Thursday, March 31, 2016

We are hiring a Postdoc!

The Social Cognition Group at Lehigh University is accepting applications for a post-doctoral position starting on or after July 1, 2016.  The postdoc will collaborate most closely with Dr. Dominic Packer and will help to coordinate research in the Group Processes Lab on intergroup relations (e.g., prejudice, stereotyping) and intragroup processes (e.g., social influence, dissent).  The postdoc will additionally have opportunity to collaborate with other members of the department, including social psychologists Drs. Gordon Moskowitz, Michael Gill, Lucy Napper and Christopher Burke.  The person in this position will also teach one course a semester (2/year) in the Psychology Department.

We are seeking a talented researcher with a PhD in psychology, expertise in the relevant research areas and a strong record of publication.  This is a one year position with the possibility of renewal.  The position will pay an annual salary of $40,000 plus benefits.  Interested applicants should submit a CV, research statement, description of teaching interests (including any prior teaching evaluations) and contact information for 2-3 referees to Dominic Packer at djp208@lehigh.edu

Lehigh University’s Psychology Department is a small but thriving PhD-granting program with specialties in three areas: social, developmental and cognitive psychology.  Please visit our websites for more information about the Group Processes Lab (www.groupprocesseslab.blogspot.com) and the Psychology Department (https://psychology.cas2.lehigh.edu/)

Lehigh University is an affirmative action/equal opportunity employer and does not discriminate on the basis of age, color, disability, gender, gender identity, genetic information, marital status, national or ethnic origin, race, religion, sexual orientation, or veteran status.

Wednesday, July 1, 2015

Pride or Prejudice? What is the meaning of the Confederate Flag?

Our #LUMountaintop team has been investigating the meaning of the Confederate flag by analyzing relationships between Google search trend data and state-level IAT scores.  Check our their post here.

Sunday, March 15, 2015

Reverberations of the Distant Past: Legacies of Slavery

This is the second in a series of posts reflecting on how intergroup relations can be influenced by factors operating on different time-scales (including events from the distant past), with particular attention to the influence of social institutions. The first post is here.

Considering how long rhythms from the past might affect current day intergroup dynamics, we have recently begun to think about the impact of slavery in the United States – an institution that still haunts this country. Slavery was a set of policies and rules that allowed one group of human beings to own another group as property, and to do with that property more or less whatever they wanted, up to and including the desecration and destruction of their bodies.  The institution of slavery allowed White people to exploit the lives and labor of Black people for their own massive economic gain, and also to torture and murder them largely at will.

Economists have in recent years started to study the effects of slavery on current day economic outcomes.  US counties, for example, where more people were enslaved in the 1800s tend to underperform economically still today (e.g., Nunn, 2008). There is debate as to why this is the case, but one interesting theory proposes that it has to do with a lack of public goods in these areas.  Counties with more slaves, and with more free Blacks after emancipation, invested less in public goods like libraries and schools, and that absence of investment in the potential of their people continues to affect outcomes today, generations later.

Our lab has similarly begun to investigate whether and how slavery in the United States might affect current day intergroup attitudes. We are not alone in this endeavor.  A sophisticated recent working paper by Acharya, Blackwell and Sen (2014), for example, examines effects on explicit attitudes. But here I am going to focus on our (to date much more limited) work investigating relationships with implicit attitudes.

Here is a state-by-state map of implicit bias, based on data from about 1.8 million White participants who took an online Black/White IAT on the Project Implicit website between 2003 and 2013.  In December of last year, we conducted an analysis for the Washington Post’s Wonkblog to identify what sorts of factors might predict this distribution of bias.  There appears, after all, to be a spatial logic to it – with some the highest levels of bias clustering in the south.  Indeed, statistical analysis reveals that levels of implicit bias are indeed greater in former slave-holding states.  But bias is also higher in some perhaps less expected locations: New York, Illinois, Pennsylvania, Ohio…

What unites these less expected locations with the slaveholding south?  These are states where many African Americans moved after emancipation, and particularly during the ‘great migration’ between the 1920s and 1970s.  Black people moved in large numbers from the south to cities like New York, Chicago, Philadelphia and Cleveland.  And the number of African Americans – or, more specifically, the number of African Americans relative to White people – turns out to be a potent predictor of implicit bias at the state level. Indeed, this ratio (shown below in reverse, so that higher numbers indicate more White relative to Black people) predicts more than 50% of the variance in IAT scores.  Further, entering this ratio into a regression equation reduces the influence of former slaveholding status to statistical non-significance.*

Now, a common interpretation of this statistical pattern would be to say that the effect of the historical variable (slavery) is accounted for or mediated by a current day variable.  Slavery is associated with current day implicit bias because it (and events following its abolition) brought Black people to certain locations, and where there are more Black people today relative to the White population, bias is heightened.  But it would, I think, be a mistake to assume that all of the important psychological action in these relationships is taking place in the present.

