UNIT 1:- Research Methodology: An Introduction
MEANING OF RESEARCH
➢ Research in common parlance refers to a search for knowledge.
➢ Once can also define research as a scientific and systematic search for pertinent
information on a specific topic. In fact, research is an art of scientific investigation.
➢ The Advanced Learner’s Dictionary of Current English lays down the meaning of
research as “a careful investigation or inquiry specially through search for new
facts in any branch of knowledge.
➢ Redman and Mory define research as a “systematized effort to gain new
knowledge.
➢ Research is an academic activity and as such the term should be used in a
technical sense.
➢ According to Clifford Woody research comprises defining and redefining
problems, formulating hypothesis or suggested solutions; collecting, organising
and evaluating data; making deductions and reaching conclusions; and at last
carefully testing the conclusions to determine whether they fit the formulating
hypothesis.
Research is, thus, an original contribution to the existing stock of knowledge making for
its advancement. It is the persuit of truth with the help of study, observation,
comparison and experiment. In short, the search for knowledge through objective and
systematic method of finding solution to a problem is research. The systematic
approach concerning generalisation and the formulation of a theory is also research. As
such the term ‘research’ refers to the systematic method consisting of enunciating the
problem, formulating a hypothesis, collecting the facts or data, analysing the facts and
reaching certain conclusions either in the form of solutions(s) towards the concerned
problem or in certain generalisations for some theoretical formulation.
BUSINESS RESEARCH
▪
▪
The term ‘business research, refers to academic research on topics relating to
questions that are Relevant to the field of business and management and have a
Social science orientation.
We include in this category research in areas such as Organizational behaviour,
marketing, accounting, HRM, and Strategy, which draw on the social sciences for
conceptual and Theoretical inspiration.
OBJECTIVES OF RESEARCH
1. To gain familiarity with a phenomenon or to achieve new insights into it (studies
with this object in view are termed as exploratory or formulative research
studies);
2. To portray accurately the characteristics of a particular individual, situation or a
group (studies with this object in view are known as descriptive research studies);
3. To determine the frequency with which something occurs or with which it is
associated with something else (studies with this object in view are known as
diagnostic research studies);
4. To test a hypothesis of a causal relationship between variables (such studies are
known as Hypothesis-testing research studies).
MOTIVATION IN RESEARCH
1. Desire to get a research degree along with its consequential benefits;
2. Desire to face the challenge in solving the unsolved problems, i.e., concern over
practical problems initiates research;
3. Desire to get intellectual joy of doing some creative work;
4. Desire to be of service to society;
5. Desire to get respectability.
Why is it important to study methods?
•
•
•
•
Training in research methods sensitizes you to the choices that are available to
business and management researchers.
Training in research methods provides you with an awareness of the 'dos' and
'don'ts' when employing, a particular approach to collecting or analysing data.
Training in research methods provides you with insights into the Overall research
process. It provides a general understanding how research is done.
Training in research methods provides you with an awareness of what constitutes
good and poor research. It therefore provides a platform for developing a critical
awareness of the limits and limitations of research that you read.
•
The skills that training in research methods imparts are transferable ones. How to
sample, how to design a questionnaire, how to conduct semi-structured
interviewing or focus groups and so on are skills that are relevant to research in
other spheres (such as firms, public-sector organizations, and so on).
TYPES OF RESEARCHES
1) Exploratory research:- Exploratory research is defined as a research used to
investigate a problem which is not clearly defined. It is conducted to have a
better understanding of the existing problem, but will not provide conclusive
results. For such a research, a researcher starts with a general idea and uses this
research as a medium to identify issues, that can be the focus for future research.
An important aspect here is that the researcher should be willing to change
his/her direction subject to the revelation of new data or insight. Such a research
is usually carried out when the problem is at a preliminary stage. It is often
referred to as grounded theory approach or interpretive research as it used to
answer questions like what, why and how.
For example: Consider a scenario where a juice bar owner feels that increasing
the variety of juices will enable increase in customers, however he is not sure and
needs more information. The owner intends to carry out an exploratory research
to find out and hence decides to do an exploratory research to find out if
expanding their juices selection will enable him to get more customers of if there
is a better idea.
2) Descriptive research:- It refers to the methods that describe the characteristics of the
variables under study. This methodology focuses on answering questions relating to
“what” than the “why” of the research subject. The primary focus of descriptive
research is to simply describe the nature of the demographics under study instead of
focusing on the “why”. Descriptive research is called an observational research
method as none of the variables in the study are influenced during the process of the
research. For example, let’s assume if a UK based brand is trying to establish itself in
New York and wants to understand the demographics of the buyers who generally
purchase from brands similar to it. Here, the information gathered from the survey
will be focused on the demographics of the population only. It will uncover details on
the buying patterns of different age cohorts in New York. It will not study why such
patterns exist, because the brand is trying to establish itself in New York. All that they
want to understand is the buying behavior of the population, and not why such
associations exist. Descriptive research is a part of quantitative market research
or social research study which involves conducting survey research using quantitative
variables on a market research tool or social research tool.
3) Causative Research:- Causative research is also known as explanatory research.
Ideally, it is a follow-up to explain and measure the strength of the cause-effect
relationship between variables, such as the first impression and the employee
experience in a company.
Causative research answers the “how” and “why questions by collecting numerical
data from observations or experiments involving a representative sample. On that
note, it is fair to say that causative research is mainly quantitative. The findings
are not only generalisable to the population but also convincing to put into
action.
The picture below visualises how exploratory, descriptive, and causative research differ
from each other. It helps you to define a more specific research objective in
your research design.
Exploratory,
descriptive, causative?
The more specific the research objective is, the more confident you are that your
research brings valuable deliverable to the table.
Research Approaches
There are two basic approaches to research, viz., quantitative approach and the
qualitative approach.
The former involves the generation of data in quantitative form which can be subjected
to rigorous quantitative analysis in a formal and rigid fashion. This approach can be
further sub-classified into inferential, experimental and simulation approaches to
research. The purpose of inferential approach to research is to form a data base from
which to infer characteristics or relationships of population. This usually means survey
research where a sample of population is studied (questioned or observed) to determine
its characteristics, and it is then inferred that the population has the same characteristics.
Experimental approach is characterised by much greater control over the research
environment and in this case some variables are manipulated to observe their effect on
other variables. Simulation approach involves the construction of an artificial
environment within which relevant information and data can be generated. This permits
an observation of the dynamic behaviour of a system (or its sub-system) under
controlled conditions. The term ‘simulation’ in the context of business and social
sciences applications refers to “the operation of a numerical model that represents the
structure of a dynamic process. Given the values of initial conditions, parameters and
exogenous variables, a simulation is run to represent the behaviour of the process over
time. Simulation approach can also be useful in building models for understanding
future conditions.
