top of page

Group

Public·267 members
Jacob Vishnyakov
Jacob Vishnyakov

Research Methodology: Techniques And Trends ((LINK))


Citation: Bolt T, Nomi JS, Bzdok D, Uddin LQ (2021) Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods. PLoS Biol 19(7): e3001313.




Research Methodology: Techniques and Trends


Download File: https://www.google.com/url?q=https%3A%2F%2Fjinyurl.com%2F2ufr2U&sa=D&sntz=1&usg=AOvVaw3p3Z6WhWx4zVQWvpDU159s



The data analytic landscape of the BLS sciences is subject to change. The democratization and commoditization of tools for quantitative analysis have grown exponentially in the 21th century and only accelerated in pace in the past decade. This tectonic shift is due to the increased accessibility of computational resources, open-source software and abundance of big data in more areas of human activity. When learning to conduct data analysis, the scientist in training is faced with a steep hill to climb. To make this climb easier, graduate and undergraduate education must reflect the current practices and trends in data analysis. We offer an automated 12-year survey of approximately 1.3 million open research papers to characterize the data analytic landscape of the BLS sciences. This study aimed to provide a snapshot of the ongoing methodological shifts across a variety of scientific communities.


Literature reviews play a critical role in scholarship because science remains, first and foremost, a cumulative endeavour (vom Brocke et al., 2009). As in any academic discipline, rigorous knowledge syntheses are becoming indispensable in keeping up with an exponentially growing eHealth literature, assisting practitioners, academics, and graduate students in finding, evaluating, and synthesizing the contents of many empirical and conceptual papers. Among other methods, literature reviews are essential for: (a) identifying what has been written on a subject or topic; (b) determining the extent to which a specific research area reveals any interpretable trends or patterns; (c) aggregating empirical findings related to a narrow research question to support evidence-based practice; (d) generating new frameworks and theories; and (e) identifying topics or questions requiring more investigation (Paré, Trudel, Jaana, & Kitsiou, 2015).


Analyzing and synthesizing data: As a final step, members of the review team must collate, summarize, aggregate, organize, and compare the evidence extracted from the included studies. The extracted data must be presented in a meaningful way that suggests a new contribution to the extant literature (Jesson et al., 2011). Webster and Watson (2002) warn researchers that literature reviews should be much more than lists of papers and should provide a coherent lens to make sense of extant knowledge on a given topic. There exist several methods and techniques for synthesizing quantitative (e.g., frequency analysis, meta-analysis) and qualitative (e.g., grounded theory, narrative analysis, meta-ethnography) evidence (Dixon-Woods, Agarwal, Jones, Young, & Sutton, 2005; Thomas & Harden, 2008).


The primary goal of a descriptive review is to determine the extent to which a body of knowledge in a particular research topic reveals any interpretable pattern or trend with respect to pre-existing propositions, theories, methodologies or findings (King & He, 2005; Paré et al., 2015). In contrast with narrative reviews, descriptive reviews follow a systematic and transparent procedure, including searching, screening and classifying studies (Petersen, Vakkalanka, & Kuzniarz, 2015). Indeed, structured search methods are used to form a representative sample of a larger group of published works (Paré et al., 2015). Further, authors of descriptive reviews extract from each study certain characteristics of interest, such as publication year, research methods, data collection techniques, and direction or strength of research outcomes (e.g., positive, negative, or non-significant) in the form of frequency analysis to produce quantitative results (Sylvester et al., 2013). In essence, each study included in a descriptive review is treated as the unit of analysis and the published literature as a whole provides a database from which the authors attempt to identify any interpretable trends or draw overall conclusions about the merits of existing conceptualizations, propositions, methods or findings (Paré et al., 2015). In doing so, a descriptive review may claim that its findings represent the state of the art in a particular domain (King & He, 2005).


In the fields of health sciences and medical informatics, reviews that focus on examining the range, nature and evolution of a topic area are described by Anderson, Allen, Peckham, and Goodwin (2008) as mapping reviews. Like descriptive reviews, the research questions are generic and usually relate to publication patterns and trends. There is no preconceived plan to systematically review all of the literature although this can be done. Instead, researchers often present studies that are representative of most works published in a particular area and they consider a specific time frame to be mapped.


