Categorical Data Analysis

By Jeremy Freese, Jason Beckfield. 2003

14,007 citations recorded by the source corpus.

Subfields: Food Science, Physical and Theoretical Chemistry, Statistics and Probability

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Citations in the corpus

Marginal Release Under Local Differential Privacy (2017, methodology): “The χ 2 test of independence compares the observed cell counts to expected counts assuming the independence (null hypothesis) and compute the χ 2 value (see e.g. [2]).”
Meta-Learning: A Survey (2018, neutral): “Feature importance [3] min,max, µ , σ Equiv. nr. feats Intrinsic dimensionality [99] Noise-signal ratio Noisiness of data [99] Fisher’s discrimin.”
Ordinal Common-sense Inference (2016, background): “uations in ordinal prediction tasks (Baccianella et al., 2009; Bennett and Lanning, 2007; Gaudette and Japkowicz, 2009; Agresti, 2003; Popescu and Dinu, 2009; Liu et al., 2015; Gella et al., 2013).”
Causal Inference on Discrete Data Using Additive Noise Models (2009, background): “Agresti (2002)), which is most commonly used. It computes the difference between observed frequencies and expe ct d frequencies in the contingency table. The test statistic is known to converge towards aχ(2) distribution, which is taken as an approximation even in the finite sample case. In the case”
On measurement properties of continuation ratio models (2001, neutral): “We discuss such an item and its sequential scoring rule here because it appears to be well suited for the class of continuation ratio IRT models (CRMs; Agresti, 1990, pp. 319-321; Mellenbergh, 1995; Molenaar, 1983) that is central to this paper.”
Bayesian Posterior Estimation of Logit Parameters with Small Samples (2004, positive): “For example, Agresti (2002) proposed adding a constant that smooths toward the model of independence to construct logit confidence intervals for odds ratios.”
Latent Class Models (2004, positive): “As a starting point Agresti (2002) formulated a model containing 2 latent classes, in an attempt to confirm the hypothesis that slides are either ‘true positive’ or ‘true negative’.”
Introduction to Latent Class Analysis With Applications (2017, background): “The probability of class membership is related to the values or levels of the covariates through multinomial logistic regression, where the dependent variable is latent rather than directly observed (Agresti, 2002).”
The Employees of Baby Boomers Generation, Generation X, Generation Y and Generation Z in Selected Czech Corporations as Conceivers of Development and Competitiveness in their Corporation (2016, methodology): “(Agresti, 2013; Řezanková, 2011, 1997) According to Anděl (2011), where the Pearson’s Chi-square test could not be applied, Fisher’s exact test was used or the simulated p-value of the χ2 statistic was calculated.”
The learning burden of collocations: The role of interlexical and intralexical factors (2016, methodology): “To answer the research questions, a repeated measures logistic regression (= a Generalized Estimating Equation or GEE in SPSS) was deemed appropriate (Agresti, 2002; Peng & So, 2002) because it is suited for the analysis of dichotomous data (e.g. correct or incorrect response in a posttest).”
How Preschoolers Use Cues of Dominance to Make Sense of Their Social Environment (2015, methodology): “Statistical Analysis In order to investigate the relationships between the experimental variables, data were analyzed using log-linear models (Agresti, 2002).”
Ontology Matching: A Machine Learning Approach (2004, background): “This function is widely used to combine multiple sources of evidence [4].”