Online data collection methods are expanding the ease and access of developmental research for researchers and participants alike. While its popularity among developmental scientists has soared during the COVID-19 pandemic, its potential goes beyond just a means for safe, socially distanced data collection. In particular, advances in video conferencing software has enabled researchers to engage in face-to-face interactions with participants from nearly any location at any time. Due to the novelty of these methods, however, (...) many researchers still remain uncertain about the differences in available approaches as well as the validity of online methods more broadly. In this article, we aim to address both issues with a focus on moderated data collected using video-conferencing software. First, we review existing approaches for designing and executing moderated online studies with young children. We also present concrete examples of studies that implemented choice and verbal measures and looking time across both in-person and online moderated data collection methods. Direct comparison of the two methods within each study as well as a meta-analysis of all studies suggest that the results from the two methods are comparable, providing empirical support for the validity of moderated online data collection. Finally, we discuss current limitations of online data collection and possible solutions, as well as its potential to increase the accessibility, diversity, and replicability of developmental science. (shrink)

Research in psychopathology is booming in an unprecedented way, at least, in terms of increasing number of publications. Yet, a few questions arise: Does quantity also give us quality? Are the collected data generally of sound quality? How are data typically collected in psychopathology? Are the applied methods of data collection appropriate for this particular field of study? This article explores three different methods of data collection in psychopathology, namely self-rating scales, structured (...) interviews, and semi-structured, phenomenological interviews. To identify the most adequate methodological approach, we first establish the nature of the object of psychopathology and then we critically assess each method’s appropriateness to this field of study. We emphasize fundamental issues that make self-rating scales and structured interviews unfit for the task of adequately examining psychopathology. By contrast, we propose that a semi-structured, phenomenological interview presents a more appropriate method. Finally, we describe two types of semi-structured, phenomenological interviews that can be applied to assess and explore psychopathology, respectively. (shrink)

Research in psychopathology is booming in an unprecedented way, at least, in terms of increasing number of publications. Yet, a few questions arise: Does quantity also give us quality? Are the collected data generally of sound quality? How are data typically collected in psychopathology? Are the applied methods of data collection appropriate for this particular field of study? This article explores three different methods of data collection in psychopathology, namely self-rating scales, structured (...) interviews, and semi-structured, phenomenological interviews. To identify the most adequate methodological approach, we first establish the nature of the object of psychopathology and then we critically assess each method’s appropriateness to this field of study. We emphasize fundamental issues that make self-rating scales and structured interviews unfit for the task of adequately examining psychopathology. By contrast, we propose that a semi-structured, phenomenological interview presents a more appropriate method. Finally, we describe two types of semi-structured, phenomenological interviews that can be applied to assess and explore psychopathology, respectively. (shrink)

Since its onset, scholars have characterized social media as a valuable source for data collection since it presents several benefits (e.g. exploring research questions with hard-to-reach populations). Nonetheless, methods of online data collection are riddled with ethical and methodological challenges that researchers must consider if they want to adopt good practices when collecting and analyzing online data. Drawing from our primary research project, where we collected passive online data on Reddit, we explore and (...) detail the steps that researchers must consider before collecting online data: (1) planning online data collection; (2) ethical considerations; and (3) data collection. We also discuss two atypical questions that researchers should also consider: (1) how to handle deleted user-generated content; and (2) how to quote user-generated content. Moving on from the dichotomous discussion between what is public and private data, we present recommendations for good practices when collecting and analyzing qualitative online data. (shrink)

In the literature, the distinction between positivism and interpretivism based on ontological assumptions is somehow clear. Problems in dividing them based on epistemological assumptions continue to exist and appear in several works; for instance, Bhattacherjee’s book (2012) introduced the concept of research design and categorization of methods of data collection based on positivist and interpretivist epistemological assumptions. While acknowledging Bhattacherjee's contribution, nonetheless, the paper argues that since both the quantitative and qualitative research approaches share a few epistemological (...) assumptions, strictly categorizing them into positivism and interpretivism raises some confusion. Consequently, impacting communication among researchers and the implementation of the research projects. Therefore, the paper describes Bhattacherjee's contributions and limitations in understanding the dichotomy existing between positivist and interpretivist methods of data collection. The paper's suggestions aim to resolve such confusion and restore research approaches and their methods of data collection to their original philosophical positions. Thus minimizing the existing confusion on research methodology in the current literature. (shrink)

In this article, interviewing from a descriptive, phenomenological, human scientific perspective is examined. Methodological issues are raised in relation to evaluative criteria as well as reflective matters that concern the phenomenological researcher. The data collection issues covered are 1) the selection of participants, 2) the number of participants in a study, 3) the interviewer and the questions, and 4) data collection procedures. Certain conclusions were drawn indicating that phenomenological research methods cannot be evaluated on the (...) basis of an empiricist theory of science, but must be critiqued from within a phenomenological theory of science. Some reflective matters, experienced by the phenomenological researcher, are also elaborated upon. (shrink)

In early 2020, in-person data collection dramatically slowed or was completely halted across the world as many labs were forced to close due to the COVID-19 pandemic. Developmental researchers who assess looking time were forced to re-think their methods of data collection. While a variety of remote or online platforms are available for gathering behavioral data outside of the typical lab setting, few are specifically designed for collecting and processing looking time data in (...) infants and young children. To address these challenges, our lab developed several novel approaches for continuing data collection and coding for a remotely administered audiovisual looking time protocol. First, we detail a comprehensive approach for successfully administering the Multisensory Attention Assessment Protocol, developed by our lab to assess multisensory attention skills. The MAAP is administered from a distance by using Zoom, Gorilla Experiment Builder, an internet connection, and a home computer. This new data collection approach has the advantage that participants can be tested in their homes. We discuss challenges and successes in implementing our approach for remote testing and data collection during an ongoing longitudinal project. Second, we detail an approach for estimating gaze direction and duration collected remotely from webcam recordings using a post processing toolkit and demonstrate its effectiveness and precision. However, because OpenFace derives gaze estimates without translating them to an external frame of reference, we developed a machine learning approach to overcome this limitation. Thus, third, we trained a ML algorithm [] to classify gaze estimates from OpenFace with respect to areas of interest on the participant's screen. We then demonstrate reliability between this approach and traditional coding approaches. The combination of OpenFace and ML will provide a method to automate the coding of looking time for data collected remotely. Finally, we outline a series of best practices for developmental researchers conducting remote data collection for looking time studies. (shrink)

Investigating the influence of perception on the control of visually guided action typically involves controlled experimentation within the laboratory setting. When appropriate, however, behavioral research of this nature may benefit from the use of methods that allow for remote data collection outside of the lab. This study tested the feasibility of using remote data collection methods to explore the influence of perceived target size on visually guided cursor movements using the Ebbinghaus illusion. Participants completed (...) the experiment remotely, using the trackpad of their personal laptop computers. The task required participants to click on a single circular target presented at either the left or right side of their screen as quickly and accurately as possible, or to emphasize speed or accuracy. On each trial the target was either surrounded by small or large context circles, or no context circles. Participants’ judgments of the targets’ perceived size were influenced by the illusion, however, the illusion failed to produce differences in click-point accuracy or movement time. Interestingly, the illusion appeared to affect participants’ movement of the cursor toward the target; more directional changes were made when clicking the Perceived Large version of the illusion compared to the Perceived Small version. These results suggest the planning of the cursor movement may have been influenced by the illusion, while later stages of the movement were not, and cursor movements directed toward targets perceived as smaller required less correction compared to targets perceived as larger. (shrink)

Purpose This study aims to ascertain the impact of data collecting awareness on perceived information security concerns and information-sharing behavior on social networking sites. Design/methodology/approach Based on communication privacy management theory, the study forecasted the relationship between information-sharing behavior and awareness of data collecting purposes, data collection tactics and perceived security risk using structural equation modeling analysis and one-way ANOVA. The sample size of 521 young social media users in Vietnam, ages 18 to 34, was made (...) up of 26.7% men and 73.3% women. When constructing the questionnaire survey method with lone source respondents, the individual’s unique awareness and experiences with using online social networks (OSNs) were taken into account. Findings The results of the investigation demonstrate a significant relationship between information-sharing and awareness of data collecting, perceptions of information security threats and behavior. Social media users have used OSN privacy settings and paid attention to the sharing restriction because they are concerned about data harvesting. Research limitations/implications This study was conducted among young Vietnamese social media users, reflecting specific characteristics prevalent in the Vietnamese environment, and hence may be invalid in other nations’ circumstances. Practical implications Social media platform providers should improve user connectivity by implementing transparent privacy policies that allow users to choose how their data are used; have clear privacy statements and specific policies governing the use of social media users’ data that respect users’ consent to use their data; and thoroughly communicate how they collect and use user data while promptly detecting any potential vulnerabilities within their systems. Originality/value The authors ascertain that the material presented in this manuscript will not infringe upon any statutory copyright and that the manuscript will not be submitted elsewhere while under Journal of Information, Communication and Ethics in Society review. (shrink)

In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s (...) path choice with multisource data, in which the impact of the network time-varying state on passenger path choice is fully considered. First, according to the number and characteristics of OD candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice. (shrink)

The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining (...) gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process. (shrink)

Likert scales are useful for collecting data on attitudes and perceptions from large samples of people. In particular, they have become a well-established tool in soundscape studies for conducting in situ surveys to determine how people experience urban public spaces. However, it is still unclear whether the metrics of the scales are consistently interpreted during a typical assessment task. The current work aims at identifying some general trends in the interpretation of Likert scale metrics and introducing a procedure for (...) the derivation of metric corrections by analyzing a case study dataset of 984 soundscape assessments across 11 urban locations in London. According to ISO/TS 12913-2:2018, soundscapes can be assessed through the scaling of 8 dimensions: pleasant, annoying, vibrant, monotonous, eventful, uneventful, calm, and chaotic. The hypothesis underlying this study is that a link exists between correlations across the percentage of assessments falling in each Likert scale category and a dilation/compression factor affecting the interpretation of the scales metric. The outcome of this metric correction value derivation is introduced for soundscape, and a new projection of the London soundscapes according to the corrected circumplex space is compared with the initial ISO circumplex space. The overall results show a general non-equidistant interpretation of the scales, particularly on the vibrant-monotonous direction. The implications of this correction have been demonstrated through a Linear Ridge Classifier task for predicting the London soundscape responses using objective acoustic parameters, which shows significant improvement when applied to the corrected data. The results suggest that the corrected values account for the non-equidistant interpretation of the Likert metrics, thereby allowing mathematical operations to be viable when applied to the data. (shrink)

ArgumentData collections are a hallmark of nineteenth-century administrative knowledge making, and they were by no means confined to Europe. All colonial empires transferred and translated these techniques of serialised and quantified information gathering to their dominions overseas. The colonial situation affected the encounters underlying vital statistics, enquête methods and land surveying. In this paper, two of those data collections will be investigated—a survey on land and a survey on indigenous law, both conducted around 1910 on the Micronesian island (...) of Pohnpei, which had fallen under German colonial influence a decade earlier. Strikingly, there are no enumerators or envoys of the state visiting the doorsteps of Pohnpei. To facilitate the data collection on homesteads, the whole population of the island was called upon to measure their respective plots of land themselves, without resorting to certified land surveyors. The preserved cadastral lists and spreadsheets testify to a rather peculiar contact between the colonizing administration and the colonized peoples. I argue that the production of data made encounters necessary, which are best observed though a methodological focus on data practices. I argue, furthermore, that the Pohnpeians were prompted during the surveys to define their homestead in new terms. This did not only entail new two-dimensional plots but also a new regime of private property. The change in the legal concept can be seen as a continuation of colonial violence by other means, given that it happened in the aftermath of the defeated Pohnpei Rebellion. The argument of the paper is, therefore, that data collection can have formative effects on society, and that measurement and quantified information are often, as Witold Kula argued, a scene of conflict. At its core, the installation of these metric regimes signified a change in patterns of justification, resource management and the unwritten constitution of the Pacific island. (shrink)

Student performance prediction aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining. This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, problem (...) formalization, model, prediction, and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education. (shrink)

All scientific activity involves some method of observation and some method of recording what is observed. These activities can be carried out in ways that involve little interaction between subject and object, as is the case when a telescope observes a far-away star. At the other end of the scale are experiments in modern high energy physics in which there is little distinction between the observer and the observed, and the process of observation materially affects the data that are (...) recorded. In this regard, research on human phenomena resembles modern physics more than it does classical astronomy.Research on human phenomena, however, differs from modern physics in the way in which it affects that which is observed. Both the procedures and the findings of research on human phenomena alter the modes of thinking and the self-awareness of the objects of study. (shrink)

Rapid advancements in machine learning techniques allow mass surveillance to be applied on larger scales and utilize more and more personal data. These developments demand reconsideration of the privacy-security dilemma, which describes the tradeoffs between national security interests and individual privacy concerns. By investigating mass surveillance techniques that use bulk data collection and machine learning algorithms, we show why these methods are unlikely to pinpoint terrorists in order to prevent attacks. The diverse characteristics of terrorist attacks—especially (...) when considering lone-wolf terrorism—lead to irregular and isolated footprints. The irregularity of data affects the accuracy of machine learning algorithms and the mass surveillance that depends on them which can be explained by three kinds of known problems encountered in machine learning theory: class imbalance, the curse of dimensionality, and spurious correlations. Proponents of mass surveillance often invoke the distinction between collecting data and metadata, in which the latter is understood as a lesser breach of privacy. Their arguments commonly overlook the ambiguity in the definitions of data and metadata and ignore the ability of machine learning techniques to infer the former from the latter. Given the sparsity of datasets used for machine learning in counterterrorism and the privacy risks attendant with bulk data collection, policymakers and other relevant stakeholders should critically re-evaluate the likelihood of success of the algorithms and the collection of data on which they depend. (shrink)

Background: Health systems globally face ncreasing challenges from health crises and epidemic threats, necessitating the pivotal role of epidemiological teams in emergency response. This study investigates the significance of collective competence within these teams, focusing on their ability to collect, analyze, and manage laboratory data effectively. Methods: A cross-sectional descriptive study design was used to survey epidemiological teams in Saudi Arabia. A structured questionnaire was developed based on existing literature and distributed electronically to participants. Data analysis was (...) performed using the Statistical Package for the Social Sciences (SPSS), using descriptive and inferential statistics to summarize participant characteristics and explore associations between variables. Results: A total of 208 epidemiological teams member completed the questionnaire, yielding insights into their The Role of Collective Competence in Epidemiological Teams. The findings reveal high levels of agreement regarding the effectiveness of management approaches, knowledge sharing, and team collaboration, with the average score exceeding 4.0 on a 5-point scale. Notably, team efficacy was found to significantly enhance the efficiency of laboratory data collection and analysis, accounting for approximately 25.9% of the variance in efficacy. Discussion: These findings underscore the need to invest in developing team competencies within epidemiological teams to improve emergency response capabilities. The study provides practical insights into enhancing team collaboration and coordination, ultimately contributing to more effective public health interventions in future health crises. (shrink)

