Latent variable

A latent variable is a variable that cannot be directly observed or measured but is inferred from other variables that can be measured. Essentially, it’s a hidden or unobservable factor that influences the data but is not directly visible in the dataset.

Key Points:

• Hidden Influence: Latent variables are often the underlying causes of observed data. For example, a person’s intelligence might be a latent variable that influences their performance on various tests, but we can’t measure intelligence directly.
• Inferred from Data: We infer latent variables based on observable variables.
• Used in Models: Latent variables are commonly used in statistical models, such as:

1- Factor analysis: Where latent factors explain correlations between observed variables.

Get Osama Haider’s stories in your inbox

Join Medium for free to get updates from this writer.

Remember me for faster sign in

2- Structural equation modeling (SEM): Where latent variables are used to represent constructs that can’t be measured directly (e.g., “satisfaction,” “health”).

In short, latent variables are essential in understanding deeper relationships in data, especially when those relationships are complex and not directly measurable.