Analyzing Qualitative Data

2020 ◽  
pp. 87-105
Author(s):  
Janice E. Jones
Keyword(s):  
Data Analysis ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
Qualitative Data Analysis

This chapter provides an introduction to the process of qualitative analysis and uses step-by-step examples to provide an idea of how the process of qualitative analysis actually works. This chapter is intended to give the researcher a place to begin and to inspire a deeper dive into this rewarding form of data analysis. While qualitative data analysis can be time consuming, the rewards that come from immersion in the data far outweigh the time spent doing so.

Teaching Qualitative Data Analysis Through Gaming

2018 ◽  
Vol 25 (9-10) ◽  
pp. 1085-1090 ◽  
Author(s):  
Leo A. Mallette ◽  
Johnny Saldaña
Keyword(s):  
Data Analysis ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
Narrative Construction ◽  
Qualitative Data Analysis ◽  
Basic Principles

The purpose of this article is to describe the use of a party game, adapted by the two authors, to teach qualitative data analysis in consulting and classroom settings. The value of this exercise lies in its narrative construction outcomes. Qualitative methodologists frequently advise students to find the story of their study, but that task is often elusive given the overwhelming magnitude of data collected, and research novices often have difficulty grasping basic principles of qualitative analysis. Games are metaphors for life, and the activity described here and other games reviewed in this article are metaphors for the disparate and sometimes idiosyncratic data we collect in the field, and how we must pattern, unify, and make interpretive sense of them by constructing plausible organizational arrays and interrelationships. Flavor bites and dialogue from the participants attest that these are positive and beneficial learning exercises.

Reinvigorating the human instrument

2020 ◽  
Vol 27 (2) ◽  
pp. 505-530
Author(s):  
Hendi Yogi Prabowo
Keyword(s):  
Data Analysis ◽  
Public Sector ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
Qualitative Evaluation ◽  
Behavioral Changes ◽  
Computer Assisted ◽  
Qualitative Data Analysis ◽  
Content Type ◽  
Corruption Prevention

Purpose The purpose of this paper is to explore the potential of computer-assisted qualitative data analysis software (CAQDAS) to support qualitative evaluation of corruption prevention initiatives, especially those focusing on behavioral changes. To achieve this objective, this paper applies the principles of qualitative inquiries to establish a foundation for developing effective means for evaluating behavior-oriented corruption prevention initiatives in Indonesia. Design/methodology/approach Through exploratory study, this paper assesses current corruption prevention evaluation practice in the Indonesian public sector to highlight major deficiencies thereof primarily through examination of publicly available documents on anti-corruption practice in Indonesia. Furthermore, this paper also discusses how qualitative methods using CAQDAS may strengthen the existing anti-corruption regime by aiding decision-makers to better evaluate the success or failure of their corruption prevention initiatives in particular those aiming for behavioral changes. To illustrate how a qualitative data analysis application can support anti-corruption evaluators, NVivo will be used as a reference from which multiple analytical tools will be discussed to highlight their potential for qualitative qualitative analysis analysis in corruption prevention evaluation. Findings The author establishes that the existing quantitative measures of evaluation are insufficient in generating a comprehensive picture of the success or failure of the existing anti-corruption initiatives in Indonesia. Evidences suggest that the existing quantitative measures appear to be unable to cope with the growing complexity of various corruption problems in the country in particular to those related to organizational culture and behavior. Despite the numerous behavior-oriented initiatives to reduce the risk of corruption in the Indonesia public sector, it is still unclear if such initiatives actually have made a difference in preventing corruption, as many of their elements cannot be measured quantitatively. Therefore, the author believe that deeper insights into corruption problems can be obtained through proper qualitative assessments in which evaluators play the role as the primary analytical instrument. To enhance evaluators’ capability in obtaining and analyzing qualitative data, the author proposes the use of CAQDAS and the evaluation of corruption prevention initiatives. With a special reference to NVivo, the author argues that using CAQDAS will enable evaluators to conduct qualitative analysis more efficiently to identify patterns within the data, as it offers various tools to look deeper into context, diversity, nuance and process so as to gain deeper understanding of the meaning of human action and how it may affect the risk of corruption within organizations. Research limitations/implications This study is self-funded and is relying primarily on documentary analysis in assessing the existing corruption prevention evaluation measures in Indonesia. Future studies may benefit from in-depth interviews with anti-corruption evaluators in particular from the country’s anti-corruption agency. Practical implications This paper contributes to the development of corruption prevention strategy by proposing a framework for systematically performing qualitative evaluation on behavior-oriented corruption prevention initiatives. Originality/value This paper highlights the importance of qualitative measures in evaluating behavior-oriented corruption prevention initiatives in the Indonesian public sector.

scholarly journals Conducting Qualitative Data Analysis: Qualitative Data Analysis as a Metaphoric Process

2015 ◽  
Author(s):  
Ronald Chenail
Keyword(s):  
Data Analysis ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
Qualitative Data Analysis ◽  
Unit Of Analysis ◽  
Convincing Case

In the second of a series of “how-to” essays on conducting qualitative data analysis, Ron Chenail argues the process can best be understood as a metaphoric process. From this orientation he suggests researchers follow Kenneth Burke’s notion of metaphor and see qualitative data analysis as the analyst systematically considering the “this-ness” of the data from the “that-ness” of the qualitative abstraction drawn about the data. To make this metaphoric pronouncement a convincing case to judges as to the veracity of the juxtaposition of the code to that which is coded, the analyst must employ a recursive process by showing the presence of the qualities of the unit of analysis in the product of the qualitative analysis as evidence of the quality of the analysis itself. This evidentially recursive act must be made overtly because in qualitative data analysis, the data do not speak for themselves.

scholarly journals Introduction to Special Issue: Diverse Approaches to Qualitative Data Analysis for Applied Research

2021 ◽  
Author(s):  
Jessica Lester ◽  
Noah Goodman ◽  
Michelle O'Reilly
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
Applied Research ◽  
Special Issue ◽  
Qualitative Data Analysis ◽  
Data Set ◽  
Shared Data ◽  
Qualitative Approaches

This article introduces the special issue, “Diverse Approaches to Qualitative Data Analysis for Applied Research,” in which seven papers analyze one shared data set to illustrate different approaches to qualitative analysis. In addition to discussing the articles included in the special issue, this introduction provides an overview of applied research—highlighting some of the implications for qualitative research—and discusses how researchers could use the special issue to compare different qualitative approaches to choose one most appropriate for a given project’s goals.

Making Sense of All the Words

2016 ◽  
pp. 197-211
Author(s):  
Janice E. Jones ◽  
A. J. Metz
Keyword(s):  
Data Analysis ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
First Impressions ◽  
Qualitative Data Analysis ◽  
Making Sense

This chapter provides an introduction to the process of qualitative analysis and to use step by step examples to provide an idea of how the process of qualitative analysis actually works. Crabtree and Miller, 1992, note that there are many different strategies for analysis, in fact, they suggest there are as many strategies as there are qualitative researchers. This chapter is intended to give the researcher a place to begin and to inspire a deeper dive into this rewarding form of data analysis. Stake, (1995) writes that qualitative data analysis is “a matter of giving meaning to first impressions as well as to final compilations. Analysis essentially means taking something apart. We take our impressions, our observations, apart… we need to take the new impression apart, giving meaning to the parts”(p. 71). While qualitative data analysis can be time consuming the rewards that come from immersion in the data far outweigh the time spent doing so.

Bridging the Gap Between Methodology and Qualitative Data Analysis Software: A Practical Guide for Educators and Qualitative Researchers

2021 ◽  
pp. 136078042110035
Author(s):  
Neringa Kalpokas ◽  
Ivana Radivojevic
Keyword(s):  
Data Analysis ◽  
Qualitative Analysis ◽  
Qualitative Methodology ◽  
Qualitative Data ◽  
Model Building ◽  
Computer Assisted ◽  
Analysis Software ◽  
Qualitative Data Analysis ◽  
Qualitative Data Analysis Software ◽  
Data Analysis Software