Digital tools may help to study socioeconomic aspects of agricultural development that are difficult to measure such as the effects of new policies and technologies on the intra-household allocation of time. As farm technologies target different crops and tasks, they can affect the time-use of men, women, boys, and girls differently. Development strategies that overlook such effects can have negative consequences for vulnerable household members. In this paper, the time-use patterns associated with different levels of agricultural mechanization during land preparation (...) in smallholder farming households in Zambia were investigated. A novel data collection method was used: a pictorial smartphone application that allows real-time recording of time-use, which eliminates recall bias. Existing studies analyzing the intra-household allocation of resources often focus on adult males and females. This study paid particular attention to boys and girls as well as adults. The study addressed seasonal variations. Compositional data analysis was used to account for the co-dependence and sum constraint of time-use data. The study suggests a strong gender differentiation for land preparation activities among mechanized households; for households using manual labor, such differentiation was not found. There is some evidence that the surplus time associated with mechanization is used for off-farm and domestic work. The study cannot confirm concerns about negative second-round effects: mechanized land preparation is not associated with a higher workload for women and children during weeding and harvesting/processing. The study provides a proof-of-concept that smartphone applications can be used to collect socioeconomic data that are difficult to measure but of high relevance. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was (...) taught. We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

Musical performance is a multimodal experience, for performers and listeners alike. This paper reports on a pilot study which constitutes the first step toward a comprehensive approach to the experience of music as performed. We aim at bridging the gap between qualitative and quantitative approaches, by combining methods for data collection. The purpose is to build a data corpus containing multimodal measures linked to high-level subjective observations. This will allow for a systematic inclusion of the knowledge (...) of music professionals in an analytic framework, which synthesizes methods across established research disciplines. We outline the methods we are currently developing for the creation of a multimodal data corpus dedicated to the analysis and exploration of instrumental music performance from the perspective of embodied music cognition. This will enable the study of the multiple facets of instrumental music performance in great detail, as well as lead to the development of music creation techniques that take advantage of the cross-modal relationships and higher-level qualities emerging from the analysis of this multi-layered, multimodal corpus. The results of the pilot project suggest that qualitative analysis through stimulated recall is an efficient method for generating higher-level understandings of musical performance. Furthermore, the results indicate several directions for further development, regarding observational movement analysis, and computational analysis of coarticulation, chunking, and movement qualities in musical performance. We argue that the development of methods for combining qualitative and quantitative data are required to fully understand expressive musical performance, especially in a broader scenario in which arts, humanities, and science are increasingly entangled. The future work in the project will therefore entail an increasingly multimodal analysis, aiming to become as holistic as is music in performance. (shrink)

In this paper, we describe the challenges of collecting data in the Māori population for automatic detection of schizophrenia using natural language processing (NLP). Existing psychometric tools for detecting are wide ranging and do not meet the health needs of indigenous persons considered at risk of developing psychosis and/or schizophrenia. Automated methods using NLP have been developed to detect psychosis and schizophrenia but lack cultural nuance in their designs. Research incorporating the cultural aspects relevant to indigenous communities is (...) lacking in the design of existing automatic prediction tools and one of the main reasons is the scarcity of data from indigenous populations. This paper explores the current design of the New Zealand health care system and its potential impacts on access and inequities in the Māori population and details the methodology used to collect speech samples of Māori at risk of developing psychosis and schizophrenia. The paper also describes the major obstacles faced during speech data collection, key findings, and probable solutions. (shrink)

The article focuses on a method for collecting qualitative data. The method is the asynchronous email interview. The authors assess the advantages, challenges and best practices of the asynchronous email interview method. They base their assessment on the academic literature and their own experiences using this data collection method in qualitative research on women who had experienced perinatal loss. The asynchronous email interview will never fully replace traditional face-to-face interviews, but it could gain a solid position as (...) a qualitative research method thanks to its unique benefits. (shrink)

While a growing number of researchers are embracing Internet-based data collection methods, the adoption of Internet-based recruitment methods has been relatively slow. This may be because little is known regarding the relative strengths and weaknesses of different methods of Internet-based participant recruitment, nor how these different recruitment strategies impact on the data collected. These issues are addressed in this article with reference to a study comparing the effectiveness of three Internet-based strategies in recruiting cannabis (...) users for an online study. Consideration of the recruitment data leads us to recommend that researchers use multipronged Internet-based recruitment campaigns with appropriately detailed recruitment messages tailored to the population of interest and located carefully to ensure they reach the intended audience. Further, we suggest that building rapport directly with potential participants, or utilising derived rapport and implicit endorsements, is an important aspect of successful Internet-based participant recruitment strategies. (shrink)

Mass violence in the United States has been shown to cause trauma for survivors. These events may also create for survivors the experience of posttraumatic growth (PTG), the facets of which include personal strength, appreciation for life, new possibilities in life, spiritual change, and enhanced relationships with others. However, the role of collective efficacy and agency in the development of PTG following mass violence remains unknown. The purpose of this study is to assess the relationship between PTG and experiences of (...) collective efficacy and agency among survivors of the Isla Vista, California tragedy of 2014. The mixed methods objective was to explore the interpretive context within which the agency and collective efficacy experiences occurred. Quantitative data collected in a questionnaire (n = 166) were used to inform the collection of qualitative interview data (n = 12). Participating in therapy, organizing community events, and making the decision to either leave or stay in Isla Vista positively correlated with PTG based on hierarchical regression analysis. Themes of personal agency, the importance of role, and participating in collective action emerged from interviews. This study offers unique methodological approaches to using mixed methods data to understand the experiences which contributed to PTG among survivors of a mass shooting. (shrink)

