What is bibliometric Analysis? How to use it in a PhD dissertation

Bibliometric analysis is a method of quantitative research that can be used to measure and analyze the activities, characteristics and performance of individual scientists, institutions and fields of science. Bibliometric analysis uses data from publications in order to measure many aspects of research such as quality, productivity and impact. In this article we’ll learn about bibliometric analysis and steps of using it in a PhD dissertation.

What is bibliometric analysis?

Bibliometric analysis is the process of analyzing the relationship between academic papers and their authors. This can be done by identifying the number of citations an author has received