Multilevel modeling is a technique for dealing with data that has been grouped or clustered. Multilevel modeling can also be used to investigate data with repeated measurements. For instance, if we are regularly monitoring the blood pressure of a patient group, the subsequent measures may be considered to be grouped within the individual subjects. It can handle data with different measurement times from one element to the next. A multilevel model in ML can be used in such cases to simulate parameters that change at more than one level. We will define multilevel modeling and explain how it works in this post.
Multilevel Modeling and its Working
In machine learning, multilevel models are statistical models with a large number of levels of variance. Linear mixed-effect models, hierarchical linear models, mixed models, layered data models, random factors, random effects, random variables, or split-plot patterns are other names for them.
Many types of data, particularly observational data collected in human and biomedical research, have a hierarchical or grouped structure. Children from the same parents, for instance, have more physical and mental characteristics in common than people drawn at random from the general population.
People can be subdivided further into geographical areas or authorities like schools or employers. When an individual’s reactions over time are linked in longitudinal studies, multilevel data structures emerge.
The presence of such data hierarchies is detected using multilevel models that account for residual elements at each level of the hierarchy. A two-level model, for instance, that allows for the grouping of outcomes for children within schools typically includes residuals at both the child and school levels.
As a result, the residual variance is divided into two parts: between-school and within-school. School residuals, also known as school effects, are unobserved school characteristics that have an impact on children’s outcomes. These unknown influences are responsible for the link between outcomes for children.
Why should you use a Multilevel Model?
Multilevel modeling is beneficial for several reasons, some of which are listed below:
- To Get Correct Inferences
The entities of analysis are viewed as independent observations in standard multiple regression techniques. Standard errors of regressors will be underrepresented as a result of the failure to recognize hierarchical patterns, resulting in an overstatement of statistically significant. For higher-level predictor variable values, ignoring grouping has the greatest impact on prediction errors.
- Significant Interest in Group Effects
In many situations, the level of grouping in specific outcomes, as well as the identification of “outlying” groups, is an important research topic. In school performance reviews, for example, the emphasis is on obtaining ‘value-added’ school effects on student achievement. In a multilevel framework that accounts for prior achievement, such effects correspond to school-level residuals.
- Simultaneous Estimation of Group Effects
A regular (normal least squares) regression model can be extended with group dummy elements to account for group effects. This is known as an analysis of variance or a fixed-effects framework. Many predictors, such as school type, will be specified at the group level (mixed vs. single-sex).
The effects of group-level predictors are confused with the implications of group dummies in a fixed-effects model, i.e., it is impossible to separate effects due to observed and unobserved group features. To estimate the impact of both types of variables, a multilevel (random effects) model can be used.
- Inference to a Population of Groups
The groups in the data set are regarded as a random sample drawn from a community of groups in a multilevel model. A fixed-effects model cannot make inferences beyond the dataset’s units.
