Research Problem Statement
Construct, Concept and Variables
Research Process
The research process:
• Characteristics of research:
– Proprietary vs. scholarly research
– Research is based on curiosity and asking questions (creativity)
– Research is a systematic process
– Research is potentially replicable
– Conceptualization, planning and designing research, methodologies
for conducting research, analyzing and interpreting data,
reconceptualization
– Research is reflexive and self critical
– Research is cumulative and self-corrective
– Research is cyclical
• Research paradigms:
– Physical sciences
– Humanities
– Social sciences
Assumption
Ontological assumption
Epistemological
assumption
Axiological assumption
Methodological
assumption
Rhetorical assumption
Question
What is the nature of
reality
What is the relationship
of the researcher to that
being researched
Positivist paradigm
Singular
Objective
Independent
Naturalistic paradigm
Multiple
Intersubjective
Interdependent
What is the role of
values in the research
process
What is the process of
research
Value-free
Unbiased
Value-laden
Biased
Deduction
Search for cause and
effect relationships
between variables
Static design
Researcher-controlled
setting
Quantitative methods
Context-free
generalizations
Goals of explanation,
prediction and control
Induction
Holistic understanding
of patterns of behavior
Emergent behavior
Natural setting
Qualitative methods
Context-bound findings
Goals of understanding
and social change
What is the language of Formal
research reports?
Impersonal voice
Informal
Personal voice
Concept
• An abstraction encompassing observed events; a
word that represents the similarities or common
aspects of objects or events that are otherwise
quite different from one another.
• The purpose of a concept is to simplify thinking
by including a number of events (or the common
aspects of otherwise diverse things) under one
general heading (Ary 1985).
• Chair, dog, tree, liquid, a doughnut, etc…
Construct
• Constructs are the “highest highest-level abstractions” of
complicated objects and events, created by combining
concepts and less complex constructs. – used to account
for observed regularities and relationships, and to
summarize observations and explanations (Ary 1985).
• A concept with added meaning of having been deliberately
and con consciously invented or seriously adopted for a
special scientific purpose.
1) it enters into theoretical schemes and is theoretical related
in various ways to other constructs.
2) it is defined and specified so that it may be observed or
measured (Kerlinger 1986).
Construct
• Scientists measure things in three classes: direct
observables, indirect observables (not
experienced or observed first hand), and
constructs.
• These constructs are defined as constructs
theoretical creations based on observations but
cannot be observed directly or observed indirectly
(Kaplan 1964).
• Motivation, visual acuity, justice, problem solving
ability, …not a doughnut, …but hunger. problem-
Operational Definition
• It describes meaning to a concept or construct by
specifying the operations that must be performed in order
to measure or manipulate the concept, as the data
collected during research is in terms of observable events
(Ary 1985).
• It defines or gives meaning to a variable by spelling out
what the investigator must do to measure it (Kerlinger
1986).
• “Operational definitions are essential to research because
they permit investigators to measure abstract concepts
and constructs and permit scientists to permit move from
the level of constructs and theory to the level of
observation” (Ary 1985).
Operational Definition
• Two Types of Operational Definitions:
• Measured Operational Definition: Operations by
which investigators may measure a concept.
• Experimental Operational Definition: Steps
taken by a researcher to produce certain
experimental conditions.
Operational Definition
• Examples of an Operational Definition:
• Measured Operational Definition: An actual (score)
value from a test or questionnaire the researchers
would develop to measure “hunger.”
• Experimental Operational Definition: A manipulated
scenario to produce the condition of “hunger.” (such
as preventing the subject from consuming anything
for x number of hours) x-
Variable
• Characteristics or attributes of an object,
individual or organization that can be measured
or observed, and that varies among those objects
or individuals being studied (Creswell 2002).
• They possess values and levels (the dimensions
on which they vary) (Sommery 1997).
• “The concepts that are of interest in a study
become the variable variables for s investigation
(Ary 1985).”
Different Kinds of Variables
• Dichotomous: Two valued variables. Example:
Sex (male/female) Two• Polytomous: Multiple values for variables.
Example: Religion (Catholicism, Islam,
Judaism, Hinduism, Buddhism, etc…)
• Continuous: A variable that takes on an
infinite number of values within a range.
Example: Height & Weight
More Kinds of Variables
• Independent: The variable manipulated by the
experimenter (also: Experimental, Predictor, Manipulated,
Antecedent, Treatment).
