What is Monadic Testing and How to Use It


Posted on 1 June 2020 Catherine Chipeta


Monadic testing isolates concepts to maximise response quality and consumer preference towards individual products. Learn how it helps you get better results.

Monadic testing

Respondents can often become overwhelmed with surveys that include extensive lists of product features and pricing options, especially when multiple products are being tested. Monadic testing has been developed as a practical survey design that eliminates respondents’ confusion. By focusing participants’ attention on one stimulus at a time, it delivers actionable deep-dive results for product and pricing decisions.

In this article, we explore how monadic testing works and when to use it.

What is monadic testing?

Monadic testing introduces survey respondents to individual concepts, products in isolation. It is usually used in studies where independent findings for each stimulus are required, unlike in comparison testing, where several stimuli are tested side-by-side.

It is called monadic (from Greek μοναδικός, or “single”) because each product or concept is displayed and evaluated separately, one at a time.

Advantages of monadic testing in survey design

There are many advantages of using monadic testing in survey design, including:

Reduced bias

Monadic testing also reduces bias caused by order and pricing as only one offering is displayed at a time for each concept.

User-friendliness

Monadic tests are shorter in length as they only task the respondent with one stimuli at a time, lessening repetition and respondent fatigue.

Ability to deep-dive

Because respondents are focusing their attention on a specific concept for a few minutes, you can ask them various questions about that concept.

Best use and examples of monadic testing

Monadic concept testing

Monadic concept tests provide clear, detailed feedback about products as they allow respondents to focus exclusively on a product idea.

By gaining insights on a single product at a time, this testing method draws out in-depth findings about consumers’ broader opinions surrounding it, such as brand acceptance, and preference for competition.

Monadic price tests

Monadic price tests task respondents with an individual product at a single price, asking questions about acceptance, intent, or other pricing-related topics. They provide robust results for price sensitivity as only one price is shown for each product at a time, eliminating influence from other pricing options.

Splitting the total sample into separate sample cells allows the study to gauge respondents’ attitudes to different price points. Each choice is analysed cell-by-cell to provide an estimate of demand. It should be noted that:

  • Monadic tests do not usually consider competitor pricing, which can have a significant effect on research outcomes.
  • They also require quite large sample sizes, making them a more costly option when compared to discrete choice experimentation aka conjoint analysis.

Types of monadic tests

Sequential monadic

Sequential monadic testing differs from monadic testing in that whilst only one stimuli is shown at a time, an alternative design is also shown.

It is an ideal testing method for finding out specific reasons as to why respondents may prefer one product over another without distraction from the competing option. Gabor-Granger is a type of sequential monadic testing.

Discrete choice as an alternative technique

Discrete choice analysis involves examining datasets that contain choices made by people from among several alternatives. It is usually used to understand what product features drove people to make these choices.

Sample sizes required

Below are the formulae for required sample size depending on the type of monadic test.

Type of monadic Sample size (N)
A/B test (Number of respondents per stimulus) × 2
Split sample (Number of respondents per stimulus) × (Total number of stimuli in the test)
Partially sequential (Number of respondents per stimulus) × (Total number of stimuli in the test) / (Number of stimuli per respondent)
Sequential (Number of respondents per stimulus)

For example, if you:

  • need 200 responses for each stimulus (which is usually enough if you have a battery of Likert scale questions),
  • have 20 stimuli to test,
  • show only 7 stimuli per participant,

you will need 200×20/7 ≈ 570 respondents for your test.

Conclusion

Monadic testing is advantageous when isolated feedback for product concepts or pricing is desired. Its benefits include realistic design, reduced bias, user friendliness, and accurate results. Monadic tests should be carefully considered as a viable option for pricing and concept research as it is costlier than other survey designs due to the large sample size required.