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What is Multilevel Modeling

Handbook of Research on Organizational Transformations through Big Data Analytics
Multilevel modeling are advanced analytical methods to describe, explain, and capture the hierarchical relationships between variables at one level (micro-level) of the existing data that are affected by variables at higher level of the hierarchy (macro-level) in efforts to predict future performances, risks, trends, and behaviors.
Published in Chapter:
Predictive Analytics
Sema A. Kalaian (Eastern Michigan University, USA) and Rafa M. Kasim (Indiana Tech University, USA)
DOI: 10.4018/978-1-4666-7272-7.ch002
Abstract
Predictive analytics and modeling are analytical tools for knowledge discovery through examining and capturing the complex relationships and patterns among the variables in the existing data in efforts to predict the future organizational performances. Their uses become more common place due largely to collecting massive amount of data, which is referred to as “big data,” and the increased need to transform large amounts of data into intelligent information (knowledge) such as trends, patterns, and relationships. The intelligent information can then be used to make smart and informed data-based decisions and predictions using various methods of predictive analytics. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools of predictive analytics. The chapter provides a detailed coverage of some of the predictive analytics tools such as Simple and Multiple-Regression, Polynomial Regression, Logistic Regression, Discriminant Analysis, and Multilevel Modeling.
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Multilevel Modeling Methods for E-Collaboration Data
Refers to a set of different statistical methods for analyzing data with two or more levels of hierarchical or nested structures.
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The Relationship Between Online Formative Assessment and State Test Scores Using Multilevel Modeling
Statistical models (e.g., generalizations of linear models such as linear regression) of parameters that vary at more than one level (e.g., nested data).
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