Handling missing data in consumer hedonic tests arising from direct scaling
| dc.contributor.author | Franczak, Brian C. | |
| dc.contributor.author | Castura, John C. | |
| dc.contributor.author | Browne, Ryan P. | |
| dc.contributor.author | Findlay, Christopher J. | |
| dc.contributor.author | McNicholas, Paul D. | |
| dc.date.accessioned | 2020-10-09 | |
| dc.date.accessioned | 2022-05-31T01:15:36Z | |
| dc.date.available | 2022-05-31T01:15:36Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | In sensory evaluation, it may be necessary to design experiments that yield incomplete data sets. As such, sensory scientists will need to utilize statistical methods capable of handling data sets with missing values. This article demonstrates the advantages of a model-based imputation procedure that simultaneously accounts for heterogeneity while imputing. We compare this model-based approach to the current state-of-the-art imputation procedures using two real data sets that arose from central location tests. These data sets contain missing values by design. In addition, these data sets have two data sets nested within each of them. We use these nested data sets to validate the results. Compared to the considered state-of-the-art imputation procedures, we find evidence that the model-based approach is able to recover the group structure and key characteristics of the data sets when a high percentage of the data are missing. | |
| dc.description.uri | https://library.macewan.ca/full-record/edswsc/000389330500007 | |
| dc.identifier.citation | Franczak, B.C., Castura, J., Browne, R.P., Findlay, C.J. and McNicholas, P.D. (2016). Handling missing data in consumer hedonic tests arising from direct scaling: Imputation techniques for consumer hedonic tests 31(6). 514 - 532. | |
| dc.identifier.doi | https://doi.org/10.1111/joss.12241 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14078/1832 | |
| dc.language | English | |
| dc.language.iso | en | |
| dc.rights | All Rights Reserved | |
| dc.subject | missing values | |
| dc.subject | model-based approach | |
| dc.subject | nested data sets | |
| dc.subject | state-of-the-art imputation procedures | |
| dc.subject | incomplete data sets | |
| dc.subject | statistical methods | |
| dc.subject | non-Gaussian model-based imputation approach | |
| dc.subject | asymmetrical consumer segments | |
| dc.title | Handling missing data in consumer hedonic tests arising from direct scaling | en |
| dc.type | Article |