Nursing research
-
Comparative Study
Application of repeated-measures analysis of variance and hierarchical linear model in nursing research.
The aims of this study were to describe how repeated-measures analysis of variance (ANOVA) and the hierarchical linear model (HLM) are used to evaluate intervention effect and to compare these methods, especially in relation to their requirements regarding assumptions, number of repeated measures, completeness of repeated measures, and equal intervals between measurements. ⋯ Hierarchical linear model is a powerful statistical method that can be applied to longitudinal research to evaluate an intervention at multiple levels. The major differences between the repeated-measures ANOVA and the HLM can be summarized as follows: The HLM (a) has less strict assumptions, (b) has more flexible data requirements (dealing with the missing data), and (c) stresses individual change over group differences. More stringent assumptions should be satisfied in repeated-measures ANOVA than in the HLM. The HLM may resolve important statistical issues that have existed in repeated-measures ANOVA. The HLM has more flexible data requirements in that it (a) can be utilized when the measurement data collection points are unequal and (b) may be used when researchers do not have data for all follow-up points, whereas the repeated-measures ANOVA requires a fixed time series design (equal interval, equal number of time points).