REPEATED-MEASURES IN RANDOMIZED BLOCK AND SPLIT-PLOT EXPERIMENTS

Randomized block and split-plot designs are among the most commonly us ed experimental designs in forest research. Measurements for plots in a block (or subplots in a whole plot) are correlated with each other, and these correlations must be taken into account when analyzing repea ted-measures data from blocked designs. The analysis is similar to rep eated-measures analysis for a completely randomized design, but test s tatistics must allow for random block x time effects, and standard err ors for treatment means must also incorporate block to block variation and variation among plots within a block. Two types of statistical an alysis are often recommended for repeated-measures data: analysis of c ontrasts of the repeated factor and multivariate analysis of variance. A complete analysis of repeated measures should usually contain both of these components, just as in univariate analysis of variance it is often necessary to decompose the main effects into single degree of fr eedom contrasts to answer the research objectives. We demonstrate the multivariate analysis of variance and the analysis of contrasts in det ail for two experiments. In addition, estimation of coefficients assum ing a polynomial growth curve is discussed in detail for one of these experiments. The first experiment, a randomized complete block design, is a forest nutrition study of the long-term effects of midrotation n itrogen and phosphorus fertilization on loblolly pine (Pinus taeda L.) ; the second experiment, a split-plot design, is an air-pollution stud y of the effects of ozone and acid precipitation on loblolly pine grow th.