A nonlinear regression approach to estimating signal detection models for rating data

Title: A nonlinear regression approach to estimating signal detection models for rating data
Creator: Sheu, Ching-Fan; Heathcote, A.
Relation: Behavior Research Methods Instruments and Computers Vol. 33, Issue 2, p. 108-114
Relation: http://www.psychonomic.org/BRMIC
Publisher: Psychonomic Society
Resource Type: journal article
Date: 2001
Description: This paper considers a regression approach to estimating signal detection parameters for rating data. The methodology is based on the statistical modeling of ordinal data and requires only standard statistical software such as SAS (SAS/STAT User's Guide, 1999) for computation. The approach is more efficient than the current practice of extracting the parameter estimates with the use of specialized software and analyzing the estimates with the use of a standard statistical package. It greatly facilitates exploration of the effects of covariates on model parameters. The method is illustrated using a published data set from a single factor multiple-alternative perceptual task, and data from a more complex factorial design examining recognition memory rating data.
Subject: signal detection models; nonlinear models
Identifier: uon:2024
Identifier: http://hdl.handle.net/1959.13/27953
Identifier: ISSN:0743-3808
Language: eng
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