An Afrocentric engagement with the portrayal of old age in Shona proverbs

The differentiation of contemporary approaches to qualitative data analysis can seem daunting even for experienced social science researchers. Especially when they move forward in the data analysis process from general analytical strategies used in qualitative research to more specific approaches for different types of qualitative data, including interviews, text, audio, images, videos, and so-called virtual data, by discovering the domain ontology of the qualitative research field, we see that there are more than twice as many different classes of data analysis methods as qualitative research methods. This article critically reflects on qualitative research and the qualitative computer data analysis process, emphasising its significance in harnessing digital opportunities and shaping collaborative work. Using our extensive analytical and research project experience, the last research results, and a literature review, we try to show the impact of new technologies and digital possibilities on our thinking. We also try to do the qualitative data analysis. The essence of this procedure is a dialectical interplay between the new world of digital technology and the classic methodology. The use of digital possibilities in qualitative research practices shapes the researcher’s identity and their analytical and research workshop. Moreover, it teaches collaborative thinking and teamwork and fosters the development of new analytical, digital, and Information Technology (IT) skills. Imagining contemporary qualitative research and data analysis in the humanities and social sciences is difficult. Opening to modern technologies in computer-based qualitative data analysis shapes our interpretation frameworks and changes the optics and perception of research problems.

IntroductionThe benefits of Early Clinical Exposure (ECE) in medical education are often stated but there is limited evidence on how to effectively maximize its learning value. The challenge for medical educators lies in finding ways to enhance the quality of ECE in alignment with students’ feedback, while realizing the learning outcomes. The purpose of this study is to investigate undergraduate medical students’ perception of an innovative ECE intervention in Dubai, United Arab Emirates, developed using design-based research in alignment with adult, experiential learning theories.MethodsA convergent mixed methods study design was utilized. The data was collected using a tailormade survey to solicit both quantitative and qualitative feedback. Quantitative data was analyzed using SPSS. Qualitative data analysis was inductive based on constructivist epistemology. Following the conclusion of the independent data analyses of the quantitative and qualitative datasets, the primary inferences were integrated using the iterative joint display analysis process.ResultsOut of the 68 students who attended the ECE, 54 responded. The percentage of the total extent of agreement that the ECE: familiarized them with learning in the clinical environment and brought forth the institutional values were 79.60% and 86.43%, respectively. The extent of active engagement and self-directed learning, with a mean of 6.80(2.42), was significantly associated with how much the learners reaped from the learning experience (P < 0.05). A novel conceptual model, namely: ‘Early Clinical Exposure Added Value’, with five interconnected themes, was developed from the qualitative analysis. Integration of findings led to six meta-inferences: Embeddedness in context of learning, System perspective, Patient-centricity, Theory–practice link, Resilience, and Proactiveness.ConclusionThe more medical students engage in their learning, the more ECE contributes to building their academic resilience, and propels them in terms of clinical correlations, skills’ development, and values reinforcement. Securing engagement opportunities for the learners, when designing and planning for the ECE, is essential. Optimizing the ECE learning value can happen systematically through continuously developing the respective intervention in alignment with the principles of design-based research and anchoring it in constructivism experiential learning theories.

This article discusses the place of qualitative network analysis in the strategy of mixing methods in the social sciences. We conducted a systematic review of the literature that allowed us to demonstrate examples of the use of qualitative network analysis in empirical research. There are four ways of analyzing qualitative data in network mixed studies: qualitative analysis of qualitative data, quantitative analysis of qualitative data, quantitative and qualitative analysis of qualitative data and quantitative and qualitative analysis of qualitative and quantitative data. Currently, there is a lack of a single definition of the methodology of qualitative network analysis and consensus on its implementation in practice. The main possibilities of qualitative network analysis are discussed in the article. At the level of the research object, qualitative network analysis studies the personal networks of individuals’ relationships, and also make easier an access to hard-to-reach groups of respondents. At the level of the subject of research, qualitative network analysis allows us to study the deep meanings of relationships in the network and the contexts of interaction, to describe and understand networks from the inside and outside, to focus on the activities of actors and their strategies for building a network, as well as to identify the temporality of relationships in the network. The article discusses a position that questions the existence of qualitative network analysis as an independent methodology.