BACKGROUND: There is a growing interest in aggregating more biomedical and patient data into large health data sets for research and public benefits. However, collecting and processing patient data raises new ethical issues regarding patient's rights, social justice and trust in public institutions. The aim of this empirical study is to gain an in-depth understanding of the awareness of possible ethical risks and corresponding obligations among those who are involved in projects using patient data, i.e. healthcare (...) professionals, regulators and policy makers. METHODS: We used a qualitative design to examine Swiss healthcare stakeholders' experiences and perceptions of ethical challenges with regard to patient data in real-life settings where clinical registries are sponsored, created and/or used. A semi-structured interview was carried out with 22 participants (11 physicians, 7 policy-makers, 4 ethical committee members) between July 2014 and January 2015. The interviews were audio-recorded, transcribed, coded and analysed using a thematic method derived from Grounded Theory. RESULTS: All interviewees were concerned as a matter of priority with the needs of legal and operating norms for the collection and use of data, whereas less interest was shown in issues regarding patient agency, the need for reciprocity, and shared governance in the management and use of clinical registries' patient data. This observed asymmetry highlights a possible tension between public and research interests on the one hand, and the recognition of patients' rights and citizens' involvement on the other. CONCLUSIONS: The advocation of further health-related data sharing on the grounds of research and public interest, without due regard for the perspective of patients and donors, could run the risk of fostering distrust towards healthcare data collections. Ultimately, this could diminish the expected social benefits. However, rather than setting patient rights against public interest, new ethical approaches could strengthen both concurrently. On a normative level, this study thus provides material from which to develop further ethical reflection towards a more cooperative approach involving patients and citizens in the governance of their health-related big data. (shrink)

Data subject rights provide data controllers with obligations that can help with transparency, giving data subjects some control over their personal data. To date, a growing number of researchers have used these data subject rights as a methodology for data collection in research studies. No one, however, has gathered and analysed different academic research studies that use data subject rights as a methodology for data collection. To this end, we conducted (...) a systematic literature review that searched, compiled, and analysed 32 academic studies that use data subject rights as a data collection method. We find that the right of access is the most commonly-used data subject right by researchers, most studies are interested in measuring data subject rights compliance, and that a variety of difficulties exist in conducting research studies with data subject rights. We conclude that researchers should explore other data subject rights for alternative purposes, ease the process of exercising data subject rights, and improve the scalability of these studies. (shrink)

Healthcare provision, like many other sectors of society, is undergoing major changes due to the increased use of data-driven methods and technologies. This increased reliance on big data in medicine can lead to shifts in the norms that guide healthcare providers and patients. Continuous critical normative reflection is called for to track such potential changes. This article presents the results of an interview-based study with 20 German and Swiss experts from the fields of medicine, life science research, (...) informatics and humanities of digitalisation. The aim of the study was to explore expert opinions regarding current challenges and opportunities related to data-driven medicine and medical research and to provide a methodological framework for empirically grounded, continuous normative reflection. To this end, we developed a heuristic tool to map and structure empirical findings for normative analysis. Using this tool, our interview material points to a polarisation between individualistic and collectivistic orientated argumentations among experts. The study shows that a multilevel analysis is required to deal with complex normative implications of data-driven approaches in medical research and healthcare. (shrink)

Stereology is a valuable tool for neuroscientists, allowing them to obtain 3-Dimensional information from 2-Dimensional measurements made on appropriately sampled sections. This 3-D information is invaluable in correlating structural/functional relationships in the pursuit of far greater understanding of the function of the central nervous system. However, in carrying out such measurements, often based on limited data sets, there is a risk of experimenter bias. An important feature of modern design based stereology is to be aware of potential sources of (...) bias and eliminate them during the data collection. With many of the major neuroscience journals now insisting that quantitative data be presented, there is a greater need than ever for neuroscientists to understand the theory and practice behind quantitative methods, such as those offered by stereology. Quantitative Methods in Neuroscience is a cookbook of stereological methods written especially for neuroscientists. It provides clear and accessible advice about when and when not to use stereology. Throughout the book, the emphasis is on practical guidance, rather than discussions and formulae. Written by leading scientists in the field of stereology, with a Foreword by D.C. Sterio, the book will be a valuable introduction to these methods for neuroscientists, and all those involved in development of new drug programmes.Readership: Neuroscientists doing quantitative studies; Scientists involved in drug development; Pathologists. (shrink)

Due to the complexity of research objects, theoretical concepts, and stimuli in media research, researchers in psychology and communications presumably need sophisticated measures beyond self-report scales to answer research questions on media use processes. The present study evaluates stimulus-dependent structure in spontaneous eye-blink behavior as an objective, corroborative measure for the media use phenomenon of spatial presence. To this end, a mixed methods approach is used in an experimental setting to collect, combine, analyze, and interpret data from standardized (...) participant self-report, observation of participant behavior, and content analysis of the media stimulus. T-pattern detection is used to analyze stimulus-dependent blinking behavior, and this structural data is then contrasted with self-report data. The combined results show that behavioral indicators yield the predicted results, while self-report data shows unpredicted results that are not predicted by the underlying theory. The use of a mixed methods approach offered insights that support further theory development and theory testing beyond a traditional, mono-method experimental approach. (shrink)

