Content analysis

Content analysis is the study of documents and communication artifacts, which are defined as texts. Examples of texts include photographs, speeches, and essays. Social scientists employ content analysis as a method of examining patterns in communication in a replicable and systematic manner. One of the key advantages of using content analysis to analyse social phenomena is their non-invasive nature, in contrast to simulating social experiences or collecting survey answers. Practices and philosophies of content analysis vary between academic disciplines. They all involve systematic reading or observation of texts or artifacts which are assigned labels (sometimes called codes) to indicate the presence of interesting, meaningful pieces of content. By systematically labeling the content of a set of texts, researchers can analyse patterns of content quantitatively using statistical methods, or use qualitative methods to analyse meanings of content within texts. Computers are increasingly used in content analysis to automate the labeling (or coding) of documents. Simple computational techniques can provide descriptive data such as word frequencies and document lengths. Machine learning classifiers can greatly increase the number of texts that can be labeled, but the scientific utility of doing so is a matter of debate.…

Content analysis is the study of documents and communication artifacts, which are defined as texts. Examples of texts include photographs, speeches, and essays. Social scientists employ content analysis as a method of examining patterns in communication in a replicable and systematic manner. One of the key advantages of using content analysis to analyse social phenomena is their non-invasive nature, in contrast to simulating social experiences or collecting survey answers. Practices and philosophies of content analysis vary between academic disciplines. They all involve systematic reading or observation of texts or artifacts which are assigned labels (sometimes called codes) to indicate the presence of interesting, meaningful pieces of content. By systematically labeling the content of a set of texts, researchers can analyse patterns of content quantitatively using statistical methods, or use qualitative methods to analyse meanings of content within texts. Computers are increasingly used in content analysis to automate the labeling (or coding) of documents. Simple computational techniques can provide descriptive data such as word frequencies and document lengths. Machine learning classifiers can greatly increase the number of texts that can be labeled, but the scientific utility of doing so is a matter of debate. Further, numerous computer-aided text analysis (CATA) computer programs are available that analyze text for predetermined linguistic, semantic, and psychological characteristics. ==Uses== Holsti groups fifteen uses of content analysis into three basic categories: make inferences about the antecedents of a communication describe and make inferences about characteristics of a communication make inferences about the effects of a communication. He also places these uses into the context of the basic communication paradigm. The following table shows fifteen uses of content analysis in terms of their general purpose, element of the communication paradigm to which they apply, and the general question they are intended to answer. As a counterpoint, there are limits to the scope of use for the procedures that characterize content analysis. In particular, if access to the goal of analysis can be obtained by direct means without material interference, then direct measurement techniques yield better data. Thus, while content analysis attempts to quantifiably describe communications whose features are primarily categorical——limited usually to a nominal or ordinal scale——via selected conceptual units (the unitization) which are assigned values (the categorization) for enumeration while monitoring intercoder reliability, if instead the target quantity manifestly is already directly measurable——typically on an interval or ratio scale——especially a continuous physical quantity, then such targets usually are not listed among those needing the "subjective" selections and formulations of content analysis. For example (from mixed research and clinical application), as medical images communicate diagnostic features to physicians, neuroimaging's stroke (infarct) volume scale called ASPECTS is unitized as 10 qualitatively delineated (unequal) brain regions in the middle cerebral artery territory, which it categorizes as being at least partly versus not at all infarcted in order to enumerate the latter, with published series often assessing intercoder reliability by Cohen's kappa. The foregoing italicized operations impose the uncredited form of content analysis onto an estimation of infarct extent, which instead is easily enough and more accurately measured as a volume directly on the images. ("Accuracy ... is the highest form of reliability." ) The concomitant clinical assessment, however, by the National Institutes of Health Stroke Scale (NIHSS) or the modified Rankin Scale (mRS), retains the necessary form of content analysis. Recognizing potential limits of content analysis across the contents of language and images alike, Klaus Krippendorff affirms that "comprehen[sion] ... may ... not conform at all to the process of classification and/or counting by which most content analyses proceed," suggesting that content analysis might materially distort a message. ==See also== Donald Wayne Foster Hermeneutics Text mining The Polish Peasant in Europe and America Transition words Video content analysis Grounded theory ==References== ==Further reading==

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