scholarly journals Ensuring Rigor in Qualitative Data Analysis

2018 ◽  
Vol 17 (1) ◽  
pp. 160940691878636 ◽  
Author(s):  
Carmel Maher ◽  
Mark Hadfield ◽  
Maggie Hutchings ◽  
Adam de Eyto
Keyword(s):  
Grounded Theory ◽  
Data Analysis ◽  
Data Management ◽  
Qualitative Data ◽  
Design Research ◽  
Data Interpretation ◽  
Research Process ◽  
Qualitative Data Analysis ◽  
Analysis Process ◽  
Software Packages

Deep and insightful interactions with the data are a prerequisite for qualitative data interpretation, in particular, in the generation of grounded theory. The researcher must also employ imaginative insight as they attempt to make sense of the data and generate understanding and theory. Design research is also dependent upon the researchers’ creative interpretation of the data. To support the research process, designers surround themselves with data, both as a source of empirical information and inspiration to trigger imaginative insights. Constant interaction with the data is integral to design research methodology. This article explores a design researchers approach to qualitative data analysis, in particular, the use of traditional tools such as colored pens, paper, and sticky notes with the CAQDAS software, NVivo for analysis, and the associated implications for rigor. A design researchers’ approach which is grounded in a practice which maximizes researcher data interaction in a variety of learning modalities ensures the analysis process is rigorous and productive. Reflection on the authors’ research analysis process, combined with consultation with the literature, would suggest digital analysis software packages such as NVivo do not fully scaffold the analysis process. They do, however, provide excellent data management and retrieval facilities that support analysis and write-up. This research finds that coding using traditional tools such as colored pens, paper, and sticky notes supporting data analysis combined with digital software packages such as NVivo supporting data management offer a valid and tested analysis method for grounded theory generation. Insights developed from exploring a design researchers approach may benefit researchers from other disciplines engaged in qualitative analysis.

Qualitative Data Analysis

2021 ◽  
pp. 203-223
Keyword(s):  
Qualitative Research ◽  
Grounded Theory ◽  
Data Analysis ◽  
Qualitative Data ◽  
Research Process ◽  
Validity And Reliability ◽  
Qualitative Data Analysis ◽  
Grounded Theory Methodology ◽  
Research Questions ◽  
Axial Coding

This chapter illustrates how to do qualitative data analysis. The principles of grounded theory methodology are taken as the main reference for developing a rigorous analysis of the data. Several examples and case studies are discussed to show the practicalities of qualitative data analysis. While explaining the mechanisms of qualitative data analysis, this chapter frames data analysis as part of the overall research process. Methods used in qualitative research give access to people's stories and experiences through language and captures the complexities of social processes. Grounded theory methodology is used to illustrate an approach to coding. Coding is about finding key themes in qualitative data in the form of a text and developing explanations of the research questions. Several approaches to coding—from open and axial coding to selective coding—are systematically presented. Issues of validity and reliability of qualitative data are also addressed within the overall process of research and data analysis leading to the writing-up.

scholarly journals A Call for Enhancing Saturation at the Qualitative Data Analysis Stage via the Use of Multiple Qualitative Data Analysis Approaches

2019 ◽  
Author(s):  
Amber Sechelski ◽  
Anthony Onwuegbuzie
Keyword(s):  
Qualitative Research ◽  
Discourse Analysis ◽  
Data Analysis ◽  
Qualitative Data ◽  
Research Process ◽  
Comparison Analysis ◽  
Qualitative Data Analysis ◽  
Data Analyses ◽  
Multiple Data

The analysis of data represents the most important and difficult step in the qualitative research process. Thus, recently, a few authors have written methodological works that contain discussion of an array of qualitative data analysis approaches. Yet, despite the call of Leech and Onwuegbuzie (2007) a decade ago for qualitative researchers to analyze a given set of qualitative data in multiple ways, this practice has been largely ignored. Thus, in this article, we bolster the argument for conducting multiple data analyses. In particular, we use data stemming from an interview to demonstrate how using five qualitative data analysis approaches (e.g., constant comparison analysis, discourse analysis) helped to enhance what we refer to as analysis saturation, thereby increasing verstehen (i.e., understanding).

Embracing tension: using Bakhtinian theory as a means for data analysis

2016 ◽  
Vol 17 (1) ◽  
pp. 20-36 ◽  
Author(s):  
Xiaoli Hong ◽  
Michelle M Falter ◽  
Bob Fecho
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Data ◽  
Multiple Perspectives ◽  
Qualitative Data Analysis ◽  
Research Participants ◽  
Analysis Process ◽  
Bakhtinian Theory ◽  
Ongoing Dialogue

In this article we introduce tension as a means for qualitative data analysis based on Mikhail Bakhtin’s dialogical theory. We first explain the foundations of Bakhtin’s theory and show the inevitability of tension in our lives and qualitative data analysis. We then offer a review of how Bakhtin’s notion of tension has manifested itself in qualitative research, which prompts us to establish a tensional approach to qualitative data analysis. Finally, we outline our framework for a tensional approach to data analysis and illustrate examples of putting this approach into practice in our own study. Our tensional approach (1) explores key moments of tension; (2) seeks out unease and discomfort; (3) involves researcher and research participants in ongoing dialogue; (4) and embraces multiple perspectives on a range of tensions during the data analysis process. It encourages uncertainties and questions instead of pursuing certainty of meaning and fixed conclusions.

Introduction to Qualitative Data Analysis

2021 ◽  
pp. 140-172
Keyword(s):  
Qualitative Research ◽  
Grounded Theory ◽  
Data Analysis ◽  
Qualitative Data ◽  
Qualitative Data Analysis ◽  
The Third ◽  
Constant Comparative ◽  
Analytic Induction

This chapter introduces readers to the basics of data analysis and the practical handling of open, axial, and selective coding within and outside the grounded theory. Readers are introduced to segmentation/reassembling, constant comparative, and analytic induction concepts in qualitative data analysis in the first section of this chapter. They should be able to trace the origin of coding of qualitative data in qualitative research. The stages of qualitative data analysis are discussed in the second section. The third section takes readers through the practical steps of open, axial, and selective coding, and detailed examples are given.

Improving outcomes with Qualitative Data Analysis Software: A reflective journey

2017 ◽  
Vol 18 (3) ◽  
pp. 436-442 ◽  
Author(s):  
Austin G Oswald
Keyword(s):  
Qualitative Research ◽  
Social Work ◽  
Data Analysis ◽  
Qualitative Data ◽  
Research Process ◽  
Digital Tools ◽  
Analysis Software ◽  
Qualitative Data Analysis ◽  
Social Work Research ◽  
Qualitative Data Analysis Software

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.

Leveraging Two Computer-Assisted Qualitative Data Analysis Software Packages to Support Discourse Analysis

2015 ◽  
pp. 194-209 ◽  
Author(s):  
Jessica Nina Lester
Keyword(s):  
Discourse Analysis ◽  
Data Analysis ◽  
Qualitative Data ◽  
Computer Assisted ◽  
Analysis Software ◽  
Qualitative Data Analysis ◽  
Analysis Process ◽  
Video And Audio ◽  
Qualitative Data Analysis Software ◽  
Data Analysis Software

The purpose of this chapter is to illustrate how Computer-Assisted Qualitative Data Analysis Software (CAQDAS) packages, such as ATLAS.ti or Transana, can be used to support the transcription and data analysis process of large interactional data sets – specifically data analyzed from a discourse analysis perspective. Drawing from a larger ethnographic study, in this chapter the author illustrates how carrying out the transcription and analysis process within a CAQDAS package (in this case, Transana and ATLAS.ti) allows for an increase in transparency within the transcription and data analysis process, while also meeting the particular needs of the discourse analyst. By using one particular case/research study, the author demonstrates how CAQDAS packages might function to support a researcher in generating a more systematic and transparent analytical process, specifically during the early stages of the analysis process. The author gives particular attention to interactional data (i.e., 300 hours of video and audio recordings of therapy sessions) collected in a larger study and demonstrates the potential benefits of working across two CAQDAS packages, specifically Transana and ATLAS.ti, to support both the nuanced transcription process and the larger data analysis process.

Using NVIVO with Grounded Theory and other Qualitative Methods

2008 ◽  
Vol 30 (1) ◽  
pp. 22-26
Author(s):  
Elizabeth EnglandKennedy
Keyword(s):  
Grounded Theory ◽  
Data Analysis ◽  
Qualitative Methods ◽  
Research Design ◽  
Qualitative Data ◽  
Semantic Analysis ◽  
Research Process ◽  
Analysis Software ◽  
Survey Instruments ◽  
Qualitative Data Analysis Software

This paper demonstrates the potential usefulness of the NVivo7 software for developing grounded theory through semantic analysis and for making grounded theory more accessible to students and researchers. Use of qualitative data analysis software early in the research process can impact research design, including the creation of interview protocols and survey instruments. It can also be useful later in the process for content and discourse analyses. NVivo is a software program designed to facilitate coding and analysis of qualitative data; it includes "query" functions, which are specific searches that the software can perform on data.

scholarly journals Demystifying Qualitative Data Analysis for Novice Qualitative Researchers

2019 ◽  
Author(s):  
Neringa Kalpokaite ◽  
Ivana Radivojevic
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Data ◽  
Qualitative Data Analysis ◽  
Making Sense ◽  
Analysis Process ◽  
Modelling Cycle ◽  
Diverse Discipline ◽  
Solid Foundation ◽  
Qualitative Methodologies

Qualitative research is a rich and diverse discipline, yet novice qualitative researchers may struggle in discerning how to approach their qualitative data analysis among the plethora of possibilities. This paper presents a foundational model that facilitates a comprehensive yet manageable approach to qualitative data analysis, and it can be applied within an array of qualitative methodologies. Based on an exhaustive review of expert qualitative methodologists, along with our own experience of teaching qualitative research, this model synthesises commonly-used analytic strategies and methods that are likewise applicable to novice qualitative researchers. This foundational model consists of four iterative cycles: The Inspection Cycle, Coding Cycle, Categorisation Cycle, and Modelling Cycle, and memo-writing is inherent to the entire analysis process. Our goal is to offer a solid foundation from which novice qualitative researchers may begin familiarising themselves with the craft of qualitative research and continue discovering methods for making sense of qualitative data.

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