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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.
Contents
- 1 General tests
- 2 Binomial data
- 3 2 × 2 tables
- 4 Measures of association
- 5 Categorical manifest variables as latent variable
- 6 See also
General tests[edit]
- 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[edit]
- 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[edit]
- Chi-squared test
- Diagnostic odds ratio
- Fisher's exact test
- G-test
- Odds ratio
- Relative risk
- McNemar's test
- Yates's correction for continuity
Measures of association[edit]
- Aickin's α
- Andres and Marzo's delta
- Bangdiwala's B
- Bennett, Alpert, and Goldstein’s S
- Brennan and Prediger’s κ
- 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
Categorical manifest variables as latent variable[edit]
- Latent variable model
- Item response theory
- Rasch model
- Latent class analysis
- Item response theory
See also[edit]
- Categorical distribution
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What measure is best for categorical data?
The mode is the only central tendency measure for categorical data, while a median works best with ordinal data.
Is t
For categorical variables, you can use a one-sample t-test for proportion to test the distribution of categories.
What techniques of statistical analysis are used for categorical data?
General tests.
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..
Is chi
Revised on November 10, 2022. A Pearson's chi-square test is a statistical test for categorical data. It is used to determine whether your data are significantly different from what you expected.