The interpretative and flexible nature of qualitative research is one of its hallmark strengths, yet this can pose a significant obstacle for researchers who wish to incorporate computer-assisted qualitative data analysis software (CAQDAS), especially for educators of CAQDAS and researchers who may have abandoned CAQDAS following past frustrations. We seek to help qualitative researchers and teachers by illustrating how CAQDAS can be used to follow specific analytic strategies (e.g. inductive and deductive analysis, category identification and synthesis, and qualitative model building). To bridge the gap between qualitative methodology and CAQDAS, this article provides guidelines for researchers to familiarise themselves with widely used qualitative analysis strategies, and learn how ATLAS.ti, MAXQDA, and NVivo can be used in each phase of the qualitative analysis process. By effectively translating analytic strategies into CAQDAS features, CAQDAS can greatly facilitate data management, analysis, and collaboration when software features are harnessed to realise analytic strategies.

scholarly journals Step by step approach for qualitative data analysis

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Babatunde Femi Akinyode ◽  
Tareef Hayat Khan
Keyword(s):  
Data Analysis ◽  
Network Analysis ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
Data Interpretation ◽  
Higher Order ◽  
Qualitative Data Analysis ◽  
Postgraduate Students ◽  
Analytic Process ◽  
Thematic Network Analysis

The application of qualitative techniques is increasing and acceptable among the researchers. However, majority of the researchers and postgraduate students did not consider the importance of giving detailed procedures in qualitative data analysis for better understanding of the qualitative results. There is a need to uncover step by step approach in qualitative analysis for better application of qualitative techniques. This article presented a detailed step-by-step approach for qualitative analysis with the aid of a pragmatic illustration. The analytic process presented employed the example of qualitative data transcribed into narrative data to develop basic themes. The employment of Domain Analysis and Thematic Network analysis in the example given helped basic themes to converge to higher order themes. The article submitted that this approach in qualitative analysis will aid thorough understanding of qualitative data interpretation. This is extremely thoughtful approach for the systematic presentation of qualitative analysis.

scholarly journals Blending the Old and the New: Qualitative Data Analysis a s Critical Thinking and Using NVivo with a Generic Approach

2015 ◽  
Author(s):  
James Bernauer ◽  
Marilyn Lichtman ◽  
Cynthia Jacobs ◽  
Stuart Robinson
Keyword(s):  
Critical Thinking ◽  
Data Analysis ◽  
Qualitative Analysis ◽  
Qualitative Data ◽  
Qualitative Data Analysis ◽  
Inductive Approach

In this article the authors seek to make the case that qualitative data analysis can be explained within the framework of critical thinking and incorporates within this framework the role of technology – specifically NVivo. First they discuss critical thinking from the perspectives of Bloom, Adler, and Polanyi. They then link critical thinking to the concept of a general inductive approach to qualitative analysis as described by Thomas. Finally, they illustrate connections of both critical thinking and the general inductive approach to technology using NVivo screenshots.

scholarly journals Exploratory Geovisualizations for Supporting the Qualitative Analysis and Synthesis of Place-Related Emotion Data

2019 ◽  
Author(s):  
Susanne Bleisch ◽  
Daria Hollenstein
Keyword(s):  
Data Analysis ◽  
Qualitative Analysis ◽  
Data Model ◽  
Qualitative Data ◽  
Elderly Women ◽  
Qualitative Data Analysis ◽  
Data Set ◽  
Personal Significance ◽  
Generic Data ◽  
Generic Data Model

Locations become places through personal significance and experience. While place data are not emotion data, per se, personal significance and experience are often emotional. In this paper, we explore the potential of using visual data exploration to support the qualitative analysis of place-related emotion data. To do so, we draw upon Creswell’s (2009) definition of place to define a generic data model that contains emotion data for a given location and its locale. For each data dimension in our model, we present symbolization options that can be combined to create a range of interactive visualizations, specifically supporting re-expression. We discuss the usefulness of example visualizations, created based on a data set from a pilot study on how elderly women experience their neighborhood. We find that the visualizations support four broad qualitative data analysis tasks: revising categorizations, making connections and relationships, aggregating for synthesis, and corroborating evidence by combining sense of place with locale information to support a holistic interpretation of place data. In conclusion, the paper contributes to the literature in three ways. It provides a generic data model and associated symbolization options, and uses examples to show how place-related emotion data can be visualized. Further, the example visualizations make explicit how re-expression, the combination of emotion data with locale information, and visualization of vagueness and linked data support the analysis of emotion data. Finally, we advocate for visualization-supported qualitative data analysis in interdisciplinary teams so that more suitable maps are used and so that cartographers can better understand and support qualitative data analysis.

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