1. In this lecture we will explore the basic elements of scientific research. These elements are
common across all scientific inquiries, ranging from hard sciences such as math and
chemistry, to social sciences such as psychology or criminology.
Specifically, our goal is to become familiar with these key concepts:
1. Variables, Concepts and Constructs
2. Hypotheses
3.
3 Theories
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2. Let’s take the notion of “theory” as an example. We all have our own theories in everyday life. Do you like to
cook? What do you like to make? What ingredients and methods bring about the best tasting dish?
cook? What do you like to make? What ingredients and methods bring about the best tasting dish?
What do you believe would make the best hummus or vegetable soup or chili?
The ingredients are the “concepts” (or independent variables) and the taste is the outcome (or dependent
variable).
For example, perhaps you believe that the best tahini makes the best hummus. In other words, you believe
that the quality of tahini (independent variable) affects the taste of hummus (dependent variable.) these
variables will be explained in detail later.
You probably have a bunch of reasons why tahini is so important. These reasons constitute your theory about
hummus making. This theory helps you form a hypothesis about the relationship between tahini and
hummus.
We also have lots of informal theories that guide us in our lives – theories about child rearing or proper
maintenance of an automobile. The difference between such informal (or “folk”) theories and more formal,
scientific theories is simply that:
1. Scientific theories are more rigorous in logical reasoning, whereas informal theories can contain logical
inconsistencies when examined more carefully (e.g., professional athletes make a lot more money than
inconsistencies hen e amined more caref ll (e g professional athletes make a lot more mone than
the rest of us; poor people commit more white collar crime than rich people do)
2. Scientific theories are usually examined empirically using a systematic method (e.g., a systematic survey
of athletes’ income over the entire duration of their professional lives; a correlational study of socio‐
economic status and white collar crime rate)
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3. Your book defines these terminologies in a fairly general way
Here we try to illustrate these terminologies using concrete examples. Please contact the
instructor right away if you are still confused about these 3 terms after reviewing the slides
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4. Concepts are smaller units of “ideas”
A concept usually contains a single idea, along a single dimension (we’ll explain dimension
on later slides)
Construct’s on the other hand, are broader ideas that include multiple
concepts/dimensions
These examples are designed to help you see their differences more concretely
These examples are designed to help you see their differences more concretely
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5. Variables tend to be very specific and quantifiable
A variable usually measures an (abstract) concept, or a small aspect of a concept
For example, net income (as a variable) can be a measure of firm performance (an abstract
concept) or a dimension of firm performance (other dimensions may include customer
satisfaction, stock price, etc)
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6. Variables are not just limited to things that are easy to count (e.g., price, number of stocks)
They can also be measures of more abstract concepts, such as trust, morale, emotion or
culture
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7. Every concept or construct must be defined operationally if we want to use them for
empirical research. In other words, if we want to measure something abstract (e.g.,
happiness), we need to tell people how we are going to count it (e.g., number of smiles per
hour?)
Operational definition is a clear specification of how you will measure the variable.
For example, with the gender variable, one way to operationalize is to have people self
report their gender on a survey questionnaire. An alternative operationalization is to hire a
report their gender on a survey questionnaire An alternative operationalization is to hire a
nurse to give everyone a physical inspection!
Another example is customer satisfaction. There are several ways to operationalize it:
Operational definition 1: customer’s self report on a satisfaction questionnaire
Operational definition 2: average reviews on Amazon
Operational definition 3: Number of times products are returned to store
The importance of operational definition is illustrated by how the concept of “poverty” is
defined.
The level of poverty in countries in third world countries is very low with respect to the
developed with countries. However, students may be surprised to know that the $
threshold to define poverty is different in both groups is different (if the threshold were not
threshold to define poverty is different in both groups is different (if the threshold were not
different for both groups, everybody would be poor in the third world (compared to
incomes in countries like the U.S.) or everybody would be rich in the U.S. (compared to
incomes in the third world).
Why are operational definitions so important? Imagine that you read that the
unemployment in the U.S. is much lower than the unemployment in the U.K. Under what 7
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8. When you state your research questions, you should be able to identify your variables right
away from the questions. For example, if the questions is about whether men or women
are the happier gender, then you should be able to see two variables – gender as variable
#1, and level of happiness as variable #2.
Your research questions are the “report” type if you just summarize each variable alone.
For example, you report how many men vs. women there are in the sample. You also
report on average how happy they are. But you don t discuss how the two variables relate
report on average how happy they are But you don’t discuss how the two variables relate
to each other – is one gender happier than the other? That’s a “descriptive”, not “report”
type of question.
If you ask “Does gender explain why you’re happy/unhappy?” then this becomes an
explanatory research question.
If you ask “Given a particular gender, can we predict how happy a person might be?” this
becomes a predictive question.
All these questions involve exactly the same two variables – gender and happiness.
However, they specify different kinds of relationships between these two variables.
Different research questions require different research designs to obtain the answers we
Different research questions require different research designs to obtain the answers we
seek.
Caution: A variable is a variable only if it "varies" across subjects in your study.
E.g., How many people will attend a yoga class?
Truth or myth? ‐ The yoga class would be the independent variable and the number of 8
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9. When identifying your variables, make sure they are uni‐dimensional. In other words, do
they vary along a single dimension?
On this slide you can see that gender is a uni‐dimensional variable, whereas “niceness” can
be a multi‐dimensional variable.
Other examples of multidimensional variables are “City’s best interest,” “Socio‐economic
status, IQ, program success, emotion, etc
status ” “IQ ” “program success ” “emotion ” etc. these are also usually referred to as
these are also usually referred to as
constructs
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10. On the flip slide, sometimes two variables may actually be the same one.
