MARTIN de TOURS SCHOOL OF MANAGEMENT DEPARTMENT OF MARKETING
Lesson Plan MKT4855 Research in Marketing
MKT4855 RESEARCH IN
MARKETING: Lesson Plan
Lesson 1: Introduction to Marketing Research
Lesson 2: Defining the Marketing Research Problem and Developing an Approach
Lesson 3: Research Design
Lesson 4: Exploratory Research Design
Lesson 5: Descriptive Research Design
Lesson 6: Causal Research Design
Lesson 7: Measurement and Scaling
Lesson 8: Sampling: Design and Procedures
Lesson 9: Data Analysis: Frequency, Hypothesis Testing, and Cross-Tabulation
Lesson 10: Data Analysis: Hypothesis Testing Related to Differences
Lesson 11: Data Analysis: Correlation and Regression
Lesson 12: Data Analysis: Factor Analysis
Lesson 13: Data Analysis: Cluster Analysis
Lesson 14: Implementation
Lesson Content: This lesson is intended to provide an overview of the nature and
scope of marketing research. A definition and classification of marketing research is
provided. An overview of the marketing research process is presented.
DURATION: 1.5 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: After introduce myself and discuss briefly about course outline; to
stimulate interest and to break the ice the lesson could begin by asking students a
simple question such as what is marketing research?
• Lecture: Definition of marketing research. Role of marketing research. Introduce
them to marketing research process. Describe the rationale for the steps involved in
the marketing research process. Emphasize the importance of these steps since the
entire research project will be conducted within this framework. Each step will be
discussed more fully throughout the course
• ASSIGNMENTS:
Divide the class into groups of five to six students each. These assigned groups will
be fixed for the whole semester. Students have to submit their group portfolio with
their pictures in the next class.
Assign them all three assignments (for the whole semester) and talk briefly about the
term project.
Duration: 30 minutes
Suggested Questions:
• What is marketing research? Define marketing research. What are some of the
noteworthy aspects of the definition?
LESSON 1: Introduction to Marketing Research
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Define marketing research and distinguish between problem identification and problem-solving
research.
• Describe a framework for conducting marketing research as well as the steps of the marketing
research process.
• Understand the nature and scope of marketing research and its role in designing and implementing
successful marketing programs.
• Explain how the decision to conduct marketing research is made.
• Describe a classification of marketing research and give examples.
• Describe the steps in the marketing research process.
• Closure: After the students learn the steps in the marketing research process, ask
them to follow the process for their term project.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Course outline
• Term project guideline
• Details of three assignments
• Paper and pens
Lesson Content: This lesson is intended to provide an appreciation of the importance
and complexities involved in defining the marketing research problem, and provide an
overview of the process and components of an approach to a marketing research
problem.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students an
example of a company that poorly formulated the research problem and its
consequents. For the example, we could point out to the students that, in absence of a
well-defined problem, the data collected is often worthless to the decision maker
and/or often provide incomplete information.
• Lecture: introduce the topic of defining the marketing research and developing an
approach.
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Assign them a marketing
research case study. The case study assigned should be used to show the importance
of and process used for defining the marketing research problem.
Duration: 45 minutes
Suggested Questions:
• What is the first step in conducting a marketing research project?
• Why is it important to define the marketing research problem appropriately?
• Who should be involved in the research problem identification process?
• What are some reasons why management is often not clear about the real
problem?
• What is the role of the researcher in the problem definition process?
• What is problem audit?
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Describe the importance of and process used for defining the marketing research problem.
• Describe the tasks involved in problem definition, including discussion with decision maker(s),
interview with industry experts, secondary data analysis, and qualitative research.
• Define the environmental factors affecting the definition of the research problem: past information
and forecasts; resources and constraints; objectives of the decision maker; buyer behavior; legal
environment; economic environment; and marketing and technological skills of the firm.
• Distinguish the management decision problem from the marketing research problem.
• Describe the structure of a well-defined marketing research problem including the broad statement
and the specific components.
• Explain in detail the various components of the approach: objective/theoretical framework,
analytical models, research questions, hypotheses, and specification of information needed.
Lesson 2: Defining Research Problem and Developing an Approach
• What is the difference between a symptom and a problem? How can we
differentiate between the two and identify a true problem?
• What are some differences between a management decision problem and a
marketing research problem?
• What are the common types of errors encountered in defining a marketing
research problem? What can be done to reduce the incidence of such errors?
• How are the research questions related to components of the problem?
• What are the difference between research questions and hypotheses?
• Closure: From the example in the anticipatory set, after the students learn how to
identify research problem/opportunity, ask them to suggest the appropriate research
problem/opportunity for the example. Discuss the importance of reducing the total
error and not particular errors.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• One or two examples about poor formulating the research problem
• A case study of marketing search in defining the research problem and
developing an approach
• Paper and pens
Extensions (For Strong Students): Motivate them to develop a complete marketing search
approach from the identified research problem. The outputs of the approach development
process should include the following components: objective/theoretical framework, analytical
models, research questions, hypotheses, and specification of information needed.
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide an overview of research design. A
definition and a classification of research designs are presented. The differences
between exploratory and conclusive research designs are discussed. The three basic
types of research designs, namely exploratory, descriptive, and causal are described,
and a comparative analysis of these designs is presented. The potential sources of
error in research designs are covered in some detail.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by pointing out the
importance of a good research design. For example, we could define the research
design as a framework or blueprint for conducting the marketing research project. It
specifies the precise details of the procedures necessary for obtaining the required
information. Finally, stress that it is important to have a good research design in order
to ensure that the marketing research project is conducted effectively and efficiently.
• Lecture: define and discuss the importance of a good research design. Discuss the
appropriate uses of exploratory and conclusive research designs. Explain the
distinction between exploratory, descriptive, and causal research. Explain how poor
research design leads to sources of error in analysis. Discuss the importance of the
marketing research proposal and list its main components.
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Assign them a short
marketing research problem. The problem assigned should be used to show the
importance of a good research design.
Duration: 45 minutes
Suggested Questions:
• Differentiate between exploratory and conclusive research.
• Compare and contrast cross-sectional and longitudinal designs.
• What is the relationship between exploratory, descriptive, and causal research?
• List the major components of a research design.
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Define research design, classify various research designs, and explain the differences
between exploratory and conclusive designs.
• Compare and contrast the basic research designs: exploratory, descriptive, and causal.
• Describe the major sources of error in a research design including random sampling error
and the various sources of nonsampling error.
• Describe the elements of a marketing research proposal and show how it addresses the
steps of the marketing research process.
Lesson 3: Research Design
• Define and express each of the following types of error as an equation: (a) Total
error; (b) Random sampling error; (c) Nonresponse error; (d) Response error.
• Discuss the six Ws of a descriptive research design (who, what, when, where,
why, way) that may be adopted.
• What kind of research design is appropriate? Why?
• Closure: From the example in the anticipatory set, after the students learn the
necessary of having a good research design and learn sources of error in analysis. We
could link them by explaining how poor research design leads to sources of error in
analysis.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• One or two examples about poor developing the research design
• Some short research problem to be used for developing research design
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide an overview of secondary data.
Secondary data are defined and their advantages, disadvantages, and evaluations
discussed. A classification of secondary data is presented. Internal sources, published
external sources, computerized databases, and syndicated sources of secondary data
are discussed. The usefulness of combining secondary data from different sources is
emphasized.
This lesson also provides an overview of qualitative research. The distinction between
qualitative and quantitative research is made and a classification of qualitative
research is presented. The major direct techniques consisting of focus groups and
depth interviews are covered in detail.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: lesson could begin by having students suggest sources of
secondary data with which they are familiar and keep a list on the board. Then
proceed to discuss the sources outlined and see how many sources they overlooked.
Discussing the nature of secondary data. For the example, we could define secondary
data as data that have already been collected for purposes other than the problem at
hand. The data are often found internally, and also from published materials,
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Define the nature and scope of secondary data and distinguish secondary data from primary data.
• Analyze the advantages and disadvantages of secondary data and their uses in the various steps of
the marketing research process.
• Evaluate secondary data using specifications, error, currency, objectives, nature, and
dependability criteria.
• Describe in detail the different sources of secondary data including internal sources and external
sources in the form of published materials, computerized databases, and syndicated services.
• Discuss in detail the syndicated sources of secondary data including
household/consumer data obtained via surveys, purchase and media panels, and
electronic scanner services, as well as institutional data related to retailers, wholesalers,
and industrial/service firms.
• Explain the need to use multiple sources of secondary data and describe single-source data.
• Explain the difference between qualitative and quantitative research in terms of the objectives,
sampling, data collection and analysis, and outcomes.
