Purpose: Qualitative methods, mainly, do not use numbers to present and interpret the qualitative data but through themes, quotes, and transcript extracts. A comprehensive systematic literature review was undertaken to explore how qualitative information from semi-structured and unstructured interviews has been analysed using quantitative methods and by which methods. The aim of this research was to bridge the identified knowledge gap in the literature for developing a method, which can analyse complex relationships among qualitative data, as well as between qualitative and quantitative data by taking into account the measurement error and the small sample size of the qualitative dataset. Method: A new mixed method, called Enosis, was developed that consisted of two steps: quantifying the qualitative data (themes) based on a scoring system and analysing the scores using Structural Equation Modelling (SEM). The feasibility of the Enosis method was tested in a pilot study using one qualitative dataset that had primary been analysed with Interpretive Phenomenology Analysis . Its transferability was, then, explored using another two qualitative datasets, which had been analysed with Grounded Theory and Thematic Analysis. Findings: The results from the pilot study suggested that that the Enosis method is feasible for quantifying qualitative data and analysing them with SEM. Three scoring systems, the ‘Frequency’, the ‘Proportion’, and the ‘References’, were developed for quantifying the qualitative data. The final structural models were adjusted for the small sample size and the measurement error that incurred due to imperfect quantification of rich qualitative information into numbers was quantified. The transferability of the Enosis method was evident as new associations that were not identified by the primary qualitative analysis were revealed in all three datasets. The Enosis method also produced results that had been identified through the qualitative analysis or were present in the literature. Thus, the results of the Enosis method were used for initiation, complementary and triangulation, and expansion purposes. Five essential requirements were developed for planning appropriately the methodology of a research project so that to be suitable for applying the Enosis method. Conclusion: This research evidences that the Enosis method is a useful secondary analysis method for analysing complex relationships among qualitative data, as well as between qualitative and quantitative data, by taking into account the measurement error occurred through the quantification process and the small sample size of the qualitative dataset. The Enosis method contributed in strengthening the collaboration between the qualitative and quantitative researchers, and making the results and conclusions of the primary qualitative research appealing to a wider audience.