concepts: generalizable properties or characteristics associated with objects,
events, or people. While objects such as a person, a firm, or a car are not concepts, their specific
characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for
innovation, and a car’s weight can be viewed as concepts.
Knowingly or unknowingly, we use different kinds of concepts in our everyday
conversations. Some of these concepts have been developed over time through our shared
language. Sometimes, we borrow concepts from other disciplines or languages to explain a
phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be
used in business to describe why people tend to “gravitate” to their preferred shopping
destinations. Likewise, the concept of distance can be used to explain the degree of social
separation between two otherwise collocated individuals. Sometimes, we create our own
concepts to describe a unique characteristic not described in prior research. For instance,
technostress is a new concept referring to the mental stress one may face when asked to learn a
new technology.
Concepts may also have progressive levels of abstraction. Some concepts such as a
person’s weight are precise and objective, while other concepts such as a person’s personality
may be more abstract and difficult to visualize. A construct is an abstract concept that is
specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple
concept, such as a person’s weight, or a combination of a set of related concepts such as a
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person’s communication skill, which may consist of several underlying concepts such as the
person’s vocabulary, syntax, and spelling. The former instance (weight) is a unidimensional
construct, while the latter (communication skill) is a multi-dimensional construct (i.e., it
consists of multiple underlying concepts). The distinction between constructs and concepts are
clearer in multi-dimensional constructs, where the higher order abstraction is called a construct
and the lower order abstractions are called concepts. However, this distinction tends to blur in
the case of unidimensional constructs.
Constructs used for scientific research must have precise and clear definitions that
others can use to understand exactly what it means and what it does not mean. For instance, a
seemingly simple construct such as income may refer to monthly or annual income, before-tax
or after-tax income, and personal or family income, and is therefore neither precise nor clear.
There are two types of definitions: dictionary definitions and operational definitions. In the
more familiar dictionary definition, a construct is often defined in terms of a synonym. For
instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is
defined as an attitude. Such definitions of a circular nature are not particularly useful in
scientific research for elaborating the meaning and content of that construct. Scientific research
requires operational definitions that define constructs in terms of how they will be
empirically measured. For instance, the operational definition of a construct such as
temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or
Kelvin scale. A construct such as income should be defined in terms of whether we are
interested in monthly or annual income, before-tax or after-tax income, and personal or family
income. One can imagine that constructs such as learning, personality, and intelligence can be
quite hard to define operationally.
Figure 2.1. The theoretical and empirical planes of research
A term frequently associated with, and sometimes used interchangeably with, a
construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from
low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain
constant). However, in scientific research, a variable is a measurable representation of an
abstract construct. As abstract entities, constructs are not directly measurable, and hence, we
look for proxy measures called variables. For instance, a person’s intelligence is often measured
as his or her IQ (intelligence quotient) score, which is an index generated from an analytical and
pattern-matching test administered to people. In this case, intelligence is a construct, and IQ
score is a variable that measures the intelligence construct. Whether IQ scores truly measures
one’s intelligence is anyone’s guess (though many believe that they do), and depending on
12 | S o c i a l S c i e n c e R e s e a r c h
whether how well it measures intelligence, the IQ score may be a good or a poor measure of the
intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a
theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical
(abstract) plane, while variables are operationalized and measured at the empirical
(observational) plane. Thinking like a researcher implies the ability to move back and forth
between these two planes.
Depending on their intended use, variables may be classified as independent,
dependent, moderating, mediating, or control variables. Variables that explain other variables
are called independent variables, those that are explained by other variables are dependent
variables, those that are explained by independent variables while also explaining dependent
variables are mediating variables (or intermediate variables), and those that influence the
relationship between independent and dependent variables are called moderating variables.
As an example, if we state that higher intelligence causes improved learning among students,
then intelligence is an independent variable and learning is a dependent variable. There may be
other extraneous variables that are not pertinent to explaining a given dependent variable, but
may have some impact on the dependent variable. These variables must be controlled for in a
scientific study, and are therefore called control variables.
Figure 2.2. A nomological network of constructs
To understand the differences between these different variable types, consider the
example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’
academic achievement, then a measure of intelligence such as an IQ score is an independent
variable, while a measure of academic success such as grade point average is a dependent
variable. If we believe that the effect of intelligence on academic achievement also depends on
the effort invested by the student in the learning process (i.e., between two equally intelligent
students, the student who puts is more effort achieves higher academic achievement than one
who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also
view effort as an independent variable and intelligence as a moderating variable. If academic
achievement is viewed as an intermediate step to higher earning potential, then earning
potential becomes the dependent variable for the independent variable academic achievement,
and academic achievement becomes the mediating variable in the relationship between
intelligence and earning potential. Hence, variable are defined as an independent, dependent,
moderating, or mediating variable based on their nature of association with each other. The
overall network of relationships between a set of related constructs is called a nomological
network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract
constructs from observations, but also being able to mentally visualize a nomological network
linking these abstract constructs.
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Propositions and Hypotheses
Figure 2.2 shows how theoretical constructs such as intelligence, effort, academic
achievement, and earning potential are related to each other in a nomological network. Each of
these relationships is called a proposition. In seeking explanations to a given phenomenon or
behavior, it is not adequate just to identify key concepts and constructs underlying the target
phenomenon or behavior. We must also identify and state patterns of relationships between
these constructs. Such patterns of relationships are called propositions. A proposition is a
tentative and conjectural relationship between constructs that is stated in a declarative form.
An example of a proposition is: “An increase in student intelligence causes an increase in their
academic achievement.” This declarative statement does not have to be true, but must be
empirically testable using data, so that we can judge whether it is true or false. Propositions are
generally derived based on logic (deduction) or empirical observations (induction).
Because propositions are associations between abstract constructs, they cannot be
tested directly. Instead, they are tested indirectly by examining the relationship between
corresponding measures (variables) of those constructs. The empirical formulation of
propositions, stated as relationships between variables, is called hypotheses (see Figure 2.1).
Since IQ scores and grade point average are operational measures of intelligence and academic
achievement respectively, the above proposition can be specified in form of the hypothesis: “An
increase in students’ IQ score causes an increase in their grade point average.” Propositions are
specified in the theoretical plane, while hypotheses are specified in the empirical plane. Hence,
hypotheses are empirically testable using observed data, and may be rejected if not supported
by empirical observations. Of course, the goal of hypothesis testing is to infer whether the
corresponding proposition is valid.
Hypotheses can be strong or weak. “Students’ IQ scores are related to their academic
achievement” is an example of a weak hypothesis, since it indicates neither the directionality of
the hypothesis (i.e., whether the relationship is positive or negative), nor its causality (i.e.,
whether intelligence causes academic achievement or academic achievement causes
intelligence). A stronger hypothesis is “students’ IQ scores are positively related to their
academic achievement”, which indicates the directionality but not the causality. A still better
hypothesis is “students’ IQ scores have positive effects on their academic achievement”, which
specifies both the directionality and the causality (i.e., intelligence causes academic
achievement, and not the reverse). The signs in Figure 2.2 indicate the directionality of the
respective hypotheses.
Also note that scientific hypotheses should clearly specify independent and dependent
variables. In the hypothesis, “students’ IQ scores have positive effects on their academic
achievement,” it is clear that intelligence is the independent variable (the “cause”) and academic
achievement is the dependent variable (the “effect”). Further, it is also clear that this
hypothesis can be evaluated as either true (if higher intelligence leads to higher academic
achievement) or false (if higher intelligence has no effect on or leads to lower academic
achievement). Later on in this book, we will examine how to empirically test such cause-effect
relationships. Statements such as “students are generally intelligent” or “all students can
achieve academic success” are not scientific hypotheses because they do not specify
independent and dependent variables, nor do they specify a directional relationship that can be
evaluated as true or false.
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Theories and Models
A theory is a set of systematically interrelated constructs and propositions intended to
explain and predict a phenomenon or behavior of interest, within certain boundary conditions
and assumptions. Essentially, a theory is a systemic collection of related theoretical
propositions. While propositions generally connect two or three constructs, theories represent
a system of multiple constructs and propositions. Hence, theories can be substantially more
complex and abstract and of a larger scope than propositions or hypotheses.
I must note here that people not familiar with scientific research often view a theory as
a speculation or the opposite of fact. For instance, people often say that teachers need to be less
theoretical and more practical or factual in their classroom teaching. However, practice or fact
are not opposites of theory, but in a scientific sense, are essential components needed to test
the validity of a theory. A good scientific theory should be well supported using observed facts
and should also have practical value, while a poorly defined theory tends to be lacking in these
dimensions. Famous organizational research Kurt Lewin once said, “Theory without practice is
sterile; practice without theory is blind.” Hence, both theory and facts (or practice) are
essential for scientific research.
Theories provide explanations of social or natural phenomenon. As emphasized in
Chapter 1, these explanations may be good or poor. Hence, there may be good or poor theories.
Chapter 3 describes some criteria that can be used to evaluate how good a theory really is.
Nevertheless, it is important for researchers to understand that theory is not “truth,” there is
nothing sacrosanct about any theory, and theories should not be accepted just because they
were proposed by someone. In the course of scientific progress, poorer theories are eventually
replaced by better theories with higher explanatory power. The essential challenge for
researchers is to build better and more comprehensive theories that can explain a target
phenomenon better than prior theories.
A term often used in conjunction with theory is a model. A model is a representation of
all or part of a system that is constructed to study that system (e.g., how the system works or
what triggers the system). While a theory tries to explain a phenomenon, a model tries to
represent a phenomenon. Models are often used by decision makers to make important
decisions based on a given set of inputs. For instance, marketing managers may use models to
decide how much money to spend on advertising for different product lines based on
parameters such as prior year’s advertising expenses, sales, market growth, and competing
products. Likewise, weather forecasters can use models to predict future weather patterns
based on parameters such as wind speeds, wind direction, temperature, and humidity. While
these models are useful, they may not necessarily explain advertising expenditure or weather
forecasts. Models may be of different kinds, such as mathematical models, network models, and
path models. Models can also be descriptive, predictive, or normative. Descriptive models are
frequently used for representing complex systems, for visualizing variables and relationships in
such systems. An advertising expenditure model may be a descriptive model. Predictive
models (e.g., a regression model) allow forecast of future events. Weather forecasting models
are predictive models. Normative models are used to guide our activities along commonly
accepted norms or practices. Models may also be static if it represents the state of a system at
one point in time, or dynamic, if it represents a system’s evolution over time.
The process of theory or model development may involve inductive and deductive
reasoning. Recall from Chapter 1 that deduction is the process of drawing conclusions about a
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phenomenon or behavior based on theoretical or logical reasons and an initial set of premises.
As an example, if a certain bank enforces a strict code of ethics for its employees (Premise 1)
and Jamie is an employee at that bank (Premise 2), then Jamie can be trusted to follow ethical
practices (Conclusion). In deduction, the conclusions must be true if the initial premises and
reasons are correct.
In contrast, induction is the process of drawing conclusions based on facts or observed
evidence. For instance, if a firm spent a lot of money on a promotional campaign (Observation
1), but the sales did not increase (Observation 2), then possibly the promotion campaign was
poorly executed (Conclusion). However, there may be rival explanations for poor sales, such as
economic recession or the emergence of a competing product or brand or perhaps a supply
chain problem. Inductive conclusions are therefore only a hypothesis, and may be disproven.
Deductive conclusions generally tend to be stronger than inductive conclusions, but a deductive
conclusion based on an incorrect premise is also incorrect.
As shown in Figure 2.3, inductive and deductive reasoning go hand in hand in theory
and model building. Induction occurs when we observe a fact and ask, “Why is this happening?”
In answering this question, we advance one or more tentative explanations (hypotheses). We
then use deduction to narrow down the tentative explanations to the most plausible
explanation based on logic and reasonable premises (based on our understanding of the
phenomenon under study). Researchers must be able to move back and forth between
inductive and deductive reasoning if they are to post extensions or modifications to a given
model or theory, or built better ones, which are the essence of scientific research.