• Understand the various forms of qualitative research including direct procedures such as focus
groups and depth interviews, and indirect methods such as projective techniques.
• Describe focus groups in detail with an emphasis on planning and conducting focus groups, and
their advantages, disadvantages, and applications
• Describe depth interview techniques in detail citing their advantages, disadvantages, and
applications.
• Describe the general steps that should be followed when analyzing qualitative data.
Lesson 4: Exploratory Research Design
computerized databases, or from syndicated services. Finally, note that secondary
data are characterized as easily available and relatively inexpensive to obtain.
• Lecture: Describe the scope of secondary data. Identify the advantages and
disadvantages of secondary data. Explain the criteria for evaluating the quality of
secondary data with an example. List the various internal sources of secondary data
and explain their benefits to the researcher. Define and list the syndicated sources of
secondary data. Discuss the need to use multiple sources of secondary data.
Differentiate between quantitative and qualitative research with respect to the overall
objective, data collection techniques, sample size, data analysis, and outcome.
Explain the categories of qualitative research (direct and indirect techniques).
• ASSIGNMENTS:
o Divide the class into groups of five to six students each. Using the industry in
their term project, assign them to search for secondary data and then write a
report on their findings. For example, using external published sources,
obtain industry sales and sales of the major firms in that industry for the past
year. Estimate the market shares of each major firm.
Duration: 10 minutes for group discussion; assignment due in class
next week
o Divide the class into groups of five to six students each. Using the
industry in their term project, examples of focus group and/or depth
interview can be conducted for each group by asking for student
volunteers with the instructor acting as the moderator or interviewer.
Duration: 30 minutes for group discussion
Suggested Questions:
• What are the differences between primary and secondary data?
• Why is it important to obtain secondary data before primary data?
• What are the advantages and disadvantages of secondary data?
• What are the criteria to be used when evaluating secondary data?
• What is the difference between internal and external secondary data?
• Closure: From the example in the anticipatory set, after the students learn how to
identify research problem/opportunity, ask them to suggest the appropriate research
problem/opportunity for the example. Discuss the importance of reducing the total
error and not particular errors.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide an overview of descriptive
research consisting of surveys and observations. The survey methods are classified by
mode of administration as: telephone interviews, computer-assisted telephone
interviewing (CATI), personal in-home interviews, mall-intercept interviews,
computer-assisted personal interviewing (CAPI), mail surveys, mail panels, e-mail,
and Internet surveys. A comparative evaluation of these methods on 14 different
factors is presented. Structured versus unstructured, disguised versus undisguised and
natural versus contrived observations are discussed. The observation methods are
classified by mode of administration as personal observation, mechanical observation,
audit, content analysis, and trace analysis. Each of these methods is described in detail.
A comparison of survey and observational methods is made.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students an
example of a company that poorly formulated the research problem and its
consequents. For the example, we could point out to the students that, in absence of a
well-defined problem, the data collected is often worthless to the decision maker
and/or often provide incomplete information.
• Lecture: Classify the different survey methods by mode of administration. Present a
comparative evaluation of different survey methods. Explain when the different
observation methods are appropriate based on the structured versus unstructured,
disguised versus undisguised, and natural versus contrived conditions. Identify the
criteria for evaluating observation methods. Discuss relative advantages and
disadvantages of observation methods versus survey methods.
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Assign them a marketing
research case study. The case study assigned should be used to show them how to
conduct descriptive research design. Students should explain how they will obtain
information needed in the case. What methods will they use and why?
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Discuss and classify survey methods and describe the various telephone, personal, mail, and
electronic interviewing methods.
• Identify the criteria for evaluating survey methods, compare the different methods, and evaluate
which is best suited for a particular research project.
• Explain and classify the different observation methods used by marketing researchers and describe
personal observation, mechanical observation, audit, content analysis, and trace analysis.
• Identify the criteria for evaluating observation methods, compare the different methods, and
evaluate which, if any, is suited for a particular research project.
• Describe the relative advantages and disadvantages of observational methods and compare them
to survey methods.
Lesson 5: Descriptive Research Design: Survey and Observation
Duration: 45 minutes
Suggested Questions:
• Name the major modes for obtaining information via a survey.
• What are the relevant factors for evaluating which survey method is best suited to
a particular research project?
• What are the advantages and disadvantages of the structured-direct survey
method?
• What would be the most appropriate survey method for a project in which control
of field force and cost are critical factors?
• What are the relative advantages and disadvantages of observation?
• Closure:
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• A case study of marketing search in conducting the descriptive research design.
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives: Check their in-class group assignment.
Lesson Content: This lesson is intended to provide an overview of causal research
and experimentation. The concept of causality and the conditions for causality are
described. Internal and external validity in experimentation are discussed in detail. A
classification of experimental designs is presented and preexperimental, true
experimental, quasi-experimental, and statistical designs are described. Comparisons
between laboratory versus field experiments, and experimental versus
nonexperimental designs are made.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin with the concept of
causality as used in marketing. For the example, we could begin the class by asking a
few students what the term ‘causality’ means and writing components of their
definitions on the board. Afterwards, be sure to stress to the students that marketing
effects are caused by multiple variables and the relationships tend to be probabilistic.
As such, it is not possible to conclusively prove causality. Thus, we can only infer a
cause-effect relationship between the variables. Then, via an example, explain the
necessary conditions to infer causality.
• Lecture: Discuss the concept of causality as used in marketing. Introduce the
symbolic notation used in marketing research. Define validity and describe the
difference between internal and external validity. List some of the extraneous
variables that affect validity. Describe methods for controlling the effects of
extraneous variables. Discuss the differences in the types of experimental designs
(preexperimental designs, true experimental designs, quasi-experimental designs, and
statistical designs).
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Start a class discussion on
whether laboratory or field experiments are more useful in marketing research. We
could begin this discussion by simply asking students which form of experimentation
is better.
Duration: 30 minutes
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Explain the concept of causality as defined in marketing research and distinguish between the
ordinary meaning and the scientific meaning of causality..
• Define and differentiate the two types of validity: internal validity and external validity.
• Discuss the various extraneous variables that can affect the validity of results obtained through
experimentation and explain how the researcher can control extraneous variables.
• Describe and evaluate experimental designs and the differences among preexperimental, true
experimental, quasi-experimental, and statistical designs.
• Compare and contrast the use of laboratory versus field experimentation and experimental versus
nonexperimental designs in marketing research.
Lesson 6: Causal Research Design: Experimentation
Suggested Questions:
• What are the requirements for inferring a causal relationship between two
variables?
• Differentiate between internal and external validity?
• What key characteristic distinguishes true experimental designs from
preexperimental designs?
• List the steps involved in implementing the posttest-only control group design.
Describe the design symbolically.
• What advantages do statistical designs have over basic designs?
• Compare laboratory and field experimentation.
• Closure: From the class discussion on laboratory vs. field experiments. We may
focus on the discussion that laboratory experiments make up the bulk of consumer
research because of their ability to control extraneous variables and their relative
efficiency in gathering data. However, for certain studies, field experiments are used.
For example, Coca-Cola counts the shelf space it and its competitors receive in local
grocery stores when promotional variables are manipulated. Thus, each type of
experimentation has its role to play in marketing research, but because causality
cannot be inferred from field experiments, laboratory experiments predominate.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives: Check students’ participation during discussion and also
check the key answers of the topic.
Lesson Content: This lesson is intended to provide an introduction to primary scales
of measurement: nominal, ordinal, interval, and ratio. Scaling techniques are
classified as comparative and noncomparative. The comparative techniques consisting
of paired comparison, rank order, constant sum, and Q-sort scaling are discussed. This
lesson also provides a discussion of the noncomparative scales. Continuous and
itemized rating scales are discussed. The important noncomparative itemized rating
scale decisions are examined and guidelines provided. The construction of multi-item
scales is described. The evaluation of scales in terms of measurement accuracy,
reliability, validity, and generalizability is discussed at some length.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students
examples of each measurement scales.
• Lecture: Discuss and illustrate the primary scales of measurement. Distinguish the
two broad scaling measures (comparative and noncomparative scales). Describe the
different noncomparative scaling techniques. If available, bring examples of the
different scales to class to show to the students. Highlight the major decisions
involved in constructing itemized rating scales. Explain the criteria used to evaluate a
multi-item scale (measurement accuracy, reliability, validity, and generalizability.
• ASSIGNMENTS:
o Divide the class into groups of five to six students each. Assign them a
marketing research case study. Ask them to develop a questionnaire using
variety of measurement scales. Discuss the primary measurement scales that
are appropriate for the key variables. Discuss which of the comparative and
noncomparative technique are appropriate.
