Categorical Data Analysis Task

A Categorical Data Analysis Task is a data analysis task on categorical data.

Context:
- It can range from being an Exploratory Categorical Data Analysis Task to being a Confirmatory Categorical Data Analysis Task, to being a Functional Categorical Data Analysis Task.
- It can range from being Tabular Categorical Data to Multi-Relational Categorical Data (such as in a Graph Analysis Task).
- It can range from being a Binomial Data Analysis Task to being a Multinomial Data Analysis Task.
- It can employ various statistical methods and models designed for categorical data.
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Example(s):
- A Statistical Categorical Data Analysis Task, which involves using statistical techniques to analyze categorical data.
- A Categorical Data Machine Learning Task, where machine learning algorithms are applied to categorical data.
- A Categorical Data Mining Task, focusing on extracting patterns from large sets of categorical data.
- A 2x2 Matrix Analysis Task, commonly used in medical and social sciences research.
- Analyzing survey data with multiple choice questions.
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Counter-Example(s):
- A Continuous Data Analysis Task, where the focus is on analyzing data that is not categorical but continuous.
See: Categorical Variable, Statistical, Nominal Scale, Chi-Squared Test, Logistic Regression, Machine Learning.

References

2016

(Wikipedia, 2016) ⇒ http://wikipedia.org/wiki/List_of_analyses_of_categorical_data Retrieved:2016-3-21.
- This a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables.
- General
  - Bowker's test of symmetry; Categorical distribution, general model; Chi-squared test; Cochran–Armitage test for trend; Cochran–Mantel–Haenszel statistics; Correspondence analysis; Cronbach's alpha; Diagnostic odds ratio; G-test; Generalized estimating equations; Generalized linear models; Krichevsky–Trofimov estimator; Kuder–Richardson Formula 20; Linear discriminant analysis; Multinomial distribution; Multinomial logit; Multinomial probit; Multiple correspondence analysis; Odds ratio; Poisson regression; Powered partial least squares discriminant analysis; Qualitative variation; Randomization test for goodness of fit; Relative risk; Stratified analysis; Tetrachoric correlation; Uncertainty coefficient; Wald test.
- Binomial data.
  - Bernstein inequalities (probability theory); Binomial regression; Binomial proportion confidence interval; Chebyshev's inequality; Chernoff bound; Gauss's inequality; Markov's inequality; Rule of succession; Rule of three (medicine); Vysochanskiï–Petunin inequality.
- 2 × 2 tables.
  - Chi-squared test; Diagnostic odds ratio; G-test; Odds ratio; Relative risk; McNemar's test; Yates's correction for continuity.
- Measures of association
  - Aickin's a ; Andres and Marzo's delta; Bangdiwala's B; Bennett, Alpert, and Goldstein’s S; Coefficient of colligation - Yule's Y; Coefficient of consistency; Coefficient of raw agreement; Conger’s Kappa; Contingency coefficient – Pearson's C; Cramér's V; Dice's coefficient; Fleiss' kappa; Goodman and Kruskal's lambda; Guilford’s G; Gwet’s AC1 ; Hanssen–Kuipers discriminant; Heidke skill score; Jaccard index; Janson and Vegelius’ C; Kappa statistics; Klecka's tau; Krippendorff's Alpha; Kuipers performance index; Matthews correlation coefficient; Phi coefficient; Press' Q; Renkonen similarity index; Prevalence adjusted bias adjusted kappa; Sakoda's adjusted Pearson's C; Scott's Pi; Sørensen similarity index; Stouffer’s Z; True skill statistic; Tschuprow's T; Tversky index; Von Eye's kappa.