Data Collection Practices Research Articles

A practical guide for studying human behavior in the lab.

In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.

Using police data to measure criminogenic exposure in residential and school contexts: experiences from a data linkage project in Germany

ABSTRACT Police data and survey research provide different bases to inform research on crime and delinquency. We argue that linking police data on local crime incidences to criminological surveys allows for new insights on the role of residential and school contexts for juvenile delinquency and violence. We describe the challenges and solutions of combining these data sources in a collaboration between the state police of North Rhine-Westphalia – Germany’s most populous state – and social scientists from a major German university. In this academic-practitioner partnership, data from a four-wave longitudinal study of more than 3800 students were linked to spatially aggregated data from the police crime statistics for the years 2013–2016. We discuss how the simulation of nearby addresses can serve as a tool for anonymized data linkage, how knowledge of the local data collection practices is crucial to evaluate the geocoding accuracy of address-level crime data, and how sensitivity and implication analyses can help to reduce uncertainties at the analysis stage. We also give recommendations for future research and data collection practices.

Modeling Exposure to Fecal Contamination in Drinking Water due to Multiple Water Source Use.

The Joint Monitoring Programme estimated that 71% of people globally had access to “safely managed” drinking water in 2017. However, typical data collection practices focus only on a household’s primary water source, yet some households in low- and middle-income countries (LMICs) engage in multiple water source use, including supplementing improved water supplies with unimproved water throughout the year. Monte Carlo simulations and previously published data were used to simulate exposure to fecal contamination (as measured by E. coli) along a range of supplemental unimproved source use rates (e.g., 0–100% improved water use, with the remainder made up with unimproved water). The model results revealed a statistically significant increase in annual exposure to E. coli when individuals supplement their improved water with unimproved water just 2 days annually. Additionally, our analysis identified scenarios–realistic for the data set study setting–where supplementing with unimproved water counterintuitively decreases exposure to E. coli. These results highlight the need for evaluating the temporal dynamics in water quality and availability of drinking water sources in LMICs as well as capturing the use of multiple water sources for monitoring global access to safe drinking water.

Toward Data Justice: Understanding Police Shooting Data Systems and Narratives

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Background:</b></i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The Washington Post</i> 's Fatal Force fatal police shooting database was first created in 2015 to fill a gap in official data collection on police use of force. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Literature review:</b></i> Critical data studies scholarship suggests that data system design is rhetorical and communicates cultural values, not just numerical data. Narrative research methods, which focus on thick, rich contextual data, could help address the rhetorical and cultural dimensions of data system design. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Research questions:</b></i> 1. How do data collection, aggregation, and curation practices influence data stories about crime and violence? 2. How does Fatal Force, a data system about police use of force that originated outside law enforcement, prioritize and organize information? 3. How does designing data systems with explicit, highly specific goals and aims (like the inclusion criteria and purpose of Fatal Force) influence the system as a whole? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Methodology:</b></i> Using “unblackboxing,” a combination of narrative and critical data studies methods, I analyzed the Fatal Force database and its accompanying data stories. I compare this database with its institutional counterpart in the FBI's fledgling Use of Force database. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Results:</b></i> Fatal Force is constructed with particular attention to questions that users may have about police brutality, police shootings, and race. Fatal Force's data stories use narrative commonplaces like sociality and temporality to humanize the issue of police use of force and communicate greater nuance. The FBI's Use of Force database shows an orientation toward police perceptions of use-of-force incidents and a lack of attention to national conversations about police brutality. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Conclusions:</b></i> Data systems show clear perspectives on the issues that they describe, which influence how users encounter the data system, how useful the system can be for various users, and how inclusive or just the data system is.

