A qualitative approach: autoethnography and embodiment

Measures Used with Populations with Food Insecurity: A Call for Increased Psychometric Validation

Research ObjectiveHealth services researchers' use of social determinants of health (SDOH) variables in quantitative models is increasing, and many publicly available data sources contain scores of high‐quality, complete SDOH variables. However, determining which SDOH variables are most important to include among those available creates challenges for variable selection. One approach is relying on a conceptual framework, prior research, and intuition. But, often conceptual framework domains broadly describe “external context” or “community factors” that provide little help with identifying specific variables to use. Data science methods, particularly random forest regression, are a potential data‐driven approach for SDOH variable selection. This study compared a qualitative approach and a data‐driven approach to SDOH variable selection to identify key SDOH predictors of county‐level health outcomes.Study DesignWe constructed an initial dataset of county‐level SDOH variables compiled from the following data sources: Area Health Resources File, County Health Rankings, American Community Survey, Picture of Subsidized Households, Penn State University’s Social Capital Index, and the Food Environment Atlas. We then employed a qualitative variable selection approach using the Healthy People 2020 organizing framework for SDOH. We purposively selected 6 variables that touched on all 5 domains of the framework, had sufficient variation across counties, were relatively normally distributed, and had established associations with health outcomes in the literature. Next, we employed a data‐driven variable selection approach using random forest regression. We used 3 random forest regression models, each with a different county‐level health outcome specified, and determined the top 6 SDOH predictors driving each outcome. We used the following outcomes: premature death (days of life lost), proportion of the population reporting fair or poor health, and preventable hospitalization rate (ambulatory care sensitive conditions). We identified overlap among the 6 SDOH predictors determined from each random forest model to determine the final set of variables using the data‐driven approach. We then compared the SDOH variables determined using the data‐driven approach to those selected using the qualitative approach.Population StudiedWe included all 3142 U.S. counties in the analysis, and our dataset contained 81 SDOH variables.Principal FindingsWe selected the following SDOH variables using the qualitative approach: median household income, poverty rate, primary care physician‐to‐population ratio, social deprivation index, food environment index, and proportion of the population that reports severe housing problems. The following SDOH variables were selected using the data‐driven approach: median household income (3 models), poverty rate (2 models), proportion of the population with some college (2 models), proportion of the population who report excessive drinking (2 models), proportion of the population who identifies as American Indian or Alaskan Native (2 models), and social capital index (2 models). Two of the 6 variables selected using the qualitative approach (median household income and poverty rate) were validated by the data‐driven approach.ConclusionsRandom forest models can assist with SDOH variable selection for quantitative analysis. However, variables selected using these techniques may not align well with those selected using qualitative approaches.Implications for Policy or PracticeResearchers should consider using data science approaches to validate and compliment—rather than supplement—qualitative approaches to variable selection.

. The qualitative research approach is research that is descriptive and tends to use analysis. The process and meaning are more highlighted in qualitative research, the theoretical foundation is used as a guide so that the focus of research is following research facts. The qualitative approach centers on the description of the subject's perspective, process, and conceptual details of the subject. When these two methods deal with the same research theme, the natural environment is a direct source of data, humans are the main tool/instrument of data collection, and data analysis is carried out inductively. Results: Results in general in scientific activities take a qualitative approach emphasizing the quality aspect of the quantity studied. Citing information on the Ministry of Education's website, the qualitative approach is an emic perspective. The purpose from an emic point of view is a form of qualitative research approach that uses data in the form of narratives, story details, expressions, and construction results from informants. Data can be obtained from data collection techniques in the form of in-depth interviews and observations.

