Guess Taboo? Children’s Discussions of Race and Ethnic Prejudice

guesswhoIn a recent study, Cameron, Brady, and Abbott (2013) tested a group of children using a version of the children’s game, ‘Guess Who?’. The game was contrived, so that asking about the race of your opponent’s character would enable winning more quickly than not asking about it. Yet, rarely would children ask this question – and they were even less likely to do so in ethnically diverse classrooms. In other words, children would rather lose a game, than mention the category “race”. Why is it that ‘race’ is taboo for children?

I was reminded of these findings earlier this week at a talk I attended by Darren Chetty, @rapclassroom, from the IoE in London. In speaking to his 2014 paper, he argued that two books, Elmer’s Special Day and Tusk Tusk, both by David McKee, and both recommended by Philosophy for Children practitioners as starting points for philosophical enquiry into racism, multiculturalism and diversity, do not truly allow for an open discussion on race. Rather, he argues, ‘animal stories’ separate racism from its temporal and spatial context, limiting opportunities for engaging philosophically with the topic – and maybe even contributing, paradoxically, to the taboo.


Elmer, the patchwork elephant


To briefly review the texts, Elmer  is a patchwork elephant, who really wants to be like other grey elephants, who are happy. But he  is nothing like them. To resolve this, the other elephants have an Elmer day each year, where they paint themselves in a vast array of different pretty patterns, like Elmer’s patchwork. Elmer paints himself grey. Tusk Tusk  tells the tale of the black elephants and the white elephants. They don’t like each other. So, some of them fight and die. The ones that don’t fight, go to the jungle, and emerge years later as grey elephants. The book ends with the big-eared elephants giving funny looks to the little-eared elephants. I listened to Chetty’s analysis of these stories, with a social identity theorist hat on, and found myself, sometimes agreeing, and sometimes disagreeing with the points that were made.  Before going further it is worth noting that McKee states he never intended the books to be about colour or race. Nevertheless, what follows is my take on the two ‘elephant stories’, given Philosophy for Children’s recommendation.

Elmer is a patchwork elephant. But, If there are no other patchwork elephants, there is no opportunity to talk about group differences. Since ethnicity is a group categorization, this does seem at odds with a pathway into discussing ethnic prejudice. It might open up talking about exclusion, and exclusion can be on the grounds of race, so we could discuss the immorality of excluding someone on the grounds of race, on this basis – but it would be hard to cast this as a group-based exclusion – which ethnic prejudice arguably is – because there are no other patchwork elephants. The felt exclusion is of one  individual, and ethnic prejudice is more than that.

Tusk Tusk  has the group element that Elmer lacks. So, as a social identity theorist, this would make for better material. There are two groups, who dislike each other, and fight. This is resolved by all the elephants becoming grey over time. End of group differences, or not, as the text hints. So, we can open discussion about groups that dislike each other. There is no reason given in the texts about why the groups dislike each other – which Chetty argues is undesirable. Whilst I  agree that the lack of power dynamic isn’t helpful – ethnic prejudice is about a majority group’s treatment of the minority – the lack of explanation would, on the other hand, allow a competent, confident teacher to talk about the possible reasons for the intergroup hatred, and even to introduce the notion of power. …

One philiosophy for tackling ethnic prejudice is colour-blindness. This argues that everyone should be treated equally, and attempts at differential treatment  by race should be disregarded and dismantled. Perhaps this hints at the ontology for the taboo above. Maybe, if teachers use this strategy, they are implicitly telling children not to talk about race.  An alternative philosophy to colour-blindness is multiculturalism (for a discussion of these ideologies and their respective benefits, see Plaut, Thomas, & Goren, 2009). Multiculturalism acknowledges the differences between races, and in social identity terms, is about  acknowledging and celebrating group differences, because not to do so undermines the cultural heritage of non-white individuals, and, is thus detrimental to the well being of ethnic minorities. Multiculturalism doesn’t enter the picture in Tusk Tusk.  Group differences lead to hatred. The elephants are content to the extent that they all see themselves as similar. But there is celebration of difference in Elmer and I see whispers of multiculturalism here. Again though, Elmer  isn’t so much a culture, as an individual…if only there were other patchwork elephants…..


And what of the historical context of ethnic prejudice? Neither text addresses the inequalities or tensions between different elephants. And reflecting on the social identity research with children and race (and ethnic prejudice) I realize that  not much of it addresses this element either. Drew Nesdale and colleagues (e.g., Griffiths & Nesdale, 2006) and Killen and colleagues (e.g., Brenick & Killen, 2014) research real-life ethnic minority / majority affect- but only take implicit account of the group histories. These studies show consistently, when conflict is current and historical, that children have an ingroup-bias for their own ethnicity. It would be worth, I realize, from Chetty’s talk (with thanks due to him), looking at children’s understanding of the reason for group’s prejudices towards each other, and how groups should treat one another in light of these histories.  Might we then see a more positive attitude towards the outgroup than the ingroup – in stark contrast to the findings of the papers cited in this paragraph above?

