Abstract
Mixed methods, integrating qualitative (QUAL), and quantitative (QUAN) methods (e.g., research questions, data collections and analyses, and results) in empirical studies and program evaluations are becoming increasingly popular for answering complex questions. Several strategies for integrating QUAL and QUAN phases, results, and data have been proposed over the years, but their conceptualization is usually design-driven, or fragmented, or not empirically tested. In addition, researchers, graduate students, and professionals using mixed methods find it difficult to plan, conduct, and report the application of these strategies simply and clearly. In this chapter, the authors will present nine practical strategies and several combinations of strategies, illustrated with published mixed methods studies in information science. This chapter contributes to advance methodological knowledge on mixed methods in information science and calls for better reporting of mixed methods studies and integration strategies specifically.
TopBackground
MM empirical research or program evaluations meet three conditions: (a) at least one QUAL and one QUAN method are combined; (b) each method is used in a rigorous manner according to generally accepted criteria of the methodology or research tradition invoked; and (c) the methods are integrated at least through research question(s)/objectives(s), the MM design (be it planned or emergent), and the integration of methods and evidence (Fetters, 2020; Johnson, Onewuegbuzie, & Turner, 2007; Pluye & Hong, 2014). It follows that QUAN methods that are not integrated with rigorous QUAL methods are not MM, and vice versa. In an earlier prevalence study on the use of mixed methods in patient-oriented research, authors found that about 10% and 7% of publications entitled “mixed methods” used only quantitative and only qualitative methods, respectively (Pluye et al., 2018).
By way of illustration, a survey conducted using a structured questionnaire that includes closed-ended questions and some final open-ended questions, is an example worth highlighting as a source of MM trainees’ recurring questions. These open-ended questions can be seen as QUAL or QUAN methods depending on how they are designed and used. Answers to open-ended questions yield QUAL data when they are obtained through a rigorous QUAL methodology and research process (explicit, transparent, and reproducible) that produces plausible QUAL results (credible, contextual, confirmable, and transferable). Researchers know the participants and interact with them (by reformulating responses or stimulating the development of responses) to learn more about the context and to better understand the meaning of the data such as interviewees’ words, non-verbal language, and context.
In contrast, an optional written response to an open-ended question asked at the end of a validated, self-administered, anonymous structured online questionnaire cannot be considered qualitative data. In epidemiological surveys, responses obtained in this way traditionally provide some illustrations for discussing statistical results. While these responses are informative, they do not constitute qualitative data because they are not obtained through a rigorous qualitative scientific research process and methodology. Furthermore, these responses cannot be used to produce plausible qualitative results. Researchers cannot know who wrote these responses and why; they cannot interact with participants who responded and those who refrained to respond. Stated otherwise, they cannot better understand the meaning of the words written (feedback comment), and the reasons why nothing was written (absence of feedback comment).
Key Terms in this Chapter
Process in Integration Strategy: The looping effect between qualitative and quantitative evidence using mixed methods (e.g., qualitative results from Phase 1 are used to construct a quantitative questionnaire to be used in Phase 2).
Comparison of Results: This strategy consists of comparing results obtained either from separate or interdependent data collection and analysis. In this strategy, the similarities, differences, and contradictions between qualitative and quantitative results are identified and explained.
Resource in Integration Strategy: An input of scientific evidence in the process of managing the research project using mixed methods (e.g., qualitative results from Phase 1).
Assimilation of Data: The qualitative and quantitative data can be transformed into a single qualitative (e.g., themes) or quantitative (e.g., variables) form, or merged on a case-by-case basis.
Evidence: Scientific data or results of data analysis.
Connection of Phases: The methods for collecting and analyzing qualitative and quantitative data are kept separated. The methods and the results are presented separately in the publication. The integration occurs during the connection between two phases (e.g., between a phase-1 qualitative and a phase-2 quantitative phase). The ‘how phase-1 results inform phase-2 data collection and analysis’ is described.
Product in Integration Strategy: Mixed kind of evidence (e.g., quantitative data collection and analysis was informed or structured by the qualitative results).
Integration: Occurs when the methods are combined at least through research question(s) or objectives(s), the mixed methods design (planned or emergent), and the integration of methods and evidence.