Qualitative approach to research is concerned with subjective assessment of attitudes,
opinions and behaviour. Research in such a situation is a function of researcher’s
insights and impressions. Such an approach to research generates results either in nonquantitative form or in the form which are not subjected to rigorous quantitative
analysis. Generally, the techniques of focus group interviews, projective techniques and
depth interviews are used.
Significance of Research
▪
“All progress is born of inquiry. Doubt is often better than overconfidence, for it
leads to inquiry, and inquiry leads to invention” is a famous Hudson Maxim in
context of which the significance of research can well be understood. Increased
amounts of research make progress possible. Research inculcates scientific and
inductive thinking and it promotes the development of logical habits of thinking
and organisation.
▪
The role of research in several fields of applied economics, whether related to
business or to the economy as a whole, has greatly increased in modern times.
The increasingly complex nature of business and government has focused
attention on the use of research in solving operational problems. Research, as an
aid to economic policy, has gained added importance, both for government and
business.
▪
Research provides the basis for nearly all government policies in our economic
system. For instance, government’s budgets rest in part on an analysis of the
needs and desires of the people and on the availability of revenues to meet these
needs. The cost of needs has to be equated to probable revenues and this is a
field where research is most needed. Through research we can devise alternative
policies and can as well examine the consequences of each of these alternatives.
▪
Research has its special significance in solving various operational and planning
problems of business and industry.
▪
Research is equally important for social scientists in studying social relationships
and in seeking answers to various social problems. It provides the intellectual
satisfaction of knowing a few things just for the sake of knowledge and also has
practical utility for the social scientist to know for the sake of being able to do
something better or in a more efficient manner.
The significance of research can also be understood keeping in view the following
points:
(a) To those students who are to write a master’s or Ph.D. thesis, research may mean a
careerism or a way to attain a high position in the social structure;
(b) To professionals in research methodology, research may mean a source of livelihood;
(c) To philosophers and thinkers, research may mean the outlet for new ideas and
insights;
(d) To literary men and women, research may mean the development of new styles and
creative work;
(e) To analysts and intellectuals, research may mean the generalisations of new theories.
Thus, research is the fountain of knowledge for the sake of knowledge and an important
source of providing guidelines for solving different business, governmental and social
problems. It is a sort of formal training which enables one to understand the new
developments in one’s field in a better way
Research Methods Vs Methodology
➢ Research methods may methods/techniques that are used for conduction of
research. Research methods or techniques*, thus, refer to the methods the
researchers use in performing research operations.
➢ Research methodology is a way to systematically solve the research problem. It
may be understood as a science of studying how research is done scientifically.
➢ Thus, when we talk of research methodology we not only talk of the research
methods but also consider the logic behind the methods we use in the context of
our research study and explain why we are using a particular method or
technique and why we are not using others so that research results are capable of
being evaluated either by the researcher himself or by others.
Research Methods Vs Techniques
➢ At times, a distinction is also made between research techniques and research
methods.
➢ Research techniques refer to the behaviour and instruments we use in performing
research operations such as making observations, recording data, techniques of
processing data and the like.
➢ Research methods refer to the behaviour and instruments used in selecting and
constructing research technique.
Importance of Knowing How Research is Done
•
•
•
The knowledge of methodology provides good training specially to the new
research worker and enables him to do better research. It helps him to
develop disciplined thinking or a bent of mind' to observe the field
objectively. Hence, those aspiring for careerism in research must develop the
skill of using research techniques and must thoroughly understand the logic
behind them.
Knowledge of how to do research will inculcate the ability to evaluate and use
research results with reasonable confidence. In other words, we can state that
the government or business administration, community development and
social work where persons are increasingly called upon to evaluate and use
research results for action.
When one knows how research is done, then one may have the satisfaction of
acquiring a new intellectual tool which can become a way of looking at the
world and of judging everyday experience. Accordingly, it enables use to
make intelligent decisions concerning problems facing us in practical life at
different points of time.
The scientific method is, thus, based on certain basic postulates which can be stated as
under:
1. It relies on empirical evidence;
2. It utilizes relevant concepts;
3. It is committed to only objective considerations;
4. It presupposes ethical neutrality, i.e., it aims at nothing but making only adequate and
correct statements about population objects;
5. It results into probabilistic predictions;
6. Its methodology is made known to all concerned for critical scrutiny are for use in
testing the conclusions through replication;
7. It aims at formulating most general axioms or what can be termed as scientific
theories.
Criteria of Good Research
Whatever may be the types of research works and studies, one thing that is important is
that they all meet on the common ground of scientific method employed by them. One
expects scientific research to satisfy the following criteria:
i.
ii.
iii.
iv.
v.
vi.
The purpose of the research should be clearly defined and common concepts be
used.
The research procedure used should be described in sufficient detail to permit
another researcher to repeat the research for further advancement, keeping the
continuity of what has already been attained.
The procedural design of the research should be carefully planned to yield results
that are as objective as possible.
The researcher should report with complete frankness, flaws in procedural design
and estimate their effects upon the findings.
The analysis of data should be sufficiently adequate to reveal its significance and
the methods of analysis used should be appropriate. The validity and reliability of
the data should be checked carefully.
Conclusions should be confined to those justified by the data of the research and
limited to those for which the data provide an adequate basis.
vii.
Greater confidence in research is warranted if the researcher is experienced, has a
good reputation in research and is a person of integrity.
Problems Encountered by Researchers in India
Researchers in India, particularly those engaged in empirical research, are facing several
problems. Some of the important problems are as follows:
1. The lack of a scientific training in the methodology of research is a great
impediment for researchers in our country.
2. There is insufficient interaction between the university research departments on
one side and business establishments, government departments and research
institutions on the other side. A great deal of primary data of non-confidential nature
remain untouched/untreated by the researchers for want of proper contacts. Efforts
should be made to develop satisfactory liaison among all concerned for better and
realistic researches.
3. Most of the business units in our country do not have the confidence that the
material supplied by them to researchers will not be misused and as such they are
often reluctant in supplying the needed information to researchers. The concept of
secrecy seems to be sacrosanct to business organisations in the country so much so
that it proves an impermeable barrier to researchers. Thus, there is the need for
generating the confidence that the information/data obtained from a business unit
will not be misused.
4. Research studies overlapping one another are undertaken quite often for want of
adequate information. This results in duplication and fritters away resources. This
problem can be solved by proper compilation and revision, at regular intervals, of a
list of subjects on which and the places where the research is going on. Due
attention should be given toward identification of research problems in various
disciplines of applied science which are of immediate concern to the industries.
5. There does not exist a code of conduct for researchers and inter-university and
inter-departmental rivalries are also quite common. Hence, there is need for
developing a code of conduct for researchers which, if adhered sincerely, can win
over this problem.
