A Better Tool to Gather, Curate Research Findings

“Well, what does the research say?” People ask this question when they want science to inform their interests, and academics ask this question when figuring out what to study next.

Thanks to metaBUS, a free new online research tool, this question can now be answered more easily. Fred Oswald, an organizational psychologist and professor at Rice University, serves as one of the key advisers in developing this tool.

This article by Amy McCaig originally appeared on the Rice University news portal under the title “New tool available could revolutionize social sciences research” and is reposted with permission.

MetaBUS (www.metaBUS.org) is search engine containing more than a million quantitative findings across 25 years of research and 20-plus journals related to the fields of management, including human resource management and organizational behavior, and psychology, including industrial-organizational and personality. Students and faculty using metaBUS can search over 4,000 topics that are hierarchically arranged. Broader topics, such as job performance and cognitive ability contain narrower ones, such as specific task performance and job knowledge.

Oswald has been involved in developing and refining metaBUS topics, and he has provided the metaBUS team technical advice regarding the statistical method (meta-analysis) and graphics used to summarize research findings.

Oswald said that metaBUS can be extremely useful for researchers and practitioners across multiple areas of business, human resources and organizational behavior. For example, he said, “Personality traits, interests and motivation are the types of things that employers often want to understand about their job applicants. Solid psychological tests backed by research provide reliable sources of data to help evaluate these applicants so that employers can ultimately make smarter hiring decisions.”

MetaBUS was created in 2013 by Frank Bosco, a professor at Virginia Commonwealth University; Krista Uggerslev, a professor at the Northern Alberta Institute of Technology; and Piers Steel, a professor at the University of Calgary. The researchers merged their diverse and complementary expertise from business (industrial-organizational psychology, management and organizational behavior) and technical fields (statistics, computer science and IT security) to create this extensive database.

Bosco said that quite often, researchers and practitioners want to know about correlations found between variables being researched (for example, whether productive workers are also happy workers), but the traditional process of locating, recording and statistically summarizing these correlations across studies is almost always quite time-consuming and error-prone.

“It’s not uncommon for it to take three to six months to collect data for this type of research,” he said. He noted that this process is usually completed by two or three people (researchers and graduate students), who then compare their efforts to make sure that they reached the same conclusion.

Fred Oswald

Oswald said, “The team wondered if there would be a better open-science method to gather and curate quantitative research findings to then share with the world.”

He said the team is still fine-tuning and extending the platform as it develops additional terminology and depth configurations to target new disciplines and serve a wider range of scientific and lay audiences.

Bosco said, “Historically, we have had a bit of a ‘vocabulary problem’ in the social sciences – that is, the same ‘thing’ being studied can go by dozens of different names. Employee performance, for example, can be called a ‘supervisor rating’ or ‘number of homes sold’ and the like. If you really want to locate all relevant information on a given topic, it’s difficult to come up with all the terms you’ll need. That’s why classification systems are so valuable: Rather than coming up with all possible search terms, one may select a ‘branch’ of a classification containing all variations of the term. Having these classifications with research findings tied to them allows scientists to ask metaBUS larger ‘big science’ types of research questions.”

Oswald said, “Once fully implemented, the new system will allow researchers to locate quantitative information across published studies in the social sciences very rapidly and accurately. Researchers will be able to search an increasingly expanding range of terms and topics across multidisciplinary scientific domains.”

Since 2013, the grant-funded project has involved leading statisticians, software engineers, social science scholars and more than 20 doctoral students from around North America. Historically, the team has invested its resources primarily in software development and data collection. As for future investments, co-founders Steel and Uggerslev noted the great interest in using metaBUS to guide research and accelerating its use in evidence-informed decision-making (such as in medicine and law) in addition to the current focus on the employment and management context.

Uggerslev and Steel have high aspirations for the metaBUS platform: “If metaBUS could quickly locate, intelligently analyze, and cogently summarize a wide range of scientific findings in a way that is tailored to the needs of a wide range of stakeholders, it would be a fundamental game changer.”

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H/t to Gary Price at InfoDocket.