Duration: 30 minutes
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Introduce the concepts of measurement and scaling and show how scaling may be considered an
extension of measurement.
• Discuss the primary scales of measurement and differentiate nominal, ordinal, interval, and ratio
scales.
• Classify and discuss scaling techniques as comparative and noncomparative, and describe the
comparative techniques of paired comparison, rank order, constant sum, and Q-sort scaling.
• Describe the noncomparative scaling techniques, distinguish between continuous and itemized
rating scales, and explain Likert, semantic differential, and Stapel scales.
• Discuss the decisions involved in constructing itemized rating scales with respect to the number of
scale categories, balanced versus unbalanced scales, odd or even number of categories, forced
versus nonforced choice, degree of verbal description, and the physical form of the scale.
• Discuss the criteria used for scale evaluation and explain how to assess reliability, validity, and
generalizability.
Lesson 7: Measurement and Scaling
o Develop a questionnaire for their term project.
Duration: one week period; due at the beginning of next class.
Suggested Questions:
• What are primary scales of measurement?
• What are the implications of having an arbitrary zero point in an interval scale?
• What is a comparative rating scale?
• What are the advantages and disadvantages of paired comparison scaling?
• Describe the constant sum scale. How is it different from the other comparative
rating scales?
• What is a semantic differential scale? For what purposes is this scale used? Give
an Example.
• Describe the Likert scales. Give an example.
• What are the differences between the Stapel scale and the semantic differential
scale?
• What are the major decisions involved in constructing an itemized rating scale?
• What is the difference between balanced and unbalanced scales? Give an example
of each.
• Should an odd or even number of categories be used in an itemized rating scales?
Why? When?
• What is the difference between forced and non-forced scales? Give an example of
each.
• What are multi-item scales? Give an example.
• What is reliability?
• What is validity?
• What is the relationship between reliability and validity?
• Closure: From the examples of measurement scales in the anticipatory set, after the
students learn the material, ask them to suggest the appropriate scales of measurement
for the given examples.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Examples for every measurement scales
• A case study of marketing search in developing scales of measurement
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide a discussion of sampling design
and procedures. The sampling design process is described. The various nonprobability
and probability sampling techniques are presented and the choice between
nonprobability and probability sampling discussed. Methods for improving response
rates and adjusting for nonresponse are described.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin with how sampling is
used to achieve the objectives of marketing research. For the example, we could
begin with the objective of marketing research that we define as: obtaining
information about the characteristics of a population, either using a sample or census.
A representative sample, though not the entire population, contains the same
characteristics as the population, thus, generalizability is high and population
parameters can be inferred from the information from the sample. This information is
contained in statistics, and the inferences that are made use statistical techniques such
as estimation procedures and hypothesis testing.
• Lecture: Describe a population, a census, and a sample. Explain the relationship
between the sample design process and the research project. Explain the sampling
design process. Discuss the factors to consider in selecting a sampling technique.
Differentiate probability and nonprobability sampling techniques. Give examples of
nonprobability and probability sampling techniques. Discuss the advantages and
disadvantages of each nonprobability and probability sampling technique. Describe
the choice between nonprobability and probability samples.
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Assign them a marketing
research case study. The case study assigned should be used to show the importance
of and process used for defining the marketing research problem.
Lesson 8: Sampling: Design and Procedures
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Differentiate a sample from a census and identify the conditions that favor the use of a sample
versus a census.
• Discuss the sampling design process: definition of the target population, determination of the
sampling frame, selection of sampling technique(s), determination of sample size, and execution
of the sampling process.
• Classify sampling techniques as nonprobability and probability sampling technique.
• Describe the nonprobability sampling techniques of convenience, judgmental, quota, and snowball
sampling.
• Describe the probability sampling techniques of simple random, systematic, stratified, and cluster
sampling.
• Identify the conditions that favor the use of nonprobability sampling versus probability sampling.
Duration: 30 minutes
Suggested Questions:
• What is the major difference between a sample and a census?
• Under what conditions is a sample preferable to a sample?
• Describe the sampling design process.
• How should the target population be defined?
• What is a sampling unit? How is it different from the population element?
• How do probability sampling techniques differ from nonprobability sampling
techniques?
• What is the relationship between quota sampling and judgmental sampling?
• Describe stratified sampling. What are the criteria for the selection of
stratification variables?
