American Chamber of Commerce in Singapore
Dr. Eamon Fulcher will be presenting at the American Chamber of Commerce in Singapore in May.
10 Reasons for using an implicit survey
10 Reasons for using an implicit survey
This post also answers the related questions:
10 reasons for using an implicit reaction time survey.
10 reasons for using an implicit association test (IAT) in consumer research.
10 reasons not to use a traditional survey.
- Traditional surveys don’t have a very good way of measuring what is in consumers’ hearts, as people try to rationalise their answers.
- Implicit surveys get right at consumers’ hearts. It’s exactly what the method is all about.
- People often tell you what they think you want them to hear in a traditional survey.
- In an implicit reaction time survey, people don’t explicitly tell you how they feel, they reveal it to you in their reaction times.
- People often fail to make real discriminations in a traditional survey, hence all values converge around the average score.
- In an implicit survey people cannot influence their scores (they can’t play the game to influence the results). Hence they make highly discriminating responses, and we often get a clear winner.
- A traditional survey can often tell you which product or brand is “the winner” but not why.
- An implicit survey often not only reveals the best product, pack, brand, advert, and so on, it can tell you why because it will use 20 to 40 attributes and these are typically highly discriminating. The attributes provide you with a detailed profile of each brand/item.
- In a traditional survey, people often can’t verbalise how they feel but they are required to answer anyway.
- In an implicit survey, people don’t make evaluative judgements, they just try to press the correct keys and we can make inferences about how they feel – we get inside their inner feelings.
Take the Dads4Daughters Test
Split Second Research sponsors the Dads4Daughters campaign in collaboration with Blinc Partnership and St Paul’s Girls’ School.
Take the Dads4Daughters Test – How gender biased are you? Click the logo below:
The test has been featured in
The Dads4Daughters Test is based on a commercial test designed to measure attitudes towards brands, TV adverts, and other marketing materials. These attitudes are measured implicitly, that is, they are inferred from reaction times to words images presented on the screen. The test bypasses the need to ask explicitly about someone’s views or attitudes. This is important because often what people say they will do or what they tell you about how they feel is often at odds with how they behave! Furthermore, in difficult issues such as sexism or racism, people may be reluctant to tell you how they truly feel and in some cases they may even hold certain attitudes that they are unaware of until they are provoked.
The commercial test is itself based on the evaluative priming paradigm in academic research (e.g., Fazio, et al., 1986)1. The first phase of the test is to detect target emotion words as belonging to either one category (e.g., Happy) or another (e.g., Sad). In the second phase, the task is the same but the target emotion words are preceded very briefly by ‘primes’. These primes are either congruent with the target word (the prime is Joy when the target is Happy, or the prime is Gloomy when the target is Sad) or incongruent (the prime is Gloomy when the target is Happy, or the prime is Joy when the target is Sad). The task can be performed quicker and with fewer errors when the prime and the target are congruent than when the prime and the target are incongruent.
In the Dads4Daughters version of the test, the targets were female and male words, such as She and He. Primes were 24 words related to professions, roles, personal qualities, or career fields (e.g., engineering, manager, leader, and so on). Trials are divided into female trials (where the target is a female word that invites a specific response, e.g., press D on the keyboard or swipe left) and male trials (where the target is a male word that invites a different response, e.g., press K on the keyboard or swipe right). The logic is: if the test-taker subconsciously associates a career field (e.g., engineering) as being male, then they will be quicker to detect the male target on ‘male’ trials than the female target on ‘female’ trials when the prime is engineering.
Note that the test does not require an explicit evaluative judgement, there is always a correct answer on each trial. Also, people often think they can out-game the test – but in fact there is no way to ‘play’ the system because the task is always the same – it is a test that is difficult to fake. The way the test measures an attitude is not to do with accuracy or generally how fast the response is, but through a comparison of reaction times. So, the association between say, engineering, and the concept Female or Male is detected (or implied) by differences in reaction times to detect the female and male targets when they are both preceded by the same prime (engineering).