The Washington Post piece that reported these findings generated a fair amount of online attention.  It caused much tweeting – including, to my chagrin, tweets from the White supremacist community.  Their interpretations of the pattern tended to emphasize current day dynamics.  One tweet read, for example: “Leftist lie refuted in leftist publication: the more whites experience blacks, the more they hate them.”  To which a follow up tweet read: “If contact leads to hostility, then segregation is the moral solution. As racists said from day one. #wearethegoodguys”


As social psychologists, we would probably put it differently.  But we might actually make similar sorts of arguments (though without drawing the same egregious social conclusions): As the size of a racial outgroup increases, intergroup threats – whether symbolic or realistic – also increase, driving bias.  In this interpretation, slavery set up the conditions, but it is current day population dynamics that drive the psychology that produces the bias.

This could be the case.  But I don’t think we should write off more direct influences of the past that hastily…

Coming soon: Historical Threats to White Interests (and some county-level analyses)




*More details about these analyses can be found here.  It is interesting to consider both the variables that predicted IAT scores (state-level income inequality, social capital (inversely)), as well those that did not (Black/White segregation, state median income, 2008 electoral college vote).

Thursday, March 12, 2015

The Influence of Long Histories and Social Institutions on Intergroup Relations

This is the first in a series of blogposts reflecting on how intergroup relations can be influenced by factors operating on different time-scales (including events from the distant past), with particular attention to the influence of social institutions. These posts are adapted from a talk I presented at the 2015 Attitudes Preconference at the Society for Personality and Social Psychology.

Particularly since our “implicit revolution” in the late 1980s and 1990s, social psychologists have paid a great deal of attention to the time-course of perceivers’ evaluative responses – how, for example, rapid non-conscious responses evolve into slower deliberative ones.  It seems to me one cannot study these processes without being struck by how incredibly flexible and dynamic the evaluative system is.  The brain responds, often with millisecond timing, to contextual changes in contingencies – continually re-computing and remapping the meaning of stimuli and events.

However, in addition to the time-course of perceivers’ evaluative responses, we can also consider the time-course of factors that influence those evaluative reactions. Some of these factors - such as the influence of slavery on intergroup attitudes in the United States - may have a very long time-course indeed, still intruding today from the distant past.  And as a result, despite the dynamic potential of the evaluative system, intergroup relations – attitudes, behaviors and resulting disparities - may often remain highly static.

One way to think about this is that evaluations computed in the present are multiply determined by a complex and interactive set of influences operating on different time scales.  The result is like a complex tone – an intricate sound wave – that can be decomposed into its constituent frequencies.  Some of these frequencies (influences) oscillate rapidly, on the scale of days, minutes or seconds.  Some are slower, operating over years or decades as a person is socialized, develops and has their own experiences.  Others may be slower still, operating over generations, carrying tones from the deep past into the present.

This series of posts is going to reflect on influences affecting intergroup relations that operate on different time scales – ranging from the very long to the very short.  In each case, we will focus on an influence exerted by social institutions – a type of contextual factor that would, I think, reward a bit more attention.

What are social institutions?  Institutions turn out to be rather loosely defined by social scientists, but for current purposes I will define them as relatively formalized rules, policies or procedures that serve to organize or coordinate people.  These rules, policies and procedures are often embodied and enforced by organizations of some sort.  Institutions exist at varying levels of abstraction.  So, for example, the rule that police officers must read a suspect his or her rights upon arrest is a very specific institution within a more abstract institution that we might call “fair trial procedures”, which is itself a component of a very abstract institution called the “rule of law”. 



Seeking Students for Mountaintop Project: The Geography of Bias

The application window for this project is now closed. Thank you for your interest. 

If you submitted an application, decisions will be made shortly. 

