Veracity of the archive : a research approach to the collection and verification of urban morphological records using qualitative data analysis software

Critical to the research of urban morphologists is the availability of historical records that document the urban transformation of the study area. However, thus far little work has been done towards an empirical approach to the validation of archival data in this field. Outlined in this paper, therefore, is a new methodology for validating the accuracy of archival records and mapping data, accrued through the process of urban morphological research, so as to establish a reliable platform from which analysis can proceed. The paper particularly addresses the problems of inaccuracies in existing curated historical information, as well as errors in archival research by student assistants, which together give rise to unacceptable levels of uncertainty in the documentation. The paper discusses the problems relating to the reliability of historical information, demonstrates the importance of data verification in urban morphological research, and proposes a rigorous method for objective testing of collected archival data through the use of qualitative data analysis software.

Qualitative data analysis:A practical example

The aim of this paper is to equip readers with an understanding of the principles of qualitative data analysis and offer a practical example of how analysis might be undertaken in an interview-based study.

The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections

For a long time, electronic data analysis has been associated with quantitative methods. However, Computer Assisted Qualitative Data Analysis Software (CAQDAS) are increasingly being developed. Although the CAQDAS has been there for decades, very few qualitative health researchers report using it. This may be due to the difficulties that one has to go through to master the software and the misconceptions that are associated with using CAQDAS. While the issue of mastering CAQDAS has received ample attention, little has been done to address the misconceptions associated with CAQDAS. In this paper, the author reflects on his experience of interacting with one of the popular CAQDAS (NVivo) in order to provide evidence-based implications of using the software. The key message is that unlike statistical software, the main function of CAQDAS is not to analyse data but rather to aid the analysis process, which the researcher must always remain in control of. In other words, researchers must equally know that no software can analyse qualitative data. CAQDAS are basically data management packages, which support the researcher during analysis.

Making visible the coding process : Using qualitative data software in a post-structural study

Qualitative research methods require transparency to ensure the ‘trustworthiness’ of the data analysis. The intricate processes of organizing, coding and analyzing the data are often rendered invisible in the presentation of the research findings, which requires a ‘leap of faith’ for the reader. Computer assisted data analysis software can be used to make the research process more transparent, without sacrificing rich, interpretive analysis by the researcher. This article describes in detail how one software package was used in a poststructural study to link and code multiple forms of data to four research questions for fine-grained analysis. This description will be useful for researchers seeking to use qualitative data analysis software as an analytic tool.

A framework for qualitative analysis of focus group data in information systems

Focus groups are a popular qualitative research method for information systems researchers. However, compared with the abundance of research articles and handbooks on planning and conducting focus groups, surprisingly, there is little research on how to analyse focus group data. Moreover, those few articles that specifically address focus group analysis are all in fields other than information systems, and offer little specific guidance for information systems researchers. Further, even the studies that exist in other fields do not provide a systematic and integrated procedure to analyse both focus group ‘content’ and ‘interaction’ data. As the focus group is a valuable method to answer the research questions of many IS studies (in the business, government and society contexts), we believe that more attention should be paid to this method in the IS research. This paper offers a systematic and integrated procedure for qualitative focus group data analysis in information systems research.

Qualitative analysis of the interdisciplinary interaction between data analysis specialists and novice clinical researchers.

BACKGROUND: The inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time. METHODS/PRINCIPAL FINDINGS: We conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of "what if" situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal. CONCLUSION/SIGNIFICANCE: The communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

Structuring the Calibration of Qualitative Data as Sets for Qualitative Comparative Analysis (QCA)

Most studies that apply qualitative comparative analysis (QCA) rely on macro-level data, but an increasing number of studies focus on units of analysis at the micro or meso level (i.e., households, firms, protected areas, communities, or local governments). For such studies, qualitative interview data are often the primary source of information. Yet, so far no procedure is available describing how to calibrate qualitative data as fuzzy sets. The authors propose a technique to do so and illustrate it using examples from a study of Guatemalan local governments. By spelling out the details of this important analytic step, the authors aim at contributing to the growing literature on best practice in QCA. © The Author(s) 2012.

Generating best evidence from qualitative research: the role of data analysis

Objective: To outline the importance of the clarity of data analysis in the doing and reporting of interview-based qualitative research.

Approach: We explore the clear links between data analysis and evidence. We argue that transparency in the data analysis process is integral to determining the evidence that is generated. Data analysis must occur concurrently with data collection and comprises an ongoing process of 'testing the fit' between the data collected and analysis. We discuss four steps in the process of thematic data analysis: immersion, coding, categorising and generation of themes.

Conclusion: Rigorous and systematic analysis of qualitative data is integral to the production of high-quality research. Studies that give an explicit account of the data analysis process provide insights into how conclusions are reached while studies that explain themes anchored to data and theory produce the strongest evidence.

Qualitative chemical analysis; a guide in qualitative work, with data for analytical operations and laboratory methods in organic chemistry,

Mode of access: Internet.

Using the framework method for the analysis of qualitative data in multi-disciplinary health research

Background: The Framework Method is becoming an increasingly popular approach to the management and analysis of qualitative data in health research. However, there is confusion about its potential application and limitations. Discussion. The article discusses when it is appropriate to adopt the Framework Method and explains the procedure for using it in multi-disciplinary health research teams, or those that involve clinicians, patients and lay people. The stages of the method are illustrated using examples from a published study. Summary. Used effectively, with the leadership of an experienced qualitative researcher, the Framework Method is a systematic and flexible approach to analysing qualitative data and is appropriate for use in research teams even where not all members have previous experience of conducting qualitative research. © 2013 Gale et al.; licensee BioMed Central Ltd.

The consequences of substance use among gay and bisexual men : a consensual qualitative research analysis

- Background Substance use is common among gay/bisexual men and is associated with significant health risks (e.g. HIV transmission). The consequences of substance use, across the range of substances commonly used, have received little attention. The purpose of this study is to map participant’s beliefs about the effects of substance use to inform prevention, health promotion and clinical interventions. - Methods Participants were interviewed about experiences regarding their substance use and recruited through medical and sexual health clinics. Data were collected though a consumer panel and individual interviews. Responses regarding perceived consequences of substance use were coded using Consensual Qualitative Research (CQR) methodology. - Results Most participants reported lifetime use of alcohol, cannabis, stimulants and amyl nitrite, and recent alcohol and cannabis use. A wide range of themes were identified regarding participant’s thoughts, emotions and behaviours (including sexual behaviours) secondary to substance use, including: cognitive functioning, mood, social interaction, physical effects, sexual activity, sexual risk-taking, perception of sexual experience, arousal, sensation, relaxation, disinhibition, energy/activity level and numbing. Analyses indicated several consequences were consistent across substance types (e.g. cognitive impairment, enhanced mood), whereas others were highly specific to a given substance (e.g. heightened arousal post amyl nitrite use). - Conclusions Prevention and interventions need to consider the variety of effects of substance use in tailoring effective education programs to reduce harms. A diversity of consequences appear to have direct and indirect impacts on decision-making, sexual activity and risk-taking. Findings lend support for the role of specific beliefs (e.g. expectancies) related to substance use on risk-related cognitions, emotions and behaviours.