Analysis of Ordinal Categorical Data

By Peter McCullagh. 1985

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Citations in the corpus

Analysis of Local Dependence and Multidimensionality in Graphical Loglinear Rasch Models (2004, methodology): “with weights given by vt 1⁄4 Dt P i 1 Di: Finally, the partial g coefficient defined by Davis (1967) and discussed in details by Agresti (1984) is a partial coefficient of association defined in a way analogous to the definition of the MH estimate,”
Connecting religion and work: Patterns and influences of work-faith integration (2011, methodology): “Since the dependent variable and several independent variables were measured on an ordinal (Likert) rather than interval scale, we used ordinal logistic regression to test most relationships (Agresti, 2010).”
Schumpeter's ghost: Is hypercompetition making the best of times shorter? (2005, methodology): “Since this technique yields ordinal categorical data (Argresti, 1984), factors such as the common effects of general economic conditions, industry cycles, and product life cycles are controlled in the stratification process. However, IKS analysis can generate varying numbers of performance strata ov”
Modeling Ordered Choices: A Primer and Recent Developments (2008, unknown): “Some major references to examine for details are Agresti (1984, 1990), Clogg and Shihadeh (1994) and Greenwood and Farewell (1988).”
Hierarchical CUB Models for Ordinal Variables (2012, background): “Specifically, it may be convenient to consider instead of 2 as the reference parameter in the estimation process (Agresti, 2010).”
Don't Distract Me When I'm Media Multitasking: Toward a Theory for Raising Advertising Recall and Recognition (2016, unknown): “…according to one or more characteristics (i.e. representing clustered, and therefore dependent data with respect to student status, age and gender) we apply a multi-level modeling approach commonly referred to as mixed-effects or hierarchical modeling (Agresti 2010; Rabe-Hesketh and Skrondal 2012).”
Small Samples and Ordered Logistic Regression (2003, unknown): “…including the cumulative logit or proportional odds model (Walker and Duncan 1967; McCullagh 1980), the continuation ratio model (Feinberg 1980), the constrained and unconstrained partial proportional odds models (Peterson and Harrell 1990), and the adjacent-categorylogisticmodel (Agresti 1984).”
Ordinal regression: A review and a taxonomy of models (2021, methodology): “The distinct limitations of the use of ordinary least squares approaches to modeling ordinal responses have been outlined, for example, by Agresti (2010).”
Educational Assortative Mating and Household Income Inequality (2019, methodology): “…of the different education groups.9 There are, however, several commonly used measures of dependence for ordered contingency tables, including Spearman’s rank-order correlation coefficient, Pearson’s product-moment correlation coefficient, and Kendall’s t (see, e.g., Nelsen 2006; Agresti 2010).”
The Runaway Weed: Costs and Failures of Phragmites australis Management in the USA (2013, methodology): “To test for agreement between ordinal measures of (1) respondent agreement with an objective and (2) perceived success at achieving that objective, we used the Kendall's tau-b statistic (1=perfect agreement, −1=perfect inversion, 0=no relationship; Agresti 1984).”
The Beta Exponential Fréchet Distribution with Applications (2017, methodology): “Many techniques are available for analyzing stochastic shifts in ordinal data; for a review see Agresti (1984).”
Sustained Competitive Advantage: Temporal Dynamics and the Incidence and Persistence of Superior Economic Performance (2002, methodology): “While the performance strata developed by the Iterative Kolmogorov-Smirnov analysis form naturally ordered categories (Argresti 1984) from high performance to poor performance that are statistically significantly different from each other, the possibility of varying numbers of performance strata ove”
Some Paradoxical Results for the Quadratically Weighted Kappa (2012, methodology): “Alternatively, one can use loglinear models for modeling agreement (Tanner & Young, 1985; Agresti 1988, 2010).”
Bayesian Item Response Modeling in R with brms and Stan (2019, methodology): “There are multiple ordinal model classes (Agresti 2010; Bürkner and Vuorre 2019), one of which is the graded response model (GRM; see Section 1.”
Industry, corporate, and segment effects and business performance: a non‐parametric approach (2003, unknown): “Since the stratifications result in naturally ordered categories (Argresti, 1984) from inferior performance to modal performance to superior performance, we estimated an ordinal regression model with business unit performance stratum as the dependent variable, lagged business-level, corporate-level,”
Modelling Uncertainty and Overdispersion in Ordinal Data (2014, methodology): “Standard techniques model these data by using a continuous latent response distribution, which is partially observed through windows of adjacent intervals defined by cutpoints (McCullagh and Nelder, 1989; Agresti, 2010).”
Ordinal Regression Methods: Survey and Experimental Study (2016, unknown): “Some researchers routinely apply nominal response data analysis methods (yielding results invariant to the permutation of the categories) to both nominal and ordinal target variables alike because they are both categorical [57].”