Project Description

On the night of President Obama’s reelection in 2012, researchers at the University of Kentucky took
to Twitter to track an uptick in racist tweets.  These tweets followed a certain spatial logic.  Perhaps unsurprisingly, more racist tweets were emitted from Alabama and Mississippi than Rhode Island and Vermont.  But contrary to regional stereotypes, there were also very few racist tweets from states like Oklahoma, Montana and Wyoming, but more than might be expected from Oregon and New Jersey.

A great deal about the geography of intergroup bias – how and why, for example, racial discrimination varies by location – is not well understood.  Our lab at Lehigh has started to investigate these dynamics.  Some of our early findings - reported here by the Washington Post – include the observations that racist tweets are predictable from states’ levels of implicit bias and that those biases are, in turn, predictable from states’ histories with slavery, as well as current day civic functioning and population dynamics. 

This Mountaintop project will take students on a deep exploration into the geography of bias, using data-driven techniques to answer novel questions about how biases vary geographically and the underlying reasons why.  The project will be student-led and highly collaborative, as a group of four undergraduates and one graduate student research, debate and decide: 
  • Which form(s) of bias to investigate (e.g., racial, gender, social class, or their intersections) and in what domains (e.g., health, justice, politics, finance/economics, etc.);
  • What key variables at what levels of analysis (psychological, sociological, historical, environmental, possibly even biological) to examine as underlying contributors to bias;
  • The geographic scope of investigation (e.g., international, national, local);
  • What types and sources of data to draw upon (e.g., social media, public opinion polls, government or legal records, historical archives, census data, etc.);
  • How best to locate, acquire, aggregate and analyze the data;
  • How to interpret their findings in terms of social psychological and related theories;
  • How to present and communicate their findings, using cutting-edge data presentation and mapping tools, or perhaps even good old-fashioned paper maps and push pins;
  • What they want their final product to be.  For example: a formal report, a series of blogposts, an Op-Ed for the New York Times, a TED style talk, an exhibit on campus… 
Students can expect to gain a number of things from this experience, including: 
  • Greater knowledge and understanding of the dynamics of intergroup bias in current day society;
  • Skills in the creative use of real world data to address socially important questions and to test hypotheses;
  • Skills in the management and analysis of complex datasets;
  • Skills in scientific communication;
  • Experience planning, coordinating and managing interdisciplinary team projects


Tuesday, March 10, 2015

The rise and fall of the 'It's Not The One You Think Heuristic'

A case study. 

“Curses,” I mutter, water streaming into bleary eyes as I squeeze, for what must be the 37th time, conditioner instead of shampoo into my hand.  

The trouble all started with the purchase of these two bonus-sized bottles – one of shampoo, capped
with a white squirter, and the other of conditioner, capped with a black one. Right from the beginning, this color scheme made no sense to me.  Conditioner, being creamy, should be topped with white; shampoo, with black.  Confronted instead with this intuition-defying arrangement of colors, my first few showers with these new bottles were fraught with difficulty. A mistaken palm-full of conditioner would be washed away wasted, replaced successfully by shampoo, only to be followed again in error by a second squirt of shampoo.  

Then after the sixth or seventh day, I had a brilliant insight: “It's not the one you think!”  Such a simple heuristic, and it worked!  Ready to shampoo and reaching for the black squirter, <pause> “No, not that one.” Crisis averted!  My mornings improved perceptibly.  

Nothing good can last forever, however. Perhaps through some sort of procedural, motoric learning, I began to remember which colored cap was which. Suddenly the decision rule that had guided me so well started to backfire. Reaching for the right bottle, I would now falsely correct myself and squeeze out a burst from the wrong one. The frequency of my errors increased again and the quality of my showers decreased.  

At this point you might say that I should abandon my heuristic in favor of an even simpler rule: “It is the one you think!”  I have tried.  But I am no longer sure what I think.  I am confronted each shower by dueling beliefs. “I think the conditioner has a white cap.” <No wait, that's what I used to think> “ I think the conditioner has a black cap.”  <Is that right?>

Now, I will note in my defense that this tortured logic is usually applied at some unholy hour of the morning when I am not at my most alert.  I will also acknowledge that these problems could easily be solved if I were to bother reading the labels on the bottles. 

However, I suspect that this metacognitive heuristic has broader application than my morning shower, and that in many cases it is ultimately self-defeating.  This heuristic can be used whenever a dominant response – established by prior experience, intuitive judgment, common sense, or whatever – is known to be incorrect in the current context.  One can, for example, apply it to the meaning and use of words . (‘Enervate’, that bane of GRE takers?  It's not the one you think.) 

