Multilevel Modeling for Binary Data

By G. Guo, Hongxin Zhao. 2000

855 citations recorded by the source corpus.

Subfields: Health, Political Science and International Relations, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1770b145edfdccabdacb009a1b37661d8235e574

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Citations in the corpus

Self-Determination and Access to the General Education Curriculum (2008, methodology): “…on student variables associated with access in the general education curriculum (academic responses and competing responses), SAS GLIMMIX macro was used, which addresses categorical variables because the academic and competing responses are dichotomous categorical variables (Guo & Zhao, 2000).”
Two Genes Predict Voter Turnout (2008, positive): “To test for genetic association we employ a mixed-effects logistic regression model (Guo and Zhao 2000; Xu and Shete 2006): " where i and j index subject and family respectively.”
Engagement in entrepreneurship in emerging economies: Interactive effects of individual-level factors and institutional conditions (2016, methodology): “First, multilevel modeling can support a systematic analysis of the effects of variables across multiple levels, as well as of their cross-level interactions (Echambadi, Campbell, & Agarwal, 2006; Guo & Zhao, 2000).”
Determinants of use of maternal health services in Nigeria - looking beyond individual and household factors (2009, methodology): “This demands the use of multilevel modeling, which would calculate the standard errors more accurately and reduce the chance of misestimating the significance of variables, as some of the assumptions inherent in traditional regression methods are not valid for nested data [26].”
Adolescent's Adjustment in Four Post-Divorce Family Structures (2006, methodology): “By not using multilevel software on these variables, biased variance and/or parameter estimates are likely to occur and will typically result in a higher probability of Type I errors (Cohen et al., 2003; Guo & Zhao, 2000; Hox, 2002).”
Grandchild care and welfare state arrangements in Europe (2011, methodology): “Formally speaking, a four level random intercept model with a dichotomous dependent variable (y) can be written as (Guo and Zhao, 2000: 446ff.): log pijkl = β0 + β1 xijkl + u0jkl + v0kl + w0l (1) 1 – pijkl The parameters u0jkl, v0kl and w0l display the residues, which are independent of each other.”
Factors Influencing Domestic and Marital Violence against Women in Ghana (2013, methodology): “To control for this dependence, we employed random effects models that enabled us to estimate the magnitude and significance of clustering (Guo and Zhao 2000; Pebley et al. 1996; Raudenbush and Bryk 2002).”
Racial Wage Inequality: Job Segregation and Devaluation across U.S. Labor Markets1 (2004, background): “Hierarchical models provide accurate standard errors and significance tests when data are multileveled (Bryk and Raudenbush 1992; Guo and Zhao 2000).”
Regional Social Contexts and Individual Fertility Decisions: A Multilevel Analysis of First and Second Births in Western Germany (2001, methodology): “Hierarchical GLIM therefore allow the application of multilevel logistic regression models for the analysis of discrete dependent variables (see Guo and Zhao 2000 for an overview).”