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Introduction to Qualitative and Quantitative Analysis

This is the first of eight chapters about analyzing data. In this chapter, we begin with the basics—what analysis is and how to use matrices, tables, and flow charts to present the results of data analysis. Then we move on to two chapters on analyzing data in cognitive anthropology, two chapters on text analysis, and three chapters about statistics. You’ll notice as we go through these chapters that some of them very quantitative in orientation, some are qualitative, and some are a blend of qualitative and quantitative approaches. It’s fair to say that there are qualitative and quantitative data and that these different types of data need to be analyzed with different methods. But, in my view, forcing people in the social sciences to choose between qualitative and quantitative approaches results in trained incapacity.

QUALITATIVE, QUANTITATIVE

By a quirk of English grammar, the phrase “qualitative data analysis’’ is delightfully ambiguous. Unless someone spells it out in lots of words, you never know if the phrase means ‘‘the qualitative analysis of data’’ or the ‘‘analysis of qualitative data.’’ And the same goes for “quantitative data analysis.’’ Figure 15.1 lays out the possibilities.

FIGURE 15.1.

Qualitative-quantitative data analysis.

SOURCE: H. R. Bernard. 1996. ''Qualitative Data, QuantitativeAnalysis.'' CulturalAnthropology MethodsJour- nal, Vol. 8, pp. 9-11. Sage Publications. Used by permission.

Cell a is the qualitative analysis of qualitative data. Interpretive studies of texts are of this kind. You focus on and name themes in texts. You tell the story, as you see it, of how the themes are related to one another and how characteristics of the speaker or speakers account for the existence of certain themes and the absence of others. You may deconstruct the text, look for hidden subtexts, and, in general, try to let your audience know— using the power of good rhetoric—the deeper meaning or the multiple meanings of the text.

Looking diagonally from cell a, cell d refers to numerical or statistical analysis of numerical data. Lots of useful data about human behavior come to us as numbers. Direct observation of behavior, village censuses, time allocation studies, close-ended questions in surveys—all produce numerical data.

Cell b is the qualitative analysis of quantitative data. This can involve the search for patterns using visualization methods, like multidimensional scaling and hierarchical clustering. (We’ll get to these methods next, in chapter 16.) Cell b is also about the search for, and the presentation of, meaning in the results of quantitative data processing. It’s what quantitative analysts do after they get through doing the work in cell d. Without the work in cell b, cell d studies are sterile and superficial.

Which leaves cell c, the quantitative analysis of qualitative data. This involves turning the data from words or images into numbers. Scholars in communications, for example, tag a set of television ads from Mexico and the United States to test whether consumers are portrayed as older in one country than in the other. Political scientists code the rhetoric of a presidential debate to look for patterns and predictors of policies. Archeologists code a set of artifacts to produce emergent categories or styles or to test whether some intrusive artifacts can be traced to a source.

Most quantitative analysis in the social sciences involves reducing people (as observed directly or through their texts) to numbers; most qualitative analysis involves reducing people to words—your words about the meaning of their words or actions or artifacts. I say ‘‘most’’ because a lot of analysis these days, qualitative and quantitative, involves visualization of data: not just looking for patterns in data, but showing the patterns as maps, networks, and matrices.

It’s pretty obvious, I think, that each kind of data—qualitative and quantitative—and each kind of data reduction—qualitative and quantitative—is useful for answering certain kinds of questions. Skilled researchers can do it all.

 
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