• Describe cluster sampling procedure. What is the key distinction between cluster
sampling and stratified sampling?
• What factors should be considered in choosing between probability and
nonprobability sampling?
• Closure: After the students learn the material, ask them to suggest the appropriate
scales of measurement for the given examples.
.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• examples of every nonprobability and probability sampling
• A case study of marketing search in developing sampling design and sampling
procedure
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide a discussion of frequency
distribution, cross-tabulation, and parametric and nonparametric hypothesis testing.
The measures of location, variability, and shape associated with a frequency
distribution are discussed. The two variable and three variable cases for cross-
tabulation are described and the associated statistics explained. The one sample, two
independent samples, and paired samples parametric and nonparametric tests are
discussed. For each statistical procedure, hand calculations are shown where
appropriate. The relevant computer programs for conducting these procedures using
the microcomputer versions of SPSS and Excel are described..
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students an
example of preliminary data analysis. For the example, we could use preliminary data
analysis as relatively simple procedures that enable the researcher to get a feel for the
data. This allows the researcher to understand basic relationships among variables so
that further rigorous analysis of the data can be carried out in a focused manner. The
interpretations that are obtained in initial data analysis are sometimes very useful in
clarifying the results obtained from further analyses.
• Lecture: Explain the significance of preliminary data analysis. Discuss the
motivation for the frequencies procedure. List and differentiate the three measures of
location. Discuss the various measures of variability. Explain the cross-tabulations
procedure. Discuss the reasons for using three variable cross-tabulation. Describe the
statistics used to assess the significance and strength in cross-tabulation (i.e., Chi-
Square, Phi Correlation coefficient, Contingency coefficient, Cramer’s V, Lambda
coefficient). Classify hypothesis testing procedures (i.e., t Statistics, one sample, two
samples, Paired samples). Explain correspondent analysis.
•
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Describe the significance of preliminary data analysis and the insights that can be obtained from
such an analysis.
• Discuss data analysis associated with frequencies including measures of location, measures of
variability, and measures of shape.
• Explain data analysis associated with cross-tabulations and the associated statistics: chi-square,
phi coefficient, contingency coefficient, Cramer’s V, and lambda coefficient.
• Describe data analysis associated with parametric hypothesis testing for one sample, two
independent samples, and paired samples.
• Understand data analysis associated with nonparametric hypothesis testing for one sample, two
independent samples, and paired samples.
• Explain in detail the various components of the approach: objective/theoretical framework,
analytical models, research questions, hypotheses, and specification of information needed.
Lesson 9: Data Analysis: Frequency, Hypothesis Testing,
Cross-Tabulation
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Ask them to submit a report
of preliminary data analysis using the dataset of the term project.
Duration: One week period; due at beginning of the next class
Suggested Questions:
• What measures of location are commonly computed for frequencies?
• What measures of variability are commonly computed for frequencies?
• What is the major difference between cross-tabulation and frequency
distribution?
• What is the general rule for computing percentages in cross-tabulation?
• What is meant by the expected cell frequency?
• When is it meaningful to determine the strength of association in a cross-
tabulation?
• What statistics are available for determining the strength of association in cross-
tabulation?
• Discuss the reasons for the frequent use of cross-tabulations. What are some of its
limitations?
• Closure: After the students learn how to perform preliminary data analysis, ask them
which tools they can use to analyze the dataset of their term project.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Real examples of preliminary data analysis
• Questionnaire and dataset of the term project
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to covers analysis of variance (ANOVA)
and analysis of covariance. The relationship between analysis of variance and other
techniques is discussed. One-way analysis of variance, and n-way analysis of variance
are described in detail and issues in interpretation of results discussed. Repeated
measures ANOVA, nonmetric ANOVA, and multivariate analysis of variance are also
covered.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students an
example of preliminary data analysis.
• Lecture: Describe the relationship of both ANOVA and ANCOVA to other
multivariate techniques. Explain that ANOVA and ANCOVA are useful for testing
differences between two or more means whereas cross-tabs and t tests are applicable
only to cases involving two means or medians. Note that t tests involve only a single
binary independent variable. ANOVA tests are applicable when all of the
independent variables are categorical, whereas ANCOVA is used when some
independent variables are categorical and some are metric. If all the independent
variables are interval scaled, then a regression procedure is used. Describe the key
statistics associated with one-way ANOVA. Compare and contrast n-way ANOVA
with one-way ANOVA. Discuss the procedure for conducting n-way analysis of
variance.
