MARTIN de TOURS SCHOOL OF MANAGEMENT … plan/Lesson plan 4855.pdf• Describe a classification of...

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


• 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.



• 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?


• 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.



• 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


• 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.



• 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.


• 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.



• 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):


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


• 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,


• 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


• 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.



• Paper and pens



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



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:


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?


• 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



• 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:



• A case study of marketing search in conducting the descriptive research design.

• Paper and pens


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


• 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.



• 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


• 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.



• Paper and pens


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


• 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.



• 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.


• 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.



• Examples for every measurement scales

• A case study of marketing search in developing scales of measurement

• Paper and pens



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


• 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:


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


• 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.



• 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.

.



• examples of every nonprobability and probability sampling

• A case study of marketing search in developing sampling design and sampling

procedure

• Paper and pens



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



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.

•


• 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


• 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.



• Real examples of preliminary data analysis

• Questionnaire and dataset of the term project

• Paper and pens



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



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




• 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?


• 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


which tools they can use to analyze the dataset of their term project.



• Real examples of data analysis: hypothesis testing related to differences


• Paper and pens



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



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



• 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



• 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.



• Real examples of correlation and multiple regression data analysis


• Paper and pens



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



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:





• 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


• 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:



• Real examples of factor analysis


• Paper and pens



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


• 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:


of cluster analysis using the dataset of the term project.



• 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?


how they can use cluster analysis to analyze the dataset of their term project.


• 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



• Real examples of cluster analysis


• Paper and pens



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