For each test-taker, the result is a measure of adherence to traditional views of gender roles – that some roles are associated with men and others with women. The more a test-taker associates skilled professions, or more senior roles, or more desirable personal qualities with men and not with women, then the stronger is their measure of gender bias.
1Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. doi:10.1037/0022-35220.127.116.11
Which is better the IAT or Affective Priming?
Comparison of two Implicit Association Tests
Affective Priming versus Implicit Association test
One criticism of the IAT is that it may merely tap ‘extrapersonal associations’ – it may be a measure of culturally shared assumptions rather than personal attitudes. For example, this would argue that an IAT that detects my strong association between nurse and female is just reflecting my knowledge that society has historically given the role mainly to women, rather than this being my own personal automated attitude (i.e., that nurses ought to be female).
Another criticism of the IAT is the reliance on the switching of blocks. In the first phase, (and in a hypothetical gender bias test), the word female is paired with gender stereotype attributes, e.g., nurse, and the word male with doctor. After the respondent has learned to do this quickly, the categories are then reversed, so that the word female is now paired with words incongruent with the gender stereotype, which make the test suddenly more difficult. This yields significant reaction time differences in the second block – it is a harder task than the first block (not because of an inherent gender bias attitude, but because the respondent had already learned the correct responses, but in the second block had to unlearn them and then re-learn the new responses). This is worrying because it means that the effect is too prone to changes in procedural issues.
A further problem is that in the IAT, only two dichotomous concepts can be paired (e.g., men vs women, gender stereotypical vs not gender stereotypical), which can be very limiting when one wishes to explore their relationship in more detail. Consequently the IAT produces a single global gender bias score. However, in affective priming one may have more than just a global score and can divide attributes into dimensions and hence provide a more detailed picture of such a relationship. So for example, a gender bias test using affective priming will be based on a large number of ‘attributes’ and these can be categorised (e.g., roles, personal qualities, professions, and so on) and this kind of test produces a score for each dimension. Another statistical advantage of the affective priming approach is that one can conduct a factor analysis on the data to reveal how attributes are grouped (grouped in the minds of the respondents who took the test). Hence it can yield groups of attributes that together are likely to represent an important feature of the concept begin measured (e.g., nurse, carer, ethical, reliable, hardworking, gentle, and female) – of course this example is too obvious and not so informative, but some patterns can emerge from this approach that weren’t predicted. This is much harder to do with the IAT.
Finally, the reasons why affective priming works is because it is based on assumptions that are highly compatible with what is known about how the brain processes information. Neural network models of the brain are based on mental associations – the stronger the association between two concepts (e.g., female and nurse) the quicker one concept will mentally trigger the other. So that’s four reasons why affective priming is the preferred approach, particularly if you are looking to understand the complex processes in the minds of consumers.
What can implicit reaction time tests tell us about consumer attitudes and intentions that traditional, explicit, methods can’t?
Implicit reaction time tests, whether based on the implicit association test (the IAT) or on affective priming are on the rise in the world of market research.
Implicit reaction time tests hold the promise to unlock deep seated consumer attitudes. Using the analogy of an archaeological dig (as a colleague of mine likes to use), implicit tests, like Split Second’s Impress Test, can help uncover the hidden treasures buried in the consumer’s mind. This is something that market researchers and brand managers have been looking to use for some time, given the weaknesses of traditional methods.
The way implicit reaction time tests can tap into deep seated feelings has been likened to an archaeological dig.
Yet, market researchers and brand managers can’t work on a promise alone. There is too much to lose – not just the research budget, but the financial consequences of bad research. So an important question is what can implicit reaction time tests tell us about consumer attitudes and intentions that traditional, explicit, methods cannot?
One way to test whether implicit reaction time tests, such as Split Second’s Impress Test, can measure anything useful about consumer attitudes and intentions might be to look for the predictive ability of implicit and explicit tests – are there circumstances in which either or both of these measures are strongly related to the purchasing behaviours of consumers.
There are numerous examples in the peer-reviewed literature demonstrating that in many circumstances implicit attitudes are better predictors of subsequent behaviour than explicit responses provided at the same time. For example, Steinman and Karpinski (2009) found that implicit but not explicit attitudes towards the brand GAP predicted GAP patronage and buying intentions. Brunel, Tietje and Greenwald (2004) showed that implicit methods can detect attitudes about brands that explicit measures cannot (e.g., how different races advocated different patterns of brand preferences implicitly but not explicitly).
Other research includes Priluck and Till (2009) who found that explicit and implicit measures were both good at detecting attitudinal differences between brands when the difference was large or obvious, but only implicit methods could detect differences when they are less obvious. Other research shows that implicit methods in a consumer context are difficult to fake. For example, Chan and Sengupta (2010) found that while the claims of an advertisement were dismissed, implicit responses revealed that the ad had induced favourable attitudes to the brand.
An interesting study published in 2010 by a team of researchers in Italy headed by from Michelangelo Vianello, shows how important it is to assess true feelings as opposed to those that people like to state in order to present themselves in a favourable light. College students were given two different measures of conscientiousness, one was a traditional explicit personality self-report questionnaire and the other was an implicit reaction time test whose attributes focussed on conscientiousness. Half of the students were further told to imagine that they were being tested for their ideal job (one with a good income, low effort, and so on) and the half were not told this. Those with the job-story scored higher on conscientiousness but only on the self-report test. This shows that they could give biased answers and present themselves in a very favourable light. Yet, both groups scored about the same on the implicit measure – this is remarkable because it shows that the implicit measure was not so easy to fake.
Academic studies like this provide very strong evidence of the usefulness of implicit reaction time tests.
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Implicit Reaction Time Tests
Impress is our survey framework for all of our implicit market research tests. It can be used for:
- Brand Equity
- Brand Positioning
- Brand Tracking
- Advertising Evaluation
- TV adverts
- Radio adverts
- Print adverts
- Political speeches
- Marketing videos
- Other marketing materials (e.g., out of home)
- New pack designs
- Comparing packaging with competitors
- Front of pack compared with back of pack designs
- New Product Development
- Comparing new products with nearest competitors
- Benefits of new product features
- Risks of removing product features
- Understanding how consumers perceive the existing product vertical
- Product Claims
- The perceived value of a product’s claims
- Real-time Monitoring of Media Content
- Moment-to-moment implicit perception of a video clip, advert or speech
- Frame by frame analysis
- Biometric recording
- Electrodermal response (emotional arousal)
- Heart rate (positivity/negativity)
- Breathing rate (emotional engagement)
Our analytic tools help to identify the opportunities for new prospects and the risks to existing customers of your marketing plans.
Our analytic tools also apply economic algorithms to determine the return of investment of your marketing materials and campaigns.
Our service is international and our tests can be translated into any language.
Our online tests can be taken on most computing devices (desktop, laptop, tablet, smart phone) and any platform (Windows 7 and above, Apple IOS, android, Windows mobile).
We respond quickly to our clients’ needs – surveys can be run from start to finish as quickly as one week, and projects with broader scopes can be up to six weeks in duration.
Our Director of Research & Development, Dr. Eamon Fulcher, recently presented at the Certamente neuromarketing conference in Milan early March 2017. Check out the website for latest news about the speakers and topics at http://www.certamente2017.com/. Eamon’s talk focused on how implicit reaction time techniques can enhance our understanding of how consumers truly feel about brands and products.
Create your own implicit tests in-house
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Create your test within minutes, using our unique online interface:
Upload images of the brands (packages, logos, and so on) you want to test and select your attributes from a list, and put the survey in field
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View charts online instantly (even when still in field), download charts and data.
Ideal for regular use of a range of different types of implicit tests – charges are per interview – no licence fee to pay!
We want everyone to have access to implicit reaction time tests, so only charge per interview with no set-up or annual licence fees.
Contact us for more information: email@example.com