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Theme in Qualitative Content Analysis and Thematic Analysis
Qualitatives Design besteht aus verschiedenen Ansätzen zur Datenerhebung und -analyse, die insbesondere für die Erstellung kultureller und kontextueller Beschreibungen und die Interpretation sozialer Phänomene anwendbar sind. Qualitative Inhaltsanalyse (QCA) und thematische Analyse (TA) stellen beides qualitative Ansätze dar, die üblicherweise von Forscher_innen aus unterschiedlichen Disziplinen gleichermaßen eingesetzt werden. Allerdings besteht in der internationalen Literatur eine Leerstelle in Bezug auf den Begriff von \"Thema\" in beiden Ansätzen und des Prozesses der Entwicklung von Themen. Daher gehen wir in unserem Beitrag auf diese Leerstelle ein und stellen Unterschiede und Ähnlichkeiten zwischen beiden Methoden in Bezug auf das \"Thema\" als Endprodukt der Datenanalyse dar. Zur Unterstützung unserer Sichtweisen und zur Erstellung international fundierter analytischer Begriffe von \"Thema\" greifen wir auf die aktuelle Literatur aus unterschiedlichen Disziplinen zurück. Wir gehen davon aus, dass Forscher_innen von einem vertieften Verständnis des Prozesses der Themenentwicklung in mehreren Hinsichten profitieren, nämlich bei der Auswahl einer geeigneten Methode für die Beantwortung ihrer Forschungsfrage, bei der Erzielung qualitativ hochwertiger und valider Ergebnisse sowie dabei, den analytischen Anforderungen an QCA und TA gerecht zu werden.
Journal Article
Reflexive Content Analysis: An Approach to Qualitative Data Analysis, Reduction, and Description
Content analysis, initially a quantitative technique for identifying patterns in qualitative data, has evolved into a widely used qualitative method. However, this evolution has resulted in a confusing array of differing qualitative content analysis approaches that lack clear distinction from other methods. To address these issues, this paper introduces reflexive content analysis, a transtheoretical and flexible researcher-oriented method for the description and reduction of manifest qualitative data. RCA is used to identify patterns in the overt surface meanings of qualitative data through the use of a hierarchical structure of quantifiable analytical strata called codes, subcategories, and categories. Each stratum exists on a continuum of abstraction with codes being the closest to the original data and categories being the most abstract. During each stage of the RCA process, reflexivity is regarded as a valuable analytical resource that is crucial for ensuring adequate description of the data. RCA is intended to be used as method for data analysis, not a methodology, and therefore can be integrated with various methodological and epistemological approaches. This paper provides an introductory guide to conducting RCA. It first presents an overview of existing challenges in qualitative content analysis methods, followed by a rationale for the development of RCA. Then, the foundational principles of RCA and key concepts that support this method are discussed. The paper culminates by outlining the process for conducting an inductive RCA within a qualitative framework, using a previous application of this method as a reference point.
Journal Article
Multilingual text analysis : challenges, models, and approaches
Text analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge - facts, rules, and relationships - that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques. This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains.
Book
Bayesian Model Averaging: A Systematic Review and Conceptual Classification
Bayesian model averaging (BMA) provides a coherent and systematic mechanism for accounting for model uncertainty. It can be regarded as an direct application of Bayesian inference to the problem of model selection, combined estimation and prediction. BMA produces a straightforward model choice criterion and less risky predictions. However, the application of BMA is not always straightforward, leading to diverse assumptions and situational choices on its different aspects. Despite the widespread application of BMA in the literature, there were not many accounts of these differences and trends besides a few landmark revisions in the late 1990s and early 2000s, therefore not accounting for advancements made in the last decades. In this work, we present an account of these developments through a careful content analysis of 820 articles in BMA published between 1996 and 2016. We also develop a conceptual classification scheme to better describe this vast literature, understand its trends and future directions and provide guidance for the researcher interested in both the application and development of the methodology. The results of the classification scheme and content review are then used to discuss the present and future of the BMA literature.
Journal Article
ChatGPT for Automated Qualitative Research: Content Analysis
Data analysis approaches such as qualitative content analysis are notoriously time and labor intensive because of the time to detect, assess, and code a large amount of data. Tools such as ChatGPT may have tremendous potential in automating at least some of the analysis.
The aim of this study was to explore the utility of ChatGPT in conducting qualitative content analysis through the analysis of forum posts from people sharing their experiences on reducing their sugar consumption.
Inductive and deductive content analysis were performed on 537 forum posts to detect mechanisms of behavior change. Thorough prompt engineering provided appropriate instructions for ChatGPT to execute data analysis tasks. Data identification involved extracting change mechanisms from a subset of forum posts. The precision of the extracted data was assessed through comparison with human coding. On the basis of the identified change mechanisms, coding schemes were developed with ChatGPT using data-driven (inductive) and theory-driven (deductive) content analysis approaches. The deductive approach was informed by the Theoretical Domains Framework using both an unconstrained coding scheme and a structured coding matrix. In total, 10 coding schemes were created from a subset of data and then applied to the full data set in 10 new conversations, resulting in 100 conversations each for inductive and unconstrained deductive analysis. A total of 10 further conversations coded the full data set into the structured coding matrix. Intercoder agreement was evaluated across and within coding schemes. ChatGPT output was also evaluated by the researchers to assess whether it reflected prompt instructions.
The precision of detecting change mechanisms in the data subset ranged from 66% to 88%. Overall κ scores for intercoder agreement ranged from 0.72 to 0.82 across inductive coding schemes and from 0.58 to 0.73 across unconstrained coding schemes and structured coding matrix. Coding into the best-performing coding scheme resulted in category-specific κ scores ranging from 0.67 to 0.95 for the inductive approach and from 0.13 to 0.87 for the deductive approaches. ChatGPT largely followed prompt instructions in producing a description of each coding scheme, although the wording for the inductively developed coding schemes was lengthier than specified.
ChatGPT appears fairly reliable in assisting with qualitative analysis. ChatGPT performed better in developing an inductive coding scheme that emerged from the data than adapting an existing framework into an unconstrained coding scheme or coding directly into a structured matrix. The potential for ChatGPT to act as a second coder also appears promising, with almost perfect agreement in at least 1 coding scheme. The findings suggest that ChatGPT could prove useful as a tool to assist in each phase of qualitative content analysis, but multiple iterations are required to determine the reliability of each stage of analysis.
Journal Article
Numerical algorithms for personalized search in self-organizing information networks
\"This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks.\" The book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding web-scale data.--[book cover]
Book
Directed Qualitative Content Analysis (DQlCA): A Tool for Conflict Analysis
Qualitative Content Analysis (QlCA) is a research methodology carried on in either an inductive or deductive way. The former way is widely used by qualitative researchers and is more presented in qualitative research manuals than the latter is. While in the inductive approach, the researcher draws categories/themes from data she collected to start her research, in the deductive, aka, directed approach, she rather draws them from (an) existing theory/ies to set up the categories/themes that guide her research. The deductive or directed qualitative content analysis (DQlCA) is used to test, to corroborate the pertinence of the theory/ies guiding the study or to extend the application of the theory/ies to contexts/cultures other than those in which that/those theory/ies was/were developed. It is more used by quantitative researchers than by qualitative ones. And while using it, these create their data. This article aims at reducing the above holes in the qualitative research tradition by proposing an 8-step DQlCA within three phases (Study Preparation, Data Analysis, and Results’ Reporting) to respond to the same purposes with data not created by the researcher. Some appendixes provide, in tables/displays, illustrations to serve as models to inspire conflict analyst researchers who choose DQlCA as their research methodology.
Journal Article