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A review of commonly used statistical methods for categorical comparisons was performed and of these, the Cramer's V statistic was identified as the most appropriate for comparison of maps with different legends.
Simulation studies have repeatedly shown that estimation methods for categorical variables (e.g., WLSMV) outperform methods for continuous variables (e.g., ML) if there are less than five response categories and/or if the data is not normally distributed (Beauducel and Herzberg 2006; Rhemtulla et al. 2012).
In this paper we examine methods for categorical classification of discrete stimuli or episodic events based on the spike series responses they induce in a population of neurons.
Univariate analyses were performed using the Fisher's exact or Chi-square methods for categorical variables.
The data analysis included descriptive methods for categorical data as well as bivariate and multivariate methods.
Data analysis included descriptive methods for categorical and continuous variables as well as bivariate and multivariable methods.
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Hence, in this paper first, a clustering method for categorical data is proposed by fusing rough set and fuzzy set theories.
AMOS software uses Bayesian estimation (BE) method for categorical data via an algorithm termed the Markov Chain Monte Carlo (MCMC) algorithm.
Baseline values were compared between treatment groups with the Mann-Whitney method for continuous variables and the chi-squared method for categorical variables.
Baseline values were compared between treatment groups with the Kruskall Wallis method for continuous variables and the χ method for categorical variables.
The influence of single prognostic factors on DFS was assessed using the Kaplan Meier method (for categorical variables) or the Cox proportional hazard analysis (for continuous variables).
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