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Tutorial 1 - Your first tab

As soon as you have successfully loaded the database you are taken to the Question Selection view. This will look like this:

In order to create our first tab, we need to select the questions we wish to view in the tabulation. For the purposes of this example we are going to look at a breakdown of the gender and marital status in this survey.

The Available Topics/Studies (the 'Questions' tag on the left) utilises a tree stucture similar to that of Windows Explorer. To find the questions we need for our tab, we need to expand the topics headers. The Gender and Marital Status questions can be found under the Demographics topic heading.

Before running our tab, we have to build it by adding the questions we wish to analyse. In our example we wish to see a gender against marital status tab. This means that we must put the Gender question on the 'Columns' tag and the Marital Status question on the 'Rows' tag. This order is important as switching the row and column questions will produce completely different results.

 

  

To select a question make sure the correct pane is displayed on the right hand side of the screen, then double click or click and drag the question into that pane to select it.

We now have our tab set up with Gender on the Columns and Marital Status on the Rows (left). To run our tab and view results we have to click the 'Run Tab' button. This is the 'lightning bolt' button at the top of the screen

The Run Tab button

Pressing the Run Tab button takes us to our results. These are displayed in the Spreadsheet view, and in our example the results are as follows;

When analysing our results, it is very important to interpret what is being shown correctly. In our example we are looking at gender as the focus of our analysis broken down by marital status. In the above results we see that of males who responded to the marital status question, 78.34% were Married, 19.95% were Single and 1.71% were Other.

What is NOT SHOWN is that of Single people, 19.95% were male and 35.11% were female. Clearly this does not make sense as this only totals around 46%. You should always remember that the focus of your analysis should go on the columns!

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