Product test tables

The descriptions which follow refer to a simple product test consisting of 2 repeats of 2 questions and 100 respondents who all test both products:

  • QA. What age are you? (Young, Old)
  • QP_1. Which product did you try 1st? (list of possible products)
  • QL_1. What did you like about the 1st product? (list of likes)
  • QR_1. Please rate the 1st product (Liked, Indifferent, Disliked)
  • QP_2. Which product did you try 2nd? (list of possible products)
  • QL_2. What did you like about the 2nd product? (list of likes)
  • QR_2. Please rate the 2nd product (Liked, Indifferent, Disliked)
  • QP. Which did you prefer? (preferred 1st tested, 2nd tested, no preference)

To produce a table of Likes by Products in QPSMR Companion:

#1 is QL_1 by QP_1
#1 is QL_2 by QP_2 (overlaid)

Table 1 then shows “Likes of product” down the side by “Product” across the top (breakdown), with a base of 200.

Whenever a variable is produced it is important to decide whether it is a respondent variable or a product variable.

Respondent variables contain information that relates to the respondent. Furthermore, they may contain information from the product questions, provided there is no ambiguity about which product is being referred to. For example it is possible to define a response in a respondent variable as “Liked product A”. The definition would be (QP_1/1 and QR_1/1) or (QP_2/1 and QR_2/1).

Product variables contain information that relates to the relevant product. For example, if we want to produce a table of “likes” with summary rows broken down by how a product was rated, we need to produce two likes variables, one for each repeat:

  • VL_1 from QL_1 with summary rows
  • VL_2 from QL_2 with summary rows

These variables may then by used on product based tables:

#2 is VL_1 by QP_1
#2 is VL_2 by QP_2 (overlaid)

However, if we only want a table for product B:

#3 is VL_1 filtered by QP_1 is B
#3 is VL_2 filtered by QP_2 is B (overlaid)

If every respondent tried product B, the base for this table will be 100. However, this does not mean that it is a respondent based table, it is a filtered product based table.

In addition, whenever you produce product based tables, which includes relevant product information in a variable, then a variable will be needed for each repeat.

Confusion often arises when different sorts of information are combined. For example, if we required a breakdown with 7 columns – Age Young, Age Old, Liked, Indifferent, Not Liked, Tried first, Tried second. Because the breakdown contains some information relevant to the “likes” repeat question being tabulated we need a variable for each repeat.

VBR1 is QA/1, QA/2, QR_1/1, QR_1/2, QR_1/3, T, F
VBR2 is QA/1, QA/2, QR_2/1, QR_2/2, QR_2/3, F, T

Furthermore, you will have noticed that the first columns of the variables VBR1 and VBR2 have the same definition. The last two columns are either true (everybody) or false (nobody).

As a result, we can now produce a “Likes” table using the 2 break variables.

#4 is VL_1 by VBR1
#4 is VL_2 by VBR2 (overlaid)

When you run this table, the first 3 columns will have a base of 200. This is correct because there are two “Likes” answers for each respondent.

It does not matter how many products you have in the test. For product based tables, a table is produced for each repeat and these are then overlaid. For example, if each respondent tries three products from a list of seven possible, then you need three tables (one standard table and two overlays).