By Carl W. Roberts
This e-book offers descriptions and illustrations of state-of-the-art textual content research tools for verbal exchange and advertising and marketing learn; cultural, historical-comparative, and occasion research; curriculum overview; mental analysis; language improvement examine; and for any study within which statistical inferences are drawn from samples of texts. even supposing the booklet is obtainable to readers having no event with content material research, the textual content research professional will locate great new fabric in its pages. specifically, this assortment describes advancements in semantic and community textual content research methodologies that heretofore were available basically between a smattering of method journals.
The book's overseas and cross-disciplinary content material illustrates the breadth of quantitative textual content research functions. those functions exhibit the tools' software for overseas examine, in addition to for practitioners from the fields of sociology, political technology, journalism/communication, machine technological know-how, advertising, schooling, and English. this can be an "ecumenical" assortment that comprises functions not just of the newest semantic and community textual content research tools, but in addition of the extra conventional thematic approach to textual content research. in truth, it truly is initially with this quantity that those "relational" ways to textual content research are outlined and contrasted with extra conventional "thematic" textual content research tools. The emphasis this is on application. The book's chapters offer counsel concerning the forms of inferences that every technique gives, and updated descriptions of the human and technological assets required to use the tools. Its goal is as a source for making quantitative textual content research equipment extra obtainable to social technological know-how researchers.
Read or Download Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences From Texts and Transcripts (Routledge Communication Series) PDF
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Additional info for Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences From Texts and Transcripts (Routledge Communication Series)
Surprisingly, thematic text analysis appears to have been applied much less to studying media trends. As Neuman (1989) aptly argued, thematic analyses of media could be useful complements to public opinion polling by showing how the media set agendas or frame how people think about issues. Naisbitt (1982) described a systematic analysis of monthly data from some 6,000 local American newspapers. His company, the Naisbitt Group, found that newspapers from five states-California, Colorado, Connecticut, Florida, and Washington—served as bellwethers of major national trends, allowing him to identify what he called megatrends.
Even though thematic text analyses will become more grounded, easy access to various categorization schemes will help researchers find links to other studies, often uncovering similarities with their findings they did not anticipate. For example, three of CDC's categories map directly into three types of organizational cultures that Kabanoff, Waldersee, and Cohen (in press) derived by applying cluster analysis in a text analysis of documents from 88 Australian organizations. These in turn appear to map into three of Fiske's elementary forms of sociality.
Computers, however, may be better at scoring entire interview transcripts on a large number of themes. First, scoring many variables concurrently strains coders' bounded rationality. Second, the large span of attention this technique requires can lead to uneven evaluations, especially for coders who analyze transcripts day after day. Each approach then has its own strengths and weaknesses. Some simple text analysis tasks that one might have expected to move to the domain of the computer will continue to be better handled manually, because somewhere embedded in the analysis is a very human judgment.