6. Many researchers in our country also face the difficulty of adequate and timely
secretarial Assistance, including computerial assistance. This causes unnecessary
delays in the completion of research studies. All possible efforts be made in this
direction so that efficient secretarial assistance is made available to researchers and
that too well in time. University Grants Commission must play a dynamic role in
solving this difficulty.
7. Library management and functioning is not satisfactory at many places and much
of the time and energy of researchers are spent in tracing out the books, journals,
reports, etc., rather than in tracing out relevant material from them.
8. There is also the problem that many of our libraries are not able to get copies of
old and new Acts/Rules, reports and other government publications in time. This
problem is felt more in libraries which are away in places from Delhi and/or the state
capitals. Thus, efforts should be made for the regular and speedy supply of all
governmental publications to reach our libraries.
9. There is also the difficulty of timely availability of published data from various
government and other agencies doing this job in our country. Researcher also faces
the problem on account of the fact that the published data vary quite significantly
because of differences in coverage by the concerning agencies.
10. There may, at times, take place the problem of conceptualization and also
problems relating to the process of data collection and related things.
Defining the Research Problem
A research problem, in general, refers to some difficulty which a researcher experiences
in the context of either a theoretical or practical situation and wants to obtain a solution
for the same.
Components of a Research Problem as under:
(i)
(ii)
(iii)
There must be an individual or a group which has some difficulty or the
problem.
There must be some objective(s) to be attained at. If one wants nothing, one
cannot have a problem.
There must be alternative means (or the courses of action) for obtaining the
objective(s) one wishes to attain. This means that there must be at least two
means available to a researcher for if he has no choice of means, he cannot
have a problem.
(iv)
(v)
There must remain some doubt in the mind of a researcher with regard to the
selection of alternatives. This means that research must answer the question
concerning the relative efficiency of the possible alternatives.
There must be some environment(s) to which the difficulty pertains.
SELECTING THE PROBLEM
The following points may be observed by a researcher in selecting a research problem
or a subject for research:
(i)
(ii)
(iii)
Subject which is overdone should not be normally chosen, for it will be a
difficult task to throw any new light in such a case.
Controversial subject should not become the choice of an average researcher.
Too narrow or too vague problems should be avoided.
(iv) The subject selected for research should be familiar and feasible so that the
related research material or sources of research are within one’s reach. Even then it is
quite difficult to supply definitive ideas concerning how a researcher should obtain
ideas for his research. For this purpose, a researcher should contact an expert or a
professor in the University who is already engaged in research. He may as well read
articles published in current literature available on the subject and may think how
the techniques and ideas discussed therein might be applied to the solution of other
problems. He may discuss with others what he has in mind concerning a problem. In
this way he should make all possible efforts in selecting a problem.
(v) The importance of the subject, the qualifications and the training of a researcher,
the costs involved, the time factor are few other criteria that must also be considered
in selecting a problem. In other words, before the final selection of a problem is
done, a researcher must ask himself the following questions:
(a) Whether he is well equipped in terms of his background to carry out the
research?
(b) Whether the study falls within the budget he can afford?
(c) Whether the necessary cooperation can be obtained from those who must
participate in research as subjects?
If the answers to all these questions are in the affirmative, one may become sure
so far as the practicability of the study is concerned.
(vi)
The selection of a problem must be preceded by a preliminary study. This may
not be necessary when the problem requires the conduct of a research closely
similar to one that has already been done. But when the field of inquiry is
relatively new and does not have available a set of well developed techniques,
a brief feasibility study must always be undertaken.
UNIT 2:- Research Design
MEANING OF RESEARCH DESIGN
❖ The formidable problem that follows the task of defining the research problem is
the preparation of the design of the research project, popularly known as the
“research design”.
❖ Decisions regarding what, where, when, how much, by what means concerning an
inquiry or a research study constitute a research design.
❖ “A research design is the arrangement of conditions for collection and analysis of
data in a manner that aims to combine relevance to the research purpose with
economy in procedure.
❖ In fact, the research design is the conceptual structure within which research is
conducted; it constitutes the blueprint for the collection, measurement and
analysis of data.
More explicitly, the desing decisions happen to be in respect of:
i.
ii.
iii.
iv.
v.
vi.
What is the study about?
Why is the study being made?
Where will the study be carried out?
What type of data is required?
Where can the required data be found?
What periods of time will the study include?
vii.
viii.
ix.
x.
What will be the sample design?
What techniques of data collection will be used?
How will the data be analysed?
In what style will the report be prepared?
NEED FOR RESEARCH DESIGN
▪
▪
▪
▪
▪
Research design is needed because it facilitates the smooth sailing of the various
research operations, thereby making research as efficient as possible yielding
maximal information with minimal expenditure of effort, time and money.
A research design or a plan in advance of data collection and analysis for our
research project.
Research design stands for advance planning of the methods to be adopted for
collecting the relevant data and the techniques to be used in their analysis,
keeping in view the objective of the research and the availability of staff, time and
money.
Preparation of the research design should be done with great care as any error in
it may upset the entire project.
Research design, in fact, has a great bearing on the reliability of the results
arrived at and as such constitutes the firm foundation of the entire edifice of the
research work.
FEATURES OF A GOOD DESIGN
▪
▪
▪
▪
▪
A good design is often characterised by adjectives like flexible, appropriate,
efficient, economical and so on.
Generally, the design which minimises bias and maximises the reliability of the
data collected and analysed is considered a good design.
The design which gives the smallest experimental error is supposed to be the
best design in many investigations.
A design may be quite suitable in one case, but may be found wanting in one
respect or the other in the context of some other research problem.
One single design cannot serve the purpose of all types of research problems.
A research design appropriate for a particular research problem, usually involves the
consideration of the following factors:
(i) the means of obtaining information;
(ii) the availability and skills of the researcher and his staff, if any;
(iii) the objective of the problem to be studied;
(iv) the nature of the problem to be studied; and
(v) the availability of time and money for the research work.
IMPORTANT CONCEPTS RELATING TO RESEARCH DESIGN
•
•
•
•
•
Dependent and independent variables:- A concept which can take on different
quantitative values is called a variable. As such the concepts like weight, height,
income are all examples of variables. Qualitative phenomena (or the attributes)
are also quantified on the basis of the presence or absence of the concerning
attribute(s).
Extraneous variable:- Independent variables that are not related to the purpose of
the study, but may affect the dependent variable are termed as extraneous
variables.
Control:- One important characteristic of a good research design is to minimise
the influence or effect of extraneous variable(s). The technical term ‘control’ is
used when we design the study minimising the effects of extraneous
independent variables. In experimental researches, the term ‘control’ is used to
refer to restrain experimental conditions.
Confounded relationship:- When the dependent variable is not free from the
influence of extraneous variable(s), the relationship between the dependent and
independent variables is said to be confounded by an extraneous variable(s).
