Cross tabulation for analysing data is very important, but only if done the right way and at the right time. Essentially, it measures how different variables related to each other. Each variable has data recorded in a specific table or matrix, and this then compared. Usually, cross tabulation for analysing data involves counting how often certain variables occur, which is known as the frequency.
Cross tabulation for analysing data only works with quantitative data. It makes it far easier to manage the data, since it becomes structured. The tables in which the data is stored are known as contingency tables, which measure how likely it is for a specific relationship to exist.
Usually, a single variable is first studied. This will prove whether there is any univariation in existence, grouping different pieces of data into ranks of values. Once this has been completed, it becomes possible to perform cross tabulation for analysing data across multiple variables. This is known as bivariation or a joint contingency table. This data is used to prove that a certain relationship is a two-way street – only if, only and, if and when and so on.
Before you start with cross tabulation for analysing data, you must understand what quantitative variables actually are. There are discrete variables, which have a value of a set number. There are also continuous variables, which can choose only a set amount of values. Usually, these two are not used together and continuous variables are the most common types.
Cross tabulation is a hugely complex area of work. Although it is possible to do this manually using tools in Excel, most would use specially designed software. More often than not, this software is provided by a survey designer, to allow customers to better understand the data that they have collected through their questionnaires.