Crosstab, or Cross Tabulation, is a process or function that combines and/or summarizes data from one or more sources into a concise format for analysis or reporting. Crosstabs display the joint distribution of two or more variables and they are usually represented in the form of a contingency table in a matrix.

A Crosstab should never be mistaken for frequency distribution because the latter provides distribution of one variable only. A Cross Table has each cell showing the number of respondents which gives a particular combination of replies.

An example of Cross Tabulation would be a 3 x 2 contingency table. One variable would be age group which has three age ranges: 12-20, 21-30, and 31-up. Another variable would be the choice of polo shirt or t-shirt. With a crosstab, it would be easy to for a company to see what the choices of shirts are for the three age groups. For instance, the table would show that 20% of those aged 12-20 prefer polo, while only 10% of those aged 31-up prefer t-shirts. With the information, they can up with moves which will be beneficial to the success of the business.

Cross Tabulations are popular choices for statistical reporting because they are very easy to understand and they are laid out in a clear format. They can be used with any level of data whether the data is ordinal, nominal, interval or ratio because the Crosstab will treat all of them as if they are nominal data. Crosstab tables are provide more detailed insights to a single statistics in a simple way and they solve the problem of empty or sparse cells.

Since Cross Tabulation is widely used in statistics, there many statistical process and terms that are closely associated with it. Most of these processes are methods to test the strengths of Crosstabs which is needed to maintain consistency and come up with accurate data because data being laid out using Crosstabs may come from a wide variety of sources.

The Lambda Coefficient is a method of testing the strength of association of Crosstabs when the variables are measured at nominal level. Cramer’s V is another testing method that test the strength of Crosstabs which adjusts the number of rows and columns. Other ways to test the strength of Crosstabs associations include Chi-square, Contingency Coefficient, Phi Coefficient and the Kendall tau.

Companies find the services of a data warehouse very indispensable. But inside the data warehouse can be found billions of data which most of them are unrelated. Without the aid of tools, these data will not make any sense to the company. These data are not homogenous. They may come from various sources, often from other data suppliers and other warehouses which may be coming from other branches in other geographical locations.

Software applications like relational database monitoring systems have Cross Tabulation functionalities which allow end users to correlate and compare any piece of data. Crosstab analysis engines can examine dozens of table very fast and efficiently and these engines can even create full statistical outputs by very clicks of the mouse or keyboards.

Relational database applications have a Crosstab query function. This function can transform rows of data to columns or any table for statistical reporting. With Crosstab query, one can send a command to the database server and the server can aggregate data like breaking down reports by months, regional sales, product shipment and many more.

Many advance database systems have dynamic Crosstab features. This is very useful when dealing with columns that do not have a static number. Crosstabs are heavily used in quantitative marketing researches.