2018 Volume 4 Issue 1 Pages 22-33
It is very important to guarantee measurement results. For quantitative data, a methodology for obtaining accurate measurements in terms of both trueness and precision has been established according to ISO 5725. On the other hand, no methodology has been established for qualitative data. Moreover, in the previous study, the data are mostly supposed to be binary, rather than multinomial.
The purpose of this study is to validate a method of evaluating a measurement accuracy for ordinal-categorical (multinomial) data using the item response theory (IRT) developed in the fields of psychology and ability evaluation. The IRT model can estimate parameters indicating item difficulty and item discrimination and can evaluate whether the items can be classified appropriately according to the ability of the examinees. Two indicators of accuracy are proposed: correct-ordering probability and consistent-classification probability. We provide the characteristics of this method. The measurement data under different conditions were prepared, applied to the IRT model, and used to calculate the indicators. As a result, the two indicators could show appropriate values in most datasets, but some conditions did not work. In this study, we propose a new model to identify both consumer purchase behavior and their consciousness based on a latent class model estimated by questionnaire responses and the purchase history data for all users. In addition, we apply the proposed model to actual data to analyze its effectiveness.
