Abstract
Data with repeated measures occur frequently in agricultural research. This paper is a brief overview of statistical methods for repeated measures data. Statistical analysis of repeated measures data requires special attention due to the correlation structure, which may render standard analysis of variance techniques invalid. For balanced data, multivariate analysis of variance methods can be employed and adjustments can be applied to univariate methods, as means of accounting for the correlation structure. But these analysis of variance methods do not apply readily with unbalanced data, and they overlook the regression on time. Regression curves for treatment groups can be obtained by fitting a curve to each experimental unit; and then averaging the coefficients over the units. Treatment groups can be compared by applying univariate and multivariate methods to the group means of the coefficients. This approach does not require knowledge of the correlation structure of the repeated measures, and an approximate version of it can be applied with unbalanced data.
Keywords
Repeated measures, analysis of variance, regression, random coefficient model
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Littell, Ramon C.
(1990).
"ANALYSIS OF REPEATED MEASURES DATA,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1427
ANALYSIS OF REPEATED MEASURES DATA
Data with repeated measures occur frequently in agricultural research. This paper is a brief overview of statistical methods for repeated measures data. Statistical analysis of repeated measures data requires special attention due to the correlation structure, which may render standard analysis of variance techniques invalid. For balanced data, multivariate analysis of variance methods can be employed and adjustments can be applied to univariate methods, as means of accounting for the correlation structure. But these analysis of variance methods do not apply readily with unbalanced data, and they overlook the regression on time. Regression curves for treatment groups can be obtained by fitting a curve to each experimental unit; and then averaging the coefficients over the units. Treatment groups can be compared by applying univariate and multivariate methods to the group means of the coefficients. This approach does not require knowledge of the correlation structure of the repeated measures, and an approximate version of it can be applied with unbalanced data.
