Confidence intervals for the parameters of psychometric functions

Research output: Contribution to journalArticle

Abstract

A Monte Carlo method for computing the bias and standard deviation of estimates of the parameters of a psychometric function such as the Weibull/Quick is described. The method, based on Efron's parametric bootstrap, can also be used to estimate confidence intervals for these parameters. The method's ability to predict bias, standard deviation, and confidence intervals is evaluated in two ways. First, its predictions are compared to the outcomes of Monte Carlo simulations of psychophysical experiments. Second, its predicted confidence intervals were compared with the actual variability of human observers in a psychophysical task. Computer programs implementing the method are available from the author.

Original languageEnglish (US)
Pages (from-to)127-134
Number of pages8
JournalPerception & Psychophysics
Volume47
Issue number2
DOIs
StatePublished - Mar 1990

Fingerprint

Psychometrics
psychometrics
confidence
Confidence Intervals
Monte Carlo Method
Aptitude
data processing program
Software
trend
simulation
Confidence Interval
experiment
ability
Psychophysical
Deviation

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Sensory Systems
  • Psychology(all)

Cite this

Confidence intervals for the parameters of psychometric functions. / Maloney, Laurence T.

In: Perception & Psychophysics, Vol. 47, No. 2, 03.1990, p. 127-134.

Research output: Contribution to journalArticle

@article{03f0914850544131a91df8ec0f389c50,
title = "Confidence intervals for the parameters of psychometric functions",
abstract = "A Monte Carlo method for computing the bias and standard deviation of estimates of the parameters of a psychometric function such as the Weibull/Quick is described. The method, based on Efron's parametric bootstrap, can also be used to estimate confidence intervals for these parameters. The method's ability to predict bias, standard deviation, and confidence intervals is evaluated in two ways. First, its predictions are compared to the outcomes of Monte Carlo simulations of psychophysical experiments. Second, its predicted confidence intervals were compared with the actual variability of human observers in a psychophysical task. Computer programs implementing the method are available from the author.",
author = "Maloney, {Laurence T.}",
year = "1990",
month = "3",
doi = "10.3758/BF03205977",
language = "English (US)",
volume = "47",
pages = "127--134",
journal = "Attention, Perception, and Psychophysics",
issn = "1943-3921",
publisher = "Springer New York",
number = "2",

}

TY - JOUR

T1 - Confidence intervals for the parameters of psychometric functions

AU - Maloney, Laurence T.

PY - 1990/3

Y1 - 1990/3

N2 - A Monte Carlo method for computing the bias and standard deviation of estimates of the parameters of a psychometric function such as the Weibull/Quick is described. The method, based on Efron's parametric bootstrap, can also be used to estimate confidence intervals for these parameters. The method's ability to predict bias, standard deviation, and confidence intervals is evaluated in two ways. First, its predictions are compared to the outcomes of Monte Carlo simulations of psychophysical experiments. Second, its predicted confidence intervals were compared with the actual variability of human observers in a psychophysical task. Computer programs implementing the method are available from the author.

AB - A Monte Carlo method for computing the bias and standard deviation of estimates of the parameters of a psychometric function such as the Weibull/Quick is described. The method, based on Efron's parametric bootstrap, can also be used to estimate confidence intervals for these parameters. The method's ability to predict bias, standard deviation, and confidence intervals is evaluated in two ways. First, its predictions are compared to the outcomes of Monte Carlo simulations of psychophysical experiments. Second, its predicted confidence intervals were compared with the actual variability of human observers in a psychophysical task. Computer programs implementing the method are available from the author.

UR - http://www.scopus.com/inward/record.url?scp=0025344466&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0025344466&partnerID=8YFLogxK

U2 - 10.3758/BF03205977

DO - 10.3758/BF03205977

M3 - Article

VL - 47

SP - 127

EP - 134

JO - Attention, Perception, and Psychophysics

JF - Attention, Perception, and Psychophysics

SN - 1943-3921

IS - 2

ER -