Introduction A mobile application has the potential to involve people with chronic NSLBP in their rehabilitation. To refine the design of a smartphone application for people with chronic NSLBP using mixed quantitative and qualitative approaches. Methods We used a user-centred design approach involving people with chronic NSLBP and healthcare professionals (HCPs). We used a three-step methodology: developing consensus on the features, content, and design of the app; developing a user interface; and usability testing of the app and assessing users’ experience. Transcripts of interviews of users were analyzed by qualitative content analysis. Results A total of 18 people (aged 45 [23–53] years old) with chronic NSLBP, and 7 HCPs (aged 29.5 [25–55] years old) involved in NSLBP management were interviewed. The overall experience of using the smartphone eLombactif app was initially assessed. Then, with close-ended questions we evaluated users’ judgements on the content, its presentation and navigation. Finally, we asked for suggestions: “application content and functionality” and “content presentation” from participants regarding the use and development of the app analyzed by a qualitative methodology. Conclusions This study described how we refined the design of our application for people with chronic NSLBP using a qualitative and quantitative approaches. This methodology allows for deepening the knowledge of the needs and expectations of potential users by measuring their user experience. IMPLICATIONS FOR REHABILITATION Non-specific low back pain (NSLBP) is a major global public health issue leading to considerable economic cost and is primarily responsible for pain and disability. Mobile application has the potential to involve people with chronic NSLBP in their rehabilitation. This study described how we refined the design of our application for people with chronic NSLBP using a qualitative and quantitative approaches.

In this paper, suggestions are offered about the appropriate use of hermeneutic phenomenology and grounded theory in one study. As an alternative to selecting only one qualitative research approach to illuminate a topic of interest about which little is known, two qualitative approaches could be used in a study. Fear of 'method slurring' may prevent this alternative being used. Occasionally, however, credible qualitative researchers have advocated using various research approaches in one study, for example using hermeneutical phenomenology and grounded theory in triangulation. However, if pursuing this direction, several advances in thinking about using qualitative research approaches should be considered. An experience is presented of deciding to use grounded theory and hermeneutic phenomenology in one study. Cautions, practical considerations and alternative options are offered for using these approaches in one study, and the implications of some other possible ways to 'triangulate' qualitative approaches are discussed. Different research approaches can be creatively and successfully used in one study if there has been adequate consideration of vital factors that determine if there is a good 'fit' of the approaches not only with the research problem and question, but also with each other, while also maintaining the integrity of each approach.

Applied behavior analysis is a quantitative field. We calculate frequency of responses per minute, percentage of incorrect and correct responses, percentage of agreement across raters on a regular basis. The safety in numbers can be comfortable—they are clear and objective. However, numbers do not provide the whole picture of a person’s experience. Qualitative approaches provide valuable insights into the lived experience of people. Yet, undertaking qualitative approaches can be scary for those of us in a quantitative field. The subjective data and findings can be extremely challenging to navigate. There is also the matter of feeling like an imposter or a fraud. In this personal narrative inquiry, I tell my story of embracing qualitative approaches as a behavior analyst, the challenges and the surprising discoveries of the depths this data could help us reach. The relevance of qualitative approaches lies in understanding how various qualitative methods and approaches can enhance our understanding of lived experience. Some points about qualitative research are drawn out for context, and my personal experience is explored to show the journey, joys, and challenges of discovering and embracing qualitative research as a behavior analyst.

Yogyakarta is one of the rapidly growing Indonesian cities with its strong culture to construct a distinctive transformation, especially in the rural area. The process of transformation in rural areas is a continuous process as a form of the desire to grow. The agricultural based rural area diversify into activities other than agriculture, such as small craft industry and rural tourism. This study aims to explore tools to measure the level of transformation with a qualitative approach. The uniqueness of the transformation process in the rural area of Yogyakarta inspires the preparation of transformation measurement tools with a qualitative approach by using eight indicators to produce a depth of findings. The tools are developed by using a quadrant model of the combination of potential resources with the efforts made by the occupants. Since the case study research is being used to for the analysis, the quantitative approach could be also used to validate the result of the tools. The quantitative data is taken from secondary data of satellite imagery, government institution, and field survey. Furthermore, this research provides interesting findings by its comparative study between qualitative and quantitative approach. The qualitative approach can become a tool for explaining the dynamics of the transformation of rural area as a whole, complementing quantitative results.

Psychology has been a highly quantitative field since its conception as a science. However, a qualitative approach to psychological research has gained increasing importance in the last decades, and an enduring debate between quantitative and qualitative approaches has arisen. The recently developed Mixed Methods Research (MMR) addresses this debate by aiming to integrate quantitative and qualitative approaches. This article outlines and discusses quantitative, qualitative and mixed methods research approaches with specific reference to their (1) philosophical foundations (i.e. basic sets of beliefs that ground inquiry), (2) methodological assumptions (i.e. principles and formal conditions which guide scientific investigation), and (3) research methods (i.e. concrete procedures for data collection, analysis and interpretation). We conclude that MMR may reasonably overcome the limitation of purely quantitative and purely qualitative approaches at each of these levels, providing a fruitful context for a more comprehensive psychological research.

