From the time we get up in the morning—listening to the radio or reading the newspaper—until we go to bed—watching TV or reading a book—we are surrounded by words. Every day we have dozens of conversations, make numerous phone calls, write and receive an increasing number of e-mails, surf the Internet, and chat in chat rooms. As teachers we assign writing assignments and grade essays. As researchers we use language to communicate with our participants; we collect responses to open-ended questionnaires, conduct interviews, videotape discussions, and record conversations. It is overwhelming how our daily lives are saturated with words. Thus, it is surprising how little psychologists have used language as a source of data.
With the advent of the Internet, various new opportunities for studying linguistic phenomena have opened up. Without running a single partici pant, researchers can now collect large amounts of text from personal Web pages, chat room conversations, message board entries, and e-mails (e.g., Cohn, Mehl, & Pennebaker, in press). Also, all major newspapers, magazines, periodicals, and journals are now available online and maintain comprehensive electronic archives. Important statements of public figures such as presidential addresses or press conferences are usually available soon after they occur—often already in transcribed form. Virtually any song's lyrics and even entire movie scripts can be downloaded from the Web. In short, text analysis researchers never experience a data shortage.
However, there is more to text analysis than the opportunity to draw on easily available data. As a method for analyzing archival data it offers another critical advantage (Lee & Peterson, 1997; Simonton, 2003; Winter, 1992). The data collection is less constrained than in most other methods. Survey studies yield scaled answers on a limited set of items— selected by the investigator on conceptual grounds prior to the onset of the study. Questionnaires work by a "what you ask is what you get" principle. No further information can be obtained once the data are collected. Open-ended questions, essays, or other verbal productions are different; they allow researchers to go back to the data and explore aspects that one had not originally considered.
Going back to our initial example about students' motivation to seek out a doctor, for instance, one might later become interested in whether self-focused attention operationalized as the use of first-person singular ("I") could predict who goes to the doctor. The data is also available for unrelated research questions such as sex differences in language use (Groom, Stone, Newman, & Pennebaker, 2004). It is even possible for other researchers now or in the future to analyze the data using their own text analysis approach and interpretative framework. The analysis of verbal material provides a flexibility that is hard to obtain with other methods.
So far, the vast majority of text analysis researchers have relied on a single type of text source. From a multimethod perspective, for a more elaborate understanding of how people use language it is necessary to start comparing language effects across text sources, genres, or contexts. For example, are there systematic differences in the way humans express themselves in written as compared to spoken language (Biber, 1988; Mehl & Pennebaker, 2003; Weintraub, 1981)? Or is language use in e-mails more similar to how people actually talk or write letters (Baron, 1998)? Identifying the degree of linguistic convergence and uniqueness across different language sources is an important area for future research (Pennebaker et al., 2003).
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