• ASSIGNMENTS:
• Divide the class into groups of five to six students each. Ask them to submit a report
of preliminary data analysis using the dataset of the term project.
Duration: One week period; due at beginning of the next class
Suggested Questions:
• What is the relationship between analysis of variance and the t-test?
• What is total variance? How is it decomposed in a one-way analysis of variance?
• What is the null hypothesis in one-way ANOVA?
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Discuss the scope of the analysis of variance (ANOVA) technique and its relationship to the t test
and regression.
• Describe one-way analysis of variance including decomposition of the total variation,
measurement of effects, significance testing, and interpretation of results.
• Describe n-way analysis of variance and the testing of the significance of the overall effect, the
interaction effect, and the main effect of each factor.
• Explain key factors pertaining to the interpretation of results with emphasis on interactions,
relative importance of factors, and multiple comparisons.
Lesson 10: Data Analysis: Hypothesis Testing Related to Differences
• Closure: After the students learn how to perform preliminary data analysis, ask them
which tools they can use to analyze the dataset of their term project.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Real examples of data analysis: hypothesis testing related to differences
• Questionnaire and dataset of the term project
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide a discussion of correlation and
regression. The concepts of product moment correlation, partial, and part correlation
are described. Bivariate and multiple regression are discussed and the associated
statistics explained. This chapter also covers stepwise regression, regression with
dummy variables, analysis of variance and covariance with regression, and canonical
correlation. The relevant computer programs for conducting these procedures using
the mainframe or microcomputer versions of SPSS and Excel are described..
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students an
example of correlation and regression data analysis. For the example, we could point
out to the students that, in absence of a well-defined problem, the data collected is
often worthless to the decision maker and/or often provide incomplete information.
• Lecture: Discuss the importance of product moment correlation in regression
analysis. Discuss the importance of partial correlation in data analysis. Discuss the
importance of part correlation in data analysis. Discuss the purpose of regression
analysis. Discuss the standard bivariate regression model. Explain how hypothesis
testing can be used to determine the significance of a linear relationship between X
and Y. Discuss the assumptions made in bivariate regression analysis. Introduce the
general multiple regression model and its assumptions. Explain the procedure for
measuring the strength of association in multiple regression and its interpretation.
Discuss the stepwise regression procedure and its significance.
• ASSIGNMENTS:
• Divide the class into groups of five to six students each. Ask them to submit a report
of preliminary data analysis using the dataset of the term project.
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Discuss the concepts of product moment correlation, partial correlation, and part correlation, and
show how they provide a foundation for regression analysis.
• Explain the nature and methods of bivariate regression analysis and describe the general model,
estimation of parameters, standardized regression coefficient, significance testing, prediction
accuracy, residual analysis, and model cross-validation.
• Explain the nature and methods of multiple regression analysis and the meaning of partial
regression coefficients.
• Describe specialized techniques used in multiple regression analysis, particularly stepwise
regression, regression with dummy variables, and analysis of variance and covariance with
regression.
• Discuss nonmetric correlation and measures such as Spearman’s rho and Kendall’s tau.
Lesson 11: Data Analysis: Correlation and Regression
Duration: One week period; due at beginning of the next class
Suggested Questions:
• What is the product moment correlation coefficient?
• What are the main uses of regression analysis?
• What is the least-squares procedure?
• Explain the meaning of standardized regression coefficients.
• How is the strength of association measured in bivariate regression? In multiple
regression?
• What is standard error of estimate?
• What assumptions underlie the error term?
• State the null hypothesis in testing the significance of the overall multiple
regression equation. How is this mull hypothesis tested?
• Closure: After the students learn how to perform multiple regression data analysis,
ask them how they can use multiple regression to analyze the dataset of their term
project.
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Real examples of correlation and multiple regression data analysis
• Questionnaire and dataset of the term project
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide provides a discussion of factor
analysis. The model of factor analysis, associated statistics, and the procedure for
conducting factor analysis are discussed. Principal components analysis and common
factor analysis are covered.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students an
example of factor analysis. Give some examples of applications of factor analysis.
Begin by introducing factor analysis. Factor analysis, in general, refers to a set of
procedures primarily used for data reduction and summarization. In ANOVA,
regression, and discriminant analysis, one variable is defined as the dependent
variable and the others are used as predictor variables.
