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\"Qualitative and Mixed Methods Data Analysis using Dedoose will provide both new and experienced researchers with a guided introduction to dealing with the methodological complexity of mixed methods and qualitative inquiry using Dedoose software. The authors use their depth of experience designing and updating Dedoose as well as their significant research experience to give the reader practical strategies for using Dedoose from a wide range of research studies. Qualitative and Mixed Methods Data Analysis using Dedoose walks researchers, students and evaluators through designing a study, conducting fieldwork and reporting credible findings. In the first section the book gives a quick overview of qualitative and mixed methods research and designing studies to work easily with available software, including Dedoose. The authors pay significant attention to data analysis in the second section, addressing the challenges of working in teams, working with just qualitative data, and analyzing qualitative and quantitative data in a mixed method study. The final section is devoted to reporting results and data visualization within Dedoose. Throughout the book, case studies are presented to illustrate the topics discussed with real research examples. Working through this book will give researchers improved technological skills to use Dedoose effectively in their research\"-- Provided by publisher.

This volume brings together work developing storytelling and narrative as an educational methodological framework. Chapters foreground scholarship that helps promote creating change, both educational and societal, through the use of critical storytelling regarding diversity, equity, inclusion, and justice (DEIJ). These include both narratives of challenges and possibilities that educators sometimes encounter in research spaces when intentionally centering DEIJ in their educational practice. Chapters also pay close attention to research ethics and explore epistemological alternatives and attempt to find ways toward generative dialogue regarding the reception and implementation of culturally-relevant pedagogy. This collection offers much sustained reflection on shared and sharable ways of knowing that interrogate the very philosophical foundations of education, pointing us to ever-more equitable futures.

High technology industries are in desperate need for adequate tools to assess the validity of simulations produced by ever faster computers for perennial unstable problems. In order to meet these industrial expectations, applied mathematicians are facing a formidable challenge summarized by these words — nonlinearity and coupling. This book is unique as it proposes truly original solutions: (1) Using hypercomputation in quadratic algebras, as opposed to the traditional use of linear vector spaces in the 20th century; (2) complementing the classical linear logic by the complex logic which expresses the creative potential of the complex plane.

Los docentes se encuentran con este reto digital imprescindible. Puesto que la tecnología supone el eje central de la sociedad que ha cambiado rápidamente la forma de comunicarse, aprender, interaccionar, trabajar, comprar, etc. Ante este panorama la educación no puede avanzar al margen de esta realidad social.Las TIC empleadas en educación como recurso educativo, requieren del uso de unametodología didáctica adecuada para que cumplan con su objetivo formativo. Por sus características, posibilitan y favorecen la efectividad de las metodologías activas, por lo que no se puede hablar de TIC en educación si no se acompañan de metodologías activas adecuadas a cada contexto educativo y viceversa. [Texto de la editorial]

Das Schicksal jedes einzelnen, ist die eine Hand am Ende der vielen seidenen Fäden, die uns Menschen in Bewegung bringen oder uns verführen etwas zu tun und sei es nur einmal nichts dergleichen auszuüben. Jede noch so kleine, für das Auge unmerkliche Bewegung, ein Hauch von etwas, hat seinen Sinn, in dem etwas geschieht. So sind die Wege durch Schritte geebnet, auf denen wir Menschen das Laufen lernen, die unzähligen Kreuzungen mit den Gefühlen des Abschieds oder Zugewinns passieren, ständig dem Abendrot des Tages entgegenlaufen, um irgendwann in dieser Endlichkeit eine Leidenschaft zu finden. Jede darin erlebte Sekunde ist ein unendlicher Wert, der mit nichts aufzuwiegen sei. Über den Autoren Dan Pönicke, in Weißenfels 1981 geboren, studiert in Düsseldorf Kultur - und Sozialwissenschaften.

This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover new variations or entirely new methods for working with categorical data in neural networks. Our primary contribution is to cover these representation techniques in a single work. Practitioners working with big data often have a need to encode categorical values in their datasets in order to leverage machine learning algorithms. Moreover, the size of data sets we consider as big data may cause one to reject some encoding techniques as impractical, due to their running time complexity. Neural networks take vectors of real numbers as inputs. One must use a technique to map qualitative values to numerical values before using them as input to a neural network. These techniques are known as embeddings, encodings, representations, or distributed representations. Another contribution this work makes is to provide references for the source code of various techniques, where we are able to verify the authenticity of the source code. We cover recent research in several domains where researchers use categorical data in neural networks. Some of these domains are natural language processing, fraud detection, and clinical document automation. This study provides a starting point for research in determining which techniques for preparing qualitative data for use with neural networks are best. It is our intention that the reader should use these implementations as a starting point to design experiments to evaluate various techniques for working with qualitative data in neural networks. The third contribution we make in this work is a new perspective on techniques for using categorical data in neural networks. We organize techniques for using categorical data in neural networks into three categories. We find three distinct patterns in techniques that identify a technique as determined, algorithmic, or automated. The fourth contribution we make is to identify several opportunities for future research. The form of the data that one uses as an input to a neural network is crucial for using neural networks effectively. This work is a tool for researchers to find the most effective technique for working with categorical data in neural networks, in big data settings. To the best of our knowledge this is the first in-depth look at techniques for working with categorical data in neural networks.