In sum, Elmer  and Tusk Tusk  are each decidedly lacking in some of the elements that would be useful for direct discussion of the stories’ in relation to the topic of racism and inter-group prejudice. A competent teacher might well still be able to use them as the basis for discussion, particularly for Elmer  in terms of social exclusion, and Tusk Tusk  in terms of intergroup hatred. There is reason not to throw the baby out with the bathwater, when this is borne in mind. In light of  the taboo around race, which might lie in the colour-blind ideology, there is also good reason, given that we know children display ethnic ingroup bias, to (a) look at children’s reasoning about multiculturalism, and (b) to put ethnic prejudice  into its historical context, in our social identity research. The results could be revealing.


The Right Way To Do Statistics in Psychology

It’s that time of the year again. The time when undergraduate students in Psychology have collected their data, and are furiously trying to get it analyzed and written up, in time for their dissertation deadline.  It’s also the time of year when students tend to panic about the “right” way to analyze their data. But – as far as statistics go – there is no single right way to go about things. In fact, there is as much debate about doing statistics in Psychology as there is about psychological theories themselves – with whole journals dedicated to the topic. When it comes to dissertation stats, there is no single right way here, either…I explain.

I have one manipulated variable (call it Experimental Condition) and two continuous variables that I measured, Measure A, and Measure B. I want to know how Measure A and Experimental Condition interplay to influence Measure B. I have met all the assumptions for parametric data analysis. Although that research question is clearly defined, there are still several ways I could go about this.

Option 1

One way would be to perform a linear regression, entering Experimental Condition as a dummy variable and Measure A (centered about the mean) as predictors of my outcome, Measure B. If I found any interaction, I could analyse it using a simple slopes analysis. That would answer my research question.

Option 2

Equally viable, however, would be to run this analysis using ANOVA – because the maths underlying ANOVA and regression analyses are essentially the same. You can check this for yourself, by running the two analyses on the same variables: you will find that because both rely on what is called the General Linear Model the R squared value is the same for each. The distinction between the two in teaching terms is really just an historical artefact arising because ANOVA has been traditionally used for experimental designs and regression for correlational designs. It doesn’t have to be that way: whether the analysis you do make any sense depends on what you were trying to find out, more than anything.

Anyway – if I ran this analysis using ANOVA, there are two ways I could go about it. I could continue to treat measure A as a continuous variable and, in SPSS at least, force the program, via the syntax editor, to treat measure A as continuous but nevertheless a bona fide fixed factor, by adding it after the WITH sub-command:

Measure_B  BY Experimental Condition with Measure A_centred

Option 2a

I could, however, legitimately perform a median split on Measure A, creating a new variable where people are coded as either high A-scorers or low A-scorers. I would then enter Measure A _ split into the ANOVA alongside Experimental Condition, as above.

In either case, if I found an interaction between Measure A and Experimental Condition, I would analyse it using a simple effects analysis (to look at the effect of Experimental Condition at differing scores on Measure A).

The Right Way?

So – either ANOVAs or regression could be used for the above research question. Neither way is “wrong” although statisticians will point out the advantages and disadvantages to each approach. The classic disadvantage to median splits, for example, is that I would lose some of the variance provided in the variable scores (because I have changed a continuous variable to a dichotomous one).

Of course, that said, there are some things that we need to do, for any of the above options to be “right” before we run those tests. Here is a checklist, courtesy of Tabachnik and Fiddell (2007) – with the health warning that, the debate around statistics rages on, and these are guidelines – one high-profile  journal in Psychology decided earlier this week that reporting p values is inappropriate full-stop….

(1) Before you do anything, check for missing values and cases where weird stuff seems to be happening. Work out what is weird, and consider deletion of these cases, or checking against the questionnaires for human error in data entry.

(2) Check you meet the assumptions for the tests you want to do. See Tabachnik and Fiddell (2007) for myriad guidelines on what to do with the data, if you fail to meet an assumption.

jf16(3) This is not a fishing expedition. Define your research question clearly, and the type of test(s) you need to do to answer it with your data. Report what you find.

(4) If you do perform extra post-hoc tests, because something interesting has come up, don’t be afraid to admit to that. There are ways of statistically adjusting for the probability of finding significant results in such cases, and the important thing is being transparent about what we are doing as scientists, to allow effective evaluation of findings.

So – to sum this up, before you do anything with your data, look at it. Is it weird? Is it normal(ly distributed)? Can you use parametric statistics or not? Then, work out what research question you would like to answer, and what types of variable you now have. Based on this, choose among the options for answering that research question. All the time, remember to be transparent about the analysis and post-hoc tests that you are using. Just as one rationalizes the inclusion of different variables in your study in the Introduction, the Results section should give a rationale for what you have done with each variable, why, and what was found. Statistics in Psychology is about having a rationale, rather than a “right” answer.