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Practical Multivariate Analysis

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In this 4-day seminar, totally devoid of matrix notations or derivations, you will learn how to select and interpret the best multivariate technique for analyzing the complex relationships between many variables typically encountered in marketing research studies. You will learn: What multivariate techniques are, what they do and when to use them; How to choose between Regression, Discriminant, Factor, Cluster, M.D.S., Correspondence, Conjoint, DCM, CHAID, Logit, Canonical and other techniques to answer specific management questions from your research data; Which statistical packages perform each of the analysis and how to get started; What type of data are required for each technique, what analytical options are available and what assumptions must be made; How to interpret multivariate results and communicate what they reveal to management. Four days. Fee per person: $2925.

Provider:

Burke Institute

Topic(s):

Customer Service & Sales > Marketing

Who Should Attend?

Research practitioners

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In this 4-day seminar, totally devoid of matrix notations or derivations, you will learn how to select and interpret the best multivariate technique for analyzing the complex relationships between many variables typically encountered in marketing research studies.

You will learn:

• What multivariate techniques are, what they do and when to use them.
• How to choose between Regression, Discriminant, Factor, Cluster, M.D.S., Correspondence, Conjoint, DCM, CHAID, Logit, Canonical and other techniques to answer specific management questions from your research data.
• Which statistical packages perform each of the analysis and how to get started.
• What type of data are required for each technique, what analytical options are available and what assumptions must be made.
• How to interpret multivariate results and communicate what they reveal to management.

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Course Outline and Schedule

Day 1

8:30 am – 5:00 pm

Introduction

Session 1 – Case Studies and Applications of Multivariate Techniques:

• What is multivariate analysis?
• Why do we need multivariate analysis?
• Range of problems addressed by multivariate techniques
• Advantages over simpler approaches
• Marketing case studies illustrating the use of various multivariate techniques

Session 2 – Overview of Underlying Considerations:

• A structured framework of seven charts to help select the best analytical techniques for specific marketing research applications
• Univariate, bivariate vs. multivariate analysis
• Understanding levels of measurement: nominal, ordinal, interval, and ratio data
• Basics of statistical Inference
• Understanding variance and basic measures of data
• The types of analysis done by marketing research analysts: summarizing data, detecting difference, and studying associations
• Correlation and covariance

Lunch

Session 3 – Selecting the Most Appropriate Multivariate Techniques for Specific Marketing Research Applications:

• Flowchart to help select the best techniques
• Statistical packages for multivariate analysis
• Team Workshop: Selecting the most appropriate multivariate techniques for several applications
• A comprehensive case study of using multivariate techniques in a real-life setting

Session 4 – Multiple Regression Analysis: Fundamental Concepts:

• Applications of regression
• Conceptual understanding of regression
• Interpretation of computer regression output
• Understanding and dealing with multicollinearity
• Part and partial correlations and what they tell us
• Understanding and using Stepwise Regression

Day 2

8:30 am – 5:00 pm

Session 5 – Improving Regression Models:

• Assumptions of the regression model
• How to find and deal with violations of those assumptions, including residual analysis
• Data transformations, modeling interactions, and other improvements to the regression model

Session 6 – Specialized Regression Techniques for Categorical Variables:

• Case study exercise
• Dummy variable regression (DVR): incorporating nominal predictor variables
• MCA, an “easier to use” version of DVR
• Logistic regression: what it is, how to run it, how to evaluate the results

Lunch

Session 7 – Conjoint Analysis:

• What is conjoint analysis and why is it useful
• Case study illustrating the use of conjoint analysis
• Disjoint vs. conjoint vs. hybrid approaches
• Designing conjoint studies
• An extensive conjoint example: how to interpret the results
• Analyzing conjoint results
• Market simulation using conjoint results

Session 8 – Discrete Choice Modeling:

• What it is and how it differs from conjoint analysis
• A comprehensive case study illustrating the design analysis and interpretation of DCM
• Using DCM simulation results
• Using interactions in DCM
• Interpretation of computer outputs from DCM/CBC

Day 3

8:30 am – 5:00 pm

Session 9 – Discriminant Analysis:

• Uses of discriminant analysis for discrimination and classification
• Description and practical examples of two-group and multiple discriminant analysis
• Stepwise discriminant analysis
• Use of discriminant analysis in Segmentation Assignment Algorithms

Session 10 – Factor Analysis:

• Factor Analysis: what it is, how it works, and what it’s used for
• Performing PCA/factor analysis: inputs and outputs
• How Principal Components Analysis differs from exploratory factor analysis
• Practical considerations in performing factor analysis
• Case studies illustrating use of PCA/FA in marketing research

Lunch

Session 11 – Cluster Analysis:

• What cluster analysis is used for
• Hierarchical cluster analysis: how it works, how to run it.
• Interpreting computer outputs
• K-means cluster analysis: how it works, suggestions for running it effectively
• Application to segmentation example
• Other forms of cluster analysis including LCA

Session 12 – Multivariate Perceptual Mapping Techniques:

• Appropriateness of various perceptual mapping techniques for specific types of data
• Vector and point-based maps
• Producing perceptual maps
• Interpreting maps
• Illustrative case studies

Day 4

8:30 am – 4:30 pm

Session 13 – Data Mining Tools:

• Exploratory vs. explanatory multivariate techniques
• CHAID, CART and other tree-based techniques
• Case studies of their use in marketing research
• Neural Networks
• Illustrative case studies

Session 14 – Path Analysis and Structural Equation Modeling (CSA and PLS):

• Alternative procedures for deriving attribute importance
• Comparison of correlation, regression, path analysis, SEM and other approaches
• The basic ideas and notation of structural equation modeling (SEM)
• CSA and PLS approaches to SEM, their interpretation, pros and cons
• Basics for applying both methods in marketing research
• Case studies demonstrating how SEM provides actionable information for better marketing research decision making

Lunch

Session 15 – Latent Class and Hierarchical Bayesian Analysis:

• Latent Class Cluster Analysis
• Latent Class Regression
• Latent Class Discrete Choice
• HB for Conjoint and DCM
• Illustrative case studies

Session 16 – Linking Multivariate Techniques to Marketing Decisions:

• The analysis planning process
• Translating data into information and finally decisions
• A comprehensive case study workshop illustrating the analysis planning process
• Demonstration of how to present results from a complex study employing multivariate analysis to decision makers

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Sponsor Background:

Applying Knowledge - Improving Decisions

Burke is one of the premier international research and consulting firms in the world. For 75 years, Burke has helped manufacturing and service companies understand and accurately predict marketplace behavior. Burke's employee owners add value to research and consulting assignments by applying superior thinking to help clients solve business problems.

We provide our customers with data, information, guidance and far more. Burke strives to become a valued business partner who focuses on finding and implementing solutions to the most critical problems facing our clients today. We give our customers a competitive edge.

Burke: 100% Employee-Owned

Burke is 100% employee owned. In 2004, Burke formed an ESOP (Employee Stock Ownership Plan), ensuring that all employees are able to participate in stock ownership and have a personal stake in helping to contribute to the company's success. The ESOP, representing broad based employee ownership across all positions of the company, currently holds approximately 60% of the shares of Burke, and is expected to own 100% by 2008.

Our Mission

To provide superior decision support services that enable our clients to succeed.

Our Business Commitments and Values

• We will "do what it takes" to meet our commitment to our clients.
• We will display honesty and integrity in all our endeavors.
• We will take a disciplined approach to our work, based on scientific standards and best practices.
• We value smart people doing smart things.
• We value seeking, sharing, and applying knowledge.
• We value organizational agility and flexibility.

Our Commitment to Employees

We will hire and retain only the best people for all positions in the company.

Employees will have a work environment and tools needed to perform their jobs at maximum quality and efficiency.

Employees will be given opportunities for career development and training to enhance their skills.

Employees will be compensated fairly, based on standards of performance and contribution, and will share in the financial success of the company. Employees will treat each other with respect and dignity, recognizing the worth, quality, and importance of each individual.

We will foster an environment where open communication exists among all employees.

The work climate will be free of strict rules and structures, where employees are empowered to take risks and affect change to achieve our common vision.

Burke Timeline

1931

Alberta Burke starts Burke Marketing Research as a data collection agency.

1952

Don Miller, marketing research director for Cincinnati's WLW radio, joins Burke and begins development of the Day-After Recall (DAR) for testing TV commercials.

Late 1960's

Burke expands to international markets, adding locations in London, Paris, Frankfurt, Milan, Tokyo and Mexico City.

Mid 1980's

Industry image study rates Burke as number one research firm in study design capability and analytical sophistication.

1990

Burke Customer Satisfaction Associates (BCSA) is established to better serve businesses in listening and reacting to the "voice of the customer".

1995

Burke changes official name from Burke Marketing Research Inc., to Burke, Inc. The name change reflects Burke's broader research and consulting activities outside traditional custom marketing research.

2004

Burke re-acquires 100% of its shares and becomes 100% employee-owned. Burke establishes ESOP (Employee Stock Ownership Plan) to allow for broad based employee ownership. Burke re-acquires 100% ownership of the Burke Institute.

2005

Becomes 100% Employee-Owned

Burke Quick Facts

• Headquarters: Cincinnati, OH USA
• Founded: 1931
• Employees Full Time: 202
• Employees Part time: 178
• Total Revenues (2005) worldwide: $44.1 m
• President & CEO: Michael Baumgardner, Ph.D.

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Quote From Past Participants:

"A memorable experience - dynamic, enthusiastic, expert on his subject. He is on a par with the best professors I had at Harvard Business School" - Manager, Kimberly-Clark Corporation

"Superb! Informative, fast-moving, excellent examples tailored for individuals in the audience. Extreme clarity in presentation. Thank you!" - Marketing Research Analysis, Westwood Pharmaceuticals

"Outstanding. To me he is the finest speaker I have ever heard regarding quantitative analysis. (I have an MBA with concentration in quantitative methods, so I have heard many speakers.) Very practical. Magnetic personality." - Marketing Research Analyst, SC Johnson Company

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Past Participants Include:

• Adolph Coors Company
• Alcon Laboratories
• Allied Research, Inc.
• American Express Company
• B.C. Telephone
• Bank of America
• Barbados Telephone Co., Ltd.
• Beatrice US Foods
• Bergen Brunswig
• Blue Cross/Blue Shield
• Campbell Soup
• Canadian Parks Services
• Dannon Company
• Day-Timers, Inc.
• Del Monte USA
• E&J Gallo Winery
• El Paso Electric
• Ernst & Young
• Fia
• Frito-Lay, Inc.
• General Mills Inc.
• Glaxo Wellcome Inc.
• Hallmark Cards Inc.
• Harte-Hanks
• Hewlett Packard Company
• IBM
• IMS America
• John Hopkins Hospital
• Jostens
• K Mart Canada
• Kaiser Permanente
• LL Bean, Inc.
• Lands End
• M&M Mars
• Microsoft Corporation
• Nabisco
• Ontario Hydro
• Procter & Gamble
• Purolator
• R.J. Reynolds Tobacco
• RJR
• Sandoz
• Schering-Plough
• Taco Bell Corporation
• Texaco
• U.S. Army
• Union Gas Ltd.
• VHA Inc.
• Warner Cable
• Wrangler
• Xerox
• Zenger Miller