Exploratory Research to Test the Feasibility of Conducting Crew Coordination Training in the OH-58 Aircraft

2001 ◽  
Author(s):  
Joseph L. Zeller ◽  
Grubb Jr. ◽  
Gary
Keyword(s):  
Exploratory Research ◽  
Coordination Training ◽  
Crew Coordination

Development of Candidate Crew Coordination Training Methods and Materials

1993 ◽  
Author(s):  
Eugene A. Pawlik ◽  
Simon Sr. ◽  
Grubb Robert ◽  
Zeller G. ◽  
J.
Keyword(s):  
Training Methods ◽  
Coordination Training ◽  
Crew Coordination

Behavioral Data in the Design of Aircrew Training Devices

1975 ◽  
Vol 19 (2) ◽  
pp. 260-265
Author(s):  
B.W. Cream ◽  
F.T. Eggemeier ◽  
G.A. Klein
Keyword(s):  
Performance Measurement ◽  
Training Programs ◽  
Behavioral Data ◽  
Training Requirements ◽  
Critical Areas ◽  
Coordination Training ◽  
Crew Coordination

Attention has recently been focused on the use of ISD behavioral data for specifying aircrew training requirements. However, behavioral data are not sufficient for the actual specification of design of equipment, which frequently represents the major dollar investment for training programs. The paper presents a methodology for designing training equipment. This methodology in some ways goes beyond the collection of behavioral data, and in other ways ayoids the weaknesses of the behavioral data approach. The emphasis is on ensuring that device fidelity requirements are specifically correlated with training requirements. Four critical areas are addressed: (1) acquisition of behavioral data; (2) determination of training capabilities; (3) performance measurement; and (4) special requirements for crew coordination training.

A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 33-42
Author(s):  
Thomas Otter
Keyword(s):  
Confidence Intervals ◽  
Generalized Linear Model ◽  
Statistical Information ◽  
Regression Coefficients ◽  
Model Specification ◽  
Model Parameters ◽  
Exploratory Research ◽  
Empirical Calibration ◽  
Credible Intervals ◽  
Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

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