When I first put this on my list of blogs to write, there was no large worry over nazi’s or white supremacy. When I first put this on my list, Trump had not even been elected as the republican nominee let alone president. And while there has been a great deal of prejudice against the middle east and Islam since 2001, it is frightening how applicable a discussion of prejudice is today.
While some people will openly admit to being prejudice against religions and races, most people do not, and believe they are not. Unfortunately, prejudice runs deep in us. It is a product of how we were raised and the environments we live in. Sometimes people even work so hard against a prejudice that they become prejudice in the opposite way. Yes, positive discrimination (such as assuming all Indians are doctors or all Asians are good at math), are still prejudices, and can still be harmful.
When I first heard of Project Implicit, I was a Psychology Undergrad, so not only was I studying social behaviors, such as prejudice, but I was also studying research methods and ways to trick the mind and the participant so that they cannot skew results.
Project Implicit provides you with two categories, those that are being tested (like black vs. white in testing race), and a control group (like words that mean good things vs. words that mean bad things). They then mix and match these groups in order to test whether you have an easier time associating one test group or another with one control group or another. (It makes more sense once you try the tests, I promise.) Project Implicit does a good job of this, switching which side you are associated with what and forcing you to spend your thought process on which is what whether than cheating the test.
Now, like most (if not all) social experiments, this is not full-proof. There may be some uncontrolled variable that causes you to sway to one side or another. I would give examples that I know have swayed some of my own tests here, but I do not want to put anything in your mind that might influence you. So, for those who are curious for my own results, only for after you have taken the tests, I repeat, ONLY FOR AFTER YOU HAVE TAKEN THE TESTS, I will include my thoughts on what could influence you in a link below my results. This is the honor system here. Jedi choose honor.
When I began this post, there were only 13 tests, but there are now 14 as I write this, so by the time you take this there may be more. I will be including my results below, for the currently existing 14 tests, and if I notice in the future I will add my results for any new tests (if you are reading this now and notice a test I have not taken, please comment to alert me so that I can do so). Not only will I include the text result, but I am screencaping my results.
I challenge everyone to take all the tests, maybe not all at once, but at some point take them all. You might be surprised at what the results show. And, if you are so inclined, post your results in the comments as well, though it is not required if you are not comfortable doing so.
My final thought to give you before sending you to take the tests is this. Just because you display a prejudice one way or another does not make you a bad person. It is difficult to override the programming of our youth, of our family influence, of our environment. What defines you is how you act. Discovering that you have a prejudice you did not know about can help you to be more aware of how you act and make you aware of your opportunities to improve yourself. Like G.I. Joe used to tell us:
Here is the link to Project Implicit.
Here are my results (I will say, that some of my results surprised me as skewing to the other side of normal, most likely my own overcompensation against the norm, it is really difficult to control for that):
- Race (‘Black – White’ IAT) – “Your data suggest no automatic preference between African Americans and European Americans”
- Weight (‘Fat – Thin’ IAT) – “Your data suggest no automatic preference between Fat people and Thin people.”
- Native American (‘Native – White American’ IAT) – “Your data suggest a strong automatic association for American with Native American and Foreign with White American.”
- Asian American (‘Asian – European American’ IAT) – “Your data suggest a slight automatic association for American with Asian American and Foreign with European American.”
- Arab-Muslim (‘Arab Muslim – Other People’ IAT) – “Your data suggest a moderate automatic preference for Arab Muslims over Other People.”
- Skin-tone (‘Light Skin – Dark Skin’ IAT) – “Your data suggest a slight automatic preference for Light Skinned People over Dark Skinned People.”
- Sexuality (‘Gay – Straight’ IAT) – “Your data suggest a slight automatic preference for Gay people over Straight people.”
- Age (‘Young – Old’ IAT) – “Your data suggest a moderate automatic preference for Young people over Old people.”
- Disability (‘Disabled – Abled’ IAT) – “Your data suggest no automatic preference between Disabled Persons and Abled Persons.”
- Gender – Career – “Your data suggest a moderate automatic association for Male with Family and Female with Career.”
- Gender – Science – “Your data suggest a moderate automatic association for Male with Liberal Arts and Female with Science.”
- Presidents (‘Presidential Popularity’ IAT) – “Your data suggest a strong automatic preference for Abraham Lincoln over Donald Trump.”
- Weapons (‘Weapons – Harmless Objects’ IAT)- “Your data suggest a slight automatic association for Harmless Objects with Black Americans and Weapons with White Americans.”
- Religion – “Your data suggest a moderate automatic preference for Judaism over Christianity.”