Details
- How to develop an analysis plan for a study by linking management decisions and information needs to the available data.
- How to choose the best univariate, bivariate or multivariate statistical techniques to analyze data from virtually any marketing research study.
- How to summarize marketing research data and translate it into decision making information.
- What statistical significance tests do, how to choose the right one, how to interpret typical computer output and how to relate the results to the marketing questions.
- How to compute sample size.
- How to select the best procedure for determining associations among marketing variables and how to interpret and use correlation coefficients.
- What multivariate techniques do and when to use such techniques as Regression, Discriminant, Factor & Cluster analysis, M.D.S. and Conjoint analysis.
Outline
Day 1
8:30 am – 5:00 pm
Introduction
Session 1 – Building the Case for How to Best Analyze Research Data:
- Case study examples of improper/deceptive manipulations of data and their consequences
- How to interpret the data from the case studies
- Common errors made in data tabulations, summary measures and other analytical procedures and how to avoid them
Session 2 – Fundamental Principles for Analyzing Data:
- Purpose and goal of data analysis
- Data analysis impact on research design
- Characteristics of data and other considerations required for selecting the best analytical procedure for each situation
Lunch
Session 3 – Step-by-Step Guidelines for Selecting the Best Analytical Tool:
- Integrated flowcharts for choosing the best analytical tool
- Workshop using the flowcharts to apply the tools to a wide variety of situations commonly encountered in marketing research, such as:
- Summarizing research data and stat testing
- Deciding when to use t, chi square, f and other tests
- Choosing correlational procedures and deciding when to use factor, cluster, conjoint, regression, discriminant, MDS and other multivariate techniques
Session 4 – Group Workshop:
- Developing a detailed analysis plan
- Analyzing a real-life marketing research questionnaire to relate the research data to the marketing objectives of the study
Day 2
8:30 am – 5:00 pm
Session 5 – Review of Group Workshop:
- Review all the key analytical procedures used in the case study ranging from Chi-Square to multivariate techniques
- Learn how to transform the data into actionable information for your client
- See firsthand which analytical procedures will answer specific management questions
Session 6 – Understanding Crosstabs and Data Weighting Issues:
- Review of crosstabs -- one of the most common data analysis tools
- How to avoid common mistakes with tabular data
- Weighting data in marketing research
- Practical guidelines for getting the most out of crosstabs
Lunch
Session 7 – Summary Measures for Research Data:
- Use and misuse of various measures of average and dispersion
- Practical guidelines for the proper interpretation of mean, weighted mean, median, standard deviation, variance and other measures
- Confidence interval estimation -- what is it, what does it do and what is it used for?
- Procedures for computing sample size under a variety of conditions encountered in marketing research
Session 8 – Statistical Significance Testing:
- The framework for significance testing
- Cautions in interpreting results from tests of significance
- Key decisions in significance testing, one-tailed vs. two-tailed tests, confidence vs. significance level and other considerations
- Description of procedures for analyzing nominal, ordinal and metric data from user panels, preference evaluations, purchase likelihood, tracking studies, consumption, product performance, advertising awareness and other studies
Day 3
8:30 am – 4:30 pm
Session 9 – Analysis of Associations in Marketing Research Data:
- Bivariate correlation procedures for various types of research data
- When to use and how to interpret each coefficient
- Key terms and definitions for correlation analysis
- Multivariate associations
- Association case studies and computer output examples
Session 10 – Multivariate Approaches I:
- Factor and Cluster Analysis
- Illustrative output, terminology, key decisions, and plausible misinterpretations
- Typical marketing research applications
Lunch
Session 11 – Multivariate Approaches II:
- Perceptual Mapping, Correspondence analysis
- Conjoint Analysis and Discrete Choice Modeling
- Examples, analytical algorithms, input/output and key issues
- Typical marketing research applications
Session 12 – Multivariate Approaches III:
- Multiple Regression and Discriminant Analysis
- Appropriateness, interpretation, analytical algorithms and key issues
- Guidelines for using and communicating multivariate analysis to nontechnical decision makers
Since our founding in 1975, Burke Institute has trained more than 80,000 participants from 10,000 companies, in 40 different countries.
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When you attend our seminars, you participate in more than an exciting, on-target learning experience. You learn from what we have learned as the most experienced marketing research training organization in the world. We deliver:
Breadth
You can choose from a wide range of seminar topics to meet your continuing educational needs. Participants can select a sequence of seminars which offer coordinated state-of-the-art training with increasing levels of sophistication and depth in the chosen area. This eliminates the duplication and conflicting content which often results from attending disjointed seminars from different sources.
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We are fully committed to making each seminar a memorable learning experience - before, during and after the seminar. Our staff provides highly personalized assistance in selecting the best programs (ours or others) for your needs. And our seminar leaders are available to you to answer technical questions long after the seminar has ended. ...