This dissertation is focused on the validity of “the data” that are collected in psychotherapy research for the purpose of evidencing treatment efficacy. In the ‘Evidence Based Treatment’ paradigm, researchers rely on the so-called ‘gold standard methodology’ to gather sound and trustworthy evidence, which increasingly influences the organization of mental health care worldwide. In the gold standard, data are collected by quantified self-report measures, to assess the presence and severity of symptoms before and after treatment. When the pre-post (...) difference is larger for a group of people that received the treatment of interest and a group of people who received no or an alternative treatment, the treatment of interest is called effective. In this methodological procedure, researchers tend to assume that when this gold standard methodology is conducted properly, “the data” will speak for themselves. However, when evidence is based on data, that evidence is principally limited by that data. In other words: output depends on input. That implies that when input is flawed, output will be flawed, even when the very best methods of analysis would be used. So what if “the data” yield validity issues despite being collected by validated measures? When “the data” do not straightforwardly evidence treatment effects, will the subsequent steps in the analysis of these data be enough to secure that the evidence in the end does evidence treatment efficacy? In this dissertation, the focus is turned to validity of “the data” that are concretely provided by patient-participants by scoring their own experienced symptoms in self-report questionnaires, in function of evidencing treatment efficacy. A series of empirical case studies were conducted to scrutinize how patient-participants in a randomized controlled psychotherapy study experienced the process of data collection, and how these experiences affected the data that they provided. Each of the studied patient-participants experienced a substantial effect of the questionnaire administration on their complaints. This impacted the level of complaints, but also changed the way in which the complaints were understood at all, and how patient-participants perceived themselves. Thus, rather than neutrally measuring symptoms, questionnaire administration changed the experienced complaints ‘performatively’, which turned measurement into a clinical intervention of its own. Consequently, what is measured cannot straightforwardly be called ‘treatment efficacy’, as it may be entangled with, enabled by or even obstructed by effects of measurement and research. Therefore, the act of measurement itself can pose a vital threat to the validity of “the data”. This way, the empirical case studies exhibited that data can yield validity problems despite the use of validated measures. Consequently, the validity of a measure as such is no guarantee for the validity of data. Nonetheless, in gold standard research, the data are straightforwardly taken as input for analyses of general treatment efficacy. The question is what happens with those validity issues on the level of individual data, when they pursue their journey towards becoming evidence. In this dissertation, it was argued that the validity issues are not sufficiently solved in the methodological steps after data collection, so when data are invalid in the beginning, these validity issues will simply become part of the data set that forms the input for analysis of the final evidence. This urges that validity issues should be solved on the level of individually provided data, as the validity issues will otherwise become inherent to “the data”. Put formally: valid data is a precondition for evidence in EBT. In conclusion, it is crucial for a sound evidence-base to scrutinize the validity of data in function of the overall goal and utility of the research. For this, it is important not to take “the data” as speaking for themselves, but to regard them as clinical narratives, which are framed in a specific format to be communicated between researcher and respondent in a research context. This emphasizes that the choice for a certain format determines what can be evidenced, so it is vital that these choices indeed allow for obtaining evidence that is useful and valid to serve the clinical goal of EBT. (shrink)

BackgroundThe experience sampling method is an increasingly popular data collection method to assess interpersonal dynamics in everyday life and emotions contextualized in real-world settings. As primary advantages of ESM sampling strategies include minimization of memory biases, maximization of ecological validity, and hypothesis testing at the between- and within-person levels, ESM is suggested to be appropriate for studying the daily lives of educational actors. However, ESM appears to be underutilized in education research. We, thus, aimed to systematically evaluate the (...) methodological characteristics and quality of published ESM studies of social interactions among children and adolescents in school settings, as well as to explore how much variance in social interaction variables could be attributed to the within-person level.MethodUsing Academic Search Complete, APA PsycINFO, APA PsycArticles, ProQuest, Web of Science, Wiley Online Library, and SAGE Journals, and in accordance with PRISMA guidelines and pre-defined eligibility criteria, we conducted a systematic literature search of experience sampling studies up to November 2020. To assess methodological quality, we used a modified checklist for reporting of ESM studies.ResultsOf the originally 2 413 identified studies, a final 52 experience sampling studies were included in the present review. Findings on sample and study design characteristics generally revealed wide variability. Even if high-quality studies were associated with higher scores on the training of participants in using the ESM procedure, and use of incentives, these design strategies did not reveal a statistically significant impact on compliance. The intraclass correlation coefficient was reported in nine studies and on average 58% of the variance in social interaction variables could be attributed to within-person fluctuation between timepoints.ConclusionThe current study is the first to systematically review ESM-based studies on social interactions among children and adolescents in the school context. These observations suggest that ESM is a potentially favorable technique for extracting complex social phenomena in real-world settings. We hope that this review will contribute to improving the quality assessment of ESM studies as well as to inform and guide future experience sampling studies, particularly regarding social phenomena with children and adolescents in educational settings. (shrink)

ArgumentCollecting data about people with mental disorders living outside of asylums became a heightened concern from the early nineteenth century onwards. In Germany, so-called “insanity counts” targeted the number and sometimes the type the mentally ill who were living unattended and untreated by professional care throughout the country. An eagerly expressed assumption that the “true” extent of the gathered numbers must be much higher than the surveys could reveal came hand in glove with the emerging task of “managing” insanity (...) and its potential dangers in a modern society. The doorstep of the family home became a crucial site in psychiatrists’ and enumerators’ efforts to register the most sensitive of personal data. This article traces the ever more diligent methods that were employed to obtain the desired information, as well as the hidden agenda of the postulate of missing data itself. It also addresses the profound impact that the presumption of having only incomplete data has had on the practice of counting and surveying, as well as on the understanding of the need for professional monitoring of mental illness. (shrink)