Research hypothesis:- When a prediction or a hypothesised relationship is to be
tested by scientific methods, it is termed as research hypothesis. The research
hypothesis is a predictive statement that relates an independent variable to a
dependent variable. Usually a research hypothesis must contain, at least, one
independent and one dependent variable. Predictive statements which are not to
•
•
•
•
be objectively verified or the relationships that are assumed but not to be tested,
are not termed research hypotheses.
Experimental and non-experimental hypothesis-testing research:- When the
purpose of research is to test a research hypothesis, it is termed as hypothesistesting research. It can be of the experimental design or of the non-experimental
design. Research in which the independent variable is manipulated is termed
‘experimental hypothesis-testing research’ and a research in which an
independent variable is not manipulated is called ‘non-experimental hypothesistesting research’.
Experimental and control groups:- In an experimental hypothesis-testing research
when a group is exposed to usual conditions, it is termed a ‘control group’, but
when the group is exposed to some novel or special condition, it is termed an
‘experimental group’.
Treatments:- The different conditions under which experimental and control
groups are put are usually referred to as ‘treatments’.
Experiment:- The process of examining the truth of a statistical hypothesis,
relating to some research problem, is known as an experiment.
Research Design: Classification
Research designs may be broadly classified as exploratory or conclusive as shown in the
figure:-
The primary objective of exploratory research is to provide insights into, and an
understanding of, the problem confronting the researcher. Exploratory research is used
in cases when you must define the problem more precisely, identify relevant courses of
action, or gain additional insights before an approach can be developed.
Conclusive research is typically more formal and structured than exploratory research. It
is based on large, representative samples, and the data obtained are subjected to
quantitative analysis.
Exploratory Research
As its name implies, the objective of exploratory research is to explore or search through
a problem or situation to provide insights and understanding (Table 3.2). Exploratory
research could be used for any of the following purposes:
• Formulate a problem or define a problem more precisely.
• Identify alternative courses of action.
• Develop hypotheses.
• Isolate key variables and relationships for further examination.
• Gain insights for developing an approach to the problem.
• Establish priorities for further research.
Descriptive Research
As the name implies, the major objective of descriptive research is to describe
something. Usually market characteristics or functions (see Table 3.2). Descriptive
research is conducted for the following reasons:
i.
ii.
iii.
iv.
v.
To describe the characteristics of relevant groups, such as consumers,
salespeople, organizations, or market areas. For example, we could develop a
profile of the “heavy users” (frequent shoppers) of prestigious department stores
like Neiman Marcus.
To estimate the percentage of units in a specified population exhibiting a certain
behavior. For example, we might be interested in estimating the percentage of
heavy users of prestigious department stores who also patronize discount
department stores.
To determine the perceptions of product characteristics. For example, how do
households perceive the various department stores in terms of salient factors of
the choice criteria?
To determine the degree to which marketing variables are associated. For
example, to what extent is shopping at department stores related to eating out?
To make specific predictions. For example, what will be the retail sales of Neiman
Marcus (specific store) for fashion clothing (specific product category) in the
Dallas area (specific region)?
Project Research:- The Six W’s
1. Who—Who should be considered a patron of a particular department store? Some of
the possibilities are the following:
a. Anyone who enters the department store, whether or not she or he purchases
anything
b. Anyone who purchases anything from the store
c. Anyone who makes purchases at the department store at least once a month
d. The person in the household most responsible for department store shopping
2. What—What information should be obtained from the respondents? A wide variety of
information could be obtained, including the following:
a. Frequency with which different department stores are patronized for specific product
categories
b. Evaluation of the various department stores in terms of the salient choice criteria
c. Information pertaining to specific hypotheses to be tested
d. Psychographics and lifestyles, media consumption habits, and demographics
3. When—When should the information be obtained from the respondents? The
available options include the following:
a. Before shopping
b. While shopping
c. Immediately after shopping
d. Some time after shopping to allow time for evaluation of the shopping experience
4. Where—Where should the respondents be contacted to obtain the required
information? Possibilities include contacting the respondents:
a. In the store
b. Outside the store but in the shopping mall
c. In the parking lot
d. At home
5. Why—Why are we obtaining information from the respondents? Why is the
marketing research project being conducted? Possible reasons could be to:
a. Improve the image of the sponsoring store
b. Improve patronage and market share
c. Change the product mix
d. Develop a suitable promotional campaign
e. Decide on the location of a new store
6. Way—In what way are we going to obtain information from the respondents? The
possible ways could be the following:
a. Observations of respondents’ behavior
b. Personal interviews
c. Telephone interviews
d. Mail interviews
e. Electronic (email or Internet) interviews
f. Mobile interviews
Examples of descriptive studies are the following:
• Market studies, which describe the size of the market, market shares, buying power of
the consumers, availability of distributors, and consumer profiles
• Sales analysis studies, which describe sales by geographic region, product line, type
and size of the account
• Image studies, which determine consumer perceptions of the firm and its products
• Product usage studies, which describe consumption patterns
• Distribution studies, which determine traffic flow patterns and the number and
location of distributors
• Pricing studies, which describe the range and frequency of price changes and probable
consumer response to proposed price changes
• Advertising studies, which describe media consumption habits and audience profiles
for specific television programs and magazines.
Cross-Sectional Designs
❖ The cross-sectional study is the most frequently used descriptive design in
marketing research.
❖ Cross-sectional designs involve the collection of information from any given
sample of population elements only once. They may be either single crosssectional or multiple cross-sectional designs (Figure 3.1).
❖ In single cross-sectional designs, only one sample of respondents is drawn from
the target population, and information is obtained from this sample only once.
These designs are also called sample survey research designs.
❖ In multiple cross-sectional designs, there are two or more samples of
respondents, and information from each sample is obtained only once. Often
information from different samples is obtained at different times over long
intervals.
Longitudinal design
A type of research design involving a fixed sample of population elements that is
measured repeatedly. The sample remains the same over time, thus providing a series of
pictures that, when viewed together, portrays a vivid illustration of the situation and the
changes that are taking place over time.
Sometimes, the term panel or true panel is used interchangeably with the term
longitudinal design. A panel consists of a sample of respondents, generally households
that have agreed to provide information at specified intervals over an extended period.
Syndicated firms maintain panels, and panel members are compensated for their
participation with gifts, coupons, information, or cash. Panels can be classified as mail,
telephone, Internet, mobile, or multimedia depending on how the data are obtained
from the respondents.
Relative Advantages and Disadvantages of Longitudinal And Cross-Sectional Designs
The relative advantages and disadvantages of longitudinal versus cross-sectional
designs are summarized in Table 3.3. A major advantage of longitudinal design over the
cross-sectional design is the ability to detect change at the individual level (i.e., for an
individual respondent). This is possible because of repeated measurement of the same
variables on the same sample.
Causal Research
Causal research is used to obtain evidence of cause-and-effect (causal) relationships.
