Multilevel logistic regression for polytomous data and rankings

Skrondal, Anders; Rabe‐Hesketh, Sophia

doi:10.1007/bf02294801

Cited by 159 publications

(93 citation statements)

References 57 publications

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“…This distribution can also serve as a building block for conditional distributions of rankings (Skrondal & Rabe-Hesketh, 2003a). In pairwise comparison data, the dichotomous preference indicators for pairs of alternatives can be modeled using probit or logit regression (e.g., Takane, 1987;Böckenholt, 2001).…”

Section: Counts and Durations In Continuous Timementioning

confidence: 99%

Generalized multilevel structural equation modeling

2004

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A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

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Section: Counts and Durations In Continuous Timementioning

confidence: 99%

Generalized multilevel structural equation modeling

2004

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“…Given the multilevel structure of the data (individuals nested within census tracts), we used two-level multinomial logit models 52,53 with a random intercept for each tract to estimate neighborhood SES associations with access to USCs, adjusted for potential confounders. The use of cross-classified models, accounting for the fact that not all PCSAs perfectly nested census tracts before 2010, did not materially change our estimates and resulted in convergence problems.…”

Section: Discussionmentioning

confidence: 99%

Neighborhood Socioeconomic Status and Primary Health Care: Usual Points of Access and Temporal Trends in a Major US Urban Area

2016

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Neighborhood socioeconomic status (SES), an overall marker of neighborhood conditions, may determine residents' access to health care, independently of their own individual characteristics. It remains unclear, however, how the distinct settings where individuals seek care vary by neighborhood SES, particularly in US urban areas. With existing literature being relatively old, revealing how these associations might have changed in recent years is also timely in this US health care reform era. Using data on the Philadelphia region from 2002 to 2012, we performed multilevel analysis to examine the associations of neighborhood SES (measured as census tract median household income) with access to usual sources of primary care (physician offices, community health centers, and hospital outpatient clinics). We found no evidence that residence in a low-income (versus high-income) neighborhood was associated with poorer overall access. However, low-income neighborhood residence was associated with less reliance on physician offices [−4.40 percentage points; 95 % confidence intervals (CI) −5.80, −3.00] and greater reliance on the safety net provided by health centers [2.08; 95 % CI 1.42, 2.75] and outpatient clinics [1.61; 95 % CI 0.97, 2.26]. These patterns largely persisted over the 10 years investigated. These findings suggest that safety-net providers have continued to play an important role in ensuring access to primary care in urban, low-income communities, further underscoring the importance of supporting a strong safety net to ensure equitable access to care regardless of place of residence.

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“…A heteroskedastic random-effects model was also performed to assess if individuals within CT and ET had similar variability in rates of change in VGS scores across time when compared to the main model. Statistical analyses were conducted using Stata 12.0 (StataCorp, 2013) and the user-written program gllamm (generalised linear latent and mixed models) (Skrondal & Rabe-Hesketh, 2003).…”

Section: Discussionmentioning

confidence: 99%