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Application of Repeated-Measures Analysis of Variance and Hierarchical Linear Model in Nursing Research

Shin, Juh Hyun

doi: 10.1097/NNR.0b013e318199b5ae
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Objective: 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.

Approach: Alzheimer's Disease Assessment Scale (ADAS) data sets (101 residents in 14 nursing homes, five times) were analyzed to explain differences between repeated-measures ANOVA and the HLM.

Results: More detailed information is available when HLM is used. For example, repeated-measures ANOVA showed that there is a statistically significant difference on overall mean ADAS scores between the married and nonmarried groups. The HLM analysis showed more detailed information; the ADAS score of the married group was higher by 6.4 than that of the nonmarried group on the adjusted average ADAS scores (during the whole data collection period). Repeated-measures ANOVA does not provide results on the within-subject changes with days. The HLM provides the specific conclusion that ADAS scores were increased by the one unit of "days" variable (0.017) when days were included.

Discussion: 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).

Juh Hyun Shin, PhD, RN, is Research Assistant Professor, School of Nursing, State University of New York, Buffalo.

Accepted for publication November 25, 2008.

Thank you to Dr. David Reed (Statistical Consultant, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill), Dr. Yow-Wu Wu (Associate Professor, State University of New York, Buffalo), and Taikyoung Kim (Statistician, Beintoso, South Korea).

Corresponding author: Juh Hyun Shin, PhD, RN, 3435 Main Street, Kimball Tower 823, School of Nursing, State University of New York, Buffalo, NY 14214-3079 (iamjoohyun@gmail.com).

© 2009 Lippincott Williams & Wilkins, Inc.