You are here

Click here to qualify for Advanced Analytic Techniques Special Discount

MRII and the University of Georgia will be offering a new online Principles Express course (anticipated 10-12 hours) – Advanced Analytic Techniques!

As more and more data sources emerge in the “age of big data” – across both primary and secondary sources – the selection of the appropriate advanced analysis technique to extract insight is becoming increasingly essential to decision makers. The selection of the appropriate advanced analysis technique is first achieved by recognizing the business question at hand followed by the data available for you to work with to address the question. Certain analysis techniques are only appropriate with primary research data whereas other analysis techniques are only appropriate with secondary data, followed by some techniques which can be applied to either data source. This Principles Express course serves as a primer for some of the more advanced common statistical methods you may require as a researcher, with greater attention to techniques which are applied to secondary data. Topics include: conjoint analysis, multiple regression, cluster analysis for segmentation, and factor analysis. You are not expected to memorize complicated formulas; rather, this course teaches the principles behind commonly used advanced statistical methods and when to use them.

Graduates receive University of Georgia continuing education units (CEUs) as well as a Digital Badge. PRC approval is pending.

Learning Objectives

After completing this course you should be able to:

  1. Describe a common framework that distinguishes between multivariate analytic techniques and helps guide the decision of what technique to use when, based on the following factors—dependence, interdependence, number of dependent variables, type of relationship, item being analyzed, nature of metric, and the nature of the business question being addressed.
  2. Compare and contrast the different patterns that express the relationship between two variables (e.g., nonlinear, linear, curvilinear, s-shaped, etc.).
  3. Distinguish between interpolation and extrapolation.
  4. Describe what Factor Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
  5. Describe the concept of Segmentation Analysis, what it does, what type of input data is generally acceptable, various techniques on how one may cluster data (e.g., K-Means, RFM, Pareto, etc.) and common segmentation applications in market research.
  6. Describe what Perceptual Mapping (including the use of Multidimensional Scaling) is and common applications in market research.
  7. Describe the different techniques used to measure association (i.e., Correlation, Simple Regression, and Multiple Regression), what they do, what type of input data is generally acceptable, and common applications in market research.
  8. Describe Conjoint Analysis and Choice Modeling, what they do, what type of input data is generally acceptable, and common applications in market research.
  9. Describe more advanced measures of association (e.g., Logistical Regression and Structural Equation Modeling), what they do, what type of input data is generally acceptable, and common applications in market research.
  10. Describe what Discriminant Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
  11. Identify the most popular machine learning techniques and describe how researchers can use them to generate insight.
  12. Describe what neural network analysis is, what it does, what type of input data is generally acceptable. Describe common applications in market research.
  13. Describe the concept of Marketing Mix Modeling, what it does, what type of input data is generally acceptable, techniques that are used (e.g., multiple regression, Bayesian regression, etc.) and common applications in market research.
  14. Describe Time Series Analysis, what it does, what type of input data is generally acceptable, what techniques are used, and common applications in market research.
  15. Describe the difference between statistical significance and business significance.

Who Should Attend?

  • Entry-level researchers looking for a solid introduction to quantitative data analysis.
  • Mid-level staff seeking to expand their skillset.
  • Experienced researchers looking to catch up with the latest developments.
  • Corporations seeking professional development options for their internal training portfolio.
  • Suppliers seeking courses for new-employee onboarding.
  • Researchers whose job involves leading or contributing to project design, particularly those around secondary data.
  • Analysts needing to understand how best to analyze quantitative data, and the pitfalls to avoid.
  • Client-side researchers responsible for designing research and ensuring that the analysis leads to reliable insights.
  • People just entering the research field who want to understand this important aspect of the research process.

Course Author:
Ray Poynter – Managing Director, The Future Place & Founder of NewMR

Ray Poynter Ray is the author of The Handbook of Mobile Market Research, The Handbook of Online and Social Media Research and the #IPASOCIALWORKS Guide to Measuring Not Counting. He is the founder of NewMR.org, editor of the ESOMAR book Answers to Contemporary Market Research Questions, and is the Managing Director of The Future Place, a UK-based consultancy, specializing in training.

Ray has spent the last 35 years at the intersection of innovation, technology, and Market Research, during which time Ray has held director level positions with Vision Critical, Virtual Surveys, The Research Business, Millward Brown, Sandpiper and IntelliQuest.

Course Information

Course Date Info: 

Anticipated course release in late 2018.

Details are subject to change without notice.