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

Those who are number two try harder: The effect of sex on attributions of causality.

Email consumes significant time and attention in the workplace. We conducted an organizational survey to understand how and why people attend to incoming email messages. We examined people's ratings of message importance and the actions they took on specific email messages, based on message characteristics and characteristics of receivers and senders. Respondents kept half of their new messages in the inbox and replied to about a third of them. They rated messages as important if they were about work and required action. Importance, in turn, had a modest impact on whether people replied to their incoming messages and whether they saved them. The results indicate that factors other than message importance (e.g., their social nature) also determine how people handle email. Overall, email usage reflects attentional differences due both to personal propensities and to work demands and relationships.

http://sfussell.hci.cornell.edu/pubs/Manuscripts/Dabbish_CHI2005.pdf

Understanding email use: predicting action on a message

The hidden messages in computer networks

Background: The rapid expansion of the Internet has increased the ease with which the public can obtain medical information. Most research on the utility of the Internet for health purposes has evaluated the quality of the information itself or examined its impact on clinical populations. Little is known about the consequences of its use by the general population. Objective: Is use of the Internet by the general population for health purposes associated with a subsequent change in psychological well-being and health? Is the effect different for healthy versus ill individuals? Does the impact of using the Internet for health purposes differ from the impact of other types of Internet use? Methods: Data come from a national US panel survey of 740 individuals conducted from 2000 to 2002. Across three surveys, respondents described their use of the Internet for different purposes, indicated whether they had any of 13 serious illnesses (or were taking care of someone with a serious illness), and reported their depression. In the initial and final surveys they also reported on their physical health. Lagged dependent variable regression analysis was used to predict changes in depression and general health reported on a later survey from frequency of different types of Internet use at an earlier period, holding constant prior depression and general health, respectively. Statistical interactions tested whether uses of the Internet predicted depression and general health differently for people who initially differed on their general health, chronic illness, and caregiver status. Results: Health-related Internet use was associated with small but reliable increases in depression (ie, increasing use of the Internet for health purposes from 3 to 5 days per week to once a day was associated with .11 standard deviations more symptoms of depression, P = .002). In contrast, using the Internet for communication with friends and family was associated with small but reliable decreases in depression (ie, increasing use of the Internet for communication with friends and family purposes from 3 to 5 days per week to once a day was associated with .07 standard deviations fewer symptoms of depression, p = .007). There were no significant effects of respondents’ initial health status (P = .234) or role as a caregiver (P = .911) on the association between health-related Internet use and depression. Neither type of use was associated with changes in general health (P = .705 for social uses and P = .494 for health uses). Conclusions: Using the Internet for health purposes was associated with increased depression. The increase may be due to increased rumination, unnecessary alarm, or over-attention to health problems. Additionally, those with unmeasured problems or those more prone to health anxiety may self-select online health resources. In contrast, using the Internet to communicate with friends and family was associated with declines in depression. This finding is comparable to other studies showing that social support is beneficial for well-being and lends support to the idea that the Internet is a way to strengthen and maintain social ties. [J Med Internet Res 2010;12(1):e6]

https://www.cs.cmu.edu/~kiesler/publications/2010pdfs/effects-internet-use-health-depression-longitudinal.pdf

Effects of Internet Use on Health and Depression: A Longitudinal Study

In electronic support groups, people use Internet-based electronic text communication to discuss personal problems or disorders with others who share common circumstances. Although their discussions exist only in the electronic medium, these groups can be viewed usefully as discourse communities. The authors draw on what is known about two other popular sources of help—face-to-face self-help groups and self-help books—to frame the rhetorical challenges faced by members of electronic support groups. The authors then compare the discourse of electronic support groups with that of electronic hobby groups to demonstrate that the two sets differ in terms of the rhetorical behavior of their participants. The authors analyze messages to determine how members establish legitimacy and authority in their texts and how message exchange gives rise to group identity and a sense of community. Our observations indicate that although some discourse characteristics and some rhetorical features are common to all the electr...

Sara Kiesler

Papers

Those who are number two try harder: The effect of sex on attributions of causality.

Understanding email use: predicting action on a message

The hidden messages in computer networks

Effects of Internet Use on Health and Depression: A Longitudinal Study

Legitimacy, Authority, and Community in Electronic Support Groups: