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### We Guarantee  # An Introduction to Categorical Data Analysis

Statistics is a discipline of numbers. It is through various tools that give probability and inference among other types of data that information is extracted. What statisticians are doing is to look behind black figures on the datasheet to uncover the story. There’s always a message somewhere; it just needs to be revealed. With this in mind there any number of analyzing data instruments that can be used for knowledge acquisition. Categorical data analysis is one of them.

## A Simple Introduction to Categorical Data Analysis

As introduction to categorical data analysis it is important to consider how data is collected and made ready for analysis. Research in science and mathematics will gather large amounts of information, but that only creates a pool of figures. There has to be some sense made out of what has been gathered. The numerical information will be organized in one of two types: categorical or numerical. The categorical has to do with distinct groupings.

## The Definition of Categorical Data Analysis

To begin with, a categorical variable is one that can take one value from a fixed number of possible values. It allows each individual value then to be assigned to a group or category. Categorical data is converted into such groups and the selection may come from observations of qualitative data or from quantitative data. To analyze the data statistics could be derived from tables that are defined by the categorical variables. Hypothesis tests regarding the association between the variables would be conducted, and assumptions made of a randomized process (methods randomization procedure). Another form of categorical data analysis, methods modeling procedure, looks at any association by doing a modeling of a categorical response variable. Whether the explanatory variables are categorical or continuous does not matter.

## Categorical Data Analysis Solution Possibilities

Categorical data analysis would use tables to allow for better observation. Marginal distributions can be determined within each category and simple counts can be turned into percentages (e.g., if there are a total of 100 people and category shows that 20 of them have blonde hair, then 20% of all of the individuals are blonde). It should come as no surprise that categorical data allows for immediate visual understanding of data. Segmented bar graphs can also be used to extract categorical data analysis solutions. Statistical software packages such as SAS/STAT will do categorical data analysis, models that have binary ordinal or nominal dependent variables, and do categorical data modeling, among other possibilities.

The value of categorical data analysis cannot be denied at all. While this is definitely above ordinary arithmetic, categorical data analysis is not somewhere in the stratosphere where Bayesian data analysis or other methodologies are found. It is simple to understand and use. The data results drive the analysis are being used not just in hard sciences, but behavioral and social sciences as well. The financial industry has found the use of categorical data analysis to be a great advantage.