• Lecture: Discuss factor analysis and its differences with respect to other data analysis
techniques including ANOVA, regression, and discriminant analysis. Statistics
associated with factor analysis. Explain the steps associated with conducting factor
analysis. Explain the significance of the correlation matrix and its construction.
Describe the methods by which the appropriateness of the factor model may be
tested. Discuss the methods of conducting factor analysis. The procedures for
determining the number of factors. Discuss the motivation and importance of the
rotation of factors. Explain the procedure for interpreting factors from the rotated
factor matrix. Define a surrogate variable for a factor as one of the original variables
that will be used as a representative of a factor in subsequent multivariate analysis.
State the procedure for the determination of the fit of a model.
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Ask them to submit a report
of preliminary data analysis using the dataset of the term project.
Duration: One week period; due at beginning of the next class
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Describe the concept of factor analysis and explain how it is different from analysis of variance,
multiple regression, and discriminant analysis.
• Discuss the procedure for conducting factor analysis, including problem formulation, construction
of the correlation matrix, selection of an appropriate method, determination of the number of
factors, rotation, and interpretation of factors.
• Understand the distinction between principal component factor analysis and common factor
analysis methods.
• Explain the selection of surrogate variables and their application with emphasis on their use in
subsequent analysis.
• Describe the procedure for determining the fit of a factor analysis model using the observed and
the reproduced correlations.
Lesson 12: Factor Analysis
Suggested Questions:
• How is factor analysis different from multiple regression and discriminant
analysis?
• What are major uses of factor analysis?
• What hypothesis is examined by Barlett’s test of sphericity? For what purpose is
this test used?
• What is meant by the term ‘communality of a variable’?
• Explain how eigenvalues are used to determine the number of factors.
• What is a scree plot? For what purpose is it used?
• Why is it useful to rotate the factors? Which is the most common method of
rotation?
• What guidelines are available for interpreting the factors?
• What are surrogate variables? How are they determined?
• How is the fit of the factor analysis model examined?
• Closure:
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Real examples of factor analysis
• Questionnaire and dataset of the term project
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives:
Lesson Content: This lesson is intended to provide a discussion of cluster analysis.
The procedure for conducting cluster analysis and the associated statistics are
discussed. Hierarchical as well as nonhierarchical methods are described, and the
clustering of variables is also considered.
DURATION: 3.0 hours
INSTRUCTIONAL PROCEDURES:
• Anticipatory Set: to stimulate interest the lesson could begin by showing students
how cluster analysis is used in marketing research. Show them to real example of data
analysis using the cluster analysis.
• Lecture: Define cluster analysis as a class of techniques used to classify objects or
cases into relatively homogeneous groups. Unlike discriminant analysis, there is no a
priori information about the group or cluster membership for any of the objects;
rather, it is suggested by the data itself.
• ASSIGNMENTS:
Divide the class into groups of five to six students each. Ask them to submit a report
of cluster analysis using the dataset of the term project.
Duration: One week period; due at beginning of the next class
Suggested Questions:
• What are some of the uses of cluster analysis in marketing?
• What is the most commonly used measure of similarity in cluster analysis?
• Why is the average linkage method usually preferred to single linkage and
complete linkage?
• What are the two major disadvantages of nonhierarchical clustering procedures?
• What guidelines are available for deciding on the number of clusters?
• What is involved in the interpretation of clusters?
• Describe some procedures available for assessing the quality of clustering
solutions?
• Closure: After the students learn how to perform preliminary data analysis, ask them
how they can use cluster analysis to analyze the dataset of their term project.
Learning Objectives – Upon completion of this lesson, the students should be able to:
• Describe the basic concept and scope of cluster analysis and its importance in marketing research.
• Discuss the statistics associated with cluster analysis.
• Explain the procedure for conducting cluster analysis including formulating the problem, selecting
a distance measure, selecting a clustering procedure, deciding on the number of clusters, and
interpreting and profiling clusters.
• Describe the purpose and methods for evaluating the quality of clustering results and assessing
reliability and validity.
• Discuss the applications of nonhierarchical clustering and clustering of variables.
Lesson 13: Cluster Analysis
MATERIALS AND EQUIPMENT:
• Visual aids – PowerPoint slide show presentation
• Real examples of cluster analysis
• Questionnaire and dataset of the term project
• Paper and pens
Extensions (For Strong Students):
Assessment Based On Objectives: