Repeated Measures and MANOVA
Repeated measures models are regression models in which observations have
multiple response variables. Multiple analysis of variance (MANOVA) is a
statistical technique used to determine whether vectors of response data are
statistically different. Repeated measures models and MANOVA are commonly used
in crossover and longitudinal studies and the response variables typically
correspond to measurements taken at different times. Statistics and Machine Learning Toolbox™ provides functions for working with repeated measures models
including functions for performing one-way, two-way, and N-way multiple analysis
of variance (MANOVA); analysis of variance (ANOVA); analysis of covariance
(ANCOVA); and for creating RepeatedMeasures
model
objects.
Functions
Objects
Topics
Repeated Measures
- Model Specification for Repeated Measures Models
Learn how to specify a repeated measures model infitrm
. - Mauchly’s Test of Sphericity
Learn the test of sphericity used in repeated measures models. - Compound Symmetry Assumption and Epsilon Corrections
Learn the different epsilon corrections used in p-value calculations in the repeated measures ANOVA when the compound symmetry assumption fails. - Multivariate Analysis of Variance for Repeated Measures
Learn the four different methods used in multivariate analysis of variance for repeated measures models. - Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.
MANOVA
- Perform Multivariate Analysis of Variance (MANOVA)
MANOVA is a form of ANOVA with multiple response variables. It determines whether the entire set of means is different from one group to the next.