• Active: Any variable that is manipulated by the researcher
• Attribute: Any variable that cannot be manipulated by the
researcher. For example, all human characteristics are
attribute variables: intelligence, sex, socioeconomic status
etc.
• Dependent: The dependent variable is the phenomenon
that is the object of study and investigation (also:
Outcome, Response, Criterion, Effect). - -
More Kinds of Variables
• Categorical: Referred to as nominal measurements. One
creates ‘categories,’ and classifies all variables that fall under
this definition without rank order. All variables under the
same category are considered of equal value, and not
differentiated.
• Latent: An unobserved ‘entity’ that “stands between” the
independent variable and the dependent variable, and
mediates the effect of the independent variable on the
dependent variable. It is dependent on the independent
variable as well as other constructs, yet still plays a role in
determining the outcome (possibly: Intervening, Mediating,
Hypothetical construct).
More Kinds of Variables
• Control: An independent variable that is measured in
a study because the y potentially influence the
dependent variable. It is a more clearly defined
independent variable in attempts to eliminate all bias
in regard s to its effects on the dependent variable.
(Keeps the study in check). they clearly regards
• Confounding: Variables not actually measured or
observed in a study, yet they exist, and its influence
cannot be directly detected or understood in a study.
One becomes aware of a confounding variable at the
end of a study, they realize that there is an effect that
was not measured or accounted for, but should be
addressed.
Concepts, Construct and Variables
• Concepts (Oxford definition) general notation,
abstract ideas.
• Construct (Oxford definition) make by fitting
together; build; thing constructed; especially
by the mind.
• Variables (Oxford definition) a situation,
number or quantity that can vary or be varied
• Constant (Oxford definition) a number or
quantity that does not vary
Concepts, Construct
• Concepts are abstract ideas which have been
"defined" according to particular characteristics
or generalizations (constructs) about them.
• A construct is based on concepts, or can be
thought of as a conceptual model that has
measurable aspects.
• This will allow the researcher to "measure" the
concept and have a common acceptable platform
when other researches do a similar research.
Concepts, Construct
• E.g
• Measuring advertising effectiveness is an
construct, and concepts related would be
brand awareness and consumer behavior.
• Pain is a concept, a theoretical model of pain
would be a construct, and a pain assessment
tool would give a measurable variable.
Concepts, Construct
• An idea is a plan, suggestion, or possible
course of action.
ex...I really like the idea of helping people.
A concept is an idea or abstract principle.
ex....She added that the concept of arranged
marriages is misunderstood in the West.
• Science uses:
i. Concepts
ii. Links concepts by propositions
iii. Testing theory with observable evidence
iv. Publication of definitions and procedures
v. Control of alternative explanations
vi. Unbiased selection of evidence
vii. Reconciliation between theory and observation
Concepts, Construct
• A concept is a verbal abstraction drawn from
observation of a number of specific cases
• A theoretical definition explains what is meant
by the concept.
• Operational definitions translate the verbal
concepts into corresponding variables which
• can be measured. Operational definitions can
be either: measured, or experimental.
• Also, a variable can be either measured (e.g.,
surveys) or manipulated (e.g., experiment).
Concepts, Construct
• A construct serves the same function as a concept, but it is
more abstract.
• It is not characterized by a direct link between the abstraction
and its observed manifestations.
• For instance, “source credibility” is a construct which has
been used in studying persuasion.
• This term can be used in the same way as a concept, but we
should recognize that we cannot directly observe different
levels of source credibility in individuals.
• However, we can observe the various parts which make up
the construct individually, and then combine them to get
some overall summary.
Concepts, Construct
• Constructs are built from the logical combination
of a number of more observable concepts. In the
case of source credibility, we could define the
construct as the combination of the concepts of
expertise, objectivity, and status.
• Each of these concepts can be more directly
observed in an individual.
• We might also consider some of these terms to
be constructs themselves, and break them down
into combinations of still more concrete concepts
What we see if we do this is a set of constructs at decreasing levels of
abstraction. Only at the bottom of this hierarchy are directly observable concepts.
Concepts, Construct
• A scientific concept really consists of three parts: a label, a
theoretical definition, and an operational definition.
• The theoretical definition specifies the verbal meaning
which is attached to the concept label.
• We call self-defining concepts like “age” primitive terms.
