Analyzing Qualitative Data with MAXQDA

“To begin at the beginning” is the opening line of the play Under Milk Wood by Welsh poet Dylan Thomas. So, we also want to start here at the beginning and start with some information about the history of the analysis software MAXQDA. This story is quite long; it begins in 1989 with a first version of the software, then just called “MAX,” for the operating system DOS and a book in the German language. The book’s title was Text Analysis Software for the Social Sciences. Introduction to MAX and Textbase Alpha written by Udo Kuckartz, published by Gustav Fischer in 1992. Since then, there have been many changes and innovations: technological, conceptual, and methodological. MAXQDA has its roots in social science methodology; the original name MAX was reference to the sociologist Max Weber, whose methodology combined quantitative and qualitative methods, explanation, and understanding in a way that was unique at the time, the beginning of the twentieth century. Since the first versions, MAX (later named winMAX and MAXQDA) has always been a very innovative analysis software. In 1994, it was one of the first programs with a graphical user interface; since 2001, it has used Rich Text Format with embedded graphics and objects. Later, MAXQDA was the first QDA program (QDA stands for qualitative data analysis) with a special version for Mac computers that included all analytical functions. Since autumn 2015, MAXQDA has been available in almost identical versions for Windows and Mac, so that users can switch between operating systems without having to familiarize themselves with a new interface or changed functionality. This compatibility and feature equality between Mac and Windows versions is unique and greatly facilitates team collaboration. MAXQDA has also come up with numerous innovations in the intervening years: a logically and very intuitively designed user interface, very versatile options for memos and comments, numerous visualization options, the summary grid as a middle level of analysis between primary data and categories, and much more, for instance, transcription, geolinks, weight scores for coding, analysis of PDF files, and Twitter analysis. Last but not least, the mixed methods features are worth mentioning, in which MAXQDA has long played a pioneering role. This list already shows that today MAXQDA is much more than text analysis software: the first chapter of this book contains a representation of the data types that MAXQDA can analyze today (in version 2018) and shows which file formats can be processed. The large variety of data types is contrasted by an even greater number o

Analyzing Qualitative Data about Hospitalized Children: Reflections on Bodily Expressions

Although considerable energy is invested in ensuring that pediatric hospital environments are psychosocially supportive, few researchers have connected the experiences of patients to hospital architecture, a crucial interface between healthcare delivery and patient care. Seeking to uncover childrens experiences within a contemporary hospital, I draw on data gathered during a photo walking tour with hospitalized children. Findings suggest that in addition to speaking, children express themselves physically by appropriating the gaze, pressing boundaries and finding alternate spaces. New methods and techniques are needed to reveal childrens physical competencies and abilities to determine their environmental preferences

Using Data and Statistics

This handout identifies the differences between statistics and data and how to use and analyze them. It provides numerous links to data and statistic sources available for free online or as part of our library subscriptions. Liberty University provides NVivo software for analyzing qualitative data and a link is provided to download the software

Conducting Qualitative Data Analysis: Reading Line-by-Line, but Analyzing by Meaningful Qualitative Units

In the first of a series of “how-to” essays on conducting qualitative data analysis, Ron Chenail points out the challenges of determining units to analyze qualitatively when dealing with text. He acknowledges that although we may read a document word-by-word or line-by-line, we need to adjust our focus when processing the text for purposes of conducting qualitative data analysis so we concentrate on meaningful, undivided entities or wholes as our units of analysis

Iterative Thematic Inquiry: A New Method for Analyzing Qualitative Data

Because themes play such a central role in the presentation of qualitative research results, we propose a new method, Iterative Thematic Inquiry (ITI), that is guided by the development of themes. We begin by describing how ITI uses pragmatism as a theoretical basis for linking beliefs, in the form of preconceptions, to actions, in the form of data collection and analysis. Next, we present the four basic phases that ITI relies on: assessing beliefs; building new beliefs through encounters with data; listing tentative themes; and, evaluating themes through coding. We also review several notable differences between ITI and existing methods for qualitative data analysis, such as thematic analysis, grounded theory, and qualitative content analysis. The use of ITI is then illustrated through its application in a study of exiters from fundamentalist religions. Overall, the two most notable features of ITI are that it begins the development of themes as early as possible, through an assessment of initial preconceptions, and that it relies on writing rather than coding, by using a continual revision of tentative results as the primary procedure for generating a final set of themes

Analysing qualitative data from virtual worlds: using images and text mining

There is an increasing interest within both organisational and social contexts in virtual worlds and virtual reality platforms. Virtual worlds are highly graphical systems in which avatars interact with each other, and almost every event and conversation is logged and recorded. This presents new challenges for qualitative researchers in information systems. This paper addresses the challenges of analyzing the huge amounts of qualitative data that can be obtained from virtual worlds (both images and text). It addresses how images might be used in qualitative studies of virtual worlds, and proposes a new way to analyze textual data using a qualitative software tool called Leximancer. This paper illustrates these methods using a study of a social movement in a virtual world

To Think or Not to Think with Theory in Qualitative Research

Thinking with Theory in Qualitative Research: Viewing Data across Multiple Perspectives is a book that challenges the traditional way of analyzing qualitative data. The book invites researchers who use qualitative methods to think with theory when it comes to analyzing their data since analyzing the data with no theory in mind can make injustice to the data. Thinking with theory, however, can enrich one’s study and direct the process of the data analysis. The authors offer us six theories, as examples, to think with when analyzing qualitative data. These theories are: Derrida’s Deconstruction, Spivak’s Marginality, Foucault’s Power/Knowledge, Butler’s Performativity, Deleuze’s Desire, and Barad’s Intra-action. To clarify their approach, the authors interviewed two first-generation-scholar participants: Cassandra and Sera and analyzed their data in the chapters of the book; each chapter analyzes the data from the perspective of one of the six aforementioned theories, and each chapter is proceeded by an interlude in which the authors explicate why they have chosen this particular theory

Visualizing Qualitative Data: Creative Approaches for Analyzing and Demonstrating Lively Data from Diverse Learning Settings

This structured poster session aims to showcase novel approaches of qualitativelyanalyzing and communicating lively data—data that is complex, nuanced, multimodal, and multi-voiced. Such data is rich but also messy, often defying the traditional text-based forms of description and presentation. Therefore, the session pairs creative techniques and methods to analyze, triangulate, and/or visualize qualitative findings across multiple data sources (e.g., video, digital and physical spaces, participant artifacts, and patterns of movement) from diverse learning contexts (e.g., museums, libraries, outdoor spaces, and classrooms)—beyond showing transcriptions. The visual format of the session supports our goal of sharing and communicating rich data stories for further discussion with diverse audiences

Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms