Survey Design with Square Brackets

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Posted Sep 30, 2019
Tiffany Mullin

Over the years, aytm has focused on building a self-service, user-friendly platform that can be used by researchers of all skill levels. With the recent addition of custom syntax called aytm logic, you now have access to automated advanced programming options to test your new concepts, products and other stimuli with a multitude of survey designs, like monadic and sequential monadic research.

aytm logic is a powerful tool that allows you to customize and program your survey structure. Within most question and answer fields, you can add logic statements by starting with the open square bracket, [and closing the set of rules with a closed square bracket].

Using square brackets allows you to skip, pipe, reference, and structure the ins-and-outs of your questions and answers. You can also control the survey flow operations:

  • group certain questions or nodes of questions and control how many nodes a respondent interacts with
  • skip respondents to the end of the survey if survey questions and conditions are met
  • pipe-in references to extract question/sub-question/answer texts
  • show or hide the visibility of a question (or subpart of it) to respondents

aytm Logic for Monadic Design

In order to simulate real life interaction with your concept, ask respondents to evaluate your stimuli one at a time in a traditional research methodology approach. Using the logic syntax, you are able to enhance this monadic research design. By focusing the respondent’s attention upon a single concept, the monadic test provides an accurate and actionable report because no paired-comparison test is made. You are able to ask more questions and garner more detailed feedback without fatiguing respondents.

You can add in grouping logic to create the nodes and control the order of the concepts and questions you are testing. 

Example: [Group Q2 and Q3 and Q4 max1]

Adding in “max 1” at the end of the grouping determines the limit of the number of nodes per respondent. 

aytm Logic for Sequential Monadic Design

What is the perfect formula to test concept alternatives? Sequential Monadic research designs help you understand the differences and preferences between multiple stimuli. Respondents will be shown one concept and evaluate it, then be shown a second concept to evaluate, and so on. Another advantage of this survey design is the lower cost for the respondent pools. For example, you would like to receive feedback on three flavors for meal replacement smoothies. You want to make sure that each flavor is seen by 150 respondents. In a sequential monadic test, each respondent will see all three flavors at random, requiring you to only survey 150 respondents. In monadic tests, you will only be showing one flavor to each respondent. This will require a group of 150 respondents for each flavor, totaling 450 respondents for the entire project. Sequential monadic survey testing can help reduce the sample size by showing all concepts, alternatives, flavors, etc. in a randomized order. 

Order bias can also be a possibility when asking respondents to interact with several alternatives in sequence, therefore we recommend using sequential monadic logic to randomly change the order or sequence in which the concepts are presented to each respondent.

Example: [Group Q2-6 and Q7-11 and Q12-16 max2]

Adding in “max 2” at the end of the grouping determines the limit of the number of nodes per respondent.


The Monadic and Sequential Monadic research designs differ from one another based on validity and sensitivity. Monadic testing offers greater validity, as respondents evaluate and answer questions one concept at a time as they would in the real world. Sequential Monadic offers greater sensitivity, as respondents are exposed to multiple stimuli and evaluate concepts one after the other, revealing differences between the alternatives. 

Which design is better?

We recommend using monadic concept testing when there are detectable differences between the concepts you are testing, to determine if your target audience likes or dislikes the new concept(s). We recommend using sequential monadic concept testing for understanding your target audience preferences and the difference between the alternatives.

Opportunities with aytm’s Platform

  • Consistency in your study sample size, the confidence range, methodology alternatives, and survey analysis.
  • Power to examine the personas database of consumers’ preferences from study to study.
  • Produce actionable results to illuminate key benefits of your concept’s features.

Refer to the aytm Logic reference guide to assist with your survey programming.

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