One common mistake is to consider variables that overlap or that they are not really
distinct from each other. A crude example could be if I want to study the factors affecting
students’ success in a course and I believe that it is important to measure the emotional
approach to the course which has two dimensions: how much the student loves the course
and how much he/she hates the subject! (I know it is a silly example but proves my point
☺)
On the slide you can see that it’s hard to imagine that someone would be polite and rude
at the same time, or impolite but not rude at the same time. In this case, politeness and
rudeness are probably not two separate variables, but rather the two ends of the same
spectrum (variable.)
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11. When we talk about a testable hypothesis, we mean if the hypothesis can be proved to be
false (we cannot prove if a proposition is true – we can only prove it to be wrong – we
discussed this idea in the previous session.)
Developing hypothesis is about asking questions about how variables relate to one another
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12. In “How X relates to Z conditional of Y “ Y or gender is the moderator variable
For your research project, you should concentrate on rather simple questions with 2 or 3
variables at the most.
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13. These diagrams are visual illustrations how variables may relate to each other
Causation is one of the most difficult relationships to “prove”
The third relationship, called a conditional model, will be explained in more detail in the
next slide.
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14. Variable Y is called the moderator and changes the strength of the relation between X and Z
or the nature of that relationship. In the example given, it may be that stock price
decreases more quickly than usual when a data breach is reported. However, this effect is
more dramatic, that is, positively moderated, when the firm is large (i.e., with more than
$100 million of net income.)
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15. Notice that we started using the term models in the previous slide, as soon as the
relationships we started to study moved beyond one. Remember that a model is a
simplified representation of phenomena. The first model here tries to explain how top
management beliefs and participation influence the success of complex programs. The
second model is a variation of the “Technology Acceptance Model” that tries to explain the
antecedents of Information Technology (IT) adoption by users.
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16. The meaning of “scientific theory” is different from the way we use the word “theory” in
every day language – “speculative” as opposed to fact/practice
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17. Parsimony: Using a minimal number of concepts, principles, or assumptions to explain a
maximal number of cases
An example of a theory that’s not parsimonious:
Jason may have a theory of why Enron failed – He speculated over 50 reasons –
overcompensated CEO, greedy consultants, lazy employees, lack of a hotline, too many
acquisitions, too few outside board members, disfranchised corporate culture, company
size too big, growth too fast, CEO has a private jet, etc.
size too big growth too fast CEO has a private jet etc
He speculated that all 50 factors must be present in order for a company to fail.
Turned out that these 50 factors only apply to Enron. They don’t apply to many other
companies that also failed miserably.
We would say that Jason’s theory is not parsimonious because it’s an entangled web of
principles, speculations, overlapped reasonings, etc that only explain one isolated case (i.e.,
Enron)
In contrast, if Jason could organize these 50 different thoughts into 3‐4 major themes that
not only apply to Enron, but many other similar cases (e.g, WorldCom and others), his
theory would be more powerful (more useful) and more parsimonious
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18. Why Companies Fail by Ram Charan and Jerry Useem
http://money.cnn.com/magazines/fortune/fortune_archive/2002/05/27/323712/index.ht
m
This article speculates 10 factors that explain why companies would fail.
Although it’s not technically a rigorous scientific theory (much tighter logic and more in‐
depth elaboration is needed,) it is a good illustration of the types of theories we commonly
find in the popular press
find in the popular press
If you want to apply this theory to empirical research, the first step is to identify concepts
and see if you can measure them somehow. To do that we should begin with operational
definitions.
How would you operationally define the following “concepts?”
• Softened by success
• See no evil
• Overdose on risk
Because these concepts are taken from a popular press article, they are not defined by the
writer. The concepts are loosely illustrated by examples rather than defined logically.
Perhaps we can attempt to define them more formally here.
For example, by “softened by success,” the authors attempt to theorize that the more
successfully the company is, the less likely they are to make sound decisions (blinded by
their own success.) This statement expresses the logical relationship between two
concepts: firm success and decision making abilities. The relationship between the two
concepts is negative. In other words, “softened by success” is not a concept. Rather it is a 18
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19. Earlier we discussed features that define a good scientific theory
Let’s apply these principles to examine this theory
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20. This diagram summarizes factors that have been theorized to affect tax fraud levels (based
on a review article by Torgler 2008). The diagram shows a research “model” which
graphically summarizes the different components of the theory.
The theory is intended to explain why people commit tax fraud. The model shows that the
theory provides two explanations – when people have a low level of tax morale (X1), they
are more likely to commit tax fraud (Y) X1 leads to Y would be the first hypothesis
The model also shows that when people perceive the institution to be corrupted (X2), they
are more likely to commit tax fraud (Y) X2 leads to Y would be the second hypothesis
Finally, the model shows that when people perceive the institution to be corrupted (X2),
they are more likely to have a lower level of tax morale (X1) X2 leads to X1 would be the
third hypothesis
X1, X2, and Y are “constructs” because they refer to abstract, as opposed to concrete, and
multi‐dimensional ideas.
In order to test this theoretical model, the scientific method would require empirical
testing. How would you go about testing this theory?
Independent Variables (called so because they are the ones that will cause the change
according to the theory): X1, X2
[Independent variables –IV‐ are sometimes called “predicting variables” or “predictors”
Dependent Variable (called so because it is the one that will be affected by the
independent variables): Y
[Dependent variables –DV‐ are sometimes called “predicted variables” 20

21. As a group you may want to discuss these questions and consult the instructor if you aren’t
sure about your answers
At this point you should be able to draw a very concrete research model to represent your
explanatory/predictive research question
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