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

Workers in microtask work environments such as Mechanical Turk typically do not know if or how they fit into a workflow. The research question we posed here was whether displaying information about the number of other workers doing the same task would motivate better or poorer work quality. In experiment 1, we varied the information about co-workers presented to the worker and the number of his or her co-workers: "you" or "you alone" are doing a task, or "you" plus 5, 15, or 50 co-workers. We compared these conditions with a no-social information control. In experiment 2, we crossed the number of co-workers (5 vs. 50) with the type of incentive (individual or group). Results show that visual presentations of co-workers changed workers' perceptions of co-workers, and that the more co-workers participants perceived, the lower their work quality. We suggest future work to determine the kinds of co-worker information that will reduce or increase work quality in microtask settings.

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Co-worker transparency in a microtask marketplace

One goal of assistive robotics is to design interactive robots that can help disabled people with tasks such as fetching objects. When people do this task, they coordinate their movements closely with receivers. We investigated how a robot should fetch and give household objects to a person. To develop a model for the robot, we first studied trained dogs and person-to-person handoffs. Our findings suggest two models of handoff that differ in their predictability and adaptivity.

/pdf/predictability-or-adaptivity-designing-robot-handoffs-5899gimpfq.pdf

Predictability or adaptivity?: designing robot handoffs modeled from trained dogs and people

Researchers in the field of human-robot interaction (HRI) are celebrating the transition to an official ACM Transactions journal. It is a significant achievement for HRI. This field has faced many challenges, including scarce resources for conducting HRI research, the difficulty of conducting the research itself—integrating hardware and software; building robots that can interact with people; piecing together the intricacies of seeing, thinking, and acting when a person encounters a robot; understanding and shaping ethical norms and standards; and stepping behind the mythologies that have long governed cultural attitudes for and against robots. HRI are presented here. Even as HRI becomes more mature, the core disciplines of the field face many controversies and subtleties that complicate our research questions and activities. Here’s an example: It was long thought that human beings can discriminate only the seven so-called universal emotions of surprise, happiness, sadness, anger, disgust, fear, and contempt in others’ faces and, therefore, that modeling this ability in a robot would be soon possible. But now, psychologists have reported that people can make much finer-grained emotional distinctions, raising the bar for designing a robot. Advances in technology, machine learning, and data science also present higher standards for what a robot can do with, and for, people.

https://dl.acm.org/doi/pdf/10.1145/3209701

The Science of Human-Robot Interaction

When health services involve long-term treatment over months or years, providers have the ability, not present in acute emergency care, to collaboratively reflect on clients' changing health data and adjust interventions. In this paper, we discuss temporality as a factor in the design of health information technology. We define a temporal spectrum ranging from time-critical services that benefit from standardization to long-term services that require more flexibility. We provide empirical evidence from fieldwork that we performed in organizations providing long-term behavioral and mental health services for children. Our fieldwork in this context complements and provides contrasts to previous CSCW studies performed in time-critical hospital settings. Current literature shows a bias toward standardized records and routines in the implementation of health information technology, a policy that may not be appropriate for long-term health services. We discuss how the design of information systems should vary based on temporal factors.

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Time to reflect: Supporting health services over time by focusing on collaborative reflection

Background: In the physical therapy setting, physical therapists (PTs) often prescribe exercises for their clients to perform at home. However, it is difficult for PTs to obtain information about their clients’ compliance with the prescribed exercises, the quality of performance and symptom magnitude. We present an iPod-based system for capturing this information from individuals with vestibular hypofunction while they perform gaze stabilization exercises at home. Method: The system’s accuracy for measurement of rotational velocity against an independent motion tracker was validated. Then a seven day in-home trial was conducted with 10 individuals to assess the feasibility of implementing the system. Compliance was measured by comparing the recorded frequency and duration of the exercises with the exercise prescription. The velocity and range of motion of head movements was recorded in the pitch and yaw planes. The system also recorded dizziness severity before and after each exercise was performed. Each patient was interviewed briefly after the trial to ascertain ease of use. In addition, an interview was performed with PTs in order to assess how the information would be utilized. Results: The correlation of the velocity measurements between the iPod-based system and the motion tracker was 0.99. Half of the subjects were under-compliant with the prescribed exercises. The average head velocity during performance was 140 deg/s in the yaw plane and 101 deg/s in the pitch plane. Conclusions: The iPod-based system was able to be used in-home. Interviews with PTs suggest that the quantitative data from the system will be valuable for assisting PTs in understanding exercise performance of patients, documenting progress, making treatment decisions, and communicating patient status to other PTs.

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Sara Kiesler

Papers

Co-worker transparency in a microtask marketplace

Predictability or adaptivity?: designing robot handoffs modeled from trained dogs and people

The Science of Human-Robot Interaction

Time to reflect: Supporting health services over time by focusing on collaborative reflection

iPod-based in-home system for monitoring gaze-stabilization exercise compliance of individuals with vestibular hypofunction