Grid tables come from grid axes which comprise four items:

Statistical and totalling elements can also be used in grid axes.

For example, start with the chart on the questionnaire. There are four products to be rated on a scale of 1 (Excellent) to 5 (Very poor); in the questionnaire we see:

The interviewer circles one code for each brand.

Before deciding how to write this in Quantum, look at the conditions for each cell of this table and consider how they differ from those in an ordinary table. In a normal table the axes used for the rows and columns are independent of each other. In this grid table the condition for the row depends on the condition for the columns, and vice versa.

Then, following the instructions laid out for grid axes, write:

The texts on the first four n01s provide the column headings and the values to be assigned to the symbolic parameter for each column. If you want to lay out the column headings in a particular way, write them on g statements immediately before the side line.

The col statement names the symbolic parameter to be used and lists the row texts required. Since no codes have been defined it is assumed that the first response is code ‘1’, the second is a ‘2’, and so on.

This axis produces the table shown above, except that the numbers 1 to 5 in each column are replaced by counts of respondents giving each rating.

Suppose, now, that each rating is coded into a different column, so that Brand A is always a ‘1’, Brand B is always a ‘2’ and a rating of Satisfactory for Brand C would be c136’2’. The grid on the questionnaire is:

Nevertheless, you still want to create the same table as before, with brands as columns. This time, write:

These two examples show when to use symbolic parameters for columns in a grid axis, and when to use them for codes. In the first example, the column numbers differed from column to column of the table, so they are defined with column symbolic parameters; in the second, the codes differed between columns, so they are defined with code symbolic parameters. In short, look at the columns of the table to see what alters and define the changing item with parameters.

In more complicated tables it is possible that both columns and codes will change from column to column, especially if the grid refers only to specific elements of a response list.

For example, in a survey on washing powders respondents have been questioned to test their awareness of various brands on the market and to check what sort of advertising they have seen or heard for various brands. The company who commissioned the survey is now interested in finding out more about peoples’ attitudes to their particular products. They ask for a table showing the number of people who have already purchased one of their products and also how likely people would be to buy them. c(212,213) indicate whether the respondent buys the product, c(317,320) contain the number of times each brand was bought, and c(321,324) show how likely the respondent is to buy the brand in the future.

Here is the axis which will produce the required table:

Start by looking at the row definitions after side. The first n statement is an n00 — a filter — which sets up the base for the number of people buying each of the products. It uses both column and code symbolic parameters which are defined on the column n01s above. The filter for all buying Washo is c212’6’, while all buying Sparkle are collected with c213’5’.

The first col statement reads each of columns 317 to 320 to see how many times the respondent bought each of the four products. If c317’3’ it means that the respondent bought Washo three or four times; a ‘6’ in c320 indicates that they bought Sparkle more than ten times.

The blank n00 switches off the condition on the first n00 and all respondents now become eligible for inclusion in the rest of the table. This time you can re-use one of the symbolic parameters and increment it by four for each brand. The second col statement therefore reads data from columns 321 for Washo, 322 for Suds, 323 for Gleam and 324 for Sparkle. If c322’4’, you know that the respondent probably will not buy Gleam. The second part of this example (from the blank n00 onwards) assumes that respondents were only asked about purchase intent if they said they were aware of the product and that no other respondent has a code in these columns. If this was not the case, you put a condition on the n00 to exclude anyone who was not aware of one of the key brands. (The table which this axis creates is shown overleaf.)

Even though this example looks complicated, it is by no means uncommon. Its main attraction is that it enables you to produce a grid table from several questions giving the maximum amount of input in the minimum number of statements.