Multilevel modeling is a technique for dealing with data that has been clustered or grouped. Data with repeated measures can also be analyzed using multilevel modeling. For example, If we are testing the blood pressure of a group of patients on a weekly basis, we can think of the succeeding measurements as being grouped inside the individual subjects. It can handle data with different measurement periods from one subject to the next. A multilevel model in machine learning can be applied in such cases that models the parameters that vary at more than one level. In this article, we will go over what multilevel modelling is and how it works. The following are the important points to be discussed in this article.
Table of Contents
- What is Multilevel Modeling?
- Why use a Multilevel Model?
- Different Multilevel Models
- The Assumption Made by Models
- Statistical Components
- Advantages and Disadvantages with Respect to DL
Let’s start the discussion by understanding what multilevel modelling is.
What is Multilevel Modeling?
Multilevel models are statistical models with many levels of variation. They are also known as hierarchical linear models, linear mixed-effect models, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs.
Many types of data, particularly observational data collected in the human and biological sciences, have a hierarchical or clustered structure. Children with the same parents, for example, have more physical and mental characteristics in common than people chosen at random from the broader population.
Individuals can be split further into geographic areas or entities such as schools or employers. When an individual’s responses across time are linked, multilevel data structures develop in longitudinal investigations.
The presence of such data hierarchies is recognized by multilevel models, which allow for residual components at each level of the hierarchy. A two-level model, for example, that allows for the grouping of child outcomes within schools would include residuals at both the child and school levels.
As a result, the residual variance is divided into two components: a between-school component (the variance of the school-level residuals) and a within-school component (the variance of the child-level residuals). The school residuals, often known as school effects that are unobserved school features that influence child outcomes. These unseen variables are what causes the link between outcomes for children.
These models are generalizations of linear models (especially linear regression), but they can also be used to model non-linear data. These models grew in popularity as sufficient processing power and software were available. Multilevel models are particularly effective for research methodologies that require participant data to be organized at multiple levels (i.e., nested data).
Individuals are generally nested within contextual/aggregate units as units of analysis (at a lower level). While individual measurements are frequently the lowest level of data in multilevel(at a higher level) models, repeated measurements of persons can also be explored.
Why use a Multilevel Model?
There are several reasons to use multilevel modelling, some of which are discussed below.
To Get Correct Inferences
The units of analysis are treated as independent observations in traditional multiple regression approaches. Standard errors of regression coefficients will be underestimated as a result of failure to recognize hierarchical structures, leading to an …….
Source: https://analyticsindiamag.com/a-guide-to-multilevel-modeling-in-machine-learning/