I was born in the UK, raised in Canada and now live in the United States. As a result, I have difficulty remembering where certain words are used.  Do Americans say “restroom” or “bathroom”?  Is ‘z’ pronounced “zed” or “zee”?  In both of these cases I have used the It's Not The One You Think Heuristic – initially with success, but then increasingly with failure as the correct response started to compete with the incorrect response for dominance.  At this point I am completely confused and have taken to saying both forms, “I need to use the bathroom or the restroom, whatever you say in this country…"

Wait, where are we?



[A Note: I am not aware of any previous discussion of this heuristic, although my review of the literature has certainly not been exhaustive. It most likely has been identified before. If you are aware of prior work (and this heuristic's real name) please let me know!]

[Update: Kudos to Heidi Grant Halvorson for noting that George Costanza identified and exploited the 'do the opposite heuristic' in the 1990s.  Given that it was George, it must inevitably have backfired.]



Friday, December 19, 2014

What Predicts the Spatial Distribution of IAT Scores in the United States?

On December 15, an article by Chris Mooney on the Washington Post’s Wonkblog presented an interesting spatial distribution of state-level IAT scores across the United States. Based on a sample of more than 1.5 million participants from Project Implicit, IAT scores appear to be systematically higher in the southeastern and eastern US. (For an interactive version of the map, click here.)


The map above raises (at least!) two questions. First, what might predict this distribution of IAT scores? And second, given that the differences between scores (though significant with this large sample) are quite small (range = .341 - .456), are they meaningful?

To address these questions, we examined a number of potential predictors of state-level IAT scores. We selected predictors either because they were theoretically plausible (e.g., state level indices of racial segregation, median income, income inequality, the ratio of White:Black state residents, political preferences) or because they are known to share a similar spatial distribution to the observed IAT scores (e.g., ‘social capital’, status as a former slave-holding state). In addition, we examined whether state-level IAT scores could be used to predict actual behaviors – specifically, numbers of racist tweets recorded after the 2012 Presidential election.

What Predicts this Distribution of IAT Scores? 

Correlational analyses revealed several significant correlates of state-level IAT scores.  Note that where possible we examined variables around the year 2009 because this was the modal year of IAT data collection.

·         We used the ‘Civic Life Index’as a measure of social capital.  This index captures a composite of volunteering behavior, neighborhood engagement, voter participation and civic infrastructure.  There was a strong negative correlation between this measure of social capital and IAT scores (r (49) = -.576, p < .001)
·         State levels of income inequality (GINI) were positively correlated with IAT scores (r (49) = .409, p < .01).
·         The ratio of White to Black state residents (log transformed because it is skewed) was a strong negative predictor of IAT scores (r (49) = -.733, p < .001) – the more White relative to Black state residents, the lower the IAT score.
·         We indexed political preferences in terms each state’s Electoral College vote in the 2008 Presidential election.  States that voted for McCain (M = .405, SD = .030) vs. Obama (M = .398, SD = .027) did not have significantly different IAT scores (F (1, 49) = 0.79, p > .30).
·         In contrast, status as a former slaveholding state (in 1860) was a strong predictor of current day IAT scores.  Former slave states (M = .426, SD = .022) had significantly higher IAT scores than non-slaveholding states (M = .390, SD = .023; F (1, 49) = 27.45, p <. 001).
·         An index of Black/White segregation and state-level median income did not significantly correlate with IAT scores (rs < |.17|, ps > .20)

Of course, many of these variables were correlated with each other, so we ran a series of regression analyses that allowed us to identify the unique effects of each potential predictor.  Entering all of the variables EXCEPT population composition (ratio of White to Black residents) into a regression equation showed that two predictors remained significant – the Civic Life Index negatively predicted IAT scores (β = -.449, p < .01), whereas status as a former slaveholding state positively predicted IAT scores (β = .439, p < .01). 

When, however, the ratio of White to Black state residents (log transformed) was entered into a regression equation with these two variables, the contributions of the Civic Life Index (β = -.173, p > .10) and slaveholding status (β = .214, p > .10) dropped to non-significance.  By far the strongest single predictor of state-level IAT scores was the ratio of White:Black state residents (β = -.470, p < .01), accounting for a rather astonishing 53.8% of the variance.  States where Whites outnumber Blacks substantially in the population have lower average IAT scores.  In contrast, states where Blacks make up proportionally more of the population have higher average IAT scores.

