Chapter 3: Issues of Trustworthiness in qualitative and mixed method studies

Rather than issues of validity that we saw in quantitative studies, qualitative (and mixed methods) have trustworthiness issues. The first issue is credibility, which is comparable to internal validity. This is getting at the credibility of your data, common methods used are triangulation, prolonged contact, member checks, and saturation. You want to show that your data are as accurate as possible.

The second issue is transferability, which is comparable to external validity. This is getting at the generabilizability of your data to other groups. Common methods used are thick description and a variation in participant selection.

The third issue is dependability, comparable to reliability. You want to show the accuracy of your data methods, common methods are audit trails and triangulation. Triangulation is accomplished by asking the same research questions of different study participants and by collecting data from different sources and by using different methods to answer those research questions. Member checks occur when the researcher asks participants to review both the data collected by the interviewer and the researchers' interpretation of that interview data. Participants are generally appreciative of the member check process, and knowing that they will have a chance to verify their statements tends to cause study participants to fill in any gaps from earlier interviews.

The fourth issue is confirmability, comparable to objectivity. This is the degree to which the findings are the product of the focus of the study and not of the biases of the researcher One way to do this is through an audit trail. An adequate trail (or records) should be left to enable the auditor to determine if the conclusions, interpretations, and recommendations can be traced to their sources and if they are supported by the inquiry.

If you are using another coder(s), you must show how you will demonstrate intercoder reliability. Interrater or intercoder reliability is used to reduce bias by having multiple people code the data. How you go about this and how you resolve any discrepancies needs to be detailed. 

Next time I will post an updated blog index. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu