Abstract
Saturation, a core concept in qualitative research, suggests when data collection might end. It is reached when no new relevant information emerges with additional interviews. The aim of this research was to explore whether a mixed methods design could contribute to the demonstration of saturation. Firstly, saturation was conceptualized mathematically using set theory. Secondly, a conversion mixed design was conducted: a set of codes derived from qualitative interviews were quantitized and analyzed using partial least squares (PLS) regression to document whether saturation was reached. A qualitative study conducted by other researchers prior to this work (i.e. none of the present authors was involved in this study) was used to test saturation using PLS regression. This illustrative qualitative study aimed to investigate the impact of Clostridium difficile infection (CDI) on nurses’ work in the hospital and the results were published elsewhere (Guillemin et al. 2015). Semi-structured interviews were conducted with 12 nurses. Saturation was characterized by the cumulative percentage of variability accounted for by PLS factors. After 12 interviews, this percentage was 51% which suggests that saturation was achieved at least on main themes. Two main themes identifying similarities in the experience of nurses caring for patients with CDI were identified: Organization/Coordination of the working day and Time-consuming work. Although dependent on the coding of qualitative data, PLS regression of quantitized data from qualitative interviews generated useful information for the determination of saturation.
Highlights
BackgroundThe determination of an adequate sample size to appropriately address a research question is a fundamental aspect of the data collection process in any context, qualitative or quantitative
partial least squares (PLS) regression was performed to link the block of the themes elicited in the last three Nurse Interview (NI) to the block of the themes elicited in the first nine NIs
For this first PLS factor, the underlying structure covering the themes elicited in the first set of NIs was predicting the underlying structure of the last NIs: this supports the conclusion that saturation was reached, at least on the main themes covered by that factor
Summary
The determination of an adequate sample size to appropriately address a research question is a fundamental aspect of the data collection process in any context, qualitative or quantitative. Adequate sample size is determined using an inferential statistical framework, based on assumptions on type I and II error control. The concept of saturation, known as ‘data saturation’, has been proposed as the solution to determine. Applying a mixed methods design to test saturation publication fees. The funder of the illustrative qualitative study did not play any role in the study design, quantitative data analysis, decision to publish, or preparation of the present manuscript. The specific roles of the authors are articulated in the ‘author contributions’ section
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