthly,

airy) and practical. From an ethereal perspective science can lead to insights only into that which lends

itself to measurement and/or manipulation and thus empirical testing. Thus, when one considers

phenomena which are beyond knowing empirically, such as the existence of God and the possibility of

an afterlife, science will provide little guidance. At this point, we need to turn to ways of knowing

which specifically contemplate the unknowable, such as faith and intuition, with full recognition of their

inherent limitations.

Finally we note that upon graduation many students (business and liberal arts) will find

themselves on an employment track leading to a managerial position. At the same time they will find

themselves increasingly exposed to the ‘pop’ management literature (e.g., articles in business

periodicals, books appearing on their manager’s bookshelf and in airport bookstores), and sidetracked

from a literature that has been carefully crafted by following the canons of the scientific method.

Students and managers need to be especially wary of the ‘pop’ literature because it is not necessarily

based on the careful research of the scientific method, and it has not necessarily been subjected to a

rigorous review process. Much of the ‘pop’ management literature has been motivated by the author’s

desire to tell a personal story, a consultant’s desire to sell his/her consulting services, the desire to

generate a ‘buck or two,’ and the stroking of one’s ego. Most of this literature is presented in a simply

and straightforward format, and in a passionate and convincing way. Rising to the status of a

‘management fad’ today, passing recognition tomorrow, and finally totally giving way to a new flavor in

yet another ‘best seller’ tomorrow.

When confronting this ‘pop’ literature, it will be incumbent upon you the student to separate the

valuable ‘grain’ from the worthless ‘chaff.’ We recommend that students (and managers) themselves

exercise caution, and use of the methods and questions we have articulated in this essay to better assess

the ideas that they are presented with in their professional careers.

Conclusion

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Every day, be it in the student union, on the evening news, from a favorite subscription, on the

internet, in class or a favorite pub, students are bombarded by many different assertions, presented to

them by many different people, and reflecting many different ways of knowing. Students and all other

consumers of information who unthinkingly accept all of these as ‘true’ may come to be highly

misinformed and as a consequence experience a variety of negative outcomes. However, application of

the canons of the scientific method as a lens to examine such assertions, and relying on the principles of

the scientific method to expand one’s own knowing wherever possible, will help generate a more well

informed platform from which to understand and navigate through life’s experiences.

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Figure 1. The Faces Scale.

The male faces were originally developed by T. Kunin (1955) as reported in Personnel Psychology, 8:65-78. The matching female faces were created by R. B. Dunham and J. B. Herman and published in the Journal of Applied Psychology, 60:629-631, copyright 1975 by the American Psychological Association. Reprinted with permission of the authors.

35

36

Figure 2Independent – dependent variable relationships

2a. Single independent variable 2b. Multiple independent variables

X

Control overtarget

Y

Feelings ofownership

X1Control

over target

X2Intimate knowing

of target

X3Investment of

self

YFeelings ofownership

37

Figure 3.A fully-mediated relationship

X

Formalemployeeownership

Z

Employee’spsychologicalexperiencesof ownership

Y

Employeeorganizationalcommitment

38

Figure 4A partially mediated relationship

X

Workenvironment

control

Z

Workenvironment-

basedpsychological

ownership

Y

Organizationalcitizenshipbehavior

39

Figure 5A moderated relationship

XJob complexity

YJob satisfaction

WEmployee growth needs

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Biographical Sketch

Jon L. Pierce is Horace T. Morce Distinguished Professor of Organization and Management at the Labovitz School of Business and Economics, University of Minnesota Duluth. He received his Ph.D. inmanagement and organizational studies from the University of Wisconsin-Madison. His research focuses on the psychological of work and organizations in general, and most recently on psychological ownership, and self-esteem within the work and organizational context.

Geoffrey G. Bell, PhD, CA is Associate Professor of Management at the Labovitz School of Business and Economics, the University of Minnesota Duluth. His primary research interest lies at the intersection of industrial clusters, social networks, trust, and knowledge-sharing. He also researches and teaches in the area of business ethics. He has recently published articles in Journal of Business Ethics, Strategic Management Journal, and Academy of Management Review.

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