Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

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Common method biases in behavioral research: a critical review of the literature and recommended remedies.

Basics of qualitative research: Grounded theory procedures and techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This study examines the relationship between use of Facebook, a popular online social network site, and the formation and maintenance of social capital. In addition to assessing bonding and bridging social capital, we explore a dimension of social capital that assesses one’s ability to stay connected with members of a previously inhabited community, which we call maintained social capital. Regression analyses conducted on results from a survey of undergraduate students (N = 286) suggest a strong association between use of Facebook and the three types of social capital, with the strongest relationship being to bridging social capital. In addition, Facebook usage was found to interact with measures of psychological well-being, suggesting that it might provide greater benefits for users experiencing low self-esteem and low life satisfaction.

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The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites

This paper differs from previous examinations of organizational learning in that it is broader in scope and more evaluative of the literatures. Four constructs related to organizational learning knowledge acquisition, information distribution, information interpretation, and organizational memory are articulated, and the literatures related to each are described and critiqued.

The literature on knowledge acquisition is voluminous and multi-faceted, and so the knowledge acquisition construct is portrayed here as consisting of five subconstructs or subprocesses: 1 drawing on knowledge available at the organization's birth, 2 learning from experience, 3 learning by observing other organizations, 4 grafting on to itself components that possess knowledge needed but not possessed by the organization, and 5 noticing or searching for information about the organization's environment and performance. Examination of the related literatures indicates that much has been learned about learning from experience, but also that there is a lack of cumulative work and a lack of integration of work from different research groups. Similarly, much has been learned about organizational search, but there is a lack of conceptual work, and there is a lack of both cumulative work and syntheses with which to create a more mature literature. Congenital learning, vicarious learning, and grafting are information acquisition subprocesses about which relatively little has been learned.

The literature concerning information distribution is rich and mature, but an aspect of information distribution that is central to an organization's benefitting from its learning, namely how units that possess information and units that need this information can find each other quickly and with a high likelihood, is unexplored. Information interpretation, as an organizational process, rather than an individual process, requires empirical work for further advancement. Organizational memory is much in need of systematic investigation, particularly by those whose special concerns are improving organizational learning and decision making.

https://www.jstor.org/stable/pdfplus/2634941.pdf

Organizational Learning: The Contributing Processes and the Literatures

Science policy across the world emphasizes the desirability of research teams that can integrate diverse perspectives and expertise into new knowledge, methods, and products. However, integration in research work is not well understood. Based on retrospective interviews with 55 researchers from 52 diverse research projects, we categorized teams as co-acting (50%), coordinated (15%), and integrated (35%). Integration, when it existed, usually began when PIs chose collaborators and pursued integration throughout the project. We describe researchers' experiences and research climates that discouraged or encouraged integration. Implications for policy choices and design include changes in team structuring and technology support.

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Research team integration: what it is and why it matters

1. Social Studies of Domestic Information and Communication Technologies Information Technology and Social Change 2. Protraits of American Internet Use: Frindings from the Pew Internet & American Life Project 3. Passing By and Passing Through 4. Older People and New Technologies 5. Information Technology and Family Time Displacement 6. Examining the Impact of Internet Use on TV Viewing: Details Make a Difference 7. The Neutered Computer Technology is Context-Home, Family, and Community 8. The Consumption Junction Revisited: Networks and Contexts 9. Designing the Family Portal for Home Networking 10. Children's Privacy Online: Experimenting with Boundaries Within and Beyond the Family 11. Children's Home Internet Use: Antecedents and Psychological, Social & Academic Consequences 12. Social and Civic Participation in a Community Network New Technology in Teenage Life 13. Teens on the Internet: Interpersonal Connection, Identity, and Information 14. Teenage Communication in the Instant Messaging Era 15. Control, Emancipation, and Status: The Mobile Telephone in the Teen's Parental and Peer Group Control Relationships 16. Intimate Connections: Contextualizing Japanese Youth and Mobile Messaging The Internet and Social Relationships 17. The Internet and Social Interaction: A Meta-analysis and Critique of Studies, 1995-2003 18. Communication Technology and Friendship During the Transition From High School to College 19. Considering the Interactions: The Effects of the Internet on Self and Society 20. Rhythms and Ties: Towards a Pragmatics of Technologically-mediated Sociability

Computers, Phones, and the Internet: Domesticating Information Technology (Human Technology Interaction)

In this paper we present the concept of an isovist, derived from the architectural literature, and describe how isovists can help HCI researchers understand visibility in a physical environment. An isovist is defined as the set of all points visible in all directions from a given vantage point in space. The overlap in isovists from two or more locations can be used to assess reciprocal visibility and thereby assist in the placement of large displays for public or shared use. We illustrate the value of isovists for HCI research using field data from two OR suites in two major urban hospitals. First, we show how patterns of interaction between anesthesiologists and nurses in each of two OR suites are associated with quantity of isovist overlap. Then, we show how an isovist analysis can be used to determine a better placement for the shared display in one of the OR suites to enhance coordination between groups.

Using isovist views to study placement of large displays in natural settings

This chapter contains sections titled: What Is Linux?, Enabling Conditions: Open Source and Internet, Linus Torvalds, Hackers, Community, Lessons for Organizations, Notes, References

Essence of Distributed Work: The Case of the Linux Kernel

It is our great pleasure to welcome you to the 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI 2007). HRI is a highly selective annual conference that seeks to showcase the very best research and thinking in human-robot interaction. Human-robot interaction is inherently inter-disciplinary, and the conference sought papers from researchers in robotics, human-factors, ergonomics, human-computer interaction, cognitive psychology, and other fields. The mission of the conference is to create a common venue for this broad set of researchers.This year's conference theme is "Robot as Team Member". Robots are used in such critical domains as search and rescue, military theater, mine and bomb detection, scientific exploration, law enforcement, and hospital care. Such robots must coordinate their behaviors with human team members; they are more than mere tools but rather quasi-team members whose tasks have to be integrated with those of humans. HRI 2007 is dedicated to these and other issues in human and robot interaction, highlighting the importance of building core science and understanding the social and technical issues in human-robot interaction in the context of teams and groups.Of the 93 submissions, the program committee accepted 22 papers and 26 posters that cover a variety of topics, among them field studies of robots in public spaces, operator-robot rescue teams, attributions of robot behavior, and human-robot dialogue. The program includes paper presentations, a video session, two interactive poster sessions, panels on robots in teams and the future of HRI research, and keynote speeches by human teamwork expert, J. Richard Hackman of Harvard, and by Hiroshi Ishiguro of Osaka University and ATR. We hope that these proceedings will serve as a valuable reference for HRI researchers and students.

Sara Kiesler

Papers

Research team integration: what it is and why it matters

Computers, Phones, and the Internet: Domesticating Information Technology (Human Technology Interaction)

Using isovist views to study placement of large displays in natural settings

Essence of Distributed Work: The Case of the Linux Kernel

Proceedings of the ACM/IEEE international conference on Human-robot interaction