# Transformations

*Data transformation* is the process of changing the data in some way. More formally, a transformation involves creating a new variable or set of variables from an existing variable or set of variables.

## Contents

## Objectives of transformation

Data transformation is undertaken with the following objectives:

- Making it easier to see patterns in the data (e.g., the Log transformations and Principal Components Analysis).
- Making it easier to communicate patterns in the data (e.g., the Net Promoter Score).
- To address violations of the assumptions of statistical tests (e.g., Ranks, Log transformations).
- To improve the validity of regression models (e.g., Basis Functions).
- To reduce the amount of data (e.g., Principal Components Analysis).

## Standard transformations of a categorical variable

A categorical variable can be transformed in one of two ways:

- It can be turned into a numeric variable, by coming up with some rules about the numeric interpretation of categories. For example:
- Replacing the category
`18 to 24`with`21`and`25 to 29``with``27`(this is a type of Recoding known as Midpoint Recoding. - Computing the Net Promoter Score.

- Replacing the category
- The categories of a categorical variable can be combined. Most commonly, small categories are merged into larger categories. For example:
- When a question asks for reasons for a particular behavior, any reasons that are selected by a small number of respondents can be classified as
`Other`. - Variables that collect data on Rating Scales may be converted to Binary Variables to make further analysis simpler.

- When a question asks for reasons for a particular behavior, any reasons that are selected by a small number of respondents can be classified as

## Standard transformations of numeric variables

### Univeriate

- Ranks
- Log transformations
- Trimming
- Windsorizing

### Multivariate

- Principal Components Analysis
- Cluster Analysis
- t-SNE
- Basis functions, such as:
- Dummy Variables
- Polynomials
- Orthogonal polynomials

## See also

`
`