{"title":"Evaluation of the accuracy of ordinal classifications using item response theory","authors":"Mayu Ogawa, Mirai Tanaka, X. Lu, Tomomichi Suzuki","doi":"10.17929/TQS.4.22","DOIUrl":"https://doi.org/10.17929/TQS.4.22","url":null,"abstract":"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.","PeriodicalId":301168,"journal":{"name":"Total Quality Science","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123301856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of two-level supersaturated designs using the Modified Box-Meyer method","authors":"Kyohei Funahashi, Hironobu Kawamura","doi":"10.17929/TQS.4.34","DOIUrl":"https://doi.org/10.17929/TQS.4.34","url":null,"abstract":"In a two-level supersaturated design (L2SSD), there are more runs (columns) than factors (rows). This approach is commonly used in screening experiments, where the goal is low-cost identification of active factors (i.e., have major influence on the response). Several previous studies have considered methods for selecting the active factors of L2SSDs under the assumption of effect sparsity, with the Box–Meyer method (BMM) performing the best. However, to overcome various drawbacks of BMM, a modified BMM (MBMM) is proposed by Samset and Tyssedal(1998) to analyze the data of fractionated designs. However, MBMM is not used for L2SSDs so far. Therefore we propose here a modified BMM (MBMM) for analyzing L2SSDs. Although both methods can select active factors, MBMM selects fewer inactive factors compared to BMM. The present simulation results confirm that MBMM is an excellent analytical tool for L2SSDs.","PeriodicalId":301168,"journal":{"name":"Total Quality Science","volume":"459 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116355579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}