Within the research process, the analysis of the data emerges as one of the most important steps. In qualitative research, the analysis of data is a difficult task for even the most experienced researchers and often brings up many doubts about the way to implement it. It is therefore necessary to have material that facilitates the analysis process. Even though there are numerous manuals that focus on the analysis of qualitative data, researchers often can be confused with the large number of names that this type of analysis receives (e.g. Thematic Analysis, Content Analysis) or with the various qualitative methods (e.g. Phenomenology, Grounded Theory) that are available. Each of these qualitative approaches presents a particular language to detail the research process, which makes it difficult to recognize common aspects shared by these methods. Recently, the American Psychological Association has emphasized the need to identify, within the various qualitative methods and procedures, shared standards for reporting this type of work. In agreement with the above, several qualitative researchers have pointed out that beyond the aforementioned diversity it is possible to identify a basic core with regard to qualitative analysis, without having to match the different perspectives of the qualitative method, such as Grounded Theory, Ethnography ore Phenomenology. Focusing on this communality will facilitate a simpler and clearer approach to the data analysis process. The analysis process mainly involves 1) data condensation, and 2) presentation of results. Following this line, the present manuscript aims to: (a) develop what the basic core of data analysis consists of, (b) show the necessary steps to carry out this analysis process, (c) review specific techniques for the detection of categories, (d) present examples using the Atlas.ti software, and (e) show the possible ways of presenting the results. Researchers have realized the importance of having methodological works that facilitate the analysis of qualitative data, and allow answering the question: What does qualitative analysis look like in practice?. The development of this type of work pretends on the one hand to facilitate the understanding of the process of qualitative data analysis and, on the other hand, serve to shape better and in a more standard way which was the data analysis procedure applied in the respective investigations. This material should be taken as a first step in the understanding of the process, and it should not be understood that the qualitative analysis is reduced only to what is developed in this article. For example, in the first level grouping step or first coding cycle, the researcher can make use of 25 different types or forms of coding (e.g., live coding). Even so, the development of works such as the present manuscript is intended to facilitate the understanding and reporting the process of qualitative data analysis. Beyond the name with which the researcher calls the analysis procedure carried out, it is relevant to report in his works the basic steps (i.e. Identification, First and Second Level of Categorization), and the specific techniques used to detect categories or topics (e.g. repetition or similarities). Likewise, it is advisable to follow the guidelines recently published by the APA for the publication of qualitative research. We hope that this material will be useful especially for new researchers who need an introductory text to carry out the qualitative data analysis.

Ontological assumptions that social reality is standardized and predictable, or that it is constantly reconstructed through human interactions, affect the research topics choice of the researchers, how they formulate questions, and how they conduct research. The researcher's ontological perspective is reflected in these epistemological inquiries, which indicate how to obtain information about the nature of reality. These philosophical approaches serve as instruments for framing how the research process should progress, from the type of research questions to how data will be acquired and analyzed to data interpretation and provide a viewpoint on the social world. This research focuses on qualitative data analysis, which is one of the most difficult processes in qualitative research methodologies that aim to comprehend and interpret the social world in its natural setting and is critical to the research's success. It aims to develop a guide that will assist novice researchers who desire to conduct qualitative data analysis. To achieve this aim, the steps of qualitative data analysis are explained. Content analysis and thematic analysis which are the basic qualitative data analysis methodologies are described within the context of the disciplines on which they are founded. Qualitative data analysis methodologies, which are most used in the field of social sciences, are clarified by stressing the distinctions between the fundamental ideas.

BackgroundBig qualitative data analysis is an emerging discipline in qualitative health research and has been used with online posts, open-ended survey responses, and patient health records. Traditional methods of qualitative...

A realist approach has gained popularity in evaluation research, particularly in understanding causal explanations of how a program works (or not), the circumstances, and the observed outcomes. In qualitative inquiry, the approach has contributed to better theoretically based explanations regarding causal interactions. The aim of this study was to discuss how we conducted a realist-informed data analysis to explore the causal interactions within qualitative data. We demonstrated a four-step realist approach of retroductive theorizing in qualitative data analysis using a concrete example from our empirical research rooted in the critical realism philosophical stance. These steps include (a) category identification, (b) elaboration of context-mechanism-outcome configuration, (c) demi-regularities identification, and (d) generative mechanism refinement. The four-step qualitative realist data analysis underpins the causal interactions of important factors and reveals the underlying mechanisms. The steps produce comprehensive causal explanations that can be used by related parties-especially when making complex decisions that may affect wide communities. The core process of realist data analysis is retroductive theorizing. The four-step qualitative realist data analysis facilitates this theorizing by allowing the researcher to identify (a) patterns, (b) fluctuation of patterns, (c) mechanisms from collected data, and (d) to confirm proposed mechanisms.

AAPA Statement on Race and Racism.

This study examines the common ground between lay people and scientists regarding forest values and definitions of forest health. With the forest at Pinery Provincial Park, Ontario, as a case study, the authors compared six ecological indicators to determine which were sensitive to the multiple impacts of visitor use, deer browsing and fire suppression. Plant cover and proportion native species were sensitive to these impacts. Sapling height was greater in low deer density areas. The authors also conducted focused discussions with local interest groups, followed by qualitative data analysis. Overall, there was good convergence between scientific and public views of forests and forest health, although this may partly be due to the groups' interest in nature and the Pinery. Subjects saw a connection between their health and the state of the global and local environment, including forests. There is a need for increased awareness in the public to the necessity of managing high deer populations in parks to protect other forest components such as biodiversity. Forest managers must consider that people greatly value forests near them for mental well being. Group responses suggested that messages explicitly linking forest benefits to human health and well being may motivate people to protect forests.

Household food security is faced with two important problems, namely how to expand income sources and how to properly distribute their income for life necessities. The purpose of this study was to analyze the comparison of the distribution patterns of household income and expenditure of wetland farmers with dry land and to analyze the determinants that affect the household food security of wetland and dryland farmers in Lombok Island. The research design used a cross-sectional study with a survey method. The collected data will be analyzed using qualitative and quantitative data analysis methods. In general, the qualitative data analysis used is policy analysis (program evaluation), institutional analysis, potential analysis, and priority determination of community-level problems. Qualitative data analysis was carried out through the process of filtering data, categorizing, concluding, and retesting. Quantitative data analysis used a logistic regression model. The results of the study concluded that: The sources of income of farmers in wetlands are more varied than those of dryland farmers. Meanwhile, wetland farmers household expenditures are relatively the same as wetland farmers. The food security of wetland farmer households (90%) is better than farmer households in dryland (83.3%). Meanwhile, wetland farmer household food insecurity is lower than wetland farmer households. The distribution pattern of wetland farmer household income comes from rice, and maize cultivation, while in dryland areas, it is sourced from rice, corn, and soybean farming. Distribution patterns Household expenditure of wetland and dryland farmers is divided into expenditures for food and non-food. The factors that determine household food security in dry and wetland areas are farmer household income.

Justifying the adoption of the qualitative research method to satisfy the examiners (for thesis) and reviewers (for journal articles) is a challenging task for researchers in business, management, marketing, tourism, hospitality and albeit in social science domain. The difficulty continues in establishing the justification for selecting qualitative research approaches, sample strategy, sample size, data collection methods (i.e. interview methods), saturation, and data analysis. In this guide, we aim to ground brief justifications for researchers and guidance on how to justify the section of qualitative research method in thesis and journal articles. This study also provides brief justification on selecting specific qualitative research approaches, sampling strategies, sample size, interviews, and data analysis methods. Furthermore, this study provides a glimpse of justification regarding when and how to reach saturation point in qualitative research. Keywords: Qualitative research method, Research approaches, Sampling strategy, Sample size, Interview method, Saturation, and Qualitative data analysis (QDA)

This study aims to make students have an independent character, discipline and responsibility in the digital era through the Monthly Bazaar. The method used in this research was Mixed Method with a sequential exploratory strategy. The sequential exploratory strategy involves collecting and analyzing quantitative data in the second stage based on the results of the first stage. Weights / priorities are more likely to be in the first stage, and the mixing process between these two methods occurs when the researcher makes a connection between qualitative data analysis and quantitative data collection. The results of the study were obtained from qualitative data analysis in the first stage and quantitative data in the second stage. The results of the first stage were obtained from educators' observations of students after completing the monthly bazaar. The results of the second stage are obtained through calculations using a Likert scale and it is obtained the data of 43.75% (ever) if it is categorized in interpretation of scores based on intervals. This data was obtained before the implementation of the monthly bazaar. After implementing the monthly bazaar for two times, the data is 72.75% (often). From the two stages, the relevant results obtained between qualitative and quantitative data analysis showed that there was an independent character, discipline and responsibility embedded in students after the implementation of the monthly bazaar activities.