Research in an emergent field requires the researcher to consider appropriate methodologies. This paper describes research that attempts to discover how new technologies can influence local democratic engagement. It describes an interpretive research project informed by critical social theory that uses an explanatory mixed methods approach, combining two sequential data collection methods. The study incorporates a survey instrument and qualitative interviewing, analysed using Grounded Theory Methodology. The paper will describe the context for the research, the choice (...) of a mixed methods approach and relate this to an epistemology and ontology. The paper does not discuss the research findings but focuses on the research process and methodology and provides reflection on the outcomes and the challenges of the chosen approach. It demonstrates that mixed methods research is appropriate in a narrow and emergent field of study and that it offers methodological flexibility whilst promoting rigour and grounded results. (shrink)

This study investigated how preservice teachers develop collective efficacy when preparing lesson plans for a science methods course, and how this collective efficacy changed over time. The data were collected from four preservice science teachers working in a group to fulfil the requirements of the course. Findings revealed that four factors, including collaborative work, sharing the same goal, attitudes towards group work and group cohesion, played an important role in the development of collective efficacy among preservice teachers. The (...) study also showed that collective efficacy improved continuously over the semester. (shrink)

Many of our spoken languages are endangered and rapidly becoming extinct. Due to this, there are attempts to preserve as many of those languages as possible. One preservation approach is combining data collection and artificial intelligence‐based language models. However, current data collection methods may only capture static data from a dynamic cognitive process. If data are not genuinely capturing the dynamic process, it raises questions about whether they capture all the essential knowledge about (...) how a language functions. Here, we discuss the implications of this issue and its importance in preserving endangered languages. (shrink)

Purpose As data-driven tools increasingly shape our life and tech ethics crises become strikingly frequent, data ethics coursework is urgently needed. The purpose of this study is to map the field of data ethics curricula, tracking relations between courses, instructors, texts and writers, and present a proof-of-concept interactive website for exploring these relations. This method is designed to be used in curricular research and development and provides multiple vantage points on this multidisciplinary field. Design/methodology/approach The authors use (...) data science methods to foster insights about the field of data ethics education and literature. The authors present a semantic, linked open data graph in the Resource Description Framework, along with proof-of-concept analyses and an exploratory website. Its framework is open-source and language-agnostic, providing the seed for future contributions of code, syllabi and resources from the global data ethics community. Findings This method provides a convenient means of exploring an overview of the field of data ethics’ social and textual relations. For educators designing or refining a course, the authors provide a method for curricular introspection and discovery of transdisciplinary curricula. Research limitations/implications The syllabi the authors have collected are self-selected and represent only a subset of the field. Furthermore, this method exclusively represents a course’s assigned literature rather than a holistic view of what courses teach. The authors present a prototype rather than a finished product. Originality/value This curricular survey provides a new way of modeling a field of study, using existing ontologies to organize graph data into a comprehensible overview. This framework may be repurposed to map the institutional knowledge structures of other disciplines, as well. (shrink)

Responsibilities of the data monitoring committee and motivating illustrations -- Composition of a data monitoring committee -- Independence of the data monitoring committee : avoiding conflicts of interest -- Confidentiality issues relating to the data monitoring committee -- Data monitoring committee meetings -- Data monitoring committee interactions with other trial components or related groups -- Statistical, philosophical and ethical issues in data monitoring -- Determining when a data monitoring committee is needed -- (...) Regulatory considerations for the operation of data monitoring committees -- Legal considerations for DMCs. (shrink)

Since the second half of the twentieth century, biomedical research has made increasing use of epidemiological methods to establish empirical evidence on a population level. This paper is about practices with data in epidemiological research, based on a case study in Denmark. I propose an epistemology of record linkage that invites exploration of epidemiological studies as heterogeneous assemblages. Focusing on data collecting, sampling and linkage, I examine how data organisation and processing become productive beyond the context (...) of their collection. The case study looks at how a local population database established in 1976 to investigate possibilities for the prevention of cardiovascular disease is used thirty years later to test hypotheses on the aetiology of breast cancer. For two breast cancer investigations based on the same core data set, I follow the underlying record linkage practice and describe how research objects such as molecular markers become relevant with respect to public health through information networking. Epidemiological association studies function as tools that performatively enrol different contexts into statistical risk estimation, thereby configuring options for research as well as for clinical testing and public health policy. (shrink)

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of (...) class='Hi'>data production and circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

This article introduces the methodology of the ‘Ontology Explorer’, a semantic method and JavaScript-based open-source tool to analyse data models underpinning information systems. The Ontology Explorer has been devised and developed by the authors, who recognized a need to compare data models collected in different formats and used by diverse systems. The Ontology Explorer is distinctive firstly because it supports analyses of information systems that are not immediately comparable and, secondly, because it systematically and quantitatively supports discursive analysis (...) of ‘thin’ data models – also by detecting differences and absences through comparison. When applied to data models underpinning systems for population management, the Ontology Explorer enables the apprehension of how people are ‘inscribed’ in information systems: which assumptions are made about them, and which possibilities are excluded by design. The Ontology Explorer thus constitutes a methodology to capture authorities’ own imaginaries of populations and the ‘scripts’ through which they enact actual people. Furthermore, the method allows the comparison of scripts from diverse authorities. This is exemplified by illustrating its functioning with information systems for population management deployed at the European border. Our approach integrates a number of insights from early infrastructure studies and extends their methods and analytical depth to account for contemporary data infrastructures. By doing so, we hope to trigger a systematic discussion on how to extend those early methodical innovations at the semantic level to contemporary developments in digital methods. (shrink)

