Logistic Regression in Signal Detection: Another Piece Added to the Puzzle

2013 ◽  
Vol 94 (3) ◽  
pp. 312-312 ◽  
Author(s):  
O Caster ◽  
G N Norén ◽  
D Madigan ◽  
A Bate
Keyword(s):  
Logistic Regression ◽  
Signal Detection

Do logistic regression and signal detection identify different subgroups at risk? Implications for the design of tailored interventions.

2001 ◽  
Vol 6 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Michaela Kiernan ◽  
Helena C. Kraemer ◽  
Marilyn A. Winkleby ◽  
Abby C. King ◽  
C. Barr Taylor
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Signal Detection ◽  
Tailored Interventions

Are all quantitative postmarketing signal detection methods equal? Performance characteristics of logistic regression and Multi-item Gamma Poisson Shrinker

2011 ◽  
Vol 21 (6) ◽  
pp. 622-630 ◽  
Author(s):  
Conny Berlin ◽  
Carles Blanch ◽  
David J. Lewis ◽  
Dionigi D. Maladorno ◽  
Christiane Michel ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Signal Detection ◽  
Performance Characteristics ◽  
Detection Methods ◽  
Gamma Poisson Shrinker

scholarly journals Analysis of the Relationship Between Turning Signal Detection and Motorcycle Driver’s Characteristics on Urban Roads. A Case Study

2019 ◽  
Author(s):  
Alfonso Micucci ◽  
Luca Mantecchini ◽  
Maurizio Sangermano
Keyword(s):  
Logistic Regression ◽  
Signal Detection ◽  
Test Site ◽  
Correct Detection ◽  
Stepwise Logistic Regression ◽  
Turn Signal ◽  
Suburban Areas ◽  
The Relationship ◽  
Driver’S Characteristics

The relationships among the potential causes of a car and motorcycle collision involving turn maneuvers as well as the perception of rear and front turn signal (on/off) configuration is examined in this paper. The investigation has been based on data pooled from the answers of a survey proposed to 136 people, with special regards to the correct detection of indicators aspect. Experimental videos have been realized during the tests campaign, both in urban and suburban areas, using a 360-camera attached to a motorcyclist’s helmet, reproducing vehicular conflicts able to potentially generate crash risks. The detection of the blinker was combined with other factors (e.g. age, gender, location of the test site, presence of the car behind tester vehicles and if the bikers are also habitual car or bike drivers) in a stepwise logistic regression that modelled the odds of detecting the turn signal turned on as a function of all of these factors. The results suggest the existence of a connection between the detection of the turn signal aspect and some of the variables considered (e.g. age, being a cyclist or a car driver and the presence of a protecting car).

Response to “Logistic Regression in Signal Detection: Another Piece Added to the Puzzle”

2013 ◽  
Vol 94 (3) ◽  
pp. 313-313 ◽  
Author(s):  
R Harpaz ◽  
W DuMouchel ◽  
P LePendu ◽  
A Bauer-Mehren ◽  
P Ryan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Signal Detection

A Model of Air to Ground Target Acquisition Based on Logistic Regression Analysis

2000 ◽  
Vol 44 (21) ◽  
pp. 3-484-3-487
Author(s):  
Bartholomew Elias
Keyword(s):  
Logistic Regression ◽  
Signal Detection ◽  
Human Performance ◽  
Visual Target ◽  
Target Acquisition ◽  
Independent Variables ◽  
Ground Target ◽  
Regression Techniques ◽  
Detection Capabilities ◽  
Operational Constraints

Logistic regression, a technique for describing relationships between a binary or dichotomous dependent variable and one or more independent variables that can be either discrete or continuous, is demonstrated to be an effective analytical tool for evaluating data collected using psychophysical methods and signal detection procedures. One specific application of logistic regression is the assessment of operational factors on human performance in visual target acquisition. Visual target acquisition data collected using signal detection procedures were reanalyzed using logistic regression techniques. The application of these logistic regression techniques produced empirically derived psychophysical models of target detection capabilities under various conditions. Such models can be used to predict human performance in visual target acquisition under various operational constraints.

How Useful is the Power Law of Practice for Recognizing Practice in Concentration Tests?

2007 ◽  
Vol 23 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Carmen Hagemeister
Keyword(s):  
Logistic Regression ◽  
Reaction Time ◽  
Power Law ◽  
Error Rate ◽  
Reaction Times ◽  
Practice Effect ◽  
Practice Effects ◽  
Rate Decrease ◽  
Small Practice

Abstract. When concentration tests are completed repeatedly, reaction time and error rate decrease considerably, but the underlying ability does not improve. In order to overcome this validity problem this study aimed to test if the practice effect between tests and within tests can be useful in determining whether persons have already completed this test. The power law of practice postulates that practice effects are greater in unpracticed than in practiced persons. Two experiments were carried out in which the participants completed the same tests at the beginning and at the end of two test sessions set about 3 days apart. In both experiments, the logistic regression could indeed classify persons according to previous practice through the practice effect between the tests at the beginning and at the end of the session, and, less well but still significantly, through the practice effect within the first test of the session. Further analyses showed that the practice effects correlated more highly with the initial performance than was to be expected for mathematical reasons; typically persons with long reaction times have larger practice effects. Thus, small practice effects alone do not allow one to conclude that a person has worked on the test before.

Perceived Effectiveness of Random Testing for Alcohol and Drugs in the Australian Aviation Community

2012 ◽  
Vol 2 (2) ◽  
pp. 72-81
Author(s):  
Christina M. Rudin-Brown ◽  
Eve Mitsopoulos-Rubens ◽  
Michael G. Lenné
Keyword(s):  
Logistic Regression ◽  
Online Survey ◽  
Random Testing ◽  
Regression Analyses ◽  
Perceived Effectiveness ◽  
Student Pilots ◽  
Alcohol And Drugs ◽  
Positive Attitudes ◽  
One Year ◽  
Alcohol And Other Drugs

Random testing for alcohol and other drugs (AODs) in individuals who perform safety-sensitive activities as part of their aviation role was introduced in Australia in April 2009. One year later, an online survey (N = 2,226) was conducted to investigate attitudes, behaviors, and knowledge regarding random testing and to gauge perceptions regarding its effectiveness. Private, recreational, and student pilots were less likely than industry personnel to report being aware of the requirement (86.5% versus 97.1%), to have undergone testing (76.5% versus 96.1%), and to know of others who had undergone testing (39.9% versus 84.3%), and they had more positive attitudes toward random testing than industry personnel. However, logistic regression analyses indicated that random testing is more effective at deterring AOD use among industry personnel.

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