Partitioning Variation in Multilevel Models

Goldstein, Harvey; Browne, William J.; Rasbash, Jon

doi:10.1207/s15328031us0104_02

Cited by 731 publications

(619 citation statements)

References 3 publications

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“…These values indicate the proportional amount of error of prediction reduced from the unconditional model to the final model at the within-person level [30]. To establish these values for the binary variable of lapse occurrence, an approximation of the level 1 variance term was established by treating lapse occurrence as a continuous variable [31].…”

Section: Resultsmentioning

confidence: 99%

An Ecological Momentary Assessment of Lapse Occurrences in Dieters

2014

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Section: Resultsmentioning

confidence: 99%

An Ecological Momentary Assessment of Lapse Occurrences in Dieters

2014

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“…An initial multilevel model with no predictors indicated that ϳ13% of the variation in the odds of insurance adequacy may be attributable to differences among states, as estimated by using the latent variable approach described by Goldstein et al 16,17 Adjusting …”

Section: Resultsmentioning

confidence: 99%

State Variation in Underinsurance Among Children With Special Health Care Needs in the United States

Kogan

Newacheck²,

Blumberg

et al. 2010

Pediatrics

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WHAT'S KNOWN ON THIS SUBJECT:Many studies have documented the extent to which CSHCN lack insurance. Less attention has been devoted to examining underinsurance for this population and no studies have examined state variability in underinsurance. WHAT THIS STUDY ADDS:We demonstrate that underinsurance affects far more CSHCN than does lack of insurance. Our results indicate that where a child lives is strongly related to the likelihood that his or her health insurance is adequate. abstract OBJECTIVE: National attention has focused on providing health insurance coverage for children. Less awareness has been given to underinsurance, particularly for children with special health care needs (CSHCN). Defined as having inadequate benefits, underinsurance may be a particular problem for CSHCN because of their greater needs for medical care. METHODS:We used the 2005-2006 National Survey of Children With Special Health Care Needs, a nationally representative study of Ͼ40 000 CSHCN, to address state variations in underinsurance. CSHCN with health insurance were considered underinsured when a parent reported that the child's insurance did not usually or always cover needed services and providers or reasonably cover costs. We calculated the unadjusted prevalence of underinsurance for each state. Using logistic regression, we estimated state-specific odds and prevalence for underinsurance after adjusting for poverty level, race/ethnicity, gender, family structure, language use, insurance type, and severity of child's health condition. We also conducted multilevel analyses incorporating state-level contextual data on Medicaid and the State Children's Health Insurance Program. RESULTS: Bivariate and multivariate analyses indicated that CSHCNЈs state of residence had a strong association with insurance adequacy. State-level unadjusted underinsurance rates ranged from 24% (Hawaii) to 38% (Illinois). After multivariate adjustments, the range was largely unchanged: 23% (Hawaii) to 38% (New Jersey). Multilevel analyses indicated that Medicaid income eligibility levels were inversely associated with the odds of being underinsured. CONCLUSIONS: The individual-level and macro-level factors examined only partly explain state variations in underinsurance. Furthermore, the macro-level factors explained only a small portion of the variance; however, other macro-level factors may be relevant for the observed patterns.

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“…Browne et al, 2005;Goldstein et al, 2002;Li J et al, 2008;Merlo et al, 2006b;Snijders & Bokser, 1999), as follows:…”

Section: The Variance Partition Coefficient (Vpc)mentioning

confidence: 99%