• Primitive terms are adequately defined by their attached
concept labels.
• These are the labels which appear at the bottom of the
level of abstraction hierarchy.
• RECOMMENDATION: explicitly specify the meaning
associated with each concept, regardless of the extent to
which we think the meaning is shared.
Concepts, Construct
• An operational definition translates the verbal
meaning provided by the theoretical definition
into a prescription for measurement.
• Although they may be expressed verbally,
operational definitions are fundamentally
statements that describe measurement and
mathematical operations.
• An operational definition describes the unit of
measurement. Examples of units of measurement
are minutes (to measure time), word counts (to
measure newspaper coverage of a particular
event), percent correct responses, etc.
• An operational definition adds three things to
the theoretical definition.
• Operationalization is to take a fuzzy concept,
such as 'helping behavior', and try to measure
it by specific observations, e.g. how likely are
people to help a stranger with problems.
Concepts, Construct
• An operational definition specifies the level of
measurement.
• Levels of measurement can range from the simple
nominal variables which only make distinctions
between categories like “present or absent” or “yes
or no”; to ordinal variables which contain some
information about the quantity (“more or less”) of
the concept present, but have no real measurement
scales; to continuous variables which have real scale
points which are equally spaced, and which can take
on any value.
Concepts, Construct
• The operational definition must be very closely associated with
the theoretical definition.
• It must state clearly how observations will be made so they will
reflect as fully as possible the meaning associated with the verbal
concept or construct.
• The operational definition must tell us how to observe and
quantify the concept in the “real world”.
• This connection between theoretical and operational definitions
is quite critical.
• This connection establishes the validity of the measurement.
• The amount of validity in measurement is proportional to the
extent to which we actually measure what we intend to measure,
that is, the degree to which the operational definition and the
theoretical definition correspond.
Relationships between variables/concepts
• Null relationships
• Covariance relationships:
– In a covariance relationship, changes in the values of
one variable (the measured concept) are associated
with changes in the values of the other variable.
– That is, the variables shift values simultaneously, or
covary.
– This does not mean that one concept is the cause and
the other is the effect.
– A cause-effect relationship between concepts requires
more than just covariance, as we’ll see shortly.
– A covariance relationship is typically diagramed with a
curved, double-headed arrow between the concept
Relationships between variables/concepts
– Two variables which are related proportionally can
covary either positively or negatively.
– While covariance relationships can provide prediction,
they can’t provide explanation of the relationship.
– Spurious relationships (artifacts): two variables may
covary because they are both the effects of a common
cause. The unobserved, but real, causal variable
sometimes is called a confounding variable, since it
may mislead us by producing the appearance of a
relationship between the observed variables.
– Control variables
Relationships between
variables/concepts
• Causal relationships:
– Causality means that a change which occurs in one
variable (the cause) brings about a change in another
variable (the effect).
– Alternative terms for cause and effect variable are
independent variable and dependent variable.
– This terminology is based on the logic that in a causal
relationship the state of one variable (the effect)
depends on the state of the other (the cause).
– The state of the cause is independent of the state of
the effect variable.
Relationships between
variables/concepts
• There is a critical difference between
covariance and causality:
• Covariance means that a change in one
variable is associated with a change in the
other variable; causality requires that a
change in one variable creates the change in
the other.
• In other words, covariance alone does not
imply causality.
Relationships between
variables/concepts
– Covariance is only one of four conditions which must
be met before we can state that a relationship is
causal:
•
•
•
•
Spatial contiguity
Covariance (necessary but not sufficient condition)
Temporal ordering
Necessary connection (This necessary connection is a
statement which specifies why the cause can bring about a
change in the effect. It is the logical statement of the process
or mechanism by which the two variables are related to one
another in a cause-effect relationship.
SOME DEFINITIONS OF CONSTRUCTS
PERMISSIVENESS:
• Oxford def. a) tolerant; liberal; b) giving permission
• Experimental def. Extending the boundaries of acceptable findings.
• Measured def. Confining the boundaries of acceptable findings.
REINFORCEMENT:
• Oxford def. Strengthen or support, especially with additional personnel,
material etc.
• Experimental def. To build credibility by strengthening your research findings.
• Measured def. To build structural credibility to strengthen your research
findings.
READING ABILITY:
• Oxford def. none
• Experimental def. To understand through written text the research findings.
