MALHOTRA
Chapter 3 | Research design....................................................................................................................2
Exploratory research...............................................................................................................................2
conclusive research design.....................................................................................................................3
Chapter 7 | Causal research design: experimentation..............................................................................8
Chapter 9 | Measurement and Scaling:..................................................................................................15
Noncomparative Scaling Techniques....................................................................................................15
Chapter 10 | Questionnaire and Form Design.......................................................................................20
Chapter 11 | Sampling Design and Procedures.....................................................................................26
Chapter 15 | Frequency distribution, cross-tabulation, and hypothesis testing......................................34
CHAPTER 16 | analysis of variance and covariance............................................................................38
CHAPTER 17 | Correlation and regression..........................................................................................40
CHAPTER 19 | FACTOR ANALYSIS................................................................................................43
CHAPTER 20 | CLUSTER ANALYSIS..............................................................................................47
CHAPTER 21 | MULTIDIMENSIONAL SCALING AND CONJOINT ANALYSIS........................50
, CHAPTER 3 | RESEARCH DESIGN
A research design is a framework or blueprint for conducting the marketing research project. It details
the procedures necessary for obtaining the information needed to structure or solve marketing research
problems. Although a broad approach to the problem has already been developed, the research design
specifies the details—the nuts and bolts—of implementing that approach.
Two main types of research designs: exploratory and conclusive
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.
The objective of conclusive research is to test specific hypotheses and examine specific relationships.
This requires that the researcher clearly specify the information needed. 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
The objective of exploratory research is to explore or search through a problem or situation to provide
insights and understanding.
In general, exploratory research is meaningful in any situation where the researcher does not have
enough understanding to proceed with the research project. Exploratory research is characterized by
flexibility and versatility with respect to the methods because formal research protocols and
procedures are not employed. It rarely involves structured questionnaires, large samples, and
probability sampling plans. Rather, researchers are alert to new ideas and insights as they proceed.
The creativity and ingenuity of the researcher play a major role in exploratory research.
,CONCLUSIVE RESEARCH DESIGN
DESCRIPTIVE RESEARCH
The major objective of descriptive research is to describe something, usually market characteristics or
functions. Descriptive research is conducted for the following reasons:
1. To describe the characteristics of relevant groups
2. To estimate the percentage of units in a specified population exhibiting a certain behavior
3. To determine the perceptions of product characteristics
4. To determine the degree to which marketing variables are associated
5. To make specific predictions
Descriptive research assumes that the
researcher has much prior knowledge about
the problem situation.
In fact, a major difference between
exploratory and descriptive research is that
descriptive research is
characterized by the prior formulation of
specific hypotheses. Thus, the information
needed is clearly defined. As a result,
descriptive research is preplanned and
structured. It is typically based on large
representative samples. A formal research
design specifies the methods for selecting the
sources of information and for collecting data
from those sources. A descriptive design
requires a clear specification of the who,
what, when, where, why, and way (the six
Ws) of the research (p. 107).
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 cross-sectional or multiple cross-sectional. 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.
COHORT ANALYSIS
Cohort analysis consists of a series of surveys conducted at appropriate time intervals, where the
cohort serves as the basic unit of analysis. A cohort is a group of respondents who experience the same
,event within the same time interval.8 For example, a birth (or age) cohort is a group of people who
were born during the same time interval, such as 1951 through 1960. The term cohort analysis refers
to any study in which there are measures of some characteristics of one or more cohorts at two or more
points in time.
LONGITUDINAL DESIGNS
In longitudinal designs, a fixed sample (or samples) of population elements is measured repeatedly on
the same variables. A longitudinal design differs from a cross-sectional design in that the sample or
samples remain the same over time. In other words, the same people are studied over time and the
same variables are measured. In contrast to the typical cross-sectional design, which gives a snapshot
of the variables of interest at a single point in time, a longitudinal study provides a series of pictures
that give an in-depth view of the situation and the changes that take 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.
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.
Longitudinal data enable researchers to examine changes in the behavior of individual units and to link
behavioral changes to marketing variables, such as changes in advertising, packaging, pricing, and
distribution. Since the same units are measured repeatedly, variations caused by changes in the sample
are eliminated and even small changes are apparent. Another advantage of panels is that relatively
large amounts of data can be collected. Because panel members are usually compensated for their
participation, they are willing to participate in lengthy and demanding interviews. Yet another
advantage is that panel data can be more accurate than cross-sectional data.