Qualitative research approaches are part of the intellectual landscape in educational evaluation. The use of qualitative approaches in evaluation has been fruitful. Classic qualitative approaches, representing accepted innovations, include ethnography, naturalistic inquiry, generic pragmatic (sociological) qualitative inquiry, and connoisseurship/criticism. Metaphors and phenomenography represent novel approaches with roots in the classics. Efforts to establish standards commensurate with the mainstream of scientific inquiry serve to further institutionalize qualitative approaches, anchoring them in the fertile soil of educational evaluation.

The purpose of this research is to gain a complete understanding of how students and faculty in higher education perceive the role of AI tools, their impact on academic integrity, and their potential benefits and threats in the educational milieu, while taking into account ways to help curb its disadvantages. Drawing upon a qualitative approach, this study conducted in-depth interviews with a diverse sample of faculty members and students in higher education, in universities across Lebanon. These interviews were analyzed and coded using NVivo software, allowing for the identification of recurring themes and the extraction of rich qualitative data. The findings of this study illuminated a spectrum of perceptions. While ChatGPT and AI tools are recognized for their potential in enhancing productivity, promoting interactive learning experiences, and providing tailored support, they also raise significant concerns regarding academic integrity. This research underscores the need for higher education institutions to carefully navigate the integration of AI tools like ChatGPT. It calls for the formulation of clear policies and guidelines for their ethical and responsible use, along with comprehensive support and training. This study contributes to the existing literature by presenting a comprehensive exploration of the perceptions of both students and faculty regarding AI tools in higher education, through a qualitative rich approach. By delving into the intricate dynamics of ChatGPT and academic integrity, this study offers fresh insights into the evolving educational landscape and the ongoing dialogue between technology and ethics.

Using a comprehensive orientation of the research idea from the ontology to the epistemology basis, this article examines the distinctions between qualitative and quantitative social research approaches. The main foundation of this work is a comprehensive review of the body of research on both qualitative and quantitative methods. This analysis makes it abundantly evident that the qualitative and quantitative approaches are different from one another. These distinctions are found in the various research layers. The mix-method approach is the best alternative strategy for social research, according to the discussion of qualitative and quantitative approaches and their advantages and disadvantages. Therefore, the mix-method not only offers the advantages of both the quantitative and qualitative approaches, but it also resolves the current controversy and reduces their drawbacks.

For improving the sustainability and resilience of EU farming systems, it is important to assess their likely responses to future challenges under future scenarios. In the SURE-Farm project, a five-steps framework was developed to assess the resilience of farming systems. The steps are the following: 1) characterizing the farming system (resilience of what?), 2) identifying the challenges (resilience to what?), 3) identifying the desired functions (resilience for which purpose?), 4) assessing resilience capacities, and 5) assessing resilience attributes. For assessing the resilience of future farming systems, we took the same approach as for current farming systems, with the addition that future challenges were placed in the context of a set of possible future scenarios, (i.e., Eur-Agri-SSP scenarios). We evaluated future resilience in 11 case studies across the EU, using a soft coupling of different qualitative and quantitative approaches. The qualitative approach was FoPIA-SUREFarm 2, a participatory approach in which stakeholders identified critical thresholds for current systems, evaluated expected system performance when these thresholds would be exceeded, envisaged alternative future states of the systems (and their impact on indicators and resilience attributes), as well as strategies to get there. Quantitative approaches included models simulating the behavior of the systems under some specific challenges and scenarios. The models differed in assumptions and aspects of the farming systems described: Ecosystem Service modelling focused on the biophysical level (considering land cover and nitrogen fluxes), AgriPoliS considered, with an agent-based approach, socio-economic processes and interactions within the farming system, and System Dynamics, taking a holistic approach, explored some of the feedback loops mechanisms influencing the systems resilience from both a qualitative and quantitative approach. Each method highlighted different aspects of the farming systems. For each case study, results coming from different methods were discussed and compared. The FoPIA-SURE-Farm 2 assessment highlighted that most farming systems are close to critical thresholds, primarily for system challenges, but also for system indicators and resilience attributes. System indicators related to food production and economic viability were often considered to be close to critical thresholds. The alternative systems proposed by stakeholders are mostly adaptations of the current system and not transformations. In most case studies, both the current and alternative systems are moderately compatible with 'Eur-Agri-SSP1 – Agriculture on sustainable paths’, but little with other Eur-Agri-SSPs’. From the point of view of ecosystem services and nitrogen fluxes, the more resilient case studies are those able to provide multiple services at the same time (e.g., hazelnut cultivations in Italy and vegetable and fruit cultivation in Poland, able to provide good levels of both food production and carbon storage) and those well connected with other neighbouring farming systems (e.g., the Dutch case study receiving manure by the livestock sectors). The System Dynamic simulation (applied quantitatively for the Dutch and French case study) highlighted the need to develop resources that can increase farmers’ flexibility (e.g., access to cheap credit, local research and development, and local market). It also showed that innovation, networks, and cooperation contribute to building resilience against economic disturbances while highlighting the challenges for building resilience to environmental threats. From the application of AgriPoliS to the German case study it was concluded that changes in direct payment schemes not only affect the farm size structure, but also the functions of the farming system itself and therefore its resilience. The report showed complementarity between different methods and, above all, between quantitative and qualitative approaches. Qualitative approaches are needed for interaction with stakeholders, understand perceptions of stakeholders, consider available knowledge on all aspects of the farming system, including social dimensions, and perform a good basis for developing and parameterizing quantitative models. Quantitative methods allow quantifying the consequences of mental models, operationalizing the impact of stresses and strategies to tackle them and help to unveil unintended consequences, but are limited in their reach. Both are needed to assess resilience of farming systems and suggest strategies for improvement and to help stakeholders to wider their views regarding potential challenges and ways to tackle them.

