All Data Are Not Equal

Being swamped with data can be overwhelming. Researchers quickly find that if interviews are too long, or if interviews are excessively descriptive or unfocussed, then they will have a difficult time managing an excess quantity of data. If a researcher becomes enchanted with interviewing and neglects the analytic progression, interviews pile up and rapidly become unmanageable. However, samples (i.e., the number of participants in a qualitative project) seem to be becoming smaller and smaller. Yet, as our samples become smaller, our research data become thin, our concepts become more difficult to identify, and our research suffers. Somewhere, there must be a “right” amount of data, and the determination of “how much data” is a researcher decision. Excellent researchers somehow control the amount of data, including the substance of these data that they receive from each participant, making the analysis efficient, yet strong, solid, and convincing. Expert analysts balance the needs of the study, the demands for verification, and the developing theory, without overwhelming the analysis or collecting too little to develop a confident, interesting, and rich description. Quantitative researchers are able to control the amount of data by collecting data according to the research questions asked. They treat all data as equal, letting statistical techniques evaluate data using statistical weights. In this way, they can select the more significant items, or enable the weak items to be culled. Once their instrument is developed, all respondents are expected to answer all questions, and all valid responses are included in the analysis. Someone who “agrees” with an item is given the same weight as someone who “strongly disagrees. ” Qualitative researchers, however, control the amount of data during the actual interview by focusing on what one is told (and sees) concerning the phenomena of interest, and by targeting questions relating to the topic of interest that is “emerging” in these data. Researchers are simultaneously building the research narrative by evaluating what one is told (and what one sees) with what one already knows. Good interviewing is a cognitive skill. Of course, unstructured interviews are “safer” for research rigor, as the participant has more freedom to explain the phenomenon. With semi-structured interviews, if the researcher has not included an item, or does not probe, validity may be threatened. Unstructured interviews are frequently used because we do not have information about the topic. Therefore, the researcher must be sensitive to all aspects—even to where the participant’s story starts. Because we do not know very much about the phenomena, we may not necessarily be able to identify the domain of the topic and therefore must be open to considering everything that the participant includes in the interview. But this does not mean that everything stays as data. For instance, the participant may not include all essential information in the first interview and may omit information that is pertinent, or may skip relevant detail, while adding information that is superfluous. This is where the skill of the interviewer comes into play. Excellent interviewers learn to focus on the interview, while honing in on important information. In fact, some of the improbable information in an interview may eventually be realized as being important. These interviewers learn to tolerate seemingly odd topics in their analysis, especially if the information replicates either within the data set or with other participants’ interviews. If other participants present the same odd facts, then these features are analyzed in the data set, and the researcher follows up by asking others. By being astute, sensitive, and resourceful, excellent interviewers become excellent analysts. Of course, all interviews contain “dross”—that is, particular or unnecessary details or descriptions. Do not erase the dross—It may contain a gem that you will require later, or something that you may want to use as an example—even as a bad example. Simply put the material that you consider extraneous into a separate category and label it “dross. ” This may be culled at the end of the study. With semi-structured interviews, the patterns of the question stems delimit both the domain and the type of information sought. Even if the researcher uses probes, the amount of information obtained from each individual is restricted. Therefore, the researcher must increase the number of participants to obtain adequate and interesting data. The cost of too few participants is poorer data and the lack of interesting findings. Therefore, in qualitative research, all data are not equal. Researchers attend to data according to relevance, 597655QHRXXX10.1177/1049732315597655Qualitative Health ResearchMorse research-article2015