Statistics categorical data

Intraclass correlation coefficient Kappa statistics (categorical data)... 

The problem for the statistical programmer in categorizing data comes from text variables, or more specifically, free-text variables. A free-text variable is one that may contain any characters and is typically limited only in length. As an example, let s say you need to summarize the adverse events for a set of patients in a trial. The following SAS code shows the data and a quick summarization of the adverse events. 

Obtaining Inferential Statistics from Categorical Data Analysis... 

As we shall see later the data type to a large extent determines the class of statistical tests that we undertake. Commonly for continuous data we use the t-tests and their extensions analysis of variance and analysis of covariance. For binary, categorical and ordinal data we use the class of chi-square tests (Pearson chi-square for categorical data and the Mantel-Haenszel chi-square for ordinal data) and their extension, logistic regression. 

Quantitative methodology uses large or relatively large samples of subjects (as a rule students) and tests or questionnaires to which the subjects answer. Results are treated by statistical analysis, by means of a variety of parametric methods (when we have continuous data at the interval or at the ratio scale) or nonparametric methods (when we have categorical data at the nominal or at the ordinal scale) (30). Data are usually treated by standard commercial statistical packages. Tests and questionnaires have to satisfy the criteria for content and construct validity (this is analogous to lack of systematic errors in measurement), and for reliability (this controls for random errors) (31). 

Weizhen Wang is Associate Professor of Statistics in the Department of Mathematics and Statistics of Wright State University. His interests are in bioequivalence, multiple comparisons, categorical data analysis, quality control, and nonparamet-... 

Koch GG, Tangen CM, Jung JW, Amara lA (1998) Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them. Statistics in Medicine 17 1863-1892. 

Cohen A, Sackrowitz HB, Sackrowitz M (2000) Testing whether treatment is better than control with ordered categorical data an evaluation of new methodology. Statistics in Medicine 19 2699-2712. 

Ratio data is the most useful because it can be used with all statistical procedures. Categorical data, on the other hand, is the least useful because it can only be used with statistical procedures designed specifically for categorical variables. 

Some of these variables are continuous whereas others are categorical, hi many cases, it is advantageous to study samples from the multivariate perspective. As with so many aspects of forensic chemistry, multivariate statistics (the application of statistics to data sets with more than one variable) is a separate field to which entire textbooks are devoted. Accordingly, this brief section will introduce tile concepts most used in forensic chemistry and reported in the forensic literature more detailed treatments can be formd in the references and recommended readings. Our goal here is only to attain familiarity and, hopefully, whet your appetite for further study. 

A statistical model may depend on a convenient probability distribution function (pdf) for statistically indistinguishable data generation from the actual records of the same phenomena. These models can be categorized into parametric and non-parametric types, where in the former case the pdf s parameters play role, such as the mean and variance in a normal distribution, or the coefficients for the various exponents of the independent variable. However, in case of a nonparamet-ric model the pdf parameters do not enter directly into the model construction but they are only loosely implied by assumptions. In statistics there can be mental (descriptive qualities or physical conceptual in character) event models. 

Clinical trial data come to the statistical programmer in two basic forms numeric variables and character string (text) variables. With this in mind, there are two considerations for all numeric and text variables. All data should be cleaned if they are needed for analyses, and any data entered asfree-text variables should be coded or categorized if they are needed for analyses. 

Clinical trial data come in two basic forms numeric variables and text variables. Numeric variables are easy for the statistical programmer to handle. Numbers can be analyzed with SAS in a continuous or categorical fashion without much effort. If a numeric variable needs categorization, it is easy enough to categorize the data within SAS. For example, if you had to classify patient age, a simple DATA step such as the following might serve well. 

In summary, for data to be useful in clinical trial analyses they need to be quantifiable. The data must be either a continuous measure or a categorical value. Free text poses a problem for analysis, and if it is a valuable variable for the statistical analyses it really must be coded. Finally, hardcoding should be used only when absolutely necessary, because it is inherently problematic. Organizations that do allow hardcoding should document in their standard operating procedures (SOPs) that it is an approved business practice and how it is to be used. 

There are many ways of categorizing air quality models. One differentiation is between statistical and deterministic models. The structure of statistical models is based on the patterns that appear in the extensive measured data. The structure of deterministic models is based on mechanistic principles wherever possible. Most deterministic models contain some degree of empiricism. For example, few models, if any, use turbulent-diffiision formulations that are based on first principles, but rather use measured values of dispersion. The same is true in regard... 

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