Explore the vast range of titles available.

\"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.

Purpose Qualitative research has gained in importance in the social sciences. General knowledge about qualitative data analysis, how to code qualitative data and decisions concerning related research design in the analytical process are all important for novice researchers. The purpose of this paper is to offer researchers who are new to qualitative research a thorough yet practical introduction to the vocabulary and craft of coding. Design/methodology/approach Having pooled, their experience in coding qualitative material and teaching students how to code, in this paper, the authors synthesize the extensive literature on coding in the form of a hands-on review. Findings The aim of this paper is to provide a thorough yet practical presentation of the vocabulary and craft of coding. The authors, thus, discuss the central choices that have to be made before, during and after coding, providing support for novices in practicing careful and enlightening coding work, and joining in the debate on practices and quality in qualitative research. Originality/value While much material on coding exists, it tends to be either too comprehensive or too superficial to be practically useful for the novice researcher. This paper, thus, focusses on the central decisions that need to be made when engaging in qualitative data coding in order to help researchers new to qualitative research engage in thorough coding in order to enhance the quality of their analyses and findings, as well as improve quantitative researchers’ understanding of qualitative coding.

\"Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects. If you aren't using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished. You will: get data into and out of Python code, prepare the data and its format, find the meaning of the data, visualize the data using iPython.\"--Provided by publisher.

Purpose: The present study aims to give a short introduction into the possibilities offered by Voyant Tools to quantitatively explore qualitative data on the Sales-Marketing Interface (SMI). Design/methodology/approach: The study is exploratory in nature. The sample consists of sales and marketing employees of six manufacturing companies. Answers to three open-ended questions were analysed quantitatively and visualised in various ways using the online toolset of Voyant Tools. We experimented with four different tools out of the twenty four offered by Voyant Tools. These tools were: Cyrrus tool, Correlation tool, Topics tool and Scatter plot tool. All four tools that were tested on the data have scalable parameters. Various settings were tested to demonstrate how input conditions influence modelling of the textual data. Findings: It was demonstrated that the four selected text analysis tools can yield valuable information depicted in the form of attractive visualisation formats. It is also highlighted how rushed conclusions can be arrived at by falsely interpreting the visualised data. It is shown how setting different input parameters can affect results. Out of the four examined tools the Scatter plot tool offering an analysis and modelling method based on t-SNE (t-Distributed Stochastic Neighbour Embedding) proved to yield the most complex information about the text. Research limitations/implications: As the study aimed to be exploratory a sample of convenience was used to collect qualitative data. Although quantitative methods can be invaluable tools of preliminary analysis and hypothesis adjustment in the processing of qualitative data, their results should always be checked against the traditional content analysis techniques which are more sensitive to the complex structure of semantic units. These quantitative techniques are to help early exploration of textual data. Practical implications: Managerial implications might be connected to the fact that in a fast changing global business environment managers and corporate decision makers in general might find the attractive visualisation outputs of Voyant Tool easy to analyse and interprete various aspects of business. As Voyant Tools is an open source, free online sofware not even requiring regsitration and at the same time has an impressive array of sophisticated statistical tools, it might be a cost-effective way of analysing qualitative data. Originality/value: As there is virtually no earlier literature on how quantitative data visualisation techniques can be used in marketing research, especially in the analysis of the SMI, utilisation possibilities of Voyant Tools and other quantitative data analysis and visualisation software for handling qualitative data is definitely a worthwhile area for further research.

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you'll get a chance to revisit the concepts you've covered so far. What You Will Learn Use Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.--Provided by publisher.

This paper provides an overview of secondary analysis of qualitative data, based on work examining the nature and use of the methodology in social research. It clarifies what secondary analysis is and how the methodology relates to other similar approaches used in qualitative research. It looks at the development of secondary analysis in qualitative research, and some of the factors that have shaped this. And it examines the ways in which researchers have re-used qualitative data in published studies to date, describing which sources of data have been re-used, by whom and for what purposes. As well as reflecting on the ways in which researchers have so far utilized qualitative data in secondary studies, the paper also looks forward to some of the challenges that lie ahead.