An example of this approach in the eHealth domain is offered by DeShazo, Lavallie, and Wolf (2009). The purpose of this descriptive or mapping review was to characterize publication trends in the medical informatics literature over a 20-year period (1987 to 2006). To achieve this ambitious objective, the authors performed a bibliometric analysis of medical informatics citations indexed in medline using publication trends, journal frequencies, impact factors, Medical Subject Headings (MeSH) term frequencies, and characteristics of citations. Findings revealed that there were over 77,000 medical informatics articles published during the covered period in numerous journals and that the average annual growth rate was 12%. The MeSH term analysis also suggested a strong interdisciplinary trend. Finally, average impact scores increased over time with two notable growth periods. Overall, patterns in research outputs that seem to characterize the historic trends and current components of the field of medical informatics suggest it may be a maturing discipline (DeShazo et al., 2009).


The methodological and technical aspects of identifying research fronts and trends in the development of science are considered. Based on the literature data, a comparison of scientometric methods for finding research fronts was carried out: analysis of publication activity, direct citation analysis, co-citation analysis, bibliographic coupling, and content analysis. The advantages of the combined application of various approaches are shown, the role of expert assessment and verification of the results of scientometric analysis is emphasized. We revealed topical problems associated with the detection of scientific fronts by scientometric methods and showed promising directions in their solution.


Scientific trends and fronts, as a rule, are the object of research of science itself, and their identification is an attempt to search for new growth points, as represented by the most promising ideas and developments that are important for the further development of science and technology. In other words, a search is carried out for changing objects of research in their relation to existing knowledge and to each other [4]. When identifying research trends and fronts it is predominantly scientometric methods that are used.


To date, three main scientometric approaches are widely used to identify research trends and fronts: analysis of the dynamics of changes in scientific production, citation analysis with its varieties, and content analysis, as well as their various combinations.


Analysis of publication activity is usually used to identify research trends, while citation analysis is used to identify research fronts [4, 16]. When analyzing scientific production, expressed by the number of publications, one resorts to models of the growth of scientific knowledge:


There is no consensus about which of the proposed models most closely corresponds to reality, especially since each of them, to one degree or another, explains the ongoing scientific events in various disciplines. Each of these paradigms can correspond to some mathematical model of the growth of scientific literature, for example, linear or exponential [18]. In natural science disciplines, exponential growth often prevails; when identifying scientific trends researchers therefore turn to D. Price on the exponential growth and obsolescence of scientific literature [19, 20]. The scattering law is used to identify a scientific information trend according to S. Bradford [21], which allows identification of the core of scientific journals of a given subject.


The experience of identifying research fronts not for a discipline as a whole, but for an individual organization is remarkable: in [49], the intellectual base was studied by co-citation analysis; the corpus of publications cited by the organization, on the basis of which a research fronts of the organization itself were further identified. Similar studies of the publication activity and citations of a particular organization were carried out by the authors of this work for more effective information support of scientific projects [50, 51], while the developed methods were also applicable for identifying research trends and fronts. The search for scientific fronts can also be carried out for a separate journal: for example, the Journal of the American Society for Information Science. Using the methods of bibliographic coupling and citation analysis, research fronts were identified and a significant closeness of the intellectual base with them was shown [31].


Over time, increasingly sophisticated approaches to defining research fronts are being developed, with the goal of increasing the accuracy of clustering. One of the trends in this field is the construction of weighted citation networks. With the assignment of weight to the publications of the cluster forming scientific fronts, a series of studies was carried out by K. Fujita et al., proving the benefits of weighted citation networks [39, 40, 53]. The weight of the publication, automatically determined using neural network training technologies, takes the year of publication, the number of citations of the publication, the field of knowledge, and the strength of the links between the reference list of publications and keywords into account [39, 53]. A significant advantage of the research of this group is that various bibliometric methods are widely combined here. 041b061a72


About

Welcome to the group! You can connect with other members, ge...

Members

bottom of page