Du Bois and the construction of whiteness

In The Scholar Denied, Aldon Morris argues that Du Bois should not only be seen as a prominent American sociologist, but as one of the founders of sociology, elevated to the same status as authors like Marx, Weber and Durkheim. As his argument unfolds, it’s not hard to see why – DuBois, trained by German empiricists, was possibly the first American sociologist to use statistical techniques in his groundbreaking study, The Philadelphia Negro, which probably required more legwork and math than anything that has been published in years. His writings, although somewhat antiquated relative to modern sociological jargon, present race as a social construction during a time when prominent sociologists like Robert Park were still focusing on assimilation theory. Similarly, Du Bois preempts standpoint theory with his idea of second sight, the role of language in constructing social concepts before contemporary feminism, and Wallerstein’s worlds systems theory in Darkwater. Perhaps most famously, Du Bois also correctly predicted that the defining conflict of the 20th century would be centered around the color line. In short, Du Bois was a prolific author, a gifted thinker and might as well been clairvoyant when it came to speculating about the future.

With the efforts of Black Lives Matter, increased scrutiny is being paid to both police violence and economic injustice in the media and in the national discourse, meaning that Du Bois’s work is perhaps as relevant today as it was when it was originally published. While much of Du Bois’s theory is essentially canonized in modern discussions about race, I’d like to draw attention to one of his works, The Souls of White Folks, in which Du Bois considers the concept of whiteness. Du Bois mainly focuses on imperialism and an early concept of white privilege, but even the title is somewhat radical. Too much of modern race literature focus on marginalized peoples and why/how they are oppressed. While it is rarely the intent of the author, this sort-of frame often leads to putting blame on victims, and elevates the white baseline as a model to emulate. In other words, the question the authors are often asking is “why can’t blacks capture as much of the income distribution as whites?” instead of “How did it come to be that whites captured so much more of the income distribution than every other group?”

A lot of these empirical questions are covered by the concept of white privilege, but there is perhaps still some work to be done to examine cultural whiteness, both on the domestic and global level. For example, the anthropologist Ira Bashow’s The Meaning of Whitemen provides a fascinating account on how whiteness is constructed by individuals with very few cultural frames to situate it within, but I’m not aware of similar efforts in the US. It might also be helpful to go back to Du Bois’s writings on language to see how the media might play a role in these constructions – this might be similar to many of the content analyses that consider the role that media’s use of language serves in constructing concepts of race.

RQ1: What role does the media play in the construction of race, and, specifically, in what contexts do they employ words like “white” and “black?”

RQ2: Does our current concept of whiteness play a legitimizing role in the construction of white privilege?




Morris, A. D. (2015). The Scholar Denied. Oakland, CA: University of California Press.


Sampling distribution blog

The sampling distribution is the “theoretical probability distribution of all possible sample values for the statistics in which we are interested” (Frankfort-Nachmias & Leon-Guerrero, 2015, p. 219). Since this includes every possible combination of units in a sample, the sampling frame can become fairly enormous for even small samples – thankfully, it’s mainly theoretical, and rarely calculated. This sampling distribution, for whatever statistic it’s calculated for, can be described like other distributions with mean and standard deviation, which is referred to as the standard error of the mean in this context. Conclusions drawn from these concepts serve as the basis for inferential statistics.

While largely theoretical, sampling distributions have important implications on sampling techniques and generalizability. For example, the larger the sampling frame becomes (or, in other words, the larger the sample size), the more the sampling distribution begins to resemble a normal distribution – this is known as the central limit theorem. This means that the larger the sample, the more likely that sample’s mean is to match the actual population mean. Since the goal of inferential statistics is to infer a statistic about an entire populated out of sample data, this theorem serves as the basis for inferential statistics.


This video introduction explains the concept of a sampling distribution, through an intentionally contrived example of a professor who wants to know the average age of a college class, but is unable to sample more than three students at a time. It serves as a good introduction to sampling distributions for the uninitiated, but I wanted to draw attention to it because I really like the ending thoughts.

The textbook and the video both answer the question of why the reader/viewer should care in the same way, but they do so in different ways. The video highlights the ability of sampling distributions to quantify the likelihood of a sample statistic accurately reflecting the population statistic – it’s both really cool that one is able to do this, and really important in that it allows one to provide a metric of generalizability for statistics calculated from a given sample. Of course, this is all implied by the textbook’s thorough explanation of the central limit theorem, but the textbook probably doesn’t spell it out this way since this is part of the next chapter topic.

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