TY - JOUR
T1 - The Bias of Individuals (in Crowds)
T2 - Why Implicit Bias Is Probably a Noisily Measured Individual-Level Construct
AU - Connor, Paul
AU - Evers, Ellen R.K.
N1 - Publisher Copyright:
© The Author(s) 2020.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Payne, Vuletich, and Lundberg’s bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues’ proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors’ counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted “implicit-bias-based expulsion program” within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/.
AB - Payne, Vuletich, and Lundberg’s bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues’ proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors’ counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted “implicit-bias-based expulsion program” within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/.
KW - context effects
KW - implicit bias
KW - intergroup relations
KW - measurement
KW - social cognition
UR - http://www.scopus.com/inward/record.url?scp=85089001115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089001115&partnerID=8YFLogxK
U2 - 10.1177/1745691620931492
DO - 10.1177/1745691620931492
M3 - Article
C2 - 32745439
AN - SCOPUS:85089001115
SN - 1745-6916
VL - 15
SP - 1329
EP - 1345
JO - Perspectives on Psychological Science
JF - Perspectives on Psychological Science
IS - 6
ER -