Governments around the world collect huge amounts of personal data from their citizens for counterterrorist purposes. While mining this data has arguably increased the security of populations, the practices through which these data are currently collected in many countries have been criticised for violating individuals’ rights to privacy. Yet it is not clear what a permissible data collection regime would look like and thus also how we could reform existing regimes to make them morally acceptable. (...) This article explores a number of ways in which we might justify a data collection regime to those affected in spite of the setbacks to their privacy. In contrast to existing justifications, I argue that individuals can be asked to surrender their personal data as a requirement of reciprocity in a cooperative system in which they gain security from others doing likewise. Relying on this justification, though, has significant implications for how we should reform existing data collection regimes. In particular, more stringent limits will need to be placed on the forms which these regimes can legitimately take. (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)

Collecting and deploying poverty-related data is an important starting point for leveraging data regarding social determinants of health in precision medicine. However, we must rethink how we collect and deploy such data. Current modes of collection yield imprecise data that is unsuited for research. Better data can be collected by cross-referencing other sources such as employers and public benefit programs, and by incentivizing and encouraging patients and providers to provide more accurate information. Data (...) thus collected can be used to provide appropriate individual-level clinical and non-clinical care, and to systematically determine what share of social resources healthcare should consume. (shrink)

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)

The use of Internet to aid research practice has become more popular in the recent years. In fact, some believe that Internet surveying and electronic data collection may revolutionize many disciplines by allowing for easier data collection, larger samples, and therefore more representative data. However, others are skeptical of its usability as well as its practical value. The paper highlights both positive and negative outcomes experienced in a number of e-research projects, focusing on several common (...) mistakes and difficulties experienced by the authors. The discussion focuses on ethics and review board issues, recruitment and sampling techniques, technological issues and errors, and data collection, cleaning, and analysis. (shrink)

This article examines ethical concerns surrounding research on minority issues. Specifically, it addresses whether researchers have an obligation to consider the impact that minority research can have on vulnerable populations and their own backgrounds before conducting or assessing minority research for publication. The article argues for such an obligation mainly from a consequentialist perspective and then explores possible strategies for assessing and meeting that obligation. Finally, it explores the possible negative secondary effects of those strategies and censorship concerns.

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)

In this article, I offer a partial analysis of the role of values in qualitative data collection in social research. The partial analysis shows that nonepistemic values have both required and permissible roles to play during this phase of research. By appeal to the analysis, I reject the ideal of value-free science as applied to qualitative data collection, and I demonstrate why two alternative ideals should likewise be dismissed as standards for values in qualitative data (...) collection. Also, I briefly discuss the extent to which the partial analysis carries over to quantitative data collection in social research. (shrink)

The article analyzes ethnic data collection pertaining to criminal justice in Hungary. With such a sensitive and delicate issue at hand, Hungary has decided on an evasive approach, resisting ethnic data collection by law enforcement authorities. The author argues that this approach has become one of the obstacles in fighting discrimination and ethnic profiling. Moreover, Hungary’s restrictive approach to ethno-national data classification also causes severe constitutional problems in other, noncriminal legal circumstances, where ethnic data (...) is used in the context of additional rights and affirmative protection provided for ethno-national minorities. The first part of the article describes general problems relating to ethnic data collection and analyses of the Hungarian minority protection framework, in particular, the minority self-government structure (a unique constitutional institution). The second part focuses on the criminal justice system; the author’s aim is to show that prohibiting the official recognition and collection of data on ethnicity by criminal justice authorities has potentially ethnically discriminatory consequences. (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)

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)

This commentary reflects on recent instances of anti-Asian violence and state responses to redress violence through data-driven strategies. Data collection often presents itself as an appealing strategy, due to impacted communities’ desires for evidence and metrics to substantiate political claims. Yet, data collection can bolster the carceral state. This commentary takes an antagonistic approach to policing, including the ongoing creation of data infrastructures by—and for—law enforcement through hate crimes legislation. We critically discuss the challenges (...) and possibilities in building towards anti-carceral responses amidst ongoing racial violence and crisis. (shrink)

BackgroundKinarm Standard Tests is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior. We have previously used principal component analysis to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab. Here, we performed PCA using data from the Kinarm Exoskeleton Lab, and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether (...) further dimensionality reduction was possible by using PCA across behavioral tasks.MethodsHealthy participants were assessed using the Kinarm EXO and EP. Four behavioral tasks were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching ] and three motor tasks [Visually Guided Reaching, Object Hit, and Object Hit and Avoid ]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type was decided on a per-task basis. To assess agreement, we compared principal components across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.ResultsBy applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58–75% while accounting for 76–87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.ConclusionPCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations. (shrink)

For the first time, the revised Common Rule specifies that public health surveillance activities are not research. This article reviews the historical development of the public health surveillance exclusion and implications for other foundational public health practices.

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)

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)

Health research increasingly relies on organized collections of health data and biological samples. There are many types of sample and data collections that are used for health research, though these are collected for many purposes, not all of which are health-related. These collections exist under different jurisdictional and regulatory arrangements and include: 1) Population biobanks, cohort studies, and genome databases 2) Clinical and public health data 3) Direct-to-consumer genetic testing 4) Social media 5) Fitness trackers, health apps, (...) and biometric data sensors Population biobanks, cohort studies, and genome databases Clinical and public health data Direct-to-consumer genetic testing Social media Fitness trackers, health apps, and biometric data sensors Ethical, legal, and social challenges of such collections are well recognized, but there has been limited attention to the broader societal implications of the existence of these collections. Although health research conducted using these collections is broadly recognized as beneficent, secondary uses of these data and samples may be controversial. We examine both documented and hypothetical scenarios of secondary uses of health data and samples. In particular, we focus on the use of health data for purposes of: Forensic investigations Civil lawsuits Identification of victims of mass casualty events Denial of entry for border security and immigration Making health resource rationing decisions Facilitating human rights abuses in autocratic regimes Forensic investigations Civil lawsuits Identification of victims of mass casualty events Denial of entry for border security and immigration Making health resource rationing decisions Facilitating human rights abuses in autocratic regimes Current safeguards relating to the use of health data and samples include research ethics oversight and privacy laws. These safeguards have a strong focus on informed consent and anonymization, which are aimed at the protection of the individual research subject. They are not intended to address broader societal implications of health data and sample collections. As such, existing arrangements are insufficient to protect against subversion of health databases for non-sanctioned secondary uses, or to provide guidance for reasonable but controversial secondary uses. We are concerned that existing debate in the scholarly literature and beyond has not sufficiently recognized the secondary data uses we outline in this paper. Our main purpose, therefore, is to raise awareness of the potential for unforeseen and unintended consequences, in particular negative consequences, of the increased availability and development of health data collections for research, by providing a comprehensive review of documented and hypothetical non-health research uses of such data. (shrink)

This study appeared in full in the last issue of Research Ethics Review : 53). John Harris is investigating the effectiveness of a new regime for the treatment rheumatoid arthritis. His role involves asking patients to fill out a questionnaire that normally takes about 35 minutes to complete. The case study outlines three events: a patient whose eyesight is poor, another who cannot read and a receptionist who is discovered reading some of the completed questionnaires.

This study analyzes the increasing presence and capabilities of wearable computing devices in the cornucopia of personalized digital data. We argue that the institutional data practices typical of Google Glass will pose policy challenges and herald yet another dramatic shift to personalized data marketing. We also highlight the characteristics of Google’s existing synergetic data practices that will shape the development of not only Google Glass, but also all subsequent wearable mobile devices in light of 360-degree (...) class='Hi'>data collection. The key organizing concept of our study is the disjuncture between institutional and policy forces in harnessing dual market mechanism, which frames how the new communication industry operates in the marketplace of ubiquitous personal advertising. We conclude by summarizing the three key areas of political-policy concern and suggest future solutions, with the discussion on the future of wearable computing practices related to the freedom of the human body. (shrink)

