- 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
- Reviewed
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