An important element in the Total Quality Framework Analyzability component is Verification, i.e., taking steps to establish some level of support for the data gathered in order to move the researcher closer to achieving high quality outcomes. The verification tools at the ethnographer’s disposal go beyond those identified for the in-depth interview (IDI) and group discussion methods in that they include the technique of expanded observation. For example, Lincoln and Guba (1985) stated that it is “more likely that credible findings and interpretations” will come from ethnographic data with “prolonged engagement” in the field and “persistent observation” (p. 301). The former refers to spending adequate time at an observation site to experience the breadth of stimuli and activities relevant to the research, and the purpose of the latter (i.e., persistent observation) is “to identify those characteristics and elements in the situation that are most relevant to the problem or issue” (p. 304)—that is, to provide a depth of understanding of the “salient factors.” Both prolonged engagement and persistent observation speak to the idea of expanding observation in terms of time as well as diligence in exploring variables as they emerge in the observation. Although expanding observations in this way may be unrealistic due to the realities of deadlines and research funding, it is an important verification approach unique to ethnography. When practicable, it is recommended that researchers maximize the time allotted for observation and train observers to look for the unexpected or examine more closely seemingly minor occurrences or variables that may ultimately support (or contradict) the observer’s dominant understanding.
The ultimate usefulness of expanded observation is not unlike deviant or negative case analysis (see earlier link). In both instances, the goal is to identify and investigate observational events (or particular variables in these events) that defy explanation or otherwise contradict the general patterns or themes that appear to be emerging from the data. For example, a researcher conducting in-home nonparticipant observations of young mothers Read Full Text
Fundamental to the design of a focus group study is group composition. Specifically, the researcher must determine the degree of homogeneity or heterogeneity that should be represented by the group participants. As shown below, there are many questions the researcher needs to contemplate, such as the extent of similarity or dissimilarity in participants’ demographic characteristics, as well as in their experiences and involvement with the subject matter.
Questions When Considering Heterogeneity vs. Homogeneity
A few of the questions the focus group researcher might consider when determining the desired heterogeneity or homogeneity among group participants include:
Should participants be in the same age range and/or stage of life?
Should participants be the same gender, race, and/or ethnicity?
Should participants be at a similar income, socio-economic, or educational level?
Should participants reside in the same community, be members of the same organization(s)?
Should participants have similar professions or jobs (including, job titles)?
Should participants have a similar involvement, experience, or knowledge with the research topic, e.g., the same types of problems with their 13 year old boys? the same healthcare service provider? the same purchase behavior? the same level of expertise with a new technology?
Whether or not—or the degree to which—group participants should be homogeneous in some or all characteristics has been at the center of debate for some years. On the one hand, Grønkjær, Curtis, Crespigny, and Delmar (2011) claim that at least some “homogeneity in focus group construction is considered essential for group interaction and dynamics” (p. 23)—for example, participants belonging to the same age group may have similar frames of reference and feel comfortable sharing their thoughts with people who have lived through the same experience. In the same vein, Read Full Text
Research Design Review is a blog first published in November 2009. RDR currently consists of more than 220 articles and has 650+ subscribers along with nearly 780,000 views. As in recent years, many of the articles published in 2019 centered on qualitative research. This paper — “Qualitative Research: Analysis” — represents a compilation of four of these articles pertaining to qualitative research analysis.
These articles cover a range of topics including: considerations when defining the unit of analysis; a discussion on handling “gaps” in the data; a cautionary perspective on coding, i.e., reminding researchers that an overemphasis on coding may miss the true intention of qualitative data analysis; and a look at a Total Quality Framework approach to the qualitative content analysis method.
A separate paper consisting of 14 2019 RDR articles on design and methods can be found here.