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

A robot's appearance and behavior provide cues to the robot's abilities and propensities. We hypothesize that an appropriate match between a robot's social cues and its task improve the people's acceptance of and cooperation with the robot. In an experiment, people systematically preferred robots for jobs when the robot's humanlikeness matched the sociability required in those jobs. In two other experiments, people complied more with a robot whose demeanor matched the seriousness of the task.

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Matching robot appearance and behavior to tasks to improve human-robot cooperation

IN ALL SCIENTIFIC ENDEAVOR the available tools affect the questions one can ask and the data one can collect. In this report, we examine a new tool in survey research, the electronic or computer-mediated survey. In the last two decades, electronic computers have come to figure in many phases of survey research-instrument design, sampling, moni- toring of work in the field, coding and editing, data entry, data clean- ing, scale and index construction, data base organization, data base retrieval, statistical analysis, documentation, and report writing (Kar- Abstract This report examines the electronic survey as a research tool. In an electronic survey, respondents use a text processing program to self-administer a computer-based questionnaire. As more people have access to computers, electronic surveys may be- come widespread. The electronic survey can reduce processing costs because it auto- mates the transformation of raw data into computer-readable form. It can combine advantages of interviews (e.g., prompts, complex branching) with those of paper mail surveys (e.g., standardization, anonymity). An important issue is how the electronic survey affects the responses of people who use it. We conducted an experimental sample survey on health attitudes, behaviors, and personal traits using two forms of administra- tion: electronic and paper mail. Closed-end responses in the electronic survey were less socially desirable and tended to be more extreme than were responses in the paper survey. Open-ended responses that could be edited by respondents were relatively long and disclosing. These findings are consistent with other research on computer-mediated communication, raising general issues about using computers to collect self-report data. Sara Kiesler is a Professor of Social Sciences and Social Psychology, and Lee Sproull

Response Effects in the Electronic Survey

From the Publisher:
Technological advances and changes in the global economy are increasing the geographic distribution of work in industries as diverse as banking, wine production, and clothing design. Many workers communicate regularly with distant coworkers; some monitor and manipulate tools and objects at a distance. Work teams are spread across different cities or countries. Joint ventures and multiorganizational projects entail work in many locations. Two famous examples--the Hudson Bay Company's seventeenth-century fur trading empire and the electronic community that created the original Linux computer operating system--suggest that distributed work arrangements can be flexible, innovative, and highly successful. At the same time, distributed work complicates workers' professional and personal lives. Distributed work alters how people communicate and how they organize themselves and their work, and it changes the nature of employee-employer relationships.
This book takes a multidisciplinary approach to the study of distributed work groups and organizations, the challenges inherent in distributed work, and ways to make distributed work more effective. Specific topics include division of labor, incentives, managing group members, facilitating interaction among distant workers, and monitoring performance. The final chapters focus on distributed work in one domain, collaborative scientific research. The contributors include psychologists, cognitive scientists, sociologists, anthropologists, historians, economists, and computer scientists.

Distributed Work

Scientific and engineering research increasingly involves multidisciplinary collaboration, sometimes across multiple organizations. Technological advances have made such cross-boundary projects possible, yet they can carry high coordination costs. This study investigated scientific collaboration across disciplinary and university boundaries to understand the need for coordination in these collaborations and how different levels of coordination predicted success. We conducted a study of 62 scientific collaborations supported by a program of the US National Science Foundation in 1998 and 1999. Projects with principal investigators (PIs) in more disciplines reported as many positive outcomes as did projects involving fewer disciplines. By contrast, multi-university, rather than multidisciplinary, projects were problematic. Projects with PIs from more universities were significantly less well coordinated and reported fewer positive outcomes than projects with PIs from fewer universities. Coordination mechanis...

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Collaborative Research Across Disciplinary and Organizational Boundaries

Online communities are increasingly important to organizations and the general public, but there is little theoretically based research on what makes some online communities more successful than others. In this article, we apply theory from the field of social psychology to understand how online communities develop member attachment, an important dimension of community success. We implemented and empirically tested two sets of community features for building member attachment by strengthening either group identity or interpersonal bonds. To increase identity-based attachment, we gave members information about group activities and intergroup competition, and tools for group-level communication. To increase bond-based attachment, we gave members information about the activities of individual members and interpersonal similarity, and tools for interpersonal communication. Results from a six-month field experiment show that participants' visit frequency and self-reported attachment increased in both conditions. Community features intended to foster identity-based attachment had stronger effects than features intended to foster bond-based attachment. Participants in the identity condition with access to group profiles and repeated exposure to their group's activities visited their community twice as frequently as participants in other conditions. The new features also had stronger effects on newcomers than on old-timers. This research illustrates how theory from the social science literature can be applied to gain a more systematic understanding of online communities and how theory-inspired features can improve their success.

Sara Kiesler

Papers

Matching robot appearance and behavior to tasks to improve human-robot cooperation

Response Effects in the Electronic Survey

Distributed Work

Collaborative Research Across Disciplinary and Organizational Boundaries

Building member attachment in online communities: applying theories of group identity and interpersonal bonds