A TQF research proposal clearly defines the target population for the proposed research, the target sample (if the researcher is interested in a particular subgroup of the target population, e.g., only African American and Hispanic high school seniors in the district who anticipate graduating in the coming spring), how participants will be selected for the study, what they will be asked to do (e.g., set aside school time for an in-depth interview [IDI]), and the general types of questions to which they will be asked to respond (i.e., the content areas of the interview). In discussing Scope, the researcher proposing an IDI study with African American and Hispanic high school students would identify the list that will be used to select participants (e.g., the district’s roster of seniors who are expected to graduate); the advantages and drawbacks to using this list (e.g., not everyone on the roster may consider themselves to be African American or Hispanic); the systematic (preferably random) procedure that will be used to select the sample; and the number of students that will be selected as participants, including the rationale for that number and the steps that will be taken to gain cooperation from the students and thereby ideally ensure that everyone selected actually completes an interview (e.g., gaining permission from the school principal to allow students to take school time to participate in the IDI, and from parents/guardians for students under 18 years of age who cannot give informed consent on their own behalf).
The data-gathering portion of the Research Design section of the proposal highlights the constructs and issues that will be examined in the proposed research. This discussion should provide details of the types of questions that will be asked, observations that will be recorded, or areas of interest Read Full Text
An important aspect related to Scope within the Credibility component of the Total Quality Framework (TQF) for qualitative research design is the extent to which the researcher is successful in gaining cooperation from the participants. In an in-depth interview (IDI) study, the researcher is concerned with the impact that the proportion of selected interviewees not interviewed or only partially interviewed has on the integrity of the data. This is the domain of research that is often termed “nonresponse.” If this proportion is large and/or if the group that is selected but not interviewed differs in meaningful ways from those who are interviewed, bias can infiltrate the final data of an IDI study and compromise the credibility of the research.
To avoid this, qualitative researchers need to give serious a priori thought to how they will gain high and representative levels of cooperation from the persons they have selected to interview, and how individuals who do not cooperate may differ in past experiences, attitudes, behaviors, and knowledge compared to interviewees. The researcher must keep in mind that bias may enter into the outcomes, and the credibility of the study’s findings and interpretations thereby weakened, if the characteristics of those in the sample who do not cooperate with an IDI study are correlated with the key topics the study is investigating. Likewise, qualitative researchers using the IDI method should also constantly monitor the representativeness of the group of selected participants that does cooperate and watch whether the characteristics of that group deviate from the characteristics of the target population. This may be difficult in the case of the email IDI (or other asynchronous text-based mode) where the interviewer must stay alert to the consistency of participants’ responses and recognize when the identity of the interviewee may have changed (i.e., someone other than the recruited research participant is the one now responding). For instance, in an email IDI study among Read Full Text
Data Gathering is one of two broad areas of the Total Quality Framework Credibility component that affects all qualitative research, including ethnographic research. There are three primary aspects concerning the gathering of data in ethnography that require serious consideration by the researcher in the development of the study design. To optimize the measurement of ethnographic data, and hence the quality of the outcomes, researchers need to pay attention to:
How well the observers have identified and recorded all the information (e.g., verbal and nonverbal behavior, attitudes, context, sensory cues) pertinent to the research objectives and constructs of interest. A well-developed observation guide and observation grid can assist greatly in this effort. Not unlike the development of an in-depth interview or discussion guide, the ethnographer seeks to identify those observable events—including the specific individuals (or types of individuals), the verbal and nonverbal behaviors, attitudes, sensory and other environmental cues—that will further the researcher’s understanding of the issues. During the design development phase, the researcher might isolate the observations of interest by:
Looking at earlier ethnographic research on the subject matter and/or with similar study populations.
Interviewing the clients or those who have requested the research to learn everything they know about the topic and their past work in the area.
Consulting the literature or other experts concerning the behaviors and other occurrences associated with particular constructs.
“Shagging around” (LeCompte & Goetz, 1982) the observation site(s) to casually assess the environment and begin to learn about the participants.
Observer effects, specifically—
Observer bias, that is, behavioral and other characteristics (e.g., personal attitudes, values, traits) of the observer that may alter the observed event or bias their observations. For example, an observer as a complete participant would bias the observational data if there was an attempt to “educate” participants on a subject matter for which the observer had personal expertise or knowledge.
Observer inconsistency, that is, an inconsistent manner in which the observer conducts the observations that creates unwarranted and unrepresentative variation in the data. For example, an on-site nonparticipant observer conducting in-home observations of the use of media and technology would be introducing inaccuracies in the data by observing and recording the use of television and gaming in some households but not in others where television and gaming activities took place.
Participant effects, specifically, the extent to which observed participants alter a naturally occurring event, leading to biased outcomes. This is often called the Hawthorne effect, whereby the people being observed, either consciously or unconsciously, change what is being measured in the observation because they are aware of the observer. For example, an ethnographer conducting an overt, on-site passive observation of teaching practices in a school district would come away with misleading data if one or more school teachers deviated from their usual teaching styles during the observations in order to more closely conform with district policies.
LeCompte, M. D., & Goetz, J. P. (1982). Ethnographic data collection in evaluation research. Educational Evaluation and Policy Analysis, 4(3), 387–400.
Roller, M. R., & Lavrakas, P. J. (2015). Applied qualitative research design: A total quality framework approach. New York: Guilford Press.