Analyzing Constant-Sum Multiple Criterion Data: A Segment-Level Approach

The authors propose a methodology for determining the segment-level impact of explanatory variables on multiple criterion measures obtained on a constant-sum scale. These explanatory variables could characterize different product, situation, or person related conditions that either occur naturally or are experimentally manipulated. Their proposed methodology simultaneously estimates market segment membership and multivariate segment-level parameters for each dependent criterion, using finite mixtures of conditional Dirichlet distributions. They conduct a modest Monte Carlo simulation analysis to investigate the performance of the proposed methodology. The authors also provide an empirical application to industrial buying decisions that examines the impact of the type of buying situation on multiple vendor selection criteria such as economic cost, functional performance, vendor cooperation, and vendor capability.

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A Forward Step in Performance Evaluation

Journal of Marketing ◽

10.1177/002224296603000308 ◽

1966 ◽

Vol 30 (3) ◽

pp. 26-32

Author(s):

Allan Easton

Keyword(s):

Performance Evaluation ◽

Side Effects ◽

Figure Of Merit ◽

Evaluation Methods ◽

Multiple Criterion ◽

Multiple Measures ◽

Evaluation Of Performance ◽

Single Criterion

Persons responsible for evaluation of performance have found that use of single-criterion measures invariably leads to undesirable side-effects. As a remedy, designers of evaluation methods would like to use multiple in place of single criteria, but are likely to have difficulty in amalgamating their multiple measures into a meaningful whole. Here is a method for combining multiple-criterion scores into a conceptually satisfying, overall figure-of-merit which can be used to rank subjects or projects in order of their excellence.

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Validation of an Inertial Sensor Algorithm to Quantify Head and Trunk Movement in Healthy Young Adults and Individuals with Mild Traumatic Brain Injury

Sensors ◽

10.3390/s18124501 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4501 ◽

Cited By ~ 3

Author(s):

Lucy Parrington ◽

Deborah Jehu ◽

Peter Fino ◽

Sean Pearson ◽

Mahmoud El-Gohary ◽

...

Keyword(s):

Inertial Sensor ◽

Rotational Velocity ◽

Correlation Coefficients ◽

Head Movements ◽

Home Exercise ◽

Trunk Movement ◽

Percent Error ◽

Criterion Data ◽

Measurement Units ◽

Promising Solution

Wearable inertial measurement units (IMUs) may provide useful, objective information to clinicians interested in quantifying head movements as patients’ progress through vestibular rehabilitation. The purpose of this study was to validate an IMU-based algorithm against criterion data (motion capture) to estimate average head and trunk range of motion (ROM) and average peak velocity. Ten participants completed two trials of standing and walking tasks while moving the head with and without moving the trunk. Validity was assessed using a combination of Intra-class Correlation Coefficients (ICC), root mean square error (RMSE), and percent error. Bland-Altman plots were used to assess bias. Excellent agreement was found between the IMU and criterion data for head ROM and peak rotational velocity (average ICC > 0.9). The trunk showed good agreement for most conditions (average ICC > 0.8). Average RMSE for both ROM (head = 2.64°; trunk = 2.48°) and peak rotational velocity (head = 11.76 °/s; trunk = 7.37 °/s) was low. The average percent error was below 5% for head and trunk ROM and peak rotational velocity. No clear pattern of bias was found for any measure across conditions. Findings suggest IMUs may provide a promising solution for estimating head and trunk movement, and a practical solution for tracking progression throughout rehabilitation or home exercise monitoring.

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Multiple Criterion Decision Making Application for Sustainable Material Selection

CIRP Design 2012 ◽

10.1007/978-1-4471-4507-3_40 ◽

2012 ◽

pp. 419-425

Author(s):

S. Vinodh ◽

R. Jeya Girubha

Keyword(s):

Decision Making ◽

Material Selection ◽

Multiple Criterion ◽

Sustainable Material ◽

Multiple Criterion Decision ◽

Multiple Criterion Decision Making

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