Although every research setting will be unique in some fashion, there are some generalities involved in content analysis that can be followed by novice researchers. For example, according to Riffe, Lacy and Fico (2005), "Usually, but not always, content analysis involves drawing representative samples of content. The data collected in a quantitative content analysis are then usually analyzed to describe what are typical patterns or characteristics, or to identify important relationships among the variables measured" (p. 2).
Narrative analysis. This research methodology considers the narrative stories provided by narrators as representing their authentic social reality (Etherington, 2004). According to Etherington, "Narrative analysis views life as constructed and experienced through the telling and re-telling of the story, and the analysis is the creation of a coherent and resonant story" (2004, p. 81). Narrative analyses is not intended to identify commonalties or conceptual themes among narrative accounts, but rather relies on the unique qualities of each narrative account to help inform researchers concerning the lived experiences of narrators (Etherington, 2004). In sum, "Narratives can be analyzed for the knowledge they contain (the analysis of narratives), or can be treated as knowledge in themselves (narrative analysis)" (Etherington, 2004, p. 81).
Quantitative methodologies
Although all types of quantitative methodologies involve the analysis of numbers in some form, these approaches can also assume a number of different types including those described further below.
Observational studies. Observational studies require significant resources and time (Neuman, 2003). This type of research is particularly valuable for medical research using clinical interventions that require ongoing assessments of healthcare outcomes (von Elm, Altman, Egger & Pocock, 2008).
Correlational research....
This type of research identifies and evaluates the natural relationship that exists between different variables. According to Groat and Wang, "This characteristic means that it is particularly appropriate in circumstances when variables either cannot be manipulated for practical reasons or should not be manipulated for ethical reasons" (2003, p. 244).
Developmental designs. This type of research is used to measure changes that occur over lengthy periods of time (Developmental research, 2012). For example, a developmental design would be suitable for analyzing the differences in academic and social development in low-income vs. high-income neighborhoods. This research design is most common when working with children as subjects and can be undertaken using several methods: longitudinal, cross sectional, and cross sequential (Developmental research, 2012).
Survey research. Survey research collects data from a large number of respondents in an attempt to gain a better understanding about this sample as a whole (Grinnel & Unrau, 2005). According to Grinnel and Unrau, "It is essential, therefore, that survey research procedures produce data that is accurate, reliable, and representative so that findings can be generalized from a sample to the larger population or to different research situations" (p. 272). One of the main strengths of survey research concerns its flexibility for data-gathering purposes. De Vaus (2002) notes that, "A survey is not just a particular technique of collecting information: questionnaires are widely used but other techniques, such as structured and in-depth interviews, observation, content analysis and so forth, can also be used in survey research. The distinguishing features of surveys are the form of the data and the method of analysis" (p. 3). This main strength, though, is offset somewhat by the constraints that are inherent in the approach, but these constraints are frequently related to