Research Ethics, Volume 18, Issue 1, Page 24-38, January 2022. Are social science, cross-border research projects, where recruitment and data collection are carried out remotely, required to follow similar ethical and data-sharing procedures as ‘on-the-ground’ studies that use traditional means of recruitment and participant engagement? This article reflects on our experience of dealing with this question when we had to switch to online data collection due to the restrictions posed by the COVID-19 pandemic, such as (...) the inability to travel or work in person with local communities and collaborators. Using social media platforms and online data collection when conducting research brings many advantages, such as being able to communicate remotely but directly with gatekeepers and collaborators, and in reaching potential participants on a global scale. However, neither the guidelines and advice for conducting ethically sound internet-based research, nor the academic literature focussed on building equitable research partnerships between the Global North and the Global South, offer much information regarding the ethical concerns, or address the grey areas, posed by this type of digital and distanced transnational research. In our experience, conducting research remotely made negotiations of access very challenging due to the politics of positionality between Global North and South researchers, lack of clarity on ethical processes and perceptions of gatekeepers who we could not meet in person. We hope the reflections on, and discussion of, our experience encourage deliberation on the present ethical challenges posed by online and social-media-disseminated data collection, particularly in cross-border circumstances. (shrink)

A powerful movement is afoot to create a national computerized system of health records. Advocates claim it could save the health delivery system billions of dollars and improve the quality of health services. According to Lawrence Gostin, a leading commentator on privacy and health records, this new infrastructure is “already under way and [has] an aura of inevitability.” When it is in place, almost any information that is viewed as relevant to a decision in the health care delivery system would (...) be available to a large and yet undetermined number of individuals. The transformation of the collection and communication of health information from texts housed by health care providers and facilities to data electronically transmitted through networks of linked computers has significant implications for confidentiality and for data collection in scientific research. The best evidence clearly indicates that most people in the United States consider confidentiality for health information important and worry that the increased computerization of health records will result in inappropriate disclosure. (shrink)

A powerful movement is afoot to create a national computerized system of health records. Advocates claim it could save the health delivery system billions of dollars and improve the quality of health services. According to Lawrence Gostin, a leading commentator on privacy and health records, this new infrastructure is “already under way and [has] an aura of inevitability.” When it is in place, almost any information that is viewed as relevant to a decision in the health care delivery system would (...) be available to a large and yet undetermined number of individuals. The transformation of the collection and communication of health information from texts housed by health care providers and facilities to data electronically transmitted through networks of linked computers has significant implications for confidentiality and for data collection in scientific research. The best evidence clearly indicates that most people in the United States consider confidentiality for health information important and worry that the increased computerization of health records will result in inappropriate disclosure. (shrink)

This article discusses the benefits and limitations of collecting electronic data for large-scale thematic content analysis. We will discuss a number of methodological and technical issues. The first one is the construction of a list of relevant keywords that serves as the primary data collecting device. This is not only a technical necessity, but also secures a theoretically and empirically valid collection of data. The second concern is the quality of electronic archive information. Finally, source-specific (...) class='Hi'>data characteristics and coding difficulties are dealt with. In conclusion, seven guidelines for electronic data collecting are proposed. (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)