Causal research is appropriate for the following purposes:
1. To understand which variables are the cause (independent variables) and which
variables are the effect (dependent variables) of a phenomenon
2. To determine the nature of the relationship between the causal variables and the
effect to be predicted
Like descriptive research, causal research requires a planned and structured design.
Although descriptive research can determine the degree of association between
variables, it is not appropriate for examining causal relationships. Such an examination
requires a causal design, in which the causal or independent variables are manipulated
in a relatively controlled environment.
IMPORTANT CONCEPTS RELATING TO RESEARCH DESIGN
•
•
•
•
•
Extraneous variable: Independent variables that are not related to the purpose of
the study, but may affect the dependent variable are termed as extraneous
variables. Suppose the researcher wants to test the hypothesis that there is a
relationship between children’s gains in social studies achievement and their selfconcepts. In this case self-concept is an independent variable and social studies
achievement is a dependent variable. Intelligence may as well affect the social
studies achievement, but since it is not related to the purpose of the study
undertaken by the researcher, it will be termed as an extraneous variable.
Whatever effect is noticed on dependent variable as a result of extraneous
variable(s) is technically described as an ‘experimental error’. A study must always
be so designed that the effect upon the dependent variable is attributed entirely
to the independent variable(s), and not to some extraneous variable or variables.
Control: One important characteristic of a good research design is to minimise
the influence or effect of extraneous variable(s). The technical term ‘control’ is
used when we design the study minimising the effects of extraneous
independent variables. In experimental researches, the term ‘control’ is used to
refer to restrain experimental conditions.
Confounded relationship: When the dependent variable is not free from the
influence of extraneous variable(s), the relationship between the dependent and
independent variables is said to be confounded by an extraneous variable(s).
Research hypothesis: When a prediction or a hypothesised relationship is to be
tested by scientific methods, it is termed as research hypothesis. The research
hypothesis is a predictive statement that relates an independent variable to a
dependent variable. Usually a research hypothesis must contain, at least, one
independent and one dependent variable. Predictive statements which are not to
be objectively verified or the relationships that are assumed but not to be tested,
are not termed research hypotheses.
Experimental and control groups: In an experimental hypothesis-testing research
when a group is exposed to usual conditions, it is termed a ‘control group’, but
when the group is exposed to some novel or special condition, it is termed an
‘experimental group’. It is possible to design studies which include only
experimental groups or studies which include both experimental and control
groups.
•
•
Treatments: The different conditions under which experimental and control
groups are put are usually referred to as ‘treatments’. In the illustration taken
above, the two treatments are the usual studies programme and the special
studies programme. Similarly, if we want to determine through an experiment the
comparative impact of three varieties of fertilizers on the yield of wheat, in that
case the three varieties of fertilizers will be treated as three treatments
Experiment: The process of examining the truth of a statistical hypothesis, relating
to some research problem, is known as an experiment. For example, we can
conduct experiment to examine the usefulness of a certain newly developed
drug. Experiments can be of two types viz., absolute experiment and comparative
experiment. If we want to determine the impact of a fertilizer on the yield of a
crop, it is a case of absolute experiment; but if we want to determine the impact
of one fertilizer as compared to the impact of some other fertilizer, our
experiment then will be termed as a comparative experiment. Often, we
undertake comparative experiments when we talk of designs of experiments.
Important Experimental Designs
Experimental design refers to the framework or structure of an experiment and as such
there are several experimental designs. We can classify experimental designs into two
broad categories, viz., informal experimental designs and formal experimental designs.
Informal experimental designs are those designs that normally use a less sophisticated
form of analysis based on differences in magnitudes, whereas formal experimental
designs offer relatively more control and use precise statistical procedures for analysis.
Important experiment designs are as follows:
(a) Informal experimental designs:
(i) Before-and-after without control design.
(ii) After-only with control design.
(iii) Before-and-after with control design.
(b) Formal experimental designs:
i.
ii.
Completely randomized design (C.R. Design).
Randomized block design (R.B. Design).
iii.
iv.
Latin square design (L.S. Design).
Factorial designs.
Informal Experimental Designs
• Before-and-after without control design: In such a design a single test group or area is
selected and the dependent variable is measured before the introduction of the
treatment. The treatment is then introduced and the dependent variable is measured
again after the treatment has been introduced. The effect of the treatment would be
equal to the level of the phenomenon after the treatment minus the level of the
phenomenon before the treatment. The main difficulty of such a design is that with the
passage of time considerable extraneous variations may be there in its treatment effect.
• After-only with control design: In this design two groups or areas (test area and
control area) are selected and the treatment is introduced into the test area only. The
dependent variable is then measured in both the areas at the same time. Treatment
impact is assessed by subtracting the value of the dependent variable in the control area
from its value in the test area. The basic assumption in such a design is that the two
areas are identical with respect to their behavior towards the phenomenon considered.
If this assumption is not true, there is the possibility of extraneous variation entering
into the treatment effect. However, data can be collected in such a design without the
introduction of problems with the passage of time.
• Before-and-after with control design: In this design two areas are selected and the
dependent variable is measured in both the areas for an identical time-period before
the treatment. The treatment is then introduced into the test area only, and the
dependent variable is measured in both for an identical time-period after the
introduction of the treatment. The treatment effect is determined by subtracting the
change in the dependent variable in the control area from the change in the dependent
variable in test area.
Formal Experimental Designs
Observation Methods
Disguised versus Undisguised Observation
• In disguised observation, the respondents are unaware that they are being observed.
Disguise may be accomplished by using one-way mirrors, hidden cameras, or
inconspicuous mechanical devices. Observers may be disguised as shoppers or sales
clerks.
• In undisguised observation, the respondents are aware that they are under
observation.
Natural versus Contrived Observation
• Natural observation involves observing behavior as it takes places in the environment.
For example, one could observe the behavior of respondents eating fast food in Burger
King.
• In contrived observation, respondents’ behavior is observed in an artificial
environment, such as a test kitchen.
A Classification of Observation Methods
a. Personal Observation
• A researcher observes actual behavior as it occurs.
• The observer does not attempt to manipulate the phenomenon being observed but
merely records what takes place.
• For example, a researcher might record traffic counts and observe traffic flows in a
department store.
b. Mechanical Observation
Do not require respondents' direct participation.
• the AC Nielsen audimeter
• turnstiles that record the number of people entering or leaving a building.
• On-site cameras (still, motion picture, or video)
• Optical scanners in supermarkets
Do require respondent involvement.
• eye-tracking monitors
• pupilometers
• psychogalvanometers
• voice pitch analyzers
• devices measuring response latency
c. Audit
• The researcher collects data by examining physical records or performing inventory
analysis.
• Data are collected personally by the researcher.
• The data are based upon counts, usually of physical objects.
• Retail and wholesale audits conducted by marketing research suppliers were discussed
in the context of syndicated data.
d. Content Analysis
• The objective, systematic, and quantitative description of the manifest content of a
communication.