Privacy Cynicism and its Role in Privacy Decision-Making

In the era of data-driven communication, managing one’s online privacy is a necessary, yet burdensome challenge. While individuals have concerns about firms’ data collection practices, they sometimes appear to disclose personal information for relatively small rewards. We demonstrate that privacy cynicism—an attitude toward privacy protection characterized by frustration, hopelessness, and disillusionment—explains this paradox by moderating the relationship between the appraisal of privacy threats and privacy coping behaviors on one side, and privacy protection behaviors on the other side. Results of a U.S. national survey ( N = 993) show that privacy cynicism is negatively related to privacy protection behaviors and significantly moderates relationships of perceived vulnerability, response efficacy, disclosure benefits, and response costs on protection behaviors. Hence, this work has important implications for communication theory by extending existing models of privacy management behaviors, as well as for communication practice, by stressing the importance of creating awareness about privacy cynicism.

Net Zero and the potential of consumer data - United Kingdom energy sector case study: The need for cross-sectoral best data practice principles

The nationwide rollout of smart meters marks a significant milestone in the digitalisation of the UK energy sector. It has allowed the collection of unprecedented amounts of consumer energy consumption and behavioural data. Providing real-time, spatially explicit, bi-directional connectivity between service providers and consumers, this data is expected to provide benefits of over £40B and contribute to behavioural nudges - which are integral to 62% of all initiatives required to achieve Net Zero by 2050. Concurrently, consumers are leaving behind digital trails across a broader spectrum of their lives through their smartphones and in-home devices. This data could also be used to accelerate digitalisation within the energy sector and yet the energy sector has limited or no visibility of it. While greater access to consumer data is expected to provide substantial opportunities for economic growth and the realisation of Net Zero both in the energy sector and across the UK economy, it also risks consumer exploitation.This policy perspectives seeks to provide insights from data and digital specialists in the UK rather than just actors within the energy sector. It uses psychographics as a use case to highlight the tension where data, particularly consumer data, is under regulated and siloed across the UK economy. The potential to use consumer data to change consumer behaviour to enable net zero opportunities is explored.The analysis strongly indicates that the ability for high fidelity consumer data to elicit behavioural insight is expected to become an increasingly valuable tool to inform both policy and business decisions in the energy transition - particularly around innovation and enhanced competition. These opportunities are balanced by the fact that high-definition consumer data collection practices are already perceived as exploitative. The lack of data sharing infrastructure across the economy has allowed incumbent technology companies to establish monopoly power thereby stifling competition and raising barriers to entry. The analysis makes a series of high-level recommendations around cross-economy data principles to enhance sharing and stimulate innovation whilst protecting consumer privacy to build digital trust. Options as to how these might be realised are also proposed and should form the basis of future research.

Problems with SZZ and features: An empirical study of the state of practice of defect prediction data collection

ContextThe SZZ algorithm is the de facto standard for labeling bug fixing commits and finding inducing changes for defect prediction data. Recent research uncovered potential problems in different parts of the SZZ algorithm. Most defect prediction data sets provide only static code metrics as features, while research indicates that other features are also important.ObjectiveWe provide an empirical analysis of the defect labels created with the SZZ algorithm and the impact of commonly used features on results.MethodWe used a combination of manual validation and adopted or improved heuristics for the collection of defect data. We conducted an empirical study on 398 releases of 38 Apache projects.ResultsWe found that only half of the bug fixing commits determined by SZZ are actually bug fixing. If a six-month time frame is used in combination with SZZ to determine which bugs affect a release, one file is incorrectly labeled as defective for every file that is correctly labeled as defective. In addition, two defective files are missed. We also explored the impact of the relatively small set of features that are available in most defect prediction data sets, as there are multiple publications that indicate that, e.g., churn related features are important for defect prediction. We found that the difference of using more features is not significant.ConclusionProblems with inaccurate defect labels are a severe threat to the validity of the state of the art of defect prediction. Small feature sets seem to be a less severe threat.