The main disadvantage of panels is that they may not be representative. Nonrepresentativeness
may arise because of refusal to cooperate, mortality and payment.
,Another disadvantage of panels is response bias. New panel members are often biased in their initial
responses. They tend to increase the behavior being measured, such as food purchasing. Bias also
results from boredom, fatigue, and incomplete diary or questionnaire entries.
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. The main method
of causal research is experimentation.
RELATIONSHIPS AMONG EXPLORATORY, DESCRIPTIVE AND CAUSAL
RESEARCH
General guidelines for choosing research designs:
1. When little is known about the problem situation, it is desirable to begin with exploratory
research.
2. Exploratory research is the initial step in the overall research design framework. It should, in
most instances, be followed by descriptive or causal research.
3. It is not necessary to begin every research design with exploratory research. It depends upon
the precision with which the problem has been defined and the researcher’s degree of certainty
about the approach to the problem.
4. Although exploratory research is generally the initial step, it need not be. Exploratory research
may follow descriptive or causal research. For example, descriptive or causal research results
in findings that are hard for managers to interpret. Exploratory research may provide more
insights to help understand these findings.
POTENTIAL SOURCES OF ERROR
The total error is the variation between the true
mean value in the population of the variable of
interest and the observed mean value obtained
in the marketing research project. As shown in
the figure, total error is composed of random
sampling error and nonsampling error.
RANDOM SAMPLING ERROR
Random sampling error occurs because the
particular sample selected is an imperfect
representation of the population of interest.
Random sampling error is the variation
between the true mean value for the population
and the true mean value for the original
sample.
,NONSAMPLING ERROR
Nonsampling errors can be attributed to sources other than sampling, and they may be random or
nonrandom. They result from a variety of reasons, including errors in problem definition, approach,
scales, questionnaire design, interviewing methods, and data preparation and analysis.
NONRESPONSE ERROR
Nonresponse error arises when some of the respondents included in the sample do not respond. The
primary causes of nonresponse are refusals and not-at-homes.
RESPONSE ERROR
Response error arises when respondents give inaccurate answers or their answers are misrecorded or
misanalyzed. Response error is defined as the variation between the true mean value of the variable in
the net sample and the observed mean value obtained in the marketing research project.
Errors made by the researcher include surrogate information, measurement, population definition,
sampling frame, and data analysis errors:
Surrogate information error may be defined as the variation between the information
needed for the marketing research problem and the information sought by the researcher.
Measurement error may be defined as the variation between the information sought and the
information generated by the measurement process employed by the researcher.
Population definition error may be defined as the variation between the actual population
relevant to the problem at hand and the population as defined by the researcher (problem of
appropriately defining the population).
Sampling frame error may be defined as the variation between the population defined by the
researcher and the population as implied by the sampling frame (list) used.
Data analysis error encompasses errors that occur while raw data from questionnaires are
transformed into research findings.
Response errors made by the interviewer include respondent selection, questioning, recording and
cheating errors:
Respondent selection error occurs when interviewers select respondents other than those
specified by the sampling design or in a manner inconsistent with the sampling design.
Questioning error denotes errors made in asking questions of the respondents or in not
probing when more information is needed. For example, while asking questions an interviewer
does not use the exact wording given in the questionnaire.
Recording error arises due to errors in hearing, interpreting, and recording the answers given
by the respondents.
Cheating error arises when the interviewer fabricates answers to a part or all of the interview.
Response errors made by the respondent are comprised of inability and unwillingness errors:
Inability error results from the respondent’s inability to provide accurate answers.
Respondents may provide inaccurate answers because of unfamiliarity, fatigue, boredom,
faulty recall, question format, question content, and other factors.
Unwillingness error arises from the respondent’s unwillingness to provide accurate
information. Respondents may intentionally misreport their answers because of a desire to
provide socially acceptable answers, avoid embarrassment, or please the interviewer.
,What is important here is that there are many sources of error. In formulating a research design, the
researcher should attempt to minimize the total error, not just a particular source. This admonition is
warranted by the general tendency among students and unsophisticated researchers to control sampling
error with large samples. Increasing the sample size does decrease sampling error, but it may also
increase nonsampling error by increasing interview errors.