Data analysis is one of the most important, yet least understood, stages of the qualitative research process. Through rigorous analysis, data can illuminate the complexity of human behavior, inform interventions, and give voice to people’s lived experiences. While significant progress has been made in advancing the rigor of qualitative analysis, the process often remains nebulous. To better understand how our field conducts and reports qualitative analysis, we reviewed qualitative articles published in Health Education & Behavior between 2000 and 2015. Two independent reviewers abstracted information in the following categories: data management software, coding approach, analytic approach, indicators of trustworthiness, and reflexivity. Of the 48 (n = 48) articles identified, the majority (n = 31) reported using qualitative software to manage data. Double-coding transcripts was the most common coding method (n = 23); however, nearly one third of articles did not clearly describe the coding approach. Although the terminology used to describe the analytic process varied widely, we identified four overarching trajectories common to most articles (n = 37). Trajectories differed in their use of inductive and deductive coding approaches, formal coding templates, and rounds or levels of coding. Trajectories culminated in the iterative review of coded data to identify emergent themes. Few articles explicitly discussed trustworthiness or reflexivity. Member checks (n = 9), triangulation of methods (n = 8), and peer debriefing (n = 7) were the most common procedures. Variation in the type and depth of information provided poses challenges to assessing quality and enabling replication. Greater transparency and more intentional application of diverse analytic methods can advance the rigor and impact of qualitative research in our field.

The Cochrane Qualitative and Implementation Methods Group develops and publishes guidance on the synthesis of qualitative and mixed-method implementation evidence. Choice of appropriate methodologies, methods, and tools is essential when developing a rigorous protocol and conducting the synthesis. Cochrane authors who conduct qualitative evidence syntheses have thus far used a small number of relatively simple methods to address similarly written questions. Cochrane has invested in methodological work to develop new tools and to encourage the production of exemplar reviews to show the value of more innovative methods that address a wider range of questions. In this paper, in the series, we report updated guidance on the selection of tools to assess methodological limitations in qualitative studies and methods to extract and synthesize qualitative evidence. We recommend application of Grades of Recommendation, Assessment, Development, and Evaluation–Confidence in the Evidence from Qualitative Reviews to assess confidence in qualitative synthesized findings. This guidance aims to support review authors to undertake a qualitative evidence synthesis that is intended to be integrated subsequently with the findings of one or more Cochrane reviews of the effects of similar interventions. The review of intervention effects may be undertaken concurrently with or separate to the qualitative evidence synthesis. We encourage further development through reflection and formal testing.

ChatGPT (OpenAI) is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the use of ChatGPT in three core phases of thematic analysis within a medical context: (1) direct coding of transcripts, (2) generating themes from a predefined list of codes, and (3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data. While ChatGPT may not adequately capture the full context of each participant, it can serve as an additional member of the analysis team, contributing to researcher triangulation through knowledge building and sensemaking.

Large language models including GPT-4 (OpenAI) have opened new avenues in health care and qualitative research. Traditional qualitative methods are time-consuming and require expertise to capture nuance. Although large language models have demonstrated enhanced contextual understanding and inferencing compared with traditional natural language processing, their performance in qualitative analysis versus that of humans remains unexplored. We evaluated the effectiveness of GPT-4 versus human researchers in qualitative analysis of interviews with patients with adult-acquired buried penis (AABP). Qualitative data were obtained from semistructured interviews with 20 patients with AABP. Human analysis involved a structured 3-stage process-initial observations, line-by-line coding, and consensus discussions to refine themes. In contrast, artificial intelligence (AI) analysis with GPT-4 underwent two phases: (1) a naïve phase, where GPT-4 outputs were independently evaluated by a blinded reviewer to identify themes and subthemes and (2) a comparison phase, where AI-generated themes were compared with human-identified themes to assess agreement. We used a general qualitative description approach. The study population (N=20) comprised predominantly White (17/20, 85%), married (12/20, 60%), heterosexual (19/20, 95%) men, with a mean age of 58.8 years and BMI of 41.1 kg/m . Human qualitative analysis identified \"urinary issues\" in 95% (19/20) and GPT-4 in 75% (15/20) of interviews, with the subtheme \"spray or stream\" noted in 60% (12/20) and 35% (7/20), respectively. \"Sexual issues\" were prominent (19/20, 95% humans vs 16/20, 80% GPT-4), although humans identified a wider range of subthemes, including \"pain with sex or masturbation\" (7/20, 35%) and \"difficulty with sex or masturbation\" (4/20, 20%). Both analyses similarly highlighted \"mental health issues\" (11/20, 55%, both), although humans coded \"depression\" more frequently (10/20, 50% humans vs 4/20, 20% GPT-4). Humans frequently cited \"issues using public restrooms\" (12/20, 60%) as impacting social life, whereas GPT-4 emphasized \"struggles with romantic relationships\" (9/20, 45%). \"Hygiene issues\" were consistently recognized (14/20, 70% humans vs 13/20, 65% GPT-4). Humans uniquely identified \"contributing factors\" as a theme in all interviews. There was moderate agreement between human and GPT-4 coding (κ=0.401). Reliability assessments of GPT-4's analyses showed consistent coding for themes including \"body image struggles,\" \"chronic pain\" (10/10, 100%), and \"depression\" (9/10, 90%). Other themes like \"motivation for surgery\" and \"weight challenges\" were reliably coded (8/10, 80%), while less frequent themes were variably identified across multiple iterations. Large language models including GPT-4 can effectively identify key themes in analyzing qualitative health care data, showing moderate agreement with human analysis. While human analysis provided a richer diversity of subthemes, the consistency of AI suggests its use as a complementary tool in qualitative research. With AI rapidly advancing, future studies should iterate analyses and circumvent token limitations by segmenting data, furthering the breadth and depth of large language model-driven qualitative analyses.