Learn how and when to use advanced analytic techniques in your market research projects.
This Principles Express course, Advanced Analytic Techniques, serves as a primer for some of the more advanced statistical methods you may encounter as a researcher, with greater attention to techniques which are frequently used with secondary data. Topics include: conjoint analysis, multiple regression, cluster analysis for segmentation, linear regression, perceptual mapping 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.
Learn which analysis techniques to use with primary and secondary research data.
As more and more data primary and secondary research sources emerge in the "age of big data," selecting appropriate advanced analysis techniques to extract insights is becoming increasingly essential to decision making. The first step is to understand the business question at hand. The second is to assess the data available for you to address the business question.
Certain analysis techniques are only appropriate with primary research data, whereas other analysis techniques are only appropriate with secondary data. Some techniques can be applied to either data source.
This course will introduce you to the most common advanced analytical techniques in use today, with greater attention to techniques which are applied to secondary data. Examples are presented with each technique to demonstrate how insights can be extracted with the technique along with a conversation on what actions might be taken based on such insight. While statistical methods and terminology are discussed, explanations are purposely not detailed in order to help you focus on the overarching applied concepts behind each.
After completing this course you should be able to:
- 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.
- Compare and contrast the different patterns that express the relationship between two variables (e.g., nonlinear, linear, curvilinear, s-shaped, etc.).
- Distinguish between interpolation and extrapolation.
- Describe what Factor Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
- 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.
- Describe what Perceptual Mapping (including the use of Multidimensional Scaling) is and common applications in market research.
- 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.
- Describe Conjoint Analysis and Choice Modeling, what they do, what type of input data is generally acceptable, and common applications in market research.
- 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.
- Describe what Discriminant Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
- Identify the most popular machine learning techniques and describe how researchers can use them to generate insight.
- Describe what neural network analysis is, what it does, what type of input data is generally acceptable. Describe common applications in market research.
- 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.
- 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.
- Describe the difference between statistical significance and business significance.
Successful enrollees earn a Digital Badge and 1.2 University of Georgia Continuing Education Unit (CEU).
Who Should Attend?
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.
- Enroll at any time
- Complete the course's required graded components within 30 days
- For more details on How Does the “Advanced Analytic Techniques” Course Work, please download the file.
- For Frequently Asked Questions, please download the file.
$359 - Standard Fee
$329 - Association Discount (Members* of: Insights Association; ESOMAR; Intellus Worldwide; ARF; AMA, and the attendees of TMRE 2018 and IIeX NA 2018.)
$50 - One-Month Extension (only one extension is granted per participant)
*Membership/Attendance will be verified.
Prepayment is required to be registered. Prices listed are per person (US Funds). Prices are subject to change.
Students successfully completing graded components earn a Digital Badge and 1.2 Continuing Education Unit (CEU) from The University of Georgia. Click for details about the University of Georgia CEU.
As a graduate of the course you will be recognized by industry associations, employers, peer groups and other professionals as understanding how to translate your research findings into reports and presentations that grab your audience’s attention, address the business decision your client needs to make, and offer sound and useful recommendations. This recognition will help you advance in your company and the industry.
This course offers continuing education for research practitioners. If you are PRC certified through the Insights Association (IA), this course qualifies for 10 hours for continuing education. If you have any questions about PRC, please contact certificationATinsightsassociation.org or dial +1-202-800-2545.
CAIP Canada also recommends the course for candidates looking to fill in the gaps or gain a refresher in specific areas.
Ray Poynter – Managing Director, The Future Place and Founder, NewMR
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.