Over the past decade, place branding has become a vibrant area of research and has received increasingly widespread attention and recognition. Some scholars have discussed that many place branding studies have adopted qualitative and quantitative approaches in analysing collected data and information. This review article aligns the application of research methods and statistical analyses with place branding topic areas in articles published in three key periodicals since the year 2000. A dominance of qualitative research approaches is revealed in most of the specific topic areas in place branding including place identity, projected images, place offerings, marketing and communications, and stakeholder relationships. Several observations are also made about issues that might deserve further attention: (1) the dominance of qualitative analysis, (2) the lack of integrated research approaches and (3) the relatively low explanatory power of statistical applications in some studies. On the basis of the changing research domain in the place branding topic areas, mixed-method or more diversified quantitative approaches may yield insightful future research opportunities in a field where most research is typically conducted using individual case studies and qualitative approaches.

PurposeThe purpose of this paper is to present a methodological approach to support qualitative analysis of waste flows in food supply chains. The methodological framework introduced allows the identification of circular food waste flows that can maximise the sustainability of food supply chains.Design/methodology/approachFollowing a qualitative approach, circular economy perspectives are combined with core industrial ecology concepts in the specification of a standardised analytical method to map food waste flows and industrial synergies across a supply chain.FindingsThe mapped waste flows and industrial linkages depict two time-related scenarios: (1) current scenarios showing the status quo of existing food waste flows, and (2) future scenarios pointing out circular flows along the supply chain. The future scenarios inform potential alternatives to take waste flows up the food waste hierarchy.Research limitations/implicationsThe qualitative approach does not allow generalisations of findings out of the scope of the study. The framework is intended for providing focussed analysis, case by case. Future research involving mixed methods where quantitative approaches complement the qualitative perspectives of the framework would expand the analytical perspective.Originality/valueThe framework provides a relatively low-cost and pragmatic method to identify alternatives to minimise landfill disposals and improve the sustainability of food supply chains. Its phased methodology and standardised outcomes serve as a referential basis to inform not only comparative analysis, but also policymaking and strategic decisions aimed at transforming linear food supply chains into circular economy ecosystems.

Multiple methods are vital to understanding development as a dynamic, transactional process. This article focuses on the ways in which quantitative and qualitative methodologies can be combined to enrich developmental science and the study of human development, focusing on the practical questions of "when" and "how." Research situations that may be especially suited to mixing qualitative and quantitative approaches are described. The authors also discuss potential choices for using mixed quantitative- qualitative approaches in study design, sampling, construction of measures or interview protocols, collaborations, and data analysis relevant to developmental science. Finally, they discuss some common pitfalls that occur in mixing these methods and include suggestions for surmounting them.