Robustness of Hierarchical Linear Model Parameter Estimates - Darandari, Eqbal
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This study applied Monte Carlo simulations to investigate the robustness of parameter estimates for a two-level Hierarchical Linear Model (HLM) to the violations of the second-level residual homoskedasticity and independence assumptions. It considered these violations in the context of a typical HLM model with random intercepts as outcomes, a model that has been commonly used in value-added analysis for school settings. The results suggested that estimates of both fixed and random parameters in HLM-2L are robust to the moderate and severe violations of this assumption. It also provided new…mehr

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Produktbeschreibung
This study applied Monte Carlo simulations to investigate the robustness of parameter estimates for a two-level Hierarchical Linear Model (HLM) to the violations of the second-level residual homoskedasticity and independence assumptions. It considered these violations in the context of a typical HLM model with random intercepts as outcomes, a model that has been commonly used in value-added analysis for school settings. The results suggested that estimates of both fixed and random parameters in HLM-2L are robust to the moderate and severe violations of this assumption. It also provided new evidence concerning the contradicting study results in regard to the accuracy of HLM apparent precision associated with parameter estimates.
Autorenporträt
Eqbal Darandari,Consultant in Measurement and Statistics, Program Evaluation and Quality Assurance for Higher and Public Education. Holds Ph.D. in Measurement and Statistics and Certificate in Program Evaluation from Florida State University, USA. Presently she is associate professor, department of psychology, college of education, King Saud University.