Rapid response teams: A review of data collection practice in Victoria, Australia

BackgroundSuccessful implementation of rapid response teams (RRTs) requires robust data collection and reporting processes. However, there is variation in data collection practice in RRT activity between hospitals, leading to difficulties in quality review, collaboration and research. Although a standardised RRT data collection model would be a key step in addressing this, there is uncertainty regarding existing RRT data collection practice across Victoria. ObjectivesThis study was endorsed by Safer Care Victoria (SCV) to evaluate existing RRT data collection practice across Victoria. MethodologyBetween 2016 and 2017, hospitals in Victoria were surveyed on data collection practice for RRT activity. Data collected included the fields populated and the mode of data collection. Qualitative content analysis, utilising a blend of pre-existing frameworks and ground-up data-driven approaches for derivation of a coding frame, was used to identify common categories. Validation of the analysis and results was performed by consultation with stakeholder groups. ResultsTwenty five hospitals across 18 health networks contributed data, with a mix of tertiary (9/25), metropolitan (11/25) and rural (5/25) hospitals. Seven hospitals collected data electronically, the remainder using paper with abstraction to electronic spreadsheets. None of the hospitals linked with existing hospital data systems to reduce manual data entry requirements. Dataset size varied from 16 to 97 variables but demonstrated content consistency and could be mapped onto seven key categories (comprising antecedent, afferent, event, post-event, audit, context and patient data). Within each category, there was substantial variation in terminology and variable values, but consistency in the collection of a certain subset of variables. ConclusionDespite broad variation in data collection practice, existing datasets can be readily mapped into seven key categories, with the consistent collection of a subset of variables within each category. These variables could inform the development of a minimum dataset within a standardised RRT reporting framework and accommodate data submission from hospitals of differing resource bases.

Community first response and out-of-hospital cardiac arrest: Identifying priorities for data collection, analysis, and use via the nominal group technique

AimCommunity First Response (CFR) is an important component of Out-of-hospital Cardiac Arrest management in many countries, including Ireland. Reliable, strategic data collection and analysis are required to support the development of CFR. However, data on CFR are currently limited in Ireland and internationally. This research aimed to identify the most important CFR data to record, the most important uses of CFR data, and barriers and facilitators to CFR data collection and use. MethodsThe Nominal Group Technique structured consensus process was used. An expert panel comprising key stakeholders, including volunteers, clinicians, researchers, policy-makers, and a patient, completed a survey to generate lists of the most important CFR data to record and the most important uses of CFR data. Subsequently, they participated in a consensus meeting to agree the top ten priorities from each list. They also identified barriers and facilitators to CFR data collection and use. ResultsThe top ten CFR data items to record included volunteer response time, interventions/activities completed by volunteers, and the mental/physical impact on volunteers. The top ten most important uses of CFR data included providing feedback to volunteers, improving volunteer training, and measuring CFR effectiveness. Barriers included time constraints and limited training. Facilitators included having appropriate software/equipment and collecting minimal data. ConclusionThe results can guide CFR research and inform the development of CFR data collection and analysis policy and practice in Ireland and internationally. Ultimately, improving CFR data collection and use will help to optimise this important intervention and enhance its evidence base.

A survey of protocols from 54 infant and toddler neuroimaging research labs

Infant and toddler MRI enables unprecedented insight into the developing brain. However, consensus about optimal data collection practices is lacking, which slows growth of the field and impedes replication efforts. The goal of this study was to collect systematic data across a large number of infant/toddler research laboratories to better understand preferred practices. Survey data addressed MRI acquisition strategies, scan success rates, visit preparations, scanning protocols, accommodations for families, study design, and policies regarding incidental findings. Respondents had on average 8 years’ experience in early life neuroimaging and represented more than fifty research laboratories. Areas of consensus across labs included higher success rates among newborns compared to older infants or toddlers, high rates of data loss across age groups, endorsement of multiple layers of hearing protection, and age-specific scan preparation and participant accommodation. Researchers remain divided on decisions in longitudinal study design and practices regarding incidental findings. This study summarizes practices honed over years of work by a large collection of scientists, which may serve as an important resource for those new to the field. The ability to reference data about best practices facilitates future harmonization, data sharing, and reproducibility, all of which advance this important frontier in developmental science.

A preregistered vignette experiment on determinants of health data sharing behavior Willingness to donate sensor data, medical records, and biomarkers.

The COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data types and recipients, we argue that established social norms shape the acceptance of novel practices of data collection and use. To investigate the willingness to share health data, we conducted a preregistered vignette experiment. The scenarios experimentally varied the vignette dimensions by data type, recipient, and research purpose. While some findings contradict our hypotheses, the results indicate that all three dimensions affected respondents' data sharing decisions. Additional analyses suggest that institutional and social trust, privacy concerns, technical affinity, altruism, age, and device ownership influence the willingness to share health data.

An Ethnographic Toolkit for Studying the Networking Pathways of Hard-to-Reach Populations: The Case of Cosmetic Surgery Consumers in South Korea

This article develops a novel ethnographic toolkit for examining the networking pathways that hard-to-reach populations use to socially survive. The toolkit consists of two sampling strategies (snowball and purposive sampling) and three data collection practices (role shuttling, site shuttling, and autoethnography). This article illustrates the applications of the toolkit in an ethnography of South Korean cosmetic surgery clinics and digital forums from 2018 to 2019 by uncovering the role that furtive networks play in facilitating cosmetic surgery consumption. Longitudinal in nature, the toolkit excels in examining the network’s dynamism, informal hierarchy, and the meaning-making and networking pathways that allow members of a hard-to-reach population like cosmetic surgery consumers in South Korea to participate in stigmatized practices. In the hard-to-reach population of surgery enthusiasts, I find that surgery is purchased by consumers through persuasive reconstructions of the meanings of success, body, and self by an elusive network of clinicians, who are introduced by an ever-changing roster of past cosmetic surgery consumers perceived to be high-status.

Privacy policies of IoT-based healthcare: an empirical analysis study of the data collection practices of existing IoT medical applications

In healthcare, the internet of things (IoT) has accelerated the process of gathering patients' data. However, data processing is a significant violation of patients' privacy. This research critically examines to what extent the privacy policies of IoT medical apps provide sufficient details about their data collection practices with respect to personal data. It aims to explore the various medical data collected by IoT medical apps. An empirical analysis investigated the privacy policies of 21 of IoT medical apps. The findings revealed that these apps had provided sufficient details related to some of the data collection practices. However, details related to the security of personal data were more general. The findings indicate that most of these apps offer no guarantee regarding securing personal data. Such results open new research to be extended to validate to what extent the service providers are committed to what is explained in their privacy policies.

Privacy Policies of IoT-based Healthcare: An Empirical Analysis Study of the Data Collection Practices of Existing IoT Medical Applications

Privacy Policies of IoT-based Healthcare: An Empirical Analysis Study of the Data Collection Practices of Existing IoT Medical Applications

The Paradoxical Relationship Between Health Promotion and the Social Media Industry.

Mounting evidence suggests that problematic adolescent social media use is associated with poor mental health. To respond to increased adolescent mental health concerns, health promoters increasingly rely on social media initiatives to promote their resources, programs, and services. This creates a paradoxical situation where social-media-linked adverse mental health outcomes are addressed using the same tools and platforms that can contribute to the development of such issues. It also highlights several areas of needed critical assessment in health promotion usage of social media platform features and products, such as addictive platform design, targeted marketing tools, data collection practices, impacts on underserved groups, and conflicts of interest. To advance subsequent action on these tensions, we offer three recommendations for health promoters that build upon existing scholarship and initiatives, including adapting ethical guidelines for health promoters using social media, adopting conflicts of interest policies, and promoting interdisciplinary scholarship.

Сравнительный анализ методологии статистического наблюдения за туристской деятельностью в Российской Федерации и странах Европейского союза

The need to harmonize national and international statistical practices in data collection and presentation is based on the desire for comparability of information. In the Russian Federation and the countries of the European Union, a different approach is used to organize and present the results of statistical observation of the tourism industry, including with respect to the classification of accommodation facilities for tourists, tourist trips according to their purposes. In Russia, they do not collect information on identifying the reasons why the citizens of these countries do not take part in tourism, nor do they register quality characteristics. Analysis of international experience shows that tourism statistics receive a lot of attention, with the focus on polling tourists and collective accommodation facilities. In the Russian system of statistical observation in the field of tourism, despite its constant improvement, there is still an acute shortage of data obtained on the basis of regular sample surveys.

Privacy Within the Private Order: A Critical Evaluation of the Market-Based System Governing Data Collection in the United States

This paper critically evaluates the market-based system governing data collection in the United States. The discussion is centred around Big Tech, a group of information intermediaries responsible for the ongoing extraction and exploitation of consumer data. The exploitative system is enabled by the ubiquitous privacy policy, which ostensibly offers data subjects ‘notice’ of data collection and the ‘choice’ to consent to said collection. This paper critiques the ‘notice and choice’ model, concluding the combined ambiguity and opacity of the privacy policy fail to offer subjects meaningful control over their data. To substantiate this argument, the paper evaluates the suitability of the market-based system in a broader sense, arguing that data collection practices precludes the knowledge parity necessary for an operative and fair market-based system. The paper concludes by ascertaining the suitability of state-based regulation, identifying data’s intrinsic relationship with ideals that are core to the Western tradition: equality, democracy, and autonomy.

Conducting Community-Based Qualitative Research With LGBTQ Older Adults of Color During COVID-19

COVID-19 continues to transform the way scientists conduct research with study participants, particularly older adults who are at high risk of becoming seriously ill from the virus. For older adults who may be negatively affected by the digital divide, inclusive data collection practices become even more nuanced. Qualitative researchers moving their research into digital spaces must think critically about their use of technology, and how it affects the quality of data as well as the participant experience. This presentation highlights ethical and methodological considerations from a completely digital, community-based, qualitative research study conducted alongside LGBTQ older adults of color during COVID-19. Strategies to build and strengthen community partnerships are discussed along with challenges and opportunities for collecting data in the current digital landscape. Publicly available records are identified as a potential data source to understand the lives of LGBTQ older adults of color when in-person research is not feasible.

Health and Health Care Among Transgender Adults in the United States.

Transgender (trans) communities in the USA and globally have long organized for health and social equity but have only recently gained increased visibility within public health. In this review, we synthesize evidence demonstrating that trans adults in the USA are affected by disparities in physical and mental health and in access to health care, relative to cisgender (nontrans) persons. We draw on theory and data to situate these disparities in their social contexts, explicating the roles of gender affirmation, multilevel and intersectional stigmas, and public policies in reproducing or ameliorating trans health disparities. Until recently, trans health disparities were largely made invisible by exclusionary data collection practices. We highlight the importance of, and methodological considerations for, collecting inclusive sex and gender data. Moving forward, we recommend routine collection of gender identity data, an emphasis on intervention research to achieve trans health equity, public policy advocacy, and investment in supporting gender-diverse public health leadership.

BMI Data Collection and Communication Practices in a Multistate Sample of Head Start Programs.

Background: Although there is a federal mandate to collect anthropometric data in Head Start (HS), little is currently known about the processes used to collect the height and weight measurements across programs and how the results are communicated to parents/guardians. The goal of this study was to understand anthropometric data collection and dissemination procedures in a sample of HS programs serving children 3-5 years. Methods: A convenience sample of HS Health or Nutrition managers were recruited via personal contacts and HS state directors to complete an electronic survey. Quantitative data were analyzed using descriptive statistics (means, standard deviations and frequencies). Open-ended questions were coded using thematic analysis. All protocols and procedures were approved by the Institutional Review Board at Miami University. Results: Approximately half of the programs reported that they have a protocol in place to guide measurements (57.1%) and those measurements are primarily taken by HS staff (64.5%). Most programs explain measurements to parents (82.3%) and report that collecting height/weight data is helpful in supporting children's health (76.0%). Most programs (80.3%) provide resources to parents of children with overweight or obesity. Four themes emerged from open-ended responses: (1) Role of Community Partners (e.g., providing information that conflicts with others); (2) Communicating Children's Weight Status with Families (e.g., using sensitive communication methods); (3) Challenges Measuring Children's Weight Status (e.g., accuracy of data, children's awareness); and (4) Family Reaction to Weight Status Communication (e.g., positive or negative experiences). Conclusion: Opportunities for quality improvement include wider use of standardized, written protocols and policies on data collection and enhanced communication practices to share information with parents.