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The multilevel measurement model: introduction to the special issue
S Natasha Beretvas1, Akihito Kamata
1Department of Educational Psychology, 1 University Station, Mail Station D5800, University of Texas, Austin, TX 78712, USA. tasha.beretvas@mail.utexas.edu
Journal of Applied Measurement
|June 9, 2005
Summary
This special issue introduces the multilevel measurement model (MMM), reviewing models for continuous outcomes and demonstrating extensions for dichotomous indicators using hierarchical generalized linear models.
Area of Science:
- Statistics
- Psychometrics
- Social Sciences
Background:
- Multilevel measurement models (MMM) are crucial for analyzing nested data structures.
- Existing models primarily focus on continuous outcomes.
- There is a need to extend these models for other data types.
Purpose of the Study:
- To introduce a special issue focused on the multilevel measurement model (MMM).
- To review existing two- and three-level multilevel models for continuous outcomes.
- To demonstrate extensions of MMM for dichotomous indicators.
Main Methods:
- Review of established two- and three-level multilevel models.
- Application of hierarchical generalized linear models (HGLM).
- Demonstration of HGLM as an MMM for dichotomous data.
Main Results:
- Comprehensive overview of multilevel models for continuous data.
- Successful extension of MMM framework to dichotomous measurement indicators via HGLM.
- Introduction of six distinct articles within the special issue.
Conclusions:
- The multilevel measurement model (MMM) framework is versatile and extendable.
- Hierarchical generalized linear models (HGLM) provide a robust method for dichotomous MMM.
- The special issue offers valuable contributions to multilevel modeling techniques.