João Santos - Lisbon School of Nursing Vice President

Cristina Baixinho - Lisbon School of Nursing (WCQR2018 Coordinator)

António Pedro Costa - WCQR Coordinator

Mindsets for harnessing digital tools in qualitative and mixed-methods analysis: the Five-Level QDA® method
Christina Silver, University of Surrey - UK

Many qualitative researchers don’t use CAQDAS packages because they are concerned the software will drive the process or force them to conform to a single style of data analysis. But there are also many researchers who have mastered the art of harnessing software in the service of their analytic needs. We developed Five-Level QDA as a way of describing what these experts unconsciously do and it is a pedagogy for more quickly developing this expertise. The method recognizes that expert users of CAQDAS packages clearly distinguish their analytic strategies – what they plan to do – from their software tactics – how they plan to do it. The core of Five-Level QDA is a process of conscious translation between strategies and tactics that ensures that the needs of the data analysis, and not the capabilities of the software, drive the process. Christina will describe the genesis, development and principles of the Five-Level QDA method and discuss the heuristic mindset involved in translating between analytic strategies and software tactics. She will also outline how Five-Level QDA is being implemented by colleagues around the world and reflect on the opportunities it provides for teaching qualitative and mixed-methods in integrated curricula.

DiscoverText: Power Tools for Human & Machine Learning (Room 1.43)
Stuart Shulman,USA

Introduction to ATLAS.ti 8 Windows: Tools for Digging into your Qualitative Data (Room 1.44)
Neringa Kalpokaite and Ivana Radivojevic, Germany

How happy are your clients? webQDA as a tool for assessing customer satisfaction (Room 1.45)
Jaime Ribeiro, Portugal

User Experience Evaluation (Room 1.46)
Sónia Sousa, Estonia

(more information)

DiscoverText: Power Tools for Human & Machine Learning (Room 1.43)
Stuart Shulman,USA

Introduction to ATLAS.ti 8 Windows: Tools for Digging into your Qualitative Data (Room 1.44)
Neringa Kalpokaite and Ivana Radivojevic, Germany

How happy are your clients? webQDA as a tool for assessing customer satisfaction (Room 1.45)
Jaime Ribeiro, Portugal

User Experience Evaluation (Room 1.46)
Sónia Sousa, Estonia

(more information)

Text Mining to Improve Qualitative Data Analysis of Large Document Collections
Luís Paulo Reis, University of Porto - Portugal

Text Mining is the process of deriving high-quality information from text using analytical and natural language processing methods. Text analysis involves among others, information retrieval, lexical analysis, pattern recognition, tagging/annotation, information extraction, data mining, visualization, and predictive analytics. The main goal is, essentially, to turn text into valid information for analysis. This Talk introduces the main processes and methodologies of text mining to support qualitative data analysis of large-scale document collections. It aims to contribute to the steadily growing field of qualitative research facing the challenge to consolidate text analysis methods to be able to process vast amounts of text, in a semi-autonomous manner, using modern data and text mining algorithms. The talk will be illustrated with examples and results from recent LIACC projects on this area with emphasis on our projects on complaints analysis developed together with the Portuguese Government (Min. Economy/ASAE) and several projects developed together with large companies such as Twitómetro/TwitterEcho, VOXX, Time Machine, Financial Sentiment Analysis and Argumentation Mining.