# Categorical data analysis

Data is the basis of data analysis. However, there are different forms of data out there, and they all can be analyzed. It only depends on the software that you are using and the model that you are using. One such kind of data is categorical data.  Students always find it challenging to analyze these types of data; that is why we have devoted an article to help you with categorical data analysis. Here you will learn more about the different types of data, what categorical data is, and the various methods they can be analyzed.

### The different types of data

Data refers to information that is stored for analysis. In statistics, there are three main categories of data. These are numerical, categorical and ordinal data.

Numerical data are the type of data that are measurable.Examples are the weight of a group of people and how many pages a book has. They usually are also referred to as quantitative data. Further, they can be classified into two- discrete and continuous data. Discrete data always take a certain value—for example, the aggregate total number of people attending a meeting. A half a person doesn’t exist. Discrete data numbers are finite. Continuous data, on the other hand, is the data that is infinite. Their number on the number line cannot be counted. They usually are described as taking a certain interval. Examples of continuous data are height, weight and distance measurements such as kilometers.

Categorical data represents characteristics. An example is the data about gender or marital status. In statistics, they can be referred to as qualitative data. In some cases, they can be expressed numerically such as 1 to represent the female gender and 2 to represent the male gender.

Ordinal data is the type of data that combines the characteristics of numeric data and categorical data. An example is the ratings of an uber driver. Here the ratings are categorical data but have a mathematical meaning.

Methods of analyzing categorical data.

Analysis of variance.

Analysis of variance is used to analyze two or more groups in a dataset. The null hypothesis for the analysis of variance is that the means of all groups are equal.  We accept the null hypothesis depending on the magnitude of the p-value. If the p-value is less than the significance level, it’s rejected, and if it’s greater than the significance value, we accept the null hypothesis.

Certain assumptions must hold before conducting the ANOVA analysis. These are: all the data points should be independent, all categories have equal variances, and the dataset should be normally distributed.

Regression analysis

The most common and simplest form of regression is linear regression. However, this cannot be used for qualitative data or categorical data. Multiple regression can be used for qualitative data. Logistic regression is another form of regression ideal for categorical variables. However, it’s limited in use when there are two classes, i.e. Female/male, yes/no, or win/loose

Chi-square test.

This is a test conducted to test the relationship between the categorical variables. It’s a test for independency and the null hypothesis for this test is that there is no significant relationship between the categorical variables.  We accept the null hypothesis based on the p-values.

Other methods are also used for categorical data analysis, but these provided here are the basics. Other methods for categorical data analysis include the poison models and survival analysis.

Plotting categorical values.

The best method to plot categorical data is by using a bar chart. It has the characteristics similar to a histogram and is ideal for comparing two metrics.

How to get a categorical data analysis assignment help.

Categorical data analysis assignments are normally not easy to tackle. Sometimes they might force the student to spend a lot of time researching for the assignment solutions.  Students always have the option of seeking professional help. We are the experts who the students should approach for assignment help.

Our experts possess an in-depth knowledge of using SAS and have worked on related assignments before.  They know the marking metrics, how to avoid plagiarism, and the different formatting styles. When they work on your assignment solution, you are assured of top grades. If they provide you with categorical analysis assignment solutions, be sure you would impress your professor.