Once you have this measure, you can use a pivot table to put answers from one question in rows, and put answers from another question in columns. For example, to count how many customers have a job as a teacher and play tennis, you need to apply a calculation such as the one described in the CustomersQ1andQ2 measure below. You need two filter tables to define a logical AND condition between two questions.
Figure 3 The Survey data model includes inactive relationships between the Filter and Answers tables. As you see in Figure 3, the relationships between the Answers table and Filter1 and Filter2 are inactive. Every Filter table will become a possible slicer or filter in the pivot table used to query the survey data model. You rename the columns Question and Answer with a suffix identifying the filter they belong to. You import the Questions table twice, naming it Filter1 and Filter2. Figure 2 The Questions table defines all the possible answers to each question. You can have questions with multiple-choice answers. The Questions table in Figure 2 contains all the questions and possible answers, providing a unique key for each row. Figure 1 Every row in the Customers table (left) has many related rows in the Answers table (right). The first rows of the two tables are shown in Figure 1. In the Answers table, every row contains an answer to a question. Suppose you have an Answers table containing the answers provided to a survey by customers defined in a Customers table.
The Survey pattern uses a data model and a DAX expression to analyze correlation between different transactions related to the same entity, such as a customer’s answers to survey questions.
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