Changing Sociocultural Dynamics and Implications for National Security Proceedings of a Workshop (2018) / Chapter Skim
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3 Triangulation of Data Sources and Research Methods
3 Triangulation of Data Sources and Research Methods
Pages 21-36
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From page 21... ...
USING MULTIPLE SOURCES OF DATA TO UNDERSTAND CONFLICT IN ORGANIZATIONS Giuseppe (Joe) Labianca, University of Kentucky, discussed his research on conflict within organizations, work that entails analyzing networks of people to understand the antecedents and consequences of negative relationships and conflict through the use of survey responses and people's daily communications.
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From page 22... ...
and about people within the organization and the organization itself; and information from the organization about employee performance, salary, promotions, and turnover. Labianca reported that to understand conflict resulting from the merger, he and his team worked to develop methods for identifying negative ties from the communication data, rather than through survey responses.
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From page 23... ...
Ultimately, Labianca and his colleagues would like to be able to use a combination of content, network, attitudinal, and behavioral data to under stand negative ties in an organization. These approaches, he explained, can provide important insights into the distribution of power in networks, including who currently has power and who the emerging powerful actors are.
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USING DATA FROM SURVEYS, LABORATORY EXPERIMENTS, AND SOCIAL MEDIA TO UNDERSTAND MISINFORMATION David Broniatowski, The George Washington University, described how using different data sources and research methods can contribute to a better understanding of causality. He explained that methods with high internal validity incorporate designs for identifying causality -- Does the proposed treatment cause the proposed effect?
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The gist of these stories, the take-home meaning, he said, is that vaccines cause autism, so that people assume a causal link between the two events when they are merely spuriously correlated. According to Broniatowski, fuzzy trace theory illuminates how people search for meaning and causal explanations.
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From page 26... ...
If messages are framed in terms of gain, Broniatowski said, people will choose the possibility of gaining, whereas if messages are framed in terms of loss, people will choose no loss. Broniatowski and his colleagues tested these predictions using existing data from 30 years' worth of framing and related research and were able to successfully predict choices in 93 percent of studies.4 In addition, he and his colleagues found other support for their model, providing evidence for the internal validity of their approach for detecting causal effects of framing on decision making.
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He pointed out that individuals involved with public health and health communication would like to better understand whether providing facts or using narrative storytelling, or some combination of the two, is the most effective and ethical way to communicate messages designed to increase vaccination rates. According to Broniatowski, fuzzy trace theory suggests that people e ncode both facts and gist in parallel.
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From page 28... ...
In the case of the research he described, he said, results across multiple settings, populations, and research methods support fuzzy trace theory's predictions. He plans future work to examine the mechanisms that influence the decision to share an article and how this process influences an information cascade, in which people's decisions are influenced by observing the behavior of others in combination with their own personal information.
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tends to include terms that represent more practical considerations in that debate, such as "government shutdown" and "blame," whereas the Tea Party framing (M3) emphasizes terms associated with party principles, such as "entitlements," "debt," and "taxes." The second level of framing in Figure 3-1 illustrates how similar analyses using NLP can be applied to understanding issues in health care and mental health, including depression and suicidality, by examining how p eople frame issues in their conversations.
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In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (pp.
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He illustrated this point with examples of publicly available data sets including 1.6 million tweets, 681,288 social media posts, more than 140 million words from online bloggers, and one terabyte-sized dataset containing every publicly available Reddit comment as of 2015.11 However, he argued, the state of the art for NLP in medical or clinical settings is about 10 years behind that for NLP in other application areas because available datasets in the health care domain are far fewer and much smaller. He gave examples of orders-of-magnitude differences in data availability, including one dataset from the Mayo Clinic containing 400 manually deidentified clinical notes and pathology reports from cancer patients and another dataset containing 65,000 posts from a mental health peer support forum.
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Resnik identified data donation as one approach to increasing the amount of health data available to researchers. He reported that one company, Qntfy, maintains a Website, OurDataHelps.org, that enables people to donate private social media data for mental health research (Qntfy anonymizes the data for research use and provides a consent structure)
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For example, he observed, social media data can be collected very quickly, but rigorous methods for their analysis comparable to survey research methods have not yet been established. Alternatively, he suggested, when rapid results are required, such as during an outbreak of infectious disease, using a rigorously designed survey of people's understanding and actions might not produce results in time, even if it yields robust findings.
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A person who understands the methodology of researchers as well as the concerns of decision makers is the ideal expert for translating research, he argued. Often, he added, those skilled at data analysis are not skilled at communicating and vice versa, so he suggested considering utilizing people who can serve in translational roles.
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He added that adhering to rigorous scientific methods and replicating results in multiple settings, including within and outside of the United States, can increase trust in the results. The Roles and Limitations of Automated and Human Data Analysis Participants also discussed the importance of both automated and human data analysis in social and behavioral science research.
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From page 36... ...
He cited as an important investment establishing infrastructure across intelligence agencies to enable communication and lateral connections allowable by law at multiple levels -- the data level, the level of interpretation of the data, and the decision-making level based on the interpretation. Labianca suggested that incentives for sharing data should also be considered because some organizations, such as health insurance and social media companies, possess important data.
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