The Rotated Diagonal Factors (RDF) Approach: A Substitute for MANOVA When Analyzing Multi-Task and Multi-Criterion Data.

1997 ◽  
Author(s):  
Robert J. Wherry ◽  
Forster Jr. ◽  
Morrison Estrella M. ◽  
Jeffery
Keyword(s):  
Criterion Data

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

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4501 ◽  
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.

Biasing Effects of Missing Data on a Family Illness Criterion

1977 ◽  
Vol 41 (1) ◽  
pp. 279-288 ◽  
Author(s):  
Joe Galdi ◽  
Roland R. Bonato
Keyword(s):  
Missing Data ◽  
Original Sample ◽  
Criterion Data ◽  
Biasing Effects

A method appropriate for evaluating biases in criterion statistics attributable to excluding subjects with missing data from an original sample was illustrated using a complex family illness variable. This method incorporates non-criterion information to indirectly reduce the error in bias estimates and appears more accurate than methods which utilize criterion data alone. Since non-criterion data are required, the method is particularly suited to situations in which the original sample is complete but criterion data are missing within forms or subsequently lost through attrition of subjects.

Factors Influencing the Vicious Circle Phenomenon

1967 ◽  
Vol 21 (2) ◽  
pp. 633-642
Author(s):  
John T. Williams
Keyword(s):  
Intertrial Interval ◽  
Vicious Circle ◽  
Reactive Inhibition ◽  
Criterion Data ◽  
Factors Influencing

When rats were tested in a vicious circle paradigm, varying the location of shock in the alley from trial to trial had little or no effect. Trials to criterion data contradicted speed data in the second experiment, and this led to the rejection of speed data as a measure of the vicious circle effect. It was shown that speed data can introduce a bias which increases the probability of a significant vicious circle effect. The duration of intertrial interval was shown to be an important factor in vicious circle studies, and an explanation of the effects of intertrial interval was made based upon dissipation of reactive inhibition and of the autonomic aftereffects of shock.

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

1995 ◽  
Vol 32 (2) ◽  
pp. 222 ◽  
Author(s):  
Wayne S. Desarbo ◽  
Venkatram Ramaswamy ◽  
Rabikar Chatterjee
Keyword(s):  
Multiple Criterion ◽  
Criterion Data

A new linguistic MCDM method based on multiple-criterion data fusion

2011 ◽  
Vol 38 (6) ◽  
pp. 6985-6993 ◽  
Author(s):  
Yong Deng ◽  
Felix T.S. Chan ◽  
Ying Wu ◽  
Dong Wang
Keyword(s):  
Data Fusion ◽  
Multiple Criterion ◽  
Criterion Data ◽  
Mcdm Method
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