Learn how to develop and execute a data analysis plan.
The data analysis stage is where the researcher reviews the data and focuses on the elements that will enable the development of insight to inform the business decision to be made. This is achieved through the design and application of a meaningful analysis of the data. In this course you will learn how to select the most appropriate statistical methods when projecting your findings to the target population and determine whether different groups’ measurements are significantly different from each other. It focuses on alternative statistical analysis methods and developing a data analysis plan.
Graduates receive University of Georgia continuing education units (CEUs) as well as a digital badge.
After completing this course, you should be able to:
- Describe the process of creating an analysis plan, and give examples of alternative analytic purposes (e.g., explanatory versus confirmatory).
- Describe the key data sources.
- Name and define the key data types (nominal, ordinal, interval, ratio, etc.).
- Explain the process of matching analytic techniques to different situations and needs and give examples.
- Summarize descriptive and visual approaches used to familiarize oneself with the data and to identify problems with the data.
- Explain how to assess the impact of missing responses and select and apply appropriate remedies.
- State the reasons for and methods of statistically adjusting data; e.g., weighting, variable re-specification, and scale transformation.
- Assess the characteristics of the distribution of the data and explain the implications of normality, non-normality, skewness, and multimodal data.
- Illustrate the process for creating and testing hypotheses.
- Compare and contrast the differences between type I and type II errors, and their potential impact on business decisions.
- Describe the difference between statistical and business significance in the context of group comparisons, and explain the factors that have an impact on statistical significance.
- Describe the difference between association and causality, and the potential impact on business decisions and outcomes.
- Identify the major computer programs in current use in market research for the analysis of data.
- Explain how to turn findings into market research conclusions, link findings to business decisions, and create actionable recommendations.
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.
- 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.
Ray Poynter – Founder, NewMR.org
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.
Anticipated course release in mid-2018.
Details are subject to change without notice.