• The unit of analysis may be words, characters (individuals or objects), themes
(propositions), space and time measures (length or duration of the message), or topics
(subject of the message).
• Analytical categories for classifying the units are developed and the communication is
broken down according to prescribed rules.
e. Trace Analysis
Data collection is based on physical traces, or evidence, of past behavior.
• The selective erosion of tiles in a museum indexed by the replacement rate was used
to determine the relative popularity of exhibits.
• The number of different fingerprints on a page was used to gauge the readership of
various advertisements in a magazine.
• The position of the radio dials in cars brought in for service was used to estimate share
of listening audience of various radio stations.
• The age and condition of cars in a parking lot were used to assess the affluence of
customers.
• The magazines people donated to charity were used to determine people's favourite
magazines.
• Internet visitors leave traces which can be analyzed to examine browsing and usage
behavior by using cookies.
Relative Advantages of Observation
• They permit measurement of actual behavior rather than reports of intended or
preferred behavior.
• There is no reporting bias, and potential bias caused by the interviewer and the
interviewing process is eliminated or reduced.
• Certain types of data can be collected only by observation.
• If the observed phenomenon occurs frequently or is of short duration, observational
methods may be cheaper and faster than survey methods.
• The reasons for the observed behavior may not be determined since little is known
about the underlying motives, beliefs, attitudes, and preferences.
• Selective perception (bias in the researcher’s perception) can bias the data.
• Observational data are often time-consuming and expensive, and it is difficult to
observe certain forms of behavior.
• In some cases, the use of observational methods may be unethical, as in observing
people without their knowledge or consent.
It is best to view observation as a complement to survey methods, rather than as being
in competition with them.
CONCEPT MAP
UNIT 3:- Measurement and Scaling
Measurement
➢ It means assigning numbers or other symbols to characteristics of objects
according to certain prespecified rules.
➢ The most important aspect of measurement is the specification of rules for
assigning numbers to the characteristics. The assignment process must be
isomorphic:
➢ There must be one-to-one correspondence between the numbers and the
characteristics being measured. For example, the same dollar figures are assigned
to households with identical annual incomes.
➢ Only then can the numbers be associated with specific characteristics of the
measured object, and vice versa. In addition, the rules for assigning numbers
should be standardized and applied uniformly.
➢ They must not change over objects or time.
Scaling
➢ It may be considered an extension of measurement. Scaling involves creating a
continuum upon which measured objects are located.
➢ To illustrate, consider a scale from 1 to 100 for locating consumers according to
the characteristic “attitude toward department stores.” Each respondent is
assigned a number from 1 to 100 indicating the degree of (un)favorableness, with
1 = Extremely unfavorable to 100 = Extremely favorable. Measurement is the
actual assignment of a number from 1 to 100 to each respondent.
➢ Scaling is the process of placing the respondents on a continuum with respect to
their attitude toward department stores. In the opening example of most
admired companies, the assignment of numbers to reflect the annual revenue
was an example of measurement.
➢ The placement of individual companies on the annual revenue continuum was
scaling.
Scale Characteristics and Levels of Measurement
The level of measurement denotes what properties of an object the scale is measuring
or not measuring. An understanding of the scale characteristics is fundamental to
understanding the primary type of scales. All the scales that we use in marketing
research can be described in terms of four basic characteristics: description, order,
distance, and origin. Together they define the level of measurement of a scale.
1) Description
✓ By description, we mean the unique labels or descriptors that are used to
designate each value of the scale.
✓ Some examples of descriptors are as follows: 1. Female, 2. Male; 1 = Strongly disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, and 5 = Strongly
agree; and the number of dollars earned annually by a household. To amplify,
Female and Male are unique descriptors used to describe values 1 and 2 of the
gender scale.
✓ It is important to remember that all scales possess this characteristic of
description. Thus, all scales have unique labels or descriptors that are used to
define the scale values or response options.
2) Order
✓ By order, we mean the relative sizes or positions of the descriptors.
✓ There are no absolute values associated with order, only relative values. Order is
denoted by descriptors such as “greater than,” “less than,” and “equal to.”
✓ For example, a respondent’s preference for three brands of athletic shoes is
expressed by the following order, with the most preferred brand being listed first
and the least preferred brand last. Such as Nike, New Balance, Adidas
3) Distance
✓ The characteristic of distance means that absolute differences between the scale
descriptors are known and may be expressed in units.
✓ A five-person household has one person more than a four-person household,
which in turn has one person more than a three-person household. Thus, the
following scale possesses the distance characteristic.
✓ Number of persons living in your household _________
✓ Notice, that a scale that has distance also has order. We know that a five-person
household is greater than the four-person household in terms of the number of
persons living in the household.
✓ Likewise, a three-person household is less than a four-person household. Thus,
distance implies order but the reverse may not be true.
4) Origin
✓ The origin characteristic means that the scale has a unique or fixed beginning or
true zero point.
✓ Thus, an exact measurement of income by a scale such as: What is the annual
income of your household before taxes? $ has a fixed origin or a true zero point.
An answer of zero would mean that the household has no income at all.
✓ A scale that has origin also has distance (and order and description). Many scales
used in marketing research do not have a fixed origin or true zero point, as in the
disagree-agree scale considered earlier.
✓ Notice that such a scale was defined as 1 = Strongly disagree, 2 = Disagree, 3 =
Neither agree nor disagree, 4 = Agree, and 5 = Strongly agree. However, 1 is an
arbitrary origin or starting point
✓ Thus, this scale does not have a fixed origin or a true zero point and,
consequently, does not possess the characteristic of origin.
Primary Scales of Measurement
There are four primary scales of measurement: nominal, ordinal, interval, and ratio.
i.
▪
▪
▪
▪
ii.
▪
▪
Nominal Scale
A nominal scale is a figurative labeling scheme in which the numbers serve only
as labels or tags for identifying and classifying objects.
The only characteristic possessed by these scales is description. For example, the
numbers assigned to the respondents in a study constitute a nominal scale.
When a nominal scale is used for the purpose of identification, there is a strict
one-to-one correspondence between the numbers and the objects. Each number
is assigned to only one object and each object has only one number assigned to
it.
Common examples include Social Security numbers and numbers assigned to
football players. In marketing research, nominal scales are used for identifying
respondents, brands, attributes, stores, and other objects.
Ordinal Scale
An ordinal scale is a ranking scale in which numbers are assigned to objects to
indicate the relative extent to which the objects possess some characteristic.
An ordinal scale allows you to determine whether an object has more or less of a
characteristic than some other object, but not how much more or less.
▪
▪
▪
iii.
▪
▪
▪
▪
▪
iv.
▪
▪
▪
Thus, an ordinal scale indicates relative position, not the magnitude of the
differences between the objects. The object ranked first has more of the
characteristic as compared to the object ranked second, but whether the object
ranked second is a close second or a poor second is not known.
The ordinal scales possess description and order characteristics but do not
possess distance (or origin).
Common examples of ordinal scales include quality rankings, rankings of teams
in a tournament, socioeconomic class, and occupational status. In marketing
research, ordinal scales are used to measure relative attitudes, opinions,
perceptions, and preferences.
Interval Scale
In an interval scale, numerically equal distances on the scale represent equal
values in the characteristic being measured.
An interval scale contains all the information of an ordinal scale, but it also allows
you to compare the differences between objects.
The difference between any two adjacent scale values is identical to the
difference between any other two adjacent values of an interval scale. There is a
constant or equal interval between scale values.
The difference between 1 and 2 is the same as the difference between 2 and 3,
which is the same as the difference between 5 and 6. The distance between
descriptors is known.
A common example in everyday life is a temperature scale. In marketing research,
attitudinal data obtained from rating scales are often treated as interval data.
Ratio Scale
A ratio scale possesses all the properties of the nominal, ordinal, and interval
scales and, in addition, an absolute zero point.
Thus, ratio scales possess the characteristic of origin (and distance, order, and
description). Thus, in ratio scales we can identify or classify objects, rank the
objects, and compare intervals or differences. It is also meaningful to compute
ratios of scale values.
Not only is the difference between 2 and 5 the same as the difference between
14 and 17, but also 14 is seven times as large as 2 in an absolute sense.
▪
▪
Common examples of ratio scales include height, weight, age, and money. In
marketing, sales, costs, market share, and number of customers are variables
measured on a ratio scale.
In the opening example, the annual revenue of the most admired companies, not
shown, could be represented on a ratio scale.
A Comparison of Scaling Techniques
The scaling techniques commonly employed in marketing research can be classified into
comparative and noncomparative scales.
• Comparative scales involve the direct comparison of stimulus objects. For example,
respondents might be asked whether they prefer Coke or Pepsi. Comparative scale data
must be interpreted in relative terms and have only ordinal or rank order properties. For
this reason, comparative scaling is also referred to as nonmetric scaling. As shown in
Figure 8.2, comparative scales include paired comparisons, rank order, constant sum
scales, Q-sort, and other procedures.
• Advantage: The major benefit of comparative scaling is that small differences between
stimulus objects can be detected. As they compare the stimulus objects, respondents
are forced to choose between them. In addition, respondents approach the rating task
from the same known reference points.
• Disadvantage: The major disadvantages of comparative scales include the ordinal
nature of the data and the inability to generalize beyond the stimulus objects scaled. For
instance, to compare RC Cola to Coke and Pepsi, the researcher would have to do a new
study. These disadvantages are substantially overcome by the noncomparative scaling
techniques.
• Non-Comparative Scales: also referred to as monadic or metric scales, each object is
scaled independently of the others in the stimulus set. The resulting data are generally
assumed to be interval or ratio scaled.
• For example, respondents may be asked to evaluate Coke on a 1-to-6 preference scale
(1 not at all preferred, 6 greatly preferred). Similar evaluations would be obtained for
Pepsi and RC Cola. As can be seen in Figure 8.2, noncomparative scales can be
continuous rating or itemized rating scales. The itemized rating scales can be further
classified as Likert, semantic differential, or stapel scales.
• Noncomparative scaling is the most widely used scaling technique in marketing
research.
Comparative Scaling Techniques
1) Paired Comparison Scaling
As its name implies, in paired comparison scaling, a respondent is presented with two
objects and asked to select one according to some criterion. The data obtained are
ordinal in nature.
2) Rank Order Scaling
After paired comparisons, the most popular comparative scaling technique is rank order
scaling.
In rank order scaling, respondents are presented with several objects simultaneously
and asked to order or rank them according to some criterion. For example, respondents
may be asked to rank brands of toothpaste according to overall preference. As shown in
Figure 8.4, these rankings are typically obtained by asking the respondents to assign a
rank of 1 to the most preferred brand, 2 to the second most preferred, and so on, until a
rank of n is assigned to the least preferred brand.
3) Constant Sum Scaling
In constant sum scaling, respondents allocate a constant sum of units, such as points,
dollars, or chips, among a set of stimulus objects with respect to some criterion.
As shown in Figure, respondents may be asked to allocate 100 points to attributes of
toilet soap in a way that reflects the importance they attach to each attribute.
If an attribute is unimportant, the respondent assigns it zero points. If an attribute is
twice as important as some other attribute, it receives twice as many points. The sum of
all the points is 100.
4) Q-sort scaling
A comparative scaling technique that uses a rank order procedure to sort objects based
on similarity with respect to some criterion.
Non-Comparative Scaling Techniques
Continuous Rating Scale
❖ In a continuous rating scale, also referred to as a graphic rating scale,
respondents rate the objects by placing a mark at the appropriate position on a
line that runs from one extreme of the criterion variable to the other.
❖ Thus, the respondents are not restricted to selecting from marks previously set by
the researcher. The form of the continuous scale may vary considerably.
❖ For example, the line may be vertical or horizontal; scale points, in the form of
numbers or brief descriptions, may be provided; and, if provided, the scale points
may be few or many. Three versions of a continuous rating scale are illustrated.
Itemized Rating Scales
❖ In an itemized rating scale, the respondents are provided with a scale that has a
number or brief description associated with each category.
❖ The categories are ordered in terms of scale position, and the respondents are
required to select the specified category that best describes the object being
rated.
❖ Itemized rating scales are widely used in marketing research and form the basic
components of more complex scales, such as multi-item rating scales.
a) Likert Scale
Named after its developer, Rensis Likert, the Likert scale is a widely used rating scale
that requires the respondents to indicate a degree of agreement or disagreement
with each of a series of statements about the stimulus objects. Typically, each scale
item has five response categories, ranging from “strongly disagree” to “strongly
agree.” We illustrate with a Likert Scale for evaluating attitudes toward Wal-Mart in
the context of the department store patronage project.
b) Semantic Differential Scale
The semantic differential is a 7-point rating scale with endpoints associated with bipolar
labels that have semantic meaning. In a typical application, respondents rate objects on
a number of itemized, 7-point rating scales bounded at each end by one of two bipolar
adjectives, such as “cold” and “warm.”
c) Stapel Scale
The Stapel scale, named after its developer, Jan Stapel, is a unipolar rating scale with 10
categories numbered from 25 to 15, without a neutral point (zero).11 This scale is
usually presented vertically. Respondents are asked to indicate how accurately or
inaccurately each term describes the object by selecting an appropriate numerical
response category. The higher the number, the more accurately the term describes the
object,
Sources of Error in Measurement
Measurement should be precise and unambiguous in an ideal research study. This
objective, however, is often not met with in entirety. As such the researcher. must be
aware about the sources of error in measurement.
(a) Respondent: At times the respondent may be reluctant to express strong negative
feelings or it is just possible that he may have very little knowledge but may not admit
his ignorance. All this reluctance is likely to result in an interview of ‘guesses.’ Transient
factors like fatigue, boredom, anxiety, etc. may limit the ability of the respondent to
respond accurately and fully.
(b) Situation: Situational factors may also come in the way of correct measurement. Any
condition which places a strain on interview can have serious effects on the interviewerrespondent rapport. For instance, if someone else is present, he can distort responses by
joining in or merely by being present. If the respondent feels that anonymity is not
assured, he may be reluctant to express certain feelings
(c) Measurer: The interviewer can distort responses by rewording or reordering
questions. His behavior, style and looks may encourage or discourage certain replies
from respondents. Careless mechanical processing may distort the findings. Errors may
also creep in because of incorrect coding, faulty tabulation and/or statistical
calculations, particularly in the data-analysis stage
(d) Instrument: Error may arise because of the defective measuring instrument. The use
of complex words, beyond the comprehension of the respondent, ambiguous meanings,
poor printing, inadequate space for replies, response choice omissions, etc. are a few
things that make the measuring instrument defective and may result in measurement
errors. Another type of instrument deficiency is the poor sampling of the universe of
items of concern.
Tests of Sound Measurement
Sound measurement must meet the tests of validity, reliability and practicality. In fact,
these are the three major considerations one should use in evaluating a measurement
tool.
Validity refers to the extent to which a test measures what we actually wish to measure.
Reliability has to do with the accuracy and precision of a measurement procedure.
Practicality is concerned with a wide range of factors of economy, convenience, and
interpretability .
Reliability
Reliability refers to the extent to which a scale produces consistent results if repeated
measurements are made.23 Systematic sources of error do not have an adverse impact
on reliability, because they affect the measurement in a constant way and do not lead to
inconsistency.
Approaches for assessing reliability include as given below:TEST-RETEST RELIABILITY:- In test-retest reliability, respondents are administered
identical sets of scale items at two different times under as nearly equivalent conditions
as possible. The time interval between tests or administrations is, typically, two to four
weeks. The degree of similarity between the two measurements is determined by
computing a correlation coefficient. The higher the correlation coefficient is, the greater
the reliability.
ALTERNATIVE-FORMS RELIABILITY:- In alternative-forms reliability, two equivalent forms
of the scale are constructed. The same respondents are measured at two different times,
usually two to four weeks apart, with a different scale form being administered each
time. The scores from the administration of the alternative-scale forms are correlated to
assess reliability.
INTERNAL CONSISTENCY RELIABILITY:- Internal consistency reliability is used to assess
the reliability of a summated scale where several items are summed to form a total
score. In a scale of this type, each item measures some aspect of the construct measured
by the entire scale, and the items should be consistent in what they indicate about the
characteristic. This measure of reliability focuses on the internal consistency of the set of
items forming the scale.
•
•
The simplest measure of internal consistency is split-half reliability. The items on
the scale are divided into two halves and the resulting half scores are correlated.
High correlations between the halves indicate high internal consistency. The scale
items can be split into halves based on odd- and even-numbered items or
randomly.
The coefficient alpha, or Cronbach’s alpha, is the average of all possible split-half
coefficients resulting from different ways of splitting the scale items. This
coefficient varies from 0 to 1, and a value of 0.6 or less generally indicates
unsatisfactory internal consistency reliability. An important property of coefficient
alpha is that its value tends to increase with an increase in the number of scale
items. Therefore, coefficient alpha may be artificially, and inappropriately, inflated
by including several redundant scale items.
Validity
The validity of a scale may be defined as the extent to which differences in observed
scale scores reflect true differences among objects on the characteristic being measured,
rather than systematic or random error. Perfect validity requires that there be no
measurement error. Researchers may assess content validity, criterion validity, or
construct validity.
CONTENT VALIDITY:- Content validity, sometimes called face validity, is a subjective but
systematic evaluation of how well the content of a scale represents the measurement
task at hand. The researcher or someone else examines whether the scale items
adequately cover the entire domain of the construct being measured. Thus, a scale
designed to measure store image would be considered inadequate if it omitted any of
the major dimensions (quality, variety and assortment of merchandise, etc.). Given its
subjective nature, content validity alone is not a sufficient measure of the validity of a
scale, yet it aids in a common-sense interpretation of the scale scores. A more formal
evaluation can be obtained by examining criterion validity.
CRITERION VALIDITY:- Criterion validity reflects whether a scale performs as expected in
relation to other variables selected as meaningful criteria (criterion variables). Criterion
variables may include demographic and psychographic characteristics, attitudinal and
behavioral measures, or scores obtained from other scales. Based on the time period
involved, criterion validity can take two forms: concurrent and predictive validity.
▪
▪
Concurrent validity is assessed when the data on the scale being evaluated and
on the criterion variables are collected at the same time. To assess concurrent
validity, a researcher may develop short forms of standard personality
instruments. The original instruments and the short versions would be
administered simultaneously to a group of respondents and the results
compared.
To assess predictive validity, the researcher collects data on the scale at one point
in time and data on the criterion variables at a future time. For example,
intentions toward cereal brands could be used to predict future purchases of
cereals by members of a scanner panel. Intention data are obtained from the
panel members, and then their future purchases are tracked with scanner data.
The predicted and actual purchases are compared to assess the predictive validity
of the intention scale.
CONSTRUCT VALIDITY Construct validity addresses the question of what construct or
characteristic the scale is, in fact, measuring. When assessing construct validity, the
researcher attempts to answer theoretical questions about why the scale works and
what deductions can be made concerning the underlying theory. Thus, construct validity
requires a sound theory of the nature of the construct being measured and how it
relates to other constructs. Construct validity is the most sophisticated and difficult type
of validity to establish.
i.
ii.
iii.
Convergent validity is the extent to which the scale correlates positively with
other measures of the same construct. It is not necessary that all these measures
be obtained by using conventional scaling techniques.
Discriminant validity is the extent to which a measure does not correlate with
other constructs from which it is supposed to differ. It involves demonstrating a
lack of correlation among differing constructs.
Nomological validity is the extent to which the scale correlates in theoretically
predicted ways with measures of different but related constructs. A theoretical
model is formulated that leads to further deductions, tests, and inferences.
Gradually, a nomological net is built in which several constructs are systematically
interrelated.
Generalizability
Generalizability refers to the extent to which one can generalize from the observations
at hand to a universe of generalizations. The set of all conditions of measurement over
which the investigator wishes to generalize is the universe of generalization. These
conditions may include items, interviewers, situations of observation, and so on. A
researcher may wish to generalize a scale developed for use in personal interviews to
other modes of data collection, such as mail and telephone interviews. Likewise, one
may wish to generalize from a sample of items to the universe of items, from a sample
of times of measurement to the universe of times of measurement, from a sample of
observers to a universe of observers, and so on