What question did this study set out to answer?

To assess how parameter estimation imprecision affects standard error calculations via the inverse information matrix in two-level random-coefficient models and to propose corrective remedies.

September 1, 2000Journal of the Royal Statistical Society Series D (The Statistician)

On Estimating Standard Errors in Multilevel Analysis

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Authors

NLNicholas T. LongfordSGH Warsaw School of Economics

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Implication

Methodological analysis demonstrates inaccuracies in standard error estimation within two-level random-coefficient models, suggesting alternative remedies to improve statistical precision.

Key Points

To assess how parameter estimation imprecision affects standard error calculations via the inverse information matrix in two-level random-coefficient models and to propose corrective remedies.
Evaluated standard error derivation from the inverse of the information matrix for maximum likelihood estimators.
Analyzed parameter precision dependencies and error propagation within two-level (random-coefficient) multilevel modeling frameworks.
Demonstrated that standard errors are estimated poorly when the information matrix depends on parameters that have low estimation precision.
Formulated methodological remedies to enhance the accuracy and reliability of standard error estimates in two-level models.

Cite This Study

Nicholas T. Longford (2000) studied this question.

synapsesocial.com/papers/6a71688dfe4101aa97e08fc1 https://doi.org/10.1111/1467-9884.00242

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Implication

Methodological analysis demonstrates inaccuracies in standard error estimation within two-level random-coefficient models, suggesting alternative remedies to improve statistical precision.

On Estimating Standard Errors in Multilevel Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

On Estimating Standard Errors in Multilevel Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

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