tThis paper represents a summary of work accompliehed approximately three years ago.
Methods for determining the statistical part worth value of factors in conjoint analysis
β Scribed by H. Noguchi; H. Ishii
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 797 KB
- Volume
- 31
- Category
- Article
- ISSN
- 0895-7177
No coin nor oath required. For personal study only.
β¦ Synopsis
MONANOVA is one type of conjoint analysis used for measuring the part worth value of factors to the total evaluation, exclusively using preference ranking data of a group of commercial products designed by presorted factors. Its criterion, called Stress, is the same as that of monotone regression in MDS. Consequently, part worth values obtained from MONANOVA do not necessarily lead to definite solutions but give an approximate comparison of each factor's contribution to the total evaluation of the products.
In this paper, we would like to discuss two problems with MONANOVA: namely, its reliability and stability. We would then lie to propose two possible solutions to these problems: the first combines regression and monotone methods; the second employs quadratic fractional programming.
With these, we hope to obtain each factor's contribution to the total evaluation as a partial correlation coefficient and to demonstrate that one can compare the factor's contribution to the total evaluation with constant stability.
π SIMILAR VOLUMES
Identifying and prioritizing the most signi"cant foods and nutrients for sampling and analysis is essential in creating national food composition databases. A prioritized food list for the United States population has been developed using the Key Foods approach, where data from food consumption surv
A method for determining the dissipation and coupling loss factors of a fully assembled machinery structure is presented in this paper. The method is based on the experimental measurements of the total loss factors and the energy ratios between the subsystems of the machine structure when fully asse
Sir: It is well known that the most applied criteria for determining the number of underlying factors from a data matrix being factor-analyzed are the Malinowski's indicator @ND) function [l] and the Wold's double cross-validation (DCV) procedure [2]. Recently, we have proposed another proof that is
## Abstract Many studies have identified relationships between the forces generated by the cranial musculature during feeding and cranial design. Particularly important to understanding the diversity of cranial form amongst vertebrates is knowledge of the generated magnitudes of bite force because