- Testing and analyzing difference between treatments (independent or paired) with objective measured data
- Testing and analyzing difference between treatments (independent or paired) with subjective measure or sensory (rating/ranking) data with sensory fatigue considerations (incomplete treatment assessments)
- Testing and analyzing preference between treatments (e.g. triangle or duo-trio tests) or acceptance of a treatment
HOW YOU WILL BENEFIT
- Develop the ability to identify and use the proper statistical inferential analysis tool contingent on the product testing design and project/study intent.
- Correctly analyze, interpret and draw insights from product and design testing results.
- Structure product and sensory evaluation test aligned to the development project/study intent and not be constrained by a limited set of statistical analysis tool to use.
1.0 Brief Primer on Statistical Analysis
1.1 Scales of Data Measurement
1.2 Statistical Inference
1.3 Parametric vs Non-Parametric Statistical Test
1.4 Statistical Summaries
1.5 Graphical Data Display
2.0 Analyzing and Testing for Measured (Objective) Data
2.1 Comparing or Differentiating Two Brands or Formulations
- Independent and Paired T-Tests
- Comprehensive Look at Data: Use Both Average and Standard Deviation
- F-Test for Equality of Data Variance
- Alternative for Small Sample Size: Wilcoxon Rank-Sum and Signed Rank Test
2.2 Comparing or Differentiating Two or More Brands of Formulations
- Review of One-Way ANOVA with and without Blocks
- Test of Multiple Comparison Tools: Duncan’s Multiple Range Test and Tukey’s Test
3.0 Analyzing and Testing for Rated or Ranked (Subjective) Data
3.1 Testing for Two Brands or Formulations: Review of Rank-Sum and Signed Rank Test
3.2 Testing for Multiple Brands or Formulations:
- Friedman Test for Respondent Complete Treatments Application
- Durbin Test for Respondent Incomplete Treatments Application
- Kruskal-Wallis for Monadic Testing
- Page Test for Ordered Multiple Alternatives
- Nemenyi’s Joint Rank and Dunn’s Tests
3.3 Estimating Single Proportion and Difference of Two Proportion Values (Support for Top X Boxes Approach)
4.0 Analyzing and Testing for Preference (Subjective) Data
4.1 Testing for Two Brands or Formulations: Mc Nemar’s Test
4.2 Testing for Multiple Brands or Formulations: Cochran’s Q Test
Bryan has trained companies on a number of product research, development and testing topics and tools. These companies are in the field of consumer goods and personal care, agri-business, specialty chemicals, plastics and packaging, food ingredients to name some. He has also done work on market research and new product survey for personal care and finance company. He is a resident trainer of the Philippine Trade Training Center and a lecturer of the Department of Industrial Engineering at the Gokongwei College of Engineering of De La Salle University Manila where he also earned his bachelor in science and masters in science degree in Industrial Engineering.
|Thu, Fri||08:30 AM — 05:00 PM|
|No. of Days:||2|
|No. of Participants:||15|
We are a management advisory and consulting group focused on process and product development and improvement methodology, business analytics and decision support modeling services.
We believe in supporting process excellence initiatives of our client-partners through the creation of a clear strategic and operational mindset that emphasizes on customer and business value.