Other various postulated risk factors were analyzed as categorical data
in comparisons of inadequate levels of prenatal care with combined intermediate and adequate levels.
of Florida) provides and introduction to the most important methods of performing categorical data
analysis, covering contingency tables, generalized linear models, logistic regression, building and applying logistic regression models, multicategory logit models, loglinear models for contingency tables, models for matched pairs, modeling co-related or clustered responses, random effects in generalized linear mixed models and an historical tour of categorical data
The pseudo R-squared fit statistic operates well in a majority of conditions, and so it is recommended that educational researchers begin to explore the use of ordinal logistic regression in their modeling practice with ordered categorical data
SPSS, for example, is known for its categorical data
analysis, while Systat is best known for its analysis of variance (ANOVA) and graphing capabilities.
Hochberg's areas of research interest include multiple comparisons, categorical data
analysis, and medical applications of biostatistical methods.
Among the topics are describing bivariate numerical data, sampling variability and sampling distributions, estimating a population proportion, and learning from categorical data
Among the methods are confidence intervals, hypothesis testing, categorical data
, and epidemiological method.
A generalization of simple correspondence analysis, multiple correspondence analysis (MCA) handles larger data sets including high-dimensional categorical data
Wald (1947) and now widely used in engineering, agriculture, and computing, provides a simple model for the analysis of categorical data
He has written more than 100 research articles in the areas of categorical data
and is the author of Statistical Analysis of Categorical Data
, also published by Wiley.
The themes are as follows: fundamentals of descriptive social statistics, the development of statistical modeling, statistical modeling of longitudinal data, and the statistical modeling of ordinal categorical data
The final section deals with non-normal and categorical data
, missing and multi-level data, and simulation studies.
TABLE 1: Selected Statistical Routines Basic Statistics Descriptive statistics Frequency distribution Correlations Multiple linear regression Canonical correlations Discriminant analysis Factor analysis MANOVA Principal components analysis Power/Sample Size Nonparametric Analysis Continuous Data One sample Paired samples Two independent samples K related samples K independent samples Categorical Data
One-sample rates and proportions Poisson rates Two related binomials Two independent binomials Stratified 2X2 tables C ordered binomials Two ordered multinomials Unordered RXC table Singly ordered RXC table Doubly ordered RXC table Stratified RXC tables Measures of Association Ordinal response Nominal response Measures of agreement
Fitting latent variable models to categorical data
provides a direct means of assessing the extent to which conditional dependencies might exist among items with particular characteristics.
This book is a comprehensive resource on the key statistical tools and techniques employed during the modeling and estimation of classification errors, featuring a special focus on both latent class analysis (LCA) techniques and models for categorical data
from complex sample surveys.