Multilevel Models

(noun)

Multilevel models are statistical models of parameters that vary at more than one level. These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models.

Related Terms

Examples of Multilevel Models in the following topics:

  • Comparing Nested Models

    • Multilevel (nested) models are appropriate for research designs where data for participants are organized at more than one level.
    • Multilevel models, or nested models, are statistical models of parameters that vary at more than one level.
    • As such, multilevel models provide an alternative type of analysis for univariate or multivariate analysis of repeated measures.
    • In addition, this model provides information about intraclass correlations, which are helpful in determining whether multilevel models are required in the first place.
    • In sociological applications, multilevel models are used to examine individuals embedded within regions or countries.

  • Social Epidemiology and Health

    • Use of such multilevel models is also known as hierarchical and mixed effects models.

  • Aligning employee career development with organizational growth

    • Baruch examines transforming models of career management, arguing that there is a general shift in career trajectories from linear to multidirectional trajectories (Transforming Careers from Linear to Multidirectional Career Paths, 2004).
    • In this new model, workers' experience of career development and progression does not follow a traditional linear model of moving up organizational hierarchies.
    • The multidirectional career model suggests that as the individual career trajectories gain multiple direction and possibilities, workers are exposed to greater diversity of relationships, involving cross-functional, inter- and intra-organizational and multilevel encounters which transform the landscape of relationships involved in career experiences.
    • "[I]n the new career model, employees make major shifts within the same company, or exit and reenter the company at different career stages" (Kulik, 2004).

  • Waste comes in all shapes and sizes

    • The proposed solution (called ‘asymmetric multilevel out-phasing') works by having a special chip automatically select the right level of voltage needed by certain inner workings at any given time, which minimizes power consumption.

  • Economic Models

    • A model is simply a framework that is designed to show complex economic processes.
    • Economists use models in order to study and portray situations.
    • Models are based on theory and follow the rules of deductive logic.
    • However, creating a model does have two basic steps: 1) generate the model, and 2) checking the model for accuracy - also known as diagnostics.
    • Some economic models also use qualitative analysis.

  • Modeling Ecosystem Dynamics

    • Conceptual models describe ecosystem structure, while analytical and simulation models use algorithms to predict ecosystem dynamics.
    • Conceptual models are usually depicted graphically as flow charts.
    • These diagrams are sometimes called compartment models.
    • Like analytical models, simulation models use complex algorithms to predict ecosystem dynamics.
    • These kinds of models tend to be more widely used.

  • Introduction to Model selection

    • The best model is not always the most complicated.
    • In this section we discuss model selection strategies, which will help us eliminate from the model variables that are less important.
    • In this section, and in practice, the model that includes all available explanatory variables is often referred to as the full model.
    • Our goal is to assess whether the full model is the best model.
    • If it isn't, we want to identify a smaller model that is preferable.

  • Essential Functions for Mathematical Modeling

    • The process of developing a mathematical model is termed mathematical modeling.
    • Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models.
    • These and other types of models can overlap, with a given model involving a variety of abstract structures.
    • In general, mathematical models may include logical models, as far as logic is taken as a part of mathematics.
    • For example, a simple model of population growth is the Malthusian growth model.

  • Fama-French Three-Factor Model

    • The Fama–French three-factor model is a linear model designed by Eugene Fama and Kenneth French to describe stock returns.
    • The Fama–French three-factor model is a model designed by Eugene Fama and Kenneth French to describe stock returns .
    • Like CAPM and the Arbitrage Pricing Theory, the Fama-French three-factor model is a linear model that relates structural factors to the expected return of an asset.
    • Unlike those two models, however, the Fama-French model has three specific and defined factors.
    • Though it is more complex than CAPM, the Fama-French model has been shown to be a better at explaining the returns of a diversified portfolio: CAPM explains 70% of returns on average, while the Fama-French model explains 90% on average.

  • Two model selection strategies

    • If one of these smaller models has a higher adjusted R2 than our current model, we pick the smaller model with the largest adjusted R2.
    • That is, we fit the model including just the cond new predictor, then the model including just the stock photo variable, then a model with just duration, and a model with just wheels.
    • Each of the four models (yes, we fit four models!
    • We fit three new models:
    • If one of these models has a larger than the model with no variables, we use this new model.