Interdisciplinary examples in Research on Research

The idea of Research on Research being an interdisciplinary area is something that has been emphasized multiple times in this blog, but we have not given extensive examples of disciplines that are part of of this melting pot. So, here is a snapshot of what we currently have going on:

1. Streamlined validation of outcome scales - this area involves not only the validation of outcome instruments for specific research fields, but also the cross-cultural validation of scales across different countries and languages. Although this is a well-established field, the challenge is to create streamlined models that will allow for a fast validation of outcome scales across different cultures, thus allowing for an improved productivity, quality and cost-benefit in global clinical research.
   
2. Discrete-event models/systems dynamics/agent-based models - these are computational models to evaluate a complex system that cannot be studied through simple experimental or even observational designs such as, respectively, randomized trials and cohort studies. Under this category we have the study of clinical trial and health service research operations, along with models of diffusion of scientific innovation
3. Intelligent data analysis - this field is defined by a combination of multiple statistical methods along with data mining and computational techniques (Berthold, 2003). Examples include the analysis of large research data sets to evaluate, for example, determinants of subject enrollment in mega-trials.
4. Computational ontologies - this area includes the creation of formal computational ontologies to standardize the nomenclature for a certain research field as well as the relationship among the different elements in that field. Examples here include the use of computational ontologies to classify clinical trials and then streamline the process to generate meta-analyses.
5. Cognitive experimental psychology - this field primarily makes use of experimental designs to evaluate, for example, different cognitive methods to enhance training in scientific writing as well as a variety of experiments to evaluate decision making mechanisms used in common scientific judgments.
6. Rhetoric of science - this field specifically evaluates the rhetoric structure of scientific texts, more specifically scientific articles across multiple designs as well as grant proposals.
   
7. Streamlined models for systematic reviews and meta-analysis - this field applies a variety of computational and heuristic methods to streamline the process to conduct systematic reviews and meta-analysis. Examples include an ongoing meta-analysis of qualitative and observational studies evaluating determinants of subject enrollment among different ethnic groups in Asia.
8. Validation of imaging scales - this area evaluates a multitude of methods to streamline the validation of imaging scales from the perspective of observer reliability and comparison against gold standards (when they exist), all geared toward the improvement in the measurement reliability for future biomedical research studies. Examples include a variety of studies evaluating imaging scales for orthopedic, abdominal, and neurological conditions.
9. Gender and race disparities and academic studies - this field evaluates gender and race disparities among academic professionals, focusing on problems that might be amenable to policy and administrative solutions. Examples include a variety of studies investigating determinants of the distribution of gender and race across different clinical specialties in academic institutions.
10. Science studies (history, sociology and philosophy of science) - this is a broad area involving history, sociology and philosophy of science to evaluate common recurring patterns in the process of scientific innovation, also attempting to provide insights into contemporary policies. Examples include the study of scientific controversies, mechanisms of scientific innovation, pragmatism vs. realism, among many others.
11. Ethnography of science - this method is used in a variety of observational and participatory studies providing a first, deep investigation of causal factors that precedes quantitative designs. Examples include qualitative studies to evaluate communication patterns between statisticians and clinical researchers, scientific writing among novice researchers, determinants of success in scientific careers among novice researchers, among others.
    

Of importance, these areas are constantly combined, such as when we mix rhetoric studies with cognitive psychology experiments, research policy performance with intelligent data analysis methods, and science studies with computational ontologies (see initial draft from Pietrobon and Maldonato, 2008).