In the mid-1990s, the Internet rapidly changedfrom a venue used by a small number ofscientists to a popular phenomena affecting allaspects of life in industrialized nations. Scholars from diverse disciplines have taken aninterest in trying to understand the Internetand Internet users. However, as a variety ofresearchers have noted, guidelines for ethicalresearch on human subjects written before theInternet's growth can be difficult to extend toresearch on Internet users.In this paper, I focus on one ethicalissue: whether and to what extent to disguisematerial (...) collected online in publishedaccounts. While some people argue thatvulnerable human subjects must always be madeanonymous in publications for their ownprotection, others argue that Internet usersdeserve credit for their creative andintellectual work. Still others argue thatmuch material available online should betreated as ``published.'' To attempt to resolvethese issues, I first review my own experiencesof disguising material in research accountsfrom 1992 to 2002. Some of the thorniestissues emerge at the boundaries betweenresearch disciplines. Furthermore, manyhumanities disciplines have not historicallyviewed what they do as human subjects research. Next, I explore what it means to do humansubjects research in the humanities. Inspiredby issues raised by colleagues in thehumanities, I argue that the traditional notionof a ``human subject'' does not adequatelycharacterize Internet users. A useful alternatemental model is proposed: Internet users areamateur artists. The Internet can be seen as aplayground for amateur artists creatingsemi-published work. I argue that thisapproach helps make some ethical dilemmaseasier to reason about, because it highlightskey novel aspects of the situation,particularly with regard to disguisingmaterial. Finally, I conclude by proposing aset of practical guidelines regardingdisguising material gathered on the Internet inpublished accounts, on a continuum from nodisguise, light disguise, moderate disguise, toheavy disguise. (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)

The factors responsible for the return of fertility post-partum have not been very clearly defined. Several previous studies have reported apparently major differences between populations in the duration of infertility associated with lactation. However, these differences may be related to the differences in study designs.

Reactivity in qualitative data collection occurs when a researcher generates data about a situation with reactivity, that is, a situation in which the ongoing research affects the research participants such that they, say, diverge from their routines when the researcher is present, or tell the researcher what they think she wants to hear. In qualitative research, there are two basic approaches to reactivity. The traditional position maintains that data should ideally be collected in situations without any (...) reactivity. In other words, good data are reactivity free. By contrast, the more recent view holds that data from situations with reactivity are fine as long as the researcher is aware of the occurring reactivity so that she can take it into account when interpreting her data. In this fashion, good data are reactivity transparent. In this paper, I first spell out and defend the more recent approach to reactivity. I argue that qualitative data are reactivity transparent when conjoined with true reactivity assumptions and that, thus supplemented, data are informative about social life independently of its being studied. Next, I examine various issues raised by the requirement to put forth true reactivity assumptions. Lastly, I use my discussion of reactivity transparency as a basis for providing a framework for thinking about good qualitative data. (shrink)

Over the past two decades, the epidemic of HIV/AIDS has challenged the public health community to fundamentally rethink the framework for preventing infectious diseases. While much progress has been made on the biomedical front in treatments for HIV infection, prevention still relies on behaviour change. This book documents and explains the remarkable breakthroughs in behavioural research design that have emerged to confront this new challenge: the study of partnership networks.Traditionally, public health research focused on the "knowledge, attitudes, and practices " (...) of individuals, an approach designed for understanding health-related behaviour like seat-belt wearing and cigarette smoking. For HIV and other sexually transmitted infections, however, there are at least two people involved in transmission. This may not seem like a big difference, but in fact it changes everything. First, it means that your risk depends on your partners -- and on their partners, and their partners: it depends on your position in the network of partnerships. Consider, for example, the rise of infections among monogamous women. Second, it means that individuals are not free to simply change their behaviour -- condom use, or abstinence, needs to be negotiated with a partner. both the epidemiology of risk and constraints to behaviour are therefore a function of the partnership network. And our ability to design effective prevention strategies depends on our ability to measure and summarize that network. Using the traditional research designs, you would not see this network at all -- you would only see the unconnected nodes. They key to solving this problem lies in Network Analysis, before now a relatively obscure subfield in Sociology.For empirical studies of networks to become feasible, however, many problems had to be solved. This book documents the rapid progress that has been made. It brings together eight pioneering studies that have sought to map the networks that spread infection around the world. Each chapter reviews the questions that drove the study, the changes in methodology that were needed to implement the network survey, the mistakes and successes encountered, and the central findings that the network design made possible. An introduction provides an overview of network survey design, a glossary provides a summary of network terminology, and example questionnaires from each study provide a template for further research. This is a unique and valuable resource for the international public health research community. (shrink)