# Data Standardization

- Introduction
  - Normal vs. Robust
  - Variable-wise vs. Participant-wise
- References

# Introduction

To make sense of their data and effects, scientists might want to standardize (Z-score) their variables. They become unitless, expressed only in terms of deviation from an index of centrality (e.g., the mean or the median).

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## Normal vs. Robust

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## Variable-wise vs. Participant-wise

Standardization is an important step and extra caution is required in repeated-measures designs, in which there are three ways of standardizing data:

- Variable-wise: The most common method. A simple scaling of each column.
- Participant-wise: Variables are standardized “within” each participant, i.e., for each participant, by the participant’s mean and SD.
- Full: Participant-wise first and then re-standardizing variable-wise.

Unfortunately, the method used is often not explicitly stated. This is an issue as these methods can generate important discrepancies (that can in turn contribute to the reproducibility crisis). Let’s investigate these 3 methods.

Use this

# References