Another way that researchers can quantify the results of content analysis is to calculate the frequencies at which concepts are mentioned in all of the data, i.e., within the diverse sub-categories and categories. For example, in the study of adolescent adherence to health regimens, support from parents was mentioned over 300 times in the analysed data. Hence, various quantification methods can lead to noticeably different outcomes. For this reason, the purpose of quantification should be clearly defined to reach the required outcome of quantification. It is often the case that the sample used in the qualitative analysis is too small for any reliable statistical analyses. However, the percentages and frequencies can be reported. The study presented in this section included 51 interviewees, which enabled the researcher to perform a discriminant analysis that included the calculation of p-values. These analyses supported the creation of a model that describes good adherence to health regimens among adolescents with type1 DM (Fig. 4.2). The model indicates that support from parents and the encouragement of health care providers and friends did not directly explain adherence, but explained the participants’ motivation, energy and will power, all of which influenced adherence (see Chap. 7). A hypothetical model—which will be further discussed in Chap. 7—was created based on these outcomes.

An alternative approach for mixed methods studies is parallel data collection that combines qualitative and quantitative research methods. This approach can be used to gain a deeper understanding of quantitative data outcomes by considering how the collected qualitative data influence decision-making, and is commonly applied to interventional studies or action research in the field of nursing science. The example provided in this chapter is a Finnish study of doctoral students by Isohätälä et al. [18]. During the data collection phase, 1645 candidates from a university in northern Finland were invited to participate in a cross-sectional survey. A total of 375 doctoral candidates participated. The researchers aimed to explore and describe doctoral candidate perceptions of their doctoral degree and future career at the university. The survey included questions relating to doctoral study conditions, factors contributing to the progress of doctoral studies and perceptions of future career. These three areas of concern were measured using items that could be quantified statistically. For the factors contributing to the progress of doctoral studies area, candidates were given the additional option of sharing their personal experiences through an open question. These responses yielded qualitative data, which was analysed with content analysis. During the analysis, prior to which one researcher had read through the data several times, the identified open codes (n = 300) were grouped under two tables: (1) presenting positive factors (298 answers); and (2) presenting negative factors (312 answers). The open codes were organised in Microsoft Excel, with each open code in a separate row. Table 4.1 presents an example of the data distribution. The most frequently mentioned factors were initially grouped under ten categories, and these categories could include both positive and negative factors. The ten categories were Funding and position, Supervision, Community, Studies, Research and academic work, Practices, Infrastructure, Other work-related responsibilities, Motivation and one’s own abilities and Personal life. This phase included two researchers independently creating categories based on the collected data, after which the researchers organised the data into the identified categories. The data distribution among the ten categories is presented in Table 4.2.

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