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Finally, a data audit prior to analysis can also ensure dependability.This article introduces grounded theory and places this method in its historical context when 1960s quantitative researchers wielded harsh criticisms of qualitative research. This can be documented by a clear coding schema that identifies the codes and patterns identified in analyses. Confirmability (e.g., the steps to ensure that the data and findings are not due to participant and/or researcher bias)Ĭonfirmability of qualitative data is assured when data are checked and rechecked throughout data collection and analysis to ensure results would likely be repeatable by others. Again, rigorous data collection techniques and procedures can assure dependability of the final data set. Dependability (e.g., an in-depth description of the methodology and design to allow the study to be repeated)ĭependability of the qualitative data is demonstrated through assurances that the findings were established despite any changes within the research setting or participants during data collection. Transferability can be demonstrated by clear assumptions and contextual inferences of the research setting and participants. Generalizability is not expected in qualitative research, so transferability of qualitative data assures the study findings are applicable to similar settings or individuals. Transferability (e.g., the extent to which the findings are generalizable to other situations)

This may be done through data, investigator, or theoretical triangulation participant validation or member checks or the rigorous techniques used to gather the data. As noted in the dissertation template for qualitative studies, the section directly following the Chapter 4 introduction is to be labeled Trustworthiness of the Data, and in this section, qualitative researchers are required to articulate evidence of four primary criteria to ensure trustworthiness of the final study data set: Credibility (e.g., triangulation, member checks)Ĭredibility of qualitative data can be assured through multiple perspectives throughout data collection to ensure data are appropriate.