Graduate Thesis Or Dissertation
 

A simulation tool for evaluating sensory data analysis methods

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/td96k594n

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  • In cross-cultural studies, respondents from specific cultures may have different product preferences and scale usage. Combining data from different cultures will result in departures from the basic assumptions of analysis of variance (ANOVA) and loss of power in testing capability of finding product and culture differences. However, the result of violations on power of ANOVA is unknown by sensory researchers. The objectives of this research were by simulating consumer product evaluation data, to evaluate the robustness and testing power of ANOVA under different cross-cultural situations. The study was conducted in two parts. First, an Empirical Logit simulation model was employed for generating sensory data. This model included respondent, product, consumer segment and product by segment interaction effects. Four underlying distributions: Binomial, Beta-Binomial, Hypergeometric, and Beta-Hypergeometric were used to increase or decrease the dispersion of the responses. Alternatively, instead of using these four distributions, the same applications were achieved by a binning step. The entire simulation procedure including the Empirical Logit model and the binning step was called Discrete Empirical Logit model. In the second part of the study, the Discrete Empirical Logit model was chosen to generate specified data sets under six different cross-cultural cases. After analyzing these data sets by ANOVA reduced and full models, the empirical power of ANOVA under different cases was calculated and compared. The results showed that both Beta-Hypergeometric and Discrete Empirical Logit were flexible on simulating sensory responses, but the Discrete Empirical Logit was relatively simple to use. Comparing with the ANOVA reduced model, the full model gave better information on evaluating the case that segments differ in product preferences. This suggested segmentation was very important in cross-cultural data analysis. Under the situations that sample sizes were equal and respondents performed consistently within segment (MSE ≈ 1), ANOVA was very robust to different scale usage, losing at worst 18% in power. From the scope of this study, we recommend using the ANOVA full model in the cross-cultural research. Results from different cultures could be combined when consistency within segments was high.
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