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.

/pdf/common-method-biases-in-behavioral-research-a-critical-53ocysaemj.pdf

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.

/pdf/the-benefits-of-facebook-friends-social-capital-and-college-2j90l44gqu.pdf

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

With advances in robotics, robots can give advice and help using natural language The field of HRI, however, has not yet developed a communication strategy for giving advice effectively Drawing on literature in politeness and informal speech, we propose options for a robot's help-giving speech-using hedges or discourse markers, both of which can mitigate the commanding tone implied in direct statements of advice To test these options, we experimentally compared two help-giving strategies depicted in videos of human and robot helpers We found that when robot and human helpers used a hedge or discourse markers, they seemed more considerate and likeable, and less controlling The robot that used discourse markers had even more impact than the human helper The findings suggest that communication strategies derived from speech used when people help each other in natural settings can be effective for planning the help dialogues of robotic assistants

http://sfussell.hci.cornell.edu/pubs/Manuscripts/p275-torrey.pdf

How a robot should give advice

Heterogeneous groups are valuable, but differences among members can weaken group identification. Weak group identification may be especially problematic in larger groups, which, in contrast with smaller groups, require more attention to motivating members and coordinating their tasks. We hypothesized that as groups increase in size, productivity would decrease with greater heterogeneity. We studied the longitudinal productivity of 549 research groups varying in disciplinary heterogeneity, institutional heterogeneity, and size. We examined their publication and citation productivity before their projects started and 5 to 9 years later. Larger groups were more productive than smaller groups, but their marginal productivity declined as their heterogeneity increased, either because their members belonged to more disciplines or to more institutions. These results provide evidence that group heterogeneity moderates the effects of group size, and they suggest that desirable diversity in groups may be better leveraged in smaller, more cohesive units.

Group Heterogeneity Increases the Risks of Large Group Size A Longitudinal Study of Productivity in Research Groups

Recent developments in humanoid robotics have made possible technologically advanced robots and a vision for their everyday use as assistants in the home and workplace. Nonetheless, little is known about how we should design interactions with humanoid robots. In this paper, we argue that adaptation for user attributes (in particular gender) and task structure (in particular a competitive vs. a cooperative structure) are key design elements. We experimentally demonstrate how these two elements affect the user's social perceptions of ASIMO after playing an interactive video game with him.

Task Structure and User Attributes as Elements of Human-Robot Interaction Design

Role of forewarning in persuasive communications.

This paper is an initial description of how diverse families used the Internet in the first five months of the trial, and of variables that predicted this usage. The results have implications for design (e.g., provide more help for adults to get started), for marketing (e.g., lower income people have as much desire for on-line services as do upper income people), and for research (e.g., understand why teenagers' lead family computing).

Sara Kiesler

Papers

How a robot should give advice

Group Heterogeneity Increases the Risks of Large Group Size A Longitudinal Study of Productivity in Research Groups

Task Structure and User Attributes as Elements of Human-Robot Interaction Design

Role of forewarning in persuasive communications.

HomeNet: a field trial of residential Internet services