This article offers a step-by-step description of how qualitative data analysis software can be used for a qualitative content analysis of newspaper articles. Using NVivo as an example, it illustrates how software tools can facilitate analytical flexibility and how they can enhance transparency and trustworthiness of the qualitative research process. Following a brief discussion of the key characteristics, advantages and limitations of qualitative data analysis software, the article describes a qualitative content analysis of 230 newspaper articles, conducted to determine international media perceptions of New Zealand's environmental performance in connection with climate change and carbon emissions. The article proposes a multi-level coding approach during the analysis of news texts that combines quantitative and qualitative elements, allowing the researcher to move back and forth in coding and between analytical levels. The article concludes that while qualitative data analysis software, such as NVivo, will not do the analysis for the researcher, it can make the analytical process more flexible, transparent and ultimately more trustworthy.URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs150283

Mixed methods research is commonly defined as the combination and integration of qualitative and quantitative data. However, defining these two data types has proven difficult. In this article, I argue that qualitative and quantitative data are fundamentally different, and this difference is not about words and numbers but about condensation and structure. As qualitative data are analyzed with qualitative methods and quantitative data with quantitative methods, we cannot analyze one type of data with the other type of method. Quantitative data analysis can reveal new patterns, but these are always related to the existing variables, whereas qualitative data analysis can reveal new aspects that are hidden in the data. To consider data as quantitative or qualitative, we should judge these data as end products, not in terms of the process through which they come into being. Thus, quantitizing qualitative data results in quantitative data and the analysis thereof is quantitative, not mixed, data analysis. For mixed data analysis, both real, non-quantitized qualitative data and quantitative data are needed. As these quantitative data may be quantitized qualitative data, the implication is that, contrary to a common view, mixed methods research does not necessarily involve quantitative data collection.

Qualitative research data, such as data from focus groups and in-depth interviews, are increasingly made publicly available and used by secondary researchers, which promotes open science and improves research transparency. This has prompted concerns about the sensitivity of these data, participant confidentiality, data ownership, and the time burden and cost of de-identifying data. As more qualitative researchers (QRs) share sensitive data, they will need support to share responsibly. Few repositories provide qualitative data sharing guidance, and currently, researchers must manually de-identify data prior to sharing. To address these needs, our QDS team worked to identify and reduce ethical and practical barriers to sharing qualitative research data in health sciences research. We developed specific QDS guidelines and tools for data de-identification, depositing, and sharing. Additionally, we developed and tested Qualitative Data Sharing (QuaDS) Software to support qualitative data de-identification. We assisted 28 qualitative health science researchers in preparing and de-identifying data for deposit in a repository. Here, we describe the process of recruiting, enrolling, and assisting QRs to use the guidelines and software and report on the revisions we made to our processes and software based on feedback from QRs and curators and observations made by project team members. Through our pilot project, we demonstrate that qualitative data sharing is feasible and can be done responsibly.

Data sharing and reuse are becoming the norm in quantitative research. At the same time, significant skepticism still accompanies the sharing and reuse of qualitative research data on both ethical and epistemological grounds. Nevertheless, there is growing interest in the reuse of qualitative data, as demonstrated by the range of contributions in this special issue. In this research note, we address epistemological critiques of reusing qualitative data and argue that careful curation of data can enable what we term “epistemologically responsible reuse” of qualitative data. We begin by briefly defining qualitative data and summarizing common epistemological objections to their shareability or usefulness for secondary analysis. We then introduce the concept of curation as enabling epistemologically responsible reuse and a potential way to address such objections. We discuss three recent trends that we believe are enhancing curatorial practices and thus expand the opportunities for responsible reuse: improvements in data management practices among researchers, the development of collaborative curation practices at repositories focused on qualitative data and technological advances that support sharing rich qualitative data. Using three examples of successful reuse of qualitative data, we illustrate the potential of these three trends to further improve the availability of reusable data projects.