Implicit measures is the general name for measures that were
developed to measure social cognition (attitudes, self-concept and
stereotypes) indirectly. It is plausible that implicit measures capture
the effects of unintentional activation of social cognition.
Unintentional processes might occur very quickly, with no need for much
cognitive resources, and without awareness. However, much is still
unknown about the psychometric properties of implicit measures, and they
are still a matter of active research.
Investigating, improving, and developing indirect measures of
psychological constructs might be a useful pursuit for improving human
knowledge of these constructs and about the automatic and non-automatic
processes that underlie them. That is the main reason that our lab
investigates implicit measures. If that kind of research seems
attractive to you, you are welcome to join us.
Many researchers are interested in implicit measures because they search for better measures of the constructs that they study. This is a reasonable and common research direction. However, it should be clear that it is very difficult to know what these measures capture. When you are the first to study a topic or a mental construct with indirect measures, your research would be exciting because it would be characterized by many uncertainties, and you would be the first to collect evidence that can reduce those uncertainties. However, that kind of pioneering research is less suitable for those who hope for a simple and clear indirect measure of the mental construct they seek to study. Further, using implicit measures requires some expertise in analyzing reaction time data. It also requires a certain capacity for using appropriate technologies to implement those measures within a computerized study.
The best known implicit measure is the Implicit Association Test
(IAT). To learn about the IAT, it is best to first visit the websites of
Tony Greenwald, Mahzarin Banaji,
and Brian
Nosek. A great source for best practices is this paper.
The IAT has many
weaknesses, but it is currently the best implicit measure. There are
more than a dozen other implicit measures. To learn more about some of
them, you can read these chapters by Gawronski & De Houwer (2014),
and De Houwer & Moors (2010).
Another source for much knowledge about many implicit measures is this empirical research project from
our lab.
If you want to exprience a few implicit measures, here are online examples from our lab.
If you want your students to experience the IAT, send them to Project Implicit’s demonstration website.
Project Implicit is a non-profit organization and international collaboration between researchers who are interested in implicit social cognition - thoughts and feelings outside of conscious awareness and control. The goal of the organization is to educate the public about hidden biases and to provide a “virtual laboratory” for collecting data on the Internet. Our lab is involved in maintaining and improving Projec Implicit’s technologies (mainly the software that enables building online studies).
During her time in my lab, Mayan Navon led a new translation of Project Implicit’s website to Hebrew. If you want your students to experience the IAT, send them there.
Mayan Navon also processed the data collected in Project Implicit’s Hebrew website from 2009 to 2019. Read her post to learn more about it.
You can program reaction time tasks with most of the programs that
were developed for this purpose (e.g.,
Open Sesame and
Psychopy). Michael Pinus, a PhD
student in our lab, has created a few Open Sesame IAT scripts. You can
find them here.
If you want
Project Implicit to help you plan, program and carry out your study,
please contact services@projectimplicit.net.
Project Implicit’s software, Minno.js, is open source, and you can see it here (documentation of our player).
There are currently five methods for creating and running studies using Minno.js: