Hostname: page-component-5c6d5d7d68-sv6ng Total loading time: 0 Render date: 2024-08-17T04:51:48.570Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysing data with repeated observations on each experimental unit

Published online by Cambridge University Press:  27 March 2009

J. G. Rowell  and
D. E. Walters
J. G. Rowell
Affiliation:
Agricultural Research Council Statistics Group, Department of Applied Biology, Pembroke Street, Cambridge CB2 3DX
D. E. Walters
Affiliation:
Agricultural Research Council Statistics Group, Department of Applied Biology, Pembroke Street, Cambridge CB2 3DX
Get access Rights & Permissions [Opens in a new window]

Summary

Split-plot (or split-block) analyses are commonly applied to experimental results where several successive observations of the same variable have been recorded on each experimental unit. The assumptions required for such analyses receive scant attention and it often seems unlikely that these assumptions would be satisfied in experimental situations. Five sets of results are presented to support this proposition. An alternative analytical approach is suggested in which contrasts over time are analysed; such a method is always valid, computationally simple, and readily interpretable, and may also be used to gauge the validity of the split-plot analysis.

Type
Research Article
Information
The Journal of Agricultural Science , Volume 87 , Issue 2 , October 1976 , pp. 423 - 432
Copyright
Copyright © Cambridge University Press 1976

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

Barry, T. N. (1976). Effects of intraperitoneal injections of DL-methionine on the voluntary intake and wool growth of sheep fed sole diets of hay, silage and pasture differing in digestibility. Journal of Agricultural Science, Cambridge 86, 141–9.CrossRefGoogle Scholar
Cole, J. W. L. & Grizzle, J. E. (1966). Applications of multivariate analysis of variance to repeated measurements experiments. Biometrics 22, 810–28.Google Scholar
Danford, M. B., Hughes, H. M. & McNee, R. C. (1960). On the analysis of repeated-measurements experiments. Biometrics 16, 547–65.CrossRefGoogle Scholar
Dempster, A. P. (1963). Stepwise multivariate analysis of variance based on principal variables. Biometrics 19, 478–90.Google Scholar
Finney, D. J. (1956). Multivariate analysis and agricultural experiments. Biometrics 12, 67–71.Google Scholar
Fisher, R. A. & Yates, F. (1963). Statistical Tables for Biological, Agricultural and Medical Research, 6th ed., table XXIII. London: Oliver and Boyd.Google Scholar
Pearce, S. C. (1953). Field Experimentation with Fruit Trees and Other Perennial Plants, section 74. Commonwealth Bureau of Horticulture and Plantation Crops, Kent.Google Scholar
Preston, A. P. (1960). Pruning trials with Cox's Orange Pippin apple. Journal of Horticultural Science 35, 146–56.Google Scholar
Snedecor, G. W. (1946). Statistical Methods, 4th ed., section 11.16. Ames, Iowa: Iowa State College Press.Google Scholar
Steel, R. G. D. (1955). An analysis of perennial crop data. Biometrics 11, 201–12.Google Scholar
Steel, R. G. D. & Torrie, J. H. (1960). Principles and Procedures of Statistics, section 12.5. New York: McGraw-Hill.Google Scholar
Stevens, W. L. (1949). Análise estatistica do ensaio de variedades de café. Bragantia 9, 103–23.Google Scholar
Thomas, C. & Wilkinson, J. M. (1975). The utilization of maize silage for intensive beef production. 3. Nitrogen and acidity as factors affecting the nutritive value of ensiled maize. Journal of Agricultural Science, Cambridge 85, 255–61.CrossRefGoogle Scholar
Wilks, S. S. (1946). Sample criterion for testing equality of means, equality of variances, and equality of covariances in a normal multivariate distribution. Annals of Mathematical Statistics 17, 257–81.CrossRefGoogle Scholar
Wishart, J. (1938). Growth-rate determinations in nutrition studies with the bacon pig, and their analysis. Biometrika 30, 16–28.CrossRefGoogle Scholar