Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-17T22:20:34.715Z Has data issue: false hasContentIssue false
  • English
  • Français

Latency models: the statistical analysis of response times

Published online by Cambridge University Press:  09 July 2009

Graham Dunn  and
Din Master
Graham Dunn*
Affiliation:
Insitute of Psychiatry, London
Din Master
Affiliation:
Insitute of Psychiatry, London
*
1Address for correspondence: Dr Graham Dunn, General Practice Research Unit, Institute of Psychiatry. Dc Crespigny Park, Denmark Hill, London SE5 8AF.
Get access Rights & Permissions [Opens in a new window]

Synopsis

This paper introduces statistical methods suitable for the analysis of response, survival or failure times and, in particular, latencies measured in experiments on the speed of recall of memories. The discussion includes the use of simple descriptive statistics, as well as an explanation of the role of linear-logistic and log-linear models.

Type
Research Article
Information
Psychological Medicine , Volume 12 , Issue 3 , August 1982 , pp. 659 - 665
Copyright
Copyright © Cambridge University Press 1982

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aitken, M. & Clayton, D. (1980). The fitting of exponential, Weibull and extreme value distributions to complex censored survival data using GLIM. Applied Statistics 29, 156–163.CrossRefGoogle Scholar
Baker, R. S. & Nelder, S. A. (1978). The GLIM System, Release 3: Generalised Linear Interactive Modelling. Royal Statistical Society: London.Google Scholar
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society Series B, 34, 187–220.Google Scholar
David, H. A. & Moeschberger, M. L. (1978). The Theory of Competing Risks. Griffin: London and High Wycombe.Google Scholar
Dunn, G. (1981). The role of linear models in psychiatric epidemiology. Psychological Medicine 11, 179–184.Google Scholar
Dunn, G. & Skuse, D. (1981). The natural history of depression in general practice: stochastic models. Psychological Medicine 11, 755–764.CrossRefGoogle ScholarPubMed
Everitt, B. S. (1977). The Analysis of Contingency Tables. Chapman & Hall: London.CrossRefGoogle Scholar
Gross, A. J. & Clark, V. A. (1975). Survival Distributions: Reliability Applications in the Biomedical Sciences. wiley: New York.Google Scholar
Kalbfieisch, J. D. & Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. Wiley: New York.Google Scholar
Lee, E. T. (1980). Statistical Methods for Survival Data Analysis. Lifetime Learning Publications: Belmont. California.Google Scholar
Lishman, W. A. (1974). The speed of recall of pleasant and unpleasant experiences. Psychological Medicine 4, 212–218.CrossRefGoogle ScholarPubMed
Lloyd, G. G. & Lishman, W. A. (1975). Effect of depression on the speed of recall of pleasant and unpleasant experiences. Psychological Medicine 5, 173–180.CrossRefGoogle ScholarPubMed
Teasdale, J. D. & Fogarty, S. J. (1979). Differential effects of induced mood on retrieval of pleasant and unpleasant events from episodic memory. Journal of Abnormal Psychology 88, 248–257.CrossRefGoogle ScholarPubMed
Woolson, R. F., Tsuang, M. T. & Fleming, J. A. (1980). Utility of the proportional-hazards model for survival analysis of psychiatric data. Journal of Chronic Diseases 33, 183–195.CrossRefGoogle ScholarPubMed