A monte carlo study of alternative procedures for testing the hypothesis of parallelism for complete and incomplete growth curve data

DOI10.1080/00949658508810794zbMath0589.62096OpenAlexW2012551780MaRDI QIDQ3718050

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Publication date: 1985

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949658508810794

zbMATH Keywords

powermissing observationsanalysisdatacorrectionsignificance levelsplit-plotHotelling T-squarecomplete and incomplete growth curveGeisser-Greenhousehypothesis of parallelismsuccessive difference procedures

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Linear inference, regression (62J99) Monte Carlo methods (65C05)

Related Items (3)

A monte carlo study of successive difference analysis of growth curve data at random observation times ⋮ Small-sample adjustments to tests with unbalanced repeated measures assuming several covariance structures ⋮ The growth curve model: a review

Cites Work

• A generalization of the growth curve model which allows missing data
• Some Statistical Methods for Comparison of Growth Curves
• A Note on the Generation of Random Normal Deviates
• An Extension of Box's Results on the Use of the $F$ Distribution in Multivariate Analysis
• On the analysis of incomplete growth curve data, a Monte Carlo study of two nonparametric procedures
• Computation of the mean vector and dispersion matrix for incomplete multivariate data
• Testing hypotheses for the growth curve model when the data are incomplete
• A generalized multivariate analysis of variance model useful especially for growth curve problems

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