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).

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.

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 Seeing the Forest through the Trees: A Review of Gibson and Brown’s Working with Qualitative Data

2016 ◽  
Author(s):  
Jacquelyn Browne
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Data ◽  
Research Process ◽  
Research Report ◽  
Qualitative Data Analysis

In their 2008 book, Working with Qualitative Data, Gibson and Brown introduce their readers to the notion that qualitative data analysis is more of an on-going process integral to the overall qualitative research process than a set of techniques or tools. In making this assertion, the authors help their audience connect analysis with theory, design, and final qualitative research report.

scholarly journals ANALYSIS OF SLANG WORDS FORMATION FOUND IN THE LYRICS OF DRAKE’S SONGS

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
I.P.A. Bhuwana ◽  
I.G. Budasi ◽  
G. Mahendrayana
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Data ◽  
Document Analysis ◽  
Word Formation ◽  
Formation Processes ◽  
Qualitative Data Analysis ◽  
Music Film ◽  
Descriptive Qualitative

The development of music, film, and other industry of entertainment is stated to contribute to the existence of slang words. There are so many slang words that become popular after they are included in the lyrics of songs. One of them is YOLO (You Only Live Once) which is popularized by Drake. In relation to that, this descriptive qualitative research attempts to find out the types and word formation processes of slang words found in the lyrics of Drake’s songs. By taking the model of document analysis by Bowen (2009) and Qualitative data analysis by Miles and Hubberman (1994), it is found out that there are 190 slang words existing in the lyrics of Drake’s songs. They are respectively categorized into Clipping type (62 slangs), Fresh and Creative type (44 slangs), Compounding type (44 slangs), Imitative type (36 slangs), and Acronym type (4 slangs).

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.

Jack + Jill, and Joey in paradise: CAQDAS Conference 07

KWALON ◽  
2007 ◽  
Vol 12 (2) ◽  
Author(s):  
Hennie Boeije ◽  
Jeanine Evers
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Data ◽  
Computer Assisted ◽  
Qualitative Data Analysis

Op Royal Holloway College, universiteit van Londen, werd dit jaar de tweede conferentie over CAQDAS – computer-assisted qualitative data-analysis – gehouden. Het was er van de 17e tot de 19e april zonnig en gezellig. Organisator was het CAQDAS Networking Project van de universiteit van Surrey. De wortels van dat project, dat nu officieel dertien jaar bestaat, liggen in 1989 bij de conferentie Using Computers in Qualitative Research. Dit jaar ging het over Advances in Qualitative Computing.

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.

scholarly journals Structuring a Team-Based Approach to Coding Qualitative Data

2020 ◽  
Vol 19 ◽  
pp. 160940692096870
Author(s):  
Lindsay Giesen ◽  
Allison Roeser
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Reference Materials ◽  
Qualitative Data ◽  
Research Process ◽  
Lessons Learned ◽  
Analysis Software ◽  
Management Structure ◽  
Qualitative Data Analysis Software ◽  
Supportive Management

Improvements to qualitative data analysis software (QDAS) have both facilitated and complicated the qualitative research process. This technology allows us to work with a greater volume of data than ever before, but the increased volume of data frequently requires a large team to process and code. This paper presents insights on how to successfully structure and manage a team of staff in coding qualitative data. We draw on our experience in team-based coding of 154 interview transcripts for a study of school meal programs. The team consisted of four coders, three senior reviewers, and a lead analyst and external qualitative methodologist who shepherded the coding process together. Lessons learned from this study include: 1) establish a strong and supportive management structure; 2) build skills gradually by breaking training and coding into “bite-sized” pieces; and 3) develop detailed reference materials to guide your coding team.

Building Transparency and Trustworthiness in Inductive Research Through Computer-Aided Qualitative Data Analysis Software

2019 ◽  
Vol 24 (1) ◽  
pp. 104-139 ◽  
Author(s):  
Paula O’Kane ◽  
Anne Smith ◽  
Michael P. Lerman
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Data ◽  
Building Blocks ◽  
Analysis Software ◽  
Qualitative Data Analysis ◽  
Inductive Research ◽  
Computer Aided ◽  
Qualitative Data Analysis Software ◽  
Data Analysis Software

Many scholars have called for qualitative research to demonstrate transparency and trustworthiness in the data analysis process. Yet these processes, particularly within inductive research, often remain shrouded in mystery. We suggest that computer-aided/assisted qualitative data analysis software (CAQDAS) can support qualitative researchers in their efforts to present their analysis and findings in a transparent way, thus enhancing trustworthiness. To this end, we propose, describe, and illustrate working examples of six CAQDAS building blocks, three combined CAQDAS techniques, and two coder consistency checks. We argue that these techniques give researchers the language to write about their methods and findings in a transparent manner and that their appropriate use enhances a research project’s trustworthiness. Specific CAQDAS techniques are rarely discussed across an array of inductive research processes. Thus, we see this article as the beginning of a conversation about the utility of CAQDAS to support inductive qualitative research.

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