• Measured def. same as above
SOME DEFINITIONS OF CONSTRUCTS
ACHIEVEMENT:
• Oxford def. a) something achieved b) act of achieving achieve: a) attain by
effort acquire; gain earn b) accomplish
• Experimental def. To accomplish what you have set out to prove.
• Measured def. To put your research findings into a written format to be used as
documentation.
INTERESTS:
• Oxford def. a) curiosity, concern b) quality existing curiosity c) note worthiness,
importance 2) subject, hobby in which one is concerned 3) advantage or profit
4) self interest, excite the curiosity or attention to take a personal interest.
• Experimental def. An educational topic that concerns you and is worthy of your
research.
• Measured def. same as above
NEEDS:
• Oxford def. archaic of necessity, requirement
• Experimental def. The requirements of research in order for it to be valid.
• Measured def. The requirements for charting the research findings in order to
give documented support.
SOME DEFINITIONS OF CONSTRUCTS
TRANSFER OF TRAINING:
• Oxford def. none
• Experimental def. To give another researcher the information needed so
that they can continue researching from where you left off.
• Measured def. same as above
LEADERSHIP:
• Oxford def. A person that leads or is followed by others.
• Experimental def. A person who has the ability to direct others through a
research experiment, and who will set the tone of the research.
• Measured def. A person who will design the way in which the research
findings will be documented.
CLASS ATMOSPHERE:
• Oxford def. none
• Experimental def. The tone of a class setting that will allow for similar
testing conditions.
• Measured def. same as above
SOME DEFINITIONS OF CONSTRUCTS
DELINQUENCY:
• Oxford def. Failing in one’s duty
• Experimental def. Failing to plan out the way you are going to conduct you
research so that your findings are valid.
• Measured def. Failing to document your findings in a way that another
researcher can duplicate your research findings.
ORGANIZATIONAL CONFLICT:
• Oxford def. none
• Experimental def. Lack of agreement within a research group.
• Measured def. Lack of consistent findings and charting.
SELF-OTHER-ATTITUDE:
• Oxford def. none
• Experimental def. Ability to work together for the good of the research
• Measured def. same as above
SOME DEFINITIONS OF CONSTRUCTS
CONFORMITY:
• Oxford def. Accordance with established practice,
agreement
• Experimental def. To follow a specific plan that has
already been established and agreement as a research
team to stick to a research method that was agreed
upon.
• Measured def. To chart the research findings in a way
that would best support your findings and based on a
method that was used prior by another research team
when studying the same subject area.
Variables
• A variable is something that changes.
• It changes according to different factors.
• Some variables changes easily, like the stockexchange value, while other variables are
almost constant, like the name of someone.
• Researchers are often seeking to measure
variables.
• The variable can be a number, a name or
anything where the value can change.
Variables
• An example of a variable is temperature.
• The temperature varies according to other variable and
factors.
• You can measure different temperature inside and
outside.
• If it is a sunny day, chances are that the temperature
will be higher than if it's cloudy.
• Another thing that can make the temperature change
is whether something has been done to manipulate the
temperature, like lighting a fire in the chimney.
Variables
• A variable is any entity that can take on
different values.
• Anything that can vary can be considered a
variable.
• For instance, age can be considered a variable
because age can take different values for
different people or for the same person at
different times.
Variables
• Variables are not always 'quantitative' or numerical.
• The variable 'gender' consists of two text values: 'male' and
'female'.
• We can, if it is useful, assign quantitative values instead of (or in
place of) the text values, but we don't have to assign numbers in
order for something to be a variable.
• It's also important to realize that variables aren't only things that
we measure in the traditional sense.
• For instance, in much social research and in program evaluation,
we consider the treatment or program to be made up of one or
more variables (i.e., the 'cause' can be considered a variable).
• An educational program can have varying amounts of 'time on
task', 'classroom settings', 'student-teacher ratios', and so on.
Variables
• Variables may have the following
characteristics:
– Period: When it starts and stops.
– Pattern: Daily, weekly, ad-hoc, etc.
– Detail: Overview through to 'in depth'.
– Latency: Time between measuring dependent and
independent variable (some things take time to
take effect).
Variables
• In research, you typically define variables
according to what you're measuring.
• The independent variable is the variable which
the researcher would like to measure (the cause),
while the dependent variable is the effect (or
assumed effect), dependent on the independent
variable.
• These variables are often stated in experimental
research, in a hypothesis, e.g. "what is the effect
of personality on helping behavior?“
Variables
• In explorative research methodology, e.g. in some
qualitative research, the independent and the
dependent variables might not be identified
beforehand.
• They might not be stated because the researcher does
not have a clear idea yet on what is really going on.
• The independent variable, also known as the
manipulated variable, lies at the heart of any
quantitative experimental design.
• A researcher manipulates an independent variable, to
influence a dependent variable, or variables.
• There may be more than two dependent variables in
any experiment.
Variables
• The independent variable is what you (or
nature) manipulates -- a treatment or program
or cause.
• The dependent variable is what is affected by
the independent variable -- your effects or
outcomes.
• For example, if you are studying the effects of a
new educational program on student
achievement, the program is the independent
variable and your measures of achievement are
the dependent ones.
Variables
• The independent variable (IV) is often thought of
as our input variable.
• It is independent of everything that occurs during
the experiment because once it is chosen it does
not change.
• In our experiment on college performance, we
chose two groups at the onset, namely, those
with work experience and those without.
• This variable makes up our two independent
groups and is therefore called the independent
variable.
Variables
• The dependent variable (DV), or outcome variable, is
dependent on our independent variable or what we start
with.
• In this study, college grades would be our dependent
variable because it is dependent on work experience.
• If we chose to also look at men versus women, or older
students versus younger students, then these variables
would be other independent variables and the outcome,
our dependent variable (college grades), would be
dependent on them as well.
• Remember that whatever is the same between the two
groups is considered a constant because they do not vary
between groups but rather remain the same and therefore
do not affect the outcome of each group differently
Variables
• Descriptive variables are those that which will be reported
on, without relating them to anything in particular.
• Categorical variables result from a selection from
categories, such as 'agree' and 'disagree'. Nominal and
ordinal variables are categorical.
• Numeric variables give a number, such as age.
• Discrete variables are numeric variables that come from a
limited set of numbers. They may result from , answering
questions such as 'how many', 'how often', etc.
• Continuous variables are numeric variables that can take
any value, such as weight.
• Extraneous variables are additional variables which could
provide alternative explanations or cast doubt on
conclusions.
Variables
• Researchers must be aware that variables outside
of the independent variable(s) may confound or
alter the results of a study.
• Confounding variables are variables with a
significant effect on the dependent variable that
the researcher failed to control or eliminate sometimes because the researcher is not aware
of the effect of the confounding variable.
• The key is to identify possible confounding
variables and somehow try to eliminate or
control them.
Variables
• A confounding variable, also known as a third
variable or a mediator variable, can adversely
affect the relationship between the independent
variable and dependent variable.
• This may cause the researcher to analyze the
results incorrectly.
• The results may show a false correlation between
the dependent and independent variables,
leading to an incorrect rejection of the null
hypothesis.
Variables
• If, for instance, we had two groups in the above
mentioned study but did not control for age then age
itself may be a confound. Imagine comparing students
with work experience with a mean age of 40 with
students without work experience and a mean age of
18.
• Could we reasonably say that work experience caused
the student to receive higher grades?
• This extraneous variable can play havoc on our results
as can any intervening variable such as motivation or
attention.
• Addressing confounds before they alter the results of
your study is always a wise decision
Variables
Every Problem Needs Further
Delineation
• To comprehend fully the meaning of the problem, the researcher
should eliminate any possibility of misunderstanding by
– Stating the hypotheses and/or research questions:
Describing the specific hypotheses being tested or
questions being asked.
– Delimiting the research: Fully disclosing what the
researcher intends to do and, conversely, does not intend
to do.
– Defining the terms: Giving the meanings of all terms in the
statements of the problem and subproblems that have any
possibility of being misunderstood.
– Stating the assumptions: Presenting a clear statement of all
assumptions on which the research will rest.
• These matters facilitate understanding of the research – called the
setting of the problem
Stating the Hypotheses and/or
Research Questions
• Hypotheses are tentative, intelligent guesses
posited for the purpose of directing one’s
thinking toward the solution of the problem
• Necessary in searching for relevant data and in
establishing a tentative goal
• Hypotheses are neither proved nor disproved.
They are nothing more than tentative
propositions set forth to assist in guiding the
investigation of a problem or to provide possible
explanations for the observations made
Accept/Reject Hypotheses
• Hypotheses have nothing to do with proof
• Their acceptance or rejection is dependent on what
the data – and the data alone – ultimately reveal
• Hypotheses may originate in the subproblem, could
be 1 to 1
• Hypothesis provides a position from which a
researcher begins to initiate an exploration of
problem and subproblems and checkpoints to test
the findings that the data reveal
Accept/Reject Hypotheses
• If the data do not support the research
hypothesis, don’t be disturbed – it merely
means that the educated guess about the
outcome of the investigation was incorrect
• Frequently, rejected hypotheses are a source
of genuine and gratifying surprise – truly
made unexpected discovery
• Another type of hypothesis is the null
hypothesis
Null Hypothesis
• It is an indicator only
• Reveals some influences, forces, or factors
that have resulted in a statistical difference or
no such difference
• Most researches stop at this point – getting off
at mezzanine instead down to the basement
where the foundations are
Null Hypothesis Dynamics
• If null hypothesis shows the presence of dynamics, then the
next logical questions are as follows:
– What are these dynamics?
– What is their nature?
– How can they be isolated and studied?
• For example, let’s say that a team of social workers believe
that one type of after-school programme for teenagers (we’ll
call it Programme A) is more effective than another
programme (we’ll call it Programme B) in terms of reducing
high school dropout rates.
Null Hypothesis Dynamics
• The null hypothesis stating that there will be no difference in
the high school graduation rates of teenagers enrolled in
Programme A and those enrolled in Programme B has been
rejected – encouraging news – it is mezzanine conclusion
• What specifically were the factors within the programme that
cause the null hypothesis to be rejected?
• These are fundamental questions – will uncover facts that
may lie very close to the discovery of new substantive
knowledge – the purpose of all research
Delimiting the Research
• Know PRECISELY what the researcher intends to DO and does
NOT intend to do
• What the researcher intends to do is stated in the problem
statement
• What the researcher is not going to do is in the delimitations
• The researcher can easily be beguiled (deceived, cheated) by
discovering interesting information that lies beyond the
precincts of the problem under investigation
• Only a researcher who thinks carefully about the problem and
its focal centre can distinguish between what is relevant and
what is not relevant to the problem
• All irrelevancies to the problem must be firmly ruled out in
the statement of delimitations
Defining The Terms
• Without knowing explicitly what a term means, we cannot
evaluate the research or determine whether the researcher
has carried out what was proposed in the problem statement
• Need not necessarily agree with such a definition, but as long
as we know what the researcher means when using the term,
we are able to understand and appraise it appropriately
• A formal definition contains three parts: (a) the term to be
defined; (b) the genera, or the general class to which the
concept being defined belongs; and (c) the differentia, the
specific characteristics or traits that distinguish the concept
being defined from all other members of the general
classification
Defining The Terms
• To make the software more USER-FRIENDLY?
• What is the relationship between the user
interface metric and user acceptance?
• The researcher must be careful to avoid
circular definitions, in which the terms to be
defined are used in the definitions themselves
• A classic example is Gertrude Stein’s “A rose, is
a rose, is a rose”
Stating the Assumptions
• Assumptions are so basic that, without them, the
research problem itself could not exist
• Example, to determine by pretest-posttest whether
one method of instruction has produced the results
hypothesised
• The assumptions are:
– The test measures what it is presumed to measure
– The teacher(s) in the study can teach effectively
– The students are capable of learning the subject
matter
• Without these assumptions, we have no problem, no
research
Stating the Assumptions
• Assumptions are what researchers take for granted
with respect to the problem
• But taking everything for granted may cause
misunderstanding
• If others know the assumptions a researcher makes,
they are better prepared to evaluate the conclusions
that result from such assumptions
• Many students thought that assumption is stating
the obvious
• In research, try to leave nothing to chance in the
hope of preventing any misunderstanding
Stating the Assumptions
• All assumptions that have a material bearing
on the problem should be openly and
unreservedly set forth.
• Asking question “What am I taking for granted
with respect to the problem?” will bring
assumptions into clear view
Importance of the Study
• In dissertations or research reports, researchers frequently set
forth their reasons for undertaking the study
• In a research proposal, such a discussion may be especially
important
• Some studies seem to go far beyond any relationship to the
practical world
• Of such research efforts, one might asks “Of what use is it?
What practical value does the study have?”
• For example, the time, money, effort spent on early space
exploration flights