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## Do I need to transform my data?

**Data is transformed to make it better-organized**. Transformed data may be easier for both humans and computers to use. Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats.

## Do you have to log transform all variables?

**You should not just routinely log everything**, but it is a good practice to THINK about transforming selected positive predictors (suitably, often a log but maybe something else) before fitting a model. The same goes for the response variable. Subject-matter knowledge is important too.

## How do you remove outliers from reaction time?

Common methods used to eliminate outlier reaction times include **using the median response time, using specific cutoff response times, and using cutoffs at some number of standard deviations above the mean response time**.

## How do you find outliers in reaction time?

Tukey’s method identifies RTs as outlier, which are larger than the third quartile plus 1.5 times the IQR (> q_{0.75} + 1.5×IQR) or smaller than the first quartile minus 1.5 times the IQR (< q_{0.25}−1.5×IQR).

## When should you transform skewed data?

It’s often desirable to transform skewed data and to convert it into values **between 0 and 1**. Standard functions used for such conversions include Normalization, the Sigmoid, Log, Cube Root and the Hyperbolic Tangent.

## Do covariates need to be normally distributed?

**They do not need to be normally distributed or continuous**. It is useful, however, to understand the distribution of predictor variables to find influential outliers or concentrated values. A highly skewed independent variable may be made more symmetric with a transformation.

## Should I log transform independent variable?

**No, log transformations are not necessary for independent variables**. In any regression model, there is no assumption about the distribution shape of the independent variables, just the dependent variable.

## What effects does log transformation have on data?

The log transformation is, arguably, the most popular among the different types of transformations used to **transform skewed data to approximately conform to normality**. If the original data follows a log-normal distribution or approximately so, then the log-transformed data follows a normal or near normal distribution.

## Why do we use log transformation?

The log transformation can be used **to make highly skewed distributions less skewed**. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics. Figure 1 shows an example of how a log transformation can make patterns more visible.

## What type of scale is reaction time?

ratio scales

Examples of **ratio scales** in psychology are things such as reaction time, and individual scores such as “number of items correctly recalled” or “number of errors”.

## Is reaction time discrete or continuous?

Continuous and Discrete

Some variables (such as reaction time) are measured on a **continuous scale**. There is an infinite number of possible values these variables can take on. Other variables can only take on a limited number of values.

## Is time in hours a ratio or interval?

interval variable

The short answer: Time is considered an **interval variable** because differences between all time points are equal but there is no “true zero” value for time.