quantilope's Single Implicit Association Test (SIAT) Gets a Makeover
We’re excited to announce our brand new Single Implicit Association Test (SIAT), a new and improved version of our SAT methodology. While our team puts some finishing touches on this new version of the advanced method, below is everything you can expect from SIAT in the coming weeks!
Table of Contents:
What is SIAT?
A Single Implicit Association Test (SIAT) is one of two major methods within the field of Implicit Research - the other being the Multi Implicit Association Test (MIAT).
Implicit research is unique in that it has the ability to capture subconscious associations consumers have toward a brand, product, or category (or multiple brands, products, and categories). Implicit research thus gets an authentic representation of how consumers feel toward something. At quantilope, we’ve automated these processes.
The SIAT captures a respondent’s subconscious reaction to a single brand, product, or category. For example, if a meal delivery service wanted to measure perceptions of their business, they would ask respondents to quickly select whether or not associations ‘fit’ or ‘don’t fit’ with their perception of the service. Associations tested might include ‘quick’, ‘affordable’, ‘healthy’, etc. See the example for Uber Eats in a food delivery study below, which shows how this module would appear on a laptop, tablet, and smartphone (all of which are fully compatible):
What’s unique about implicit testing is the power to not only measure respondents’ selections, but also the respondents’ reaction time. This ensures that the SIAT is capturing immediate, gut reactions rather than respondents ruminating on associations and talking themselves in/out of something. Additionally, all associations are randomly rotated for each respondent so that certain associations don’t always appear first or last and there’s no order bias, wording bias, or external influences that might sway a respondent to answer a certain way.
New SIAT vs. Old SIAT?
For our new SIAT, we’ve taken into account all the valuable feedback we received from our clients about our previous SIAT (formerly called SAT). With this feedback, we've introduced a new structure empowering users, even those with little experience in implicit testing, to set up their SIAT method in only a few minutes.
In addition to the survey editor enhancements, the new SIAT design has been revamped to make the user experience even more straightforward for respondents; this not only includes a mobile-first design with bigger images and improved accessibility in line with international web standards, but also an updated training module to better prepare participants for the task. All these improvements help participants better understand (and enjoy) the task, thus improving the data quality in the actual SIAT exercise.
Finally, the calculation function for the SIAT scores has been optimized to more closely account for different respondent reaction times
When to leverage implicit methods
Implicit research is unique in its ability to capture subconscious associations consumers have toward a brand, product, or category (or, multiple brands, products, and categories with a Multi Implicit Association Test) - almost like reading consumers’ minds. While explicit methods have many benefits of their own, there are certain use cases in which an implicit approach might generate more interesting results.
Below are some examples of when a brand might want to opt for an implicit methodology:
- When there are a large number of associations to test
Listing too many associations in explicit multi-select or rating scales (whether 3, 5, or 7-point scales,) can become cumbersome and lead to respondent fatigue.
Beyond this, explicit questions typically give respondents an unlimited amount of time to answer. This can be problematic because it’s not how associations are typically processed in a real-world setting. Are you a fan of Apple as a company? What about Tesla? Amazon? You probably answered each of those questions within a split second, rather than having to dive deep into thought; this is the exact process that implicit research is built upon. Grounded on speed, and with respondents seeing just one association at a time, implicit methods allow brands to capture an accurate reflection of many associations while avoiding respondent fatigue at the same time: a win-win.
- When looking to measure the strength of associations
Beyond testing a large number of associations, brands may want to know the strength of those associations as well. Implicit methods don’t just measure what the response is (for example, whether a consumer thinks Apple is innovative) but to what strength they feel that's true. Rather than defining that strength as selecting a point higher on the scale (i.e. point 7 instead of point 6, on a 7-point scale), the speed of answering or engaging with the question is also used to determine the strength of an association (i.e. reaction time).
- When a topic might be hard to verbalize
Sometimes respondents have trouble verbalizing or recognizing how they actually feel about a topic. The unique part about implicit research is that it has the ability to capture true feelings that don’t always come to light in standard numerical research. By seeking an immediate response, the result is typically a stronger, more reliable read by eliminating the deep thought process. In other words, the methodology is capturing authentic reactions without the respondent even thinking about providing them.
- When a brand wants to capture advanced insights
Despite the advanced nature of implicit research and complex/ detailed findings, when automated, implicit research is simple to use and can be leveraged for a variety of objectives. This makes implicit research a broad-reaching tool to use for just about any industry, any type of product, or any research objective, and the findings are far richer than simple usage and attitude scales.
- When looking to visualize data in unique ways
Implicit research methods produce unique data outputs that go beyond standard bar or column charts. There are several options for chart outputs when it comes to implicit data analysis, including association maps, motivational maps, and comparative profiles. All types of implicit data charts are highly intuitive and visual - making it easy to extract complex, meaningful, and notable metrics at a glance.
Back to Table of Content
quantilope’s all-new SIAT method will be released on the platform in the coming weeks. Also stay tuned for an example of this method in action with quantilope’s upcoming Sneaker Category SIAT syndicated study!
In the meantime, get in touch below to learn more about this method and you might leverage it for your own research studies.