|Abstract or Summary
- 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.