eelbrain.normalize_in_cells(y, for_dim, in_cells=None, data=None, method='z-score')

Normalize data in cells to make it appropriate for ANOVA [1]

  • y (NDVar | str) – Dependent variable which should be normalized.

  • for_dim (str) – Dimension which will be included as factor in the ANOVA (e.g., 'sensor').

  • in_cells (Factor | Interaction | NestedEffect | str) – Model defining the cells within which to normalize (normally the factors that will be used as fixed effects in the ANOVA).

  • data (Dataset) – Dataset containing the data.

  • method ('z-score' | 'range') – Method used for normalizing the data: z-score: for the data in each cell, subtract the mean and divide by the standard deviation (mean and standard deviation are computed after averaging across cases in each cell) range: for the data in each cell, subtract minimum and then divide by the maximum (i.e., change the range of the data to (0, 1); range is computed after averaging across cases in each cell).

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This method normalizes data by z-scoring. A common example is a by sensor interaction effect in EEG data. ANOVA interaction effects assume additivity, but EEG topographies depend on source strength in a multiplicative fashion, which can lead to spurious interaction effects. Normalizing in each cell of the ANOVA model controls for this (see [1] for details).


See Compare topographies.