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(20.1).

Journal Article 1: Li, S., & Seale, C. (2007). Learning to do qualitative data analysis: An observational study of doctoral work. Qualitative Health Research, 17, 1442–1452.

Abstract: Using examples from written assignments and supervisory dialogues, the authors report a longitudinal observational case study of a doctoral research project, focusing on the teaching and learning of qualitative data analysis on a project that involved coding and analysis of nursing talk. Written drafts contain concrete exemplars illustrating the problems and solutions discussed in supervisions. Early problems include the difficulty of knowing where to start with coding, ambiguities in the definition of codes, inaccurate reporting and recording of data, failure to distinguish researcher and actor categories, and over interpretation of evidence. Solutions to these problems required their accurate identification, communication of practical solutions, and care in the interactional management of delivery and receipt of feedback. This detailed analysis informs readers of sources of validity, rigor, and, eventually, creativity in carrying out a social research project. It also assists in explicating an apprenticeship model for the learning of research skills.

(20.2).

Journal Article 2: Yeh, C. J., & Inman, A. G. (2007). Qualitative data analysis and interpretation in counseling psychology: Strategies for best practices. The Counseling Psychologist, 35, 369–403.

Abstract: This article presents an overview of various strategies and methods of engaging in qualitative data interpretations and analyses in counseling psychology. The authors explore the themes of self, culture, collaboration, circularity, trustworthiness, and evidence deconstruction from multiple qualitative methodologies. Commonalities and differences that span across approaches are explored. Implications for how researchers address qualitative data analysis and interpretation in counseling psychology training and research are discussed.

(20.3). Some suggestions about how to achieve validity and reliability in qualitative data analysis.

Journal Article 3: Moret, M., Reuzel, R., van der Wilt, G. J., & Grin, J. (2007). Validity and reliability of qualitative data analysis: Interobserver agreement in reconstructing interpretative frames. Field Methods, 19, 24–39.

Abstract: Many authors have discussed criteria for assessing the quality of qualitative studies. However, relatively few have presented the results of using criteria for validity of qualitative studies. We investigated the quality of reconstructing interpretative frames, a method for analyzing interview transcripts. The aim of this method is to describe a person’s perspective, distinguishing between perceived problem definitions, proposed solutions, empirical background theories, and normative preferences. Based on this description, one should be able to estimate this person’s cooperation on implementing specific changes in his or her practice. In this article, we assessed the interobserver reliability of this analytical method as an indicator of its rigor. Six analysts reconstructed interpretative frames on the basis of verbatim transcripts of three interviews. The analysts agreed only moderately about the issues identified and which problems should be prioritized. However, they showed remarkable unanimity as to the estimates of the respondents’ cooperation on proposed solutions.

(20.4). An overview of one qualitative data analysis software package.

Journal Article 4: Oswald, A. G. (2017). Improving outcomes with Qualitative Data Analysis Software: A reflective journey. Qualitative Social Work, 1–7. doi:10.1177/1473325017744860

Abstract: Now more than ever, qualitative social work researchers are being called upon to conduct increasingly complex, multifaceted, and intersectional research. Given the heightened complexity of social work research, it is necessary that scholars learn strategies to streamline the research process and digital tools for qualitative research are a mechanism to do so. In this paper, I share insights gleaned from personal experience working with Qualitative Data Analysis Software, specifically MAXQDA 12, to support a larger study that explored the social lives of older gay men. This paper highlights the various functions of MAXQDA 12 and how qualitative social work researchers can use the program to improve the research process and outcomes. Despite the rapid growth in production of digital tools for qualitative research there remains a dearth in studies that explicitly address how digital tools are used in the extant literature on qualitative research. This paper sheds light on this noted gap in the literature by exploring the functionality of MAXQDA 12 and how it can be applied to improve qualitative social work research.

(20.5). An article on another qualitative data analysis package.

Journal Article 5: Maher, C., Hadfield, M., Hutchings, M., & de Eyto, A. (2018). Ensuring rigor in qualitative data analysis: A design research approach to coding combining NVivo with traditional material methods. International Journal of Qualitative Methods, 17, 1–13. doi:10.1177/1609406918786362

No Abstract.

(20.6). An article on the evolving topic of mixed analysis.

Journal Article 6: Vogi, S. (2018). Integrating and consolidating data in mixed methods data analysis: Examples from focus group data with children. Journal of Mixed Methods Research, 1–19. doi:10.1177/1558689818796364

Abstract: The challenge in data analysis often lies in accounting for the multidimensionality and complexity of the data while simultaneously discovering patterns. Integrating and consolidating different types of data during analysis can broaden the perspective and permit obtaining complementary views. This methodological research study on data collection illustrates how one type of data collection generates different types of data, which can be linked and consolidated to reach a better understanding of the topic. Procedures and practicalities are illustrated to offer a good practice example for data integration and consolidation. With the methodological reflection of research practice, I evaluate the consequences for the field of mixed methods research, in which the practicalities of an integrated mixed analysis still need to be elaborated.

(20.7). One set of authors recommendations for integrative/mixed analysis.

Journal Article 7: Castro, F. G., Kellison, J. G., Boyd, S. J., & Kopak, A. (2010). A methodology for conducting integrative mixed methods research and data analyses. Journal of Mixed Methods Research, 4, 342–360. doi:10.1177/1558689810382916

Abstract: Mixed methods research has gained visibility within the last few years, although limitations persist regarding the scientific caliber of certain mixed methods research designs and methods. The need exists for rigorous mixed methods designs that integrate various data analytic procedures for a seamless transfer of evidence across qualitative and quantitative modalities. Such designs can offer the strength of confirmatory results drawn from quantitative multivariate analyses, along with “deep structure” explanatory descriptions as drawn from qualitative analyses. This article presents evidence generated from over a decade of pilot research in developing an integrative mixed methods methodology. It presents a conceptual framework and methodological and data analytic procedures for conducting mixed methods research studies, and it also presents illustrative examples from the authors’ ongoing integrative mixed methods research studies.