Nonsampling error is likely to be more problematic than sampling error. Sampling error can be
calculated, whereas many forms of nonsampling error defy estimation. Moreover, nonsampling error
has been found to be the major contributor to total error, whereas random sampling error is relatively
small in magnitude. The point is that total error is important. A particular type of error is important
only in that it contributes to total error.
BUDGETING AND SCHEDULING THE PROJECT
Budgeting and scheduling help to ensure that the marketing research project is completed within the
available resources - financial, time, personnel, and other.
A useful approach for managing a project is the critical path method (CPM), which involves dividing
the research project into component activities, determining the sequence of these activities, and
estimating the time required for each activity. These activities and time estimates are diagrammed in
the form of a network flowchart. The critical path, the series of activities whose delay will hold up the
project, can then be identified.
An advanced version of CPM is the program evaluation and review technique (PERT), which is a
probability-based scheduling approach that recognizes and measures the uncertainty of the project
completion times. An even more advanced scheduling technique is the graphical evaluation and
review technique (GERT), in which both the completion probabilities and the activity costs can be
built into a network representation.
MARKETING RESEARCH PROPOSAL (P. 120)
Once the research design has been formulated and budgeting and scheduling of the project
accomplished, a written research proposal should be prepared. The marketing research proposal
contains the essence of the project and serves as a contract between the researcher and management.
The research proposal covers all phases of the marketing research process. It describes the research
problem, the approach, the research design, and how the data will be collected, analyzed, and reported.
It gives a cost estimate and a time schedule for completing the project. Although the format of a
research proposal may vary considerably, most proposals address all steps of the marketing research
process and contain the following elements:
1. Executive summary
2. Background
3. Problem definition / objectives of the research
4. Approach to the problem
5. Research design
6. Fieldwork / data collection
7. Data analysis
8. Reporting
9. Cost and time
10. Appendices
, CHAPTER 7 | CAUSAL RESEARCH DESIGN: EXPERIMENTATION
Experimentation is commonly used to infer causal relationships. The scientific notion of causality
implies that we can never prove that X causes Y. At best, we can only infer that X is one of the causes
of Y in that it makes the occurrence of Y probable.
CONDITIONS FOR CAUSALITY
Before making causal inferences, or assuming causality, three conditions must be satisfied:
1. Concomitant variation
2. Time order of occurrence of variables
3. Elimination of other possible causal factors
CONCOMITANT VARIATION
Concomitant variation is the extent to which a cause, X, and an effect, Y, occur together or vary
together in the way predicted by the hypothesis under consideration. Evidence pertaining to
concomitant variation can be obtained in a qualitative or quantitative manner.
TIME ORDER OF OCCURRENCE VARIABLES
The time order of occurrence condition states that the causing event must occur either before or
simultaneously with the effect; it cannot occur afterwards. By definition, an effect cannot be produced
by an event that occurs after the effect has taken place. However, it is possible for each event in a
relationship to be both a cause and an effect of the other event. In other words, a variable can be both a
cause and an effect in the same causal relationship.
ABSENCE OF OTHER POSSIBLE CAUSAL FACTORS
The absence of other possible causal factors means that the factor or variable being investigated
should be the only possible causal explanation.
Evidence of concomitant variation, time order of occurrence of variables, and elimination of other
possible causal factors, even if combined, still do not demonstrate conclusively that a causal
relationship exists. However, if all the evidence is strong and consistent, it may be reasonable to
conclude that there is a causal relationship. Accumulated evidence from several investigations
increases our confidence that a causal relationship exists. Confidence is further enhanced if the
evidence is interpreted in light of intimate conceptual knowledge of the problem situation. Controlled
experiments can provide strong evidence on all three conditions.
DEFINITIONS AND CONCEPTS
Independent variables or treatments are variables or alternatives that are manipulated (i.e.,
the levels of these variables are changed by the researcher) and whose effects are measured
and compared.
Test units are individuals, organizations, or other entities whose response to the independent
variables or treatments is being examined (consumers, stores, etc.)
Dependent variables are the variables that measure the effect of the independent variables on
the test units.
Extraneous variables are all variables other than the independent variables that affect the
response of the test units.
