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Coding in Content Analysis

With a set of texts in hand, the next steps are to develop a codebook and actually code the text to produce a text-by-theme matrix. Consider Elizabeth Hirschman’s work (1987) on how people sell themselves to one another in personal ads. From her reading of the literature on resource theory, Hirschman thought that she would find 10 kinds of resources in personal ads: love, physical characteristics, educational status, intellectual status, occupational status, entertainment services (nonsexual), money status, demographic information (age, marital status, residence), ethnic characteristics, and personality info (not including sexual or emotional characteristics).

Hirschman formulated and tested specific hypotheses about which resources men and women would offer and seek in personal ads. She selected 20 test ads at random from the New York Magazine and The Washingtonian and checked that the 10 kinds of resources were, in fact, observable in the ads. Sexual traits and services were less than 1% of all resources coded. This was 1983-1984, but even then, ads with explicit references to sexual traits and services were more common in other periodicals than in The Washingtonian and New York Magazine.

Hirschman next gave 10 men and 11 women the list of resource categories and a list of 100 actual resources (‘‘young,’’ ‘‘attractive,’’ ‘‘fun loving,’’ ‘‘divorced,’’ ‘‘32-year-old,’’ etc.) gleaned from the 20 test ads. She asked the 21 respondents to match the 100 resources with the resource category that seemed most appropriate. This exercise demonstrated that the resource items were exhaustive and mutually exclusive: No resource items were left over, and all of them could be categorized into only 1 of the 10 resource categories.

When she was confident her codebook worked, Hirschman tested her hypotheses. She sampled approximately 100 female-placed ads and 100 male-placed ads from each magazine—a total of 400 ads. A male and a female coder, working independently (and unaware of the hypotheses of the study), coded 3,782 resource items taken from the 400 ads as belonging to 1 of the 10 resource categories. The coding took 3 weeks. This is not easy work.

Hirschman was concerned with intercoder reliability—that is, making sure that coders saw the same thing when they coded those ads. She gave the data to a third coder who identified discrepancies between the first two coders. Of 3,782 resource items coded, there were discrepancies (theme contrasts) on 636 (16.8%), and one of the coders failed to code 480 items (12.7%). Hirschman resolved the theme contrasts herself. She checked the omissions against the ads to see if the coder who had made an assignment had done so because the resource was, in fact, in the ad. This was always the case, so the 480 resource items omitted by one coder were counted as if they had been assigned to the ad by both coders.

The results? Men were more likely than women to offer monetary resources; women were more likely than men to seek monetary resources. Women were more likely than men to offer physical attractiveness. Washington, DC, and New York City are supposed to be hip places, yet the way men and women wrote their own personal ads in 1983-1984 conformed utterly to traditional gender role expectations. In 1998, a sample of 380 Internet personal ads showed that men continued to seek a particular kind of body in women whereas women continued to offer a particular kind of body and, although men and women alike mentioned their financial status, women still were more likely to explicitly seek someone who is financially secure. Gil-Burman et al. (2002), though, found evidence of what maybe a major shift in Spain: Men of all ages sought physical attractiveness in women; women under 40 sought physical attractiveness in men (Further Reading: content analysis).

 
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