What does the abundance of data and proliferation of data-making methods mean for the ordinary person, the person on the street? And, what could they come to mean? In this paper, we present an overview of a year-long project to examine just such questions and complicate, in some ways, what it is to ask them. The project is a collective exercise in which we – a mixture of social scientists, designers and makers – and those living and (...) working on one street in Cambridge, Tenison Road, are working to think through how data might be materialised and come to matter. The project aims to better understand the specificities and contingencies that arise when data is produced and used in place. Mid-way through the project, we use this commentary to give some background to the work and detail one or two of the troubles we have encountered in putting locally relevant data to work. We also touch on a methodological standpoint we are working our way into and through, one that we hope complicates the separations between subject and object in data-making and opens up possibilities for a generative refiguring of the manifold relations. (shrink)

Structural priming is a powerful method to inform linguistic theories. We argue that this method extends nicely beyond syntax to theories of meaning. Priming, however, should still be seen as only one of the tools available for linguistic data collection. Specifically, because priming can occur at different, potentially conflicting levels, it cannot detect every aspect of linguistic representations.

Data sharing – broadly defined as the exchange of health-related data among multiple controllers and processors – has gained increased relevance in the health sciences over recent years as the need and demand for collaboration has increased. This includes data obtained through healthcare provisions, clinical trials, observational studies, public health surveillance programs, and other data collection methods. The practice of data sharing presents several notable challenges, however. Compliance with a complex and dynamic regulatory (...) framework is essential, with the General Data Protection Regulation being a prominent example in a European context. Recent regulatory developments related to clinical trial transparency, trade secrecy, data access, AI training data, and health data spaces further contribute to the difficulties. Simultaneously, government initiatives often encourage scientists to embrace principles of “open data” and “open innovation.” The variety of regulations in this domain has the potential to impede widespread data sharing and hinder innovation. This edited volume, therefore, compiles comparative case studies authored by leading scholars from diverse disciplines and jurisdictions. The book aims to outline the legal complexities of data sharing. By examining real-world scenarios from diverse disciplines and a global perspective, it explores the normative, policy, and ethical dilemmas that surround data sharing in the health sciences today. Chapter Patient Perspectives on Data Sharing, Chapter Supplementary Measures and Appropriate Safeguards for International Transfers of Health Data after Schrems II are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. (shrink)

This paper explores the “triangulation approach” in social science research methods. Triangulation is an emerging method specifically in qualitative and multi-method studies. Researchers in the social sciences have increased their use of the triangulation approach recently due to the merits of this approach, especially to conduct in-depth studies through multiple inquiries. Simultaneously, this method has criticism and misconceptions among social researchers in terms of what it is, how it can be used, and the credibility of the findings when used. (...) Therefore, this paper analyses in depth what triangulation is, the types of triangulations and how they can be applied in the social sciences. The study basically adopted systematic literature review and used 30 articles published in scientific journals. The paper critically reviewed journal articles related to triangulation, revealed how different types of triangulation methods have been applied in the social sciences and highlighted the merits and cautions of applying this method. The paper theoretically contributes to understanding the phenomenon of the triangulation approach and how it connects with qualitative and multi-method studies. In addition, it revealed that the following data collection techniques from multiple sources can be exploited: in-depth interviews, focus group discussions, literature, documents, and surveys on the triangulation method to improve the credibility and reliability of the research. Furthermore, the study empirically contributes to social science researchers applying this approach with proper understanding. (shrink)

Longitudinal qualitative research in nursing is rare but becoming more common. Data collection and analysis over time provide some intriguing possibilities to better understand processes, development, and change in illness experience, healthcare organizations, and self‐management. This paper aims to present a process for analyzing qualitative longitudinal data material, namely the Pattern‐Oriented Longitudinal Analysis approach (POLA). We developed this approach after synthesizing experiences from two longitudinal qualitative projects and comparing our procedures and reflections with the relevant literature. Using (...) the POLA approach, researchers can describe complexity and variation in changes over time. During the analysis process, emphasis is put on visualizing and identifying change at both an individual and a group level. Ontological and epistemological assumptions for the approach are also described. The benefit of this approach is the possibility to describe complexity and diversity in processes over time, which is important for the development of nursing knowledge. The analysis approach can be further used and developed by researchers seeking to understand variance or contextual features in processes and changes over time. (shrink)

The basic components of data management including data ownership, collection, selection, recording, analysis, storage, retention, destruction, and sharing. A number of important principles underlie best practices for each of these components; these include recording details such that another can repeat the experiment, keeping the data safe, managing storage in such a way as to facilitate easy retrieval for the period of time required by regulatory agencies and establishing data sharing principles with colleagues before collaborations begin. (...) Experience as practicing scientists and teachers has aided in developing helpful strategies and approaches for communicating these principles, policies and practices to trainees and colleagues. We recommend didactic instruction focused by discipline, combined with the use of teachable moments in a student’s career, as well as teaching principles versus rules, because changing methods of data collection and storage have implications for data management practices. (shrink)