Figure: Relationship between White:Black Population Ratio (log) and IAT Scores



Interpretation.  The strong association between population composition and IAT scores is consistent with the idea that bias increases when and where there is greater perceived competition between groups.  In the political science and sociology literatures this sort of population composition effect is often termed the “racial threat hypothesis” (Key, 1949).  In states where the African American outgroup comprises a larger proportion of the population, Whites may perceive greater competition for political, cultural and economic resources (e.g., Tolbert & Grummel, 2003).  Further, due to negative stereotypes about African Americans, they may also perceive greater risk for cross-race crime (e.g., Eitle, D'Alessio & Stolzenberg, 2002).

In contrast, these findings are inconsistent with any simple hypothesis that contact between members of different racial groups will lead to reduced bias.  (Of course, social psychologists know this anyway!)

Critically, however, this pattern should be interpreted within its historical context.  It is not a coincidence that bias is greater in former slaveholding states.  In these and other states with large African American communities, for example, emancipation and extension of full voting rights during the civil rights era posed a significant threat to White’s political power and historical dominance. Understandings of racial identity and the nature of intergroup relationships forged in the past persist today and likely continue to exert influence (see Acharya, Blackwell & Sen, under review; Jackman, 1994). In a different historical context, however, the relative size of racial groups might not have the same relationship to bias. 

Do State-Level IAT Scores Predict Anything?

After the 2012 election, geographers at the University of Kentucky plotted the distribution of racist Tweets geolocated by state.  They created a racist tweet location quotient “that indicates each state's share of election hate speech tweet relative to its total number of tweets”. We found that the distribution of tweets they observed can be predicted by state-level IAT scores, such that states with higher average IAT scores tended to emit more racist tweets (r (49) = .326, p <. 05). 

We further examined whether this relationship varied as a function of which candidate (Romney or Obama) won the Electoral College vote in each state.  Perhaps unsurprisingly, numbers of racist tweets were greater in states that voted for Romney (who lost the election; β = .409, p < .01; R2 = .25).  However, there was also a significant interaction between state vote and IAT scores (β = 6.78, p < .01; ΔR2 = .14).  The positive relationship between IAT scores and racist tweets was significant among states that voted for Romney (β = .48, p < .05), but was non-significant among states that voted for Obama (β = -.25, p > .20).

 Figure: Relationships between IAT Scores and Racist Tweet Location Quotient for States that Voted for Romney Vs. Obama


  
Interpretation.  This pattern is consistent with contemporary theories of racial bias (e.g., aversive racism). Given that social norms generally prohibit overtly racist behavior, biased attitudes often only predict behavior when people feel they have an excuse to behave in a biased fashion or perhaps when something (like frustration or anger about a preferred candidate losing an election) reduces control over biased responses.  Alternately, the normative climate in states that voted for Romney might be have felt sufficiently accepting of prejudice to some individuals following the election to sanction the translation of biased attitudes into behavior.

Conclusion

We started with two initial questions. 

What predicts the spatial distribution of IAT scores in the United States?  A cluster of variables are correlated with state-level IAT scores, including indices of social capital, income inequality and historical status as a slaveholding state.  Notably, however, the strongest correlate was the ratio of White to Black state residents, consistent with the idea that intergroup competition and identity threat contributes to the type of  bias indexed by the IAT.

Given that the differences between states’ IAT scores are quite small, are they meaningful?  The answer to this second question appears to be yes.  First, the fact that IAT scores are related to variables like social capital and population composition suggests that they vary in a systematic fashion.  Second, we found that state-level IAT scores predicted a form of biased behavior – namely the number of racist tweets produced in each state following the 2012 election of President Barack Obama. Thus, although the observed differences between states are indeed small, the very large sample (1.5 million+) from which these estimates of bias are derived means that they are stable, predictable and predictive.

Finally, it is important to note that the current analyses sample in a limited way from a much larger universe of possible predictors.  We believe it was an informed sampling, but there are certain to be other important predictors out there.  Further, a more sophisticated approach would examine effects at the county-level.  We are currently engaged in some county-level analyses, and hope to report findings in the near future. 

The Data

If you'd like to take a look yourself or have ideas about additional predictors (urban/rural differences being an obvious contender), the data for these analyses can be downloaded here.  And many thanks to Brian Nosek and Project Implicit for providing open access to the IAT data!