This paper provides a basic outline of qualitative research techniques and the steps involved in qualitative data analysis. A comparison is made with quantitative research, arguing that this is not as objective and non-interpretative as it often made out to be. Qualitative data analysis is illustrated using an example from the field of social studies of science to outline how qualitative research may be applied.

While qualitative data analysis (QDA) is an established method in education research, QDA is less common in engineering research and may be a challenge for engineering faculty not formally trained in qualitative methods to apply it in engineering education. The following describes the collaborative effort between an engineering design instructor and an anthropologist who used QDA to evaluate the implementation of design ethnography training in a third-year biomedical engineering design course. In their partnership, the study investigators examined student perspectives regarding design ethnography training and how such training in an engineering curriculum may prepare students for careers in biomedical design. Data for the study consisted of reflective essays (N = 42) that the students completed following two primary exercises dedicated to design ethnography skills training. Investigators input typed and anonymized text files of the student essays into ATLAS.ti X7, a qualitative data analysis software program, for qualitative content analysis. QDA was conducted using the constant comparison method to inductively identify pertinent themes. Throughout the QDA process, the investigators routinely met to discuss, merge and interpret themes as needed. Upon the finalization of themes, researchers re-reviewed the data using the finalized codebook (a list of themes and their definitions) for coding reliability. This regular contact was invaluable for the engineering instructor, providing instruction on the process necessary for proper application of QDA. The unique partnership between investigators offered the engineering design instructor the opportunity to evaluate engineering student perceptions of a new curriculum implementation in an in-depth manner not commonly attempted in engineering education. Results from the QDA showed that the incorporation of design ethnography skills training into an engineering design curriculum increased student awareness of the value of ethnography in understanding user environments while offering engineering students the opportunity to develop better observation skills. This study was successful not only in demonstrating efficacy of design ethnography training among undergraduate engineering students, but it also serves as an example of how QDA may be applied by engineering instructors for the evaluation of student experience and work in engineering education.

About the editors.- About the authors.- Foreword Wilfried Griebel.- Theorising Transitions: Shifts and Tensions Sue Dockett, Anne Petriwskyj and Bob Perry.- Building on Bioecological Perspectives.- Reading of Media Accounts of Transition to School in Iceland Johanna Einarsdottir.- Thinking about Transitions - One Framework or Many? Populating the Theoretical Model over Time Aline-Wendy Dunlop.- Multiple Influences on Children's Transition to School Elizabeth Murray.- Intrapersonal and Interpersonal Influences on School Transition Linda Harrison.- Transition and Adjustment to School Kay Margetts.- Transitions and Emergent Writers Noella Mackenzie.- Borderlands, Life Course and Rites of Passage.- Chasms, Bridges and Borderlands: A Transitions Research 'Across the Border' from Early Childhood Education to School in New Zealand Sally Peters.- Transition to School - A Rite of Passage in Life Anders Garpelin.- A Sociocultural Approach to Children in the Transition from Home to Kindergarten Mei Seung Lam.- Experienced and Recalled Transition. Starting School as Part of Life History Tuija Turunen.- Critical Perspectives.- The Relation of Research on Readiness to Research/Practice of Transitions Elizabeth Graue and June Reineke.- Social Justice Dimensions of Starting School Bob Perry.- Transition to School: Normative or Relative? Sue Dockett.- Critical Theory and Inclusive Transitions to School Anne Petriwskyj.- Connecting Theory, Research, Policy and Practice.- Starting School: Synthesis and Analysis Amy MacDonald, Wendy Goff, Kathryn Hopps, Cathy Kaplun and Susanne Rogers.- The Wollongong Transition to School Experience: A Big Step for Children, Families and the Community Tracey Kirk-Downey and Shabnam Hinton.- Transitions, Inclusion and Information Technology Bronwyn Glass and Margaret Cotman.- Building Connections around Transition: Partnerships and Resources for Inclusion Marge Arnup.- Research to Policy: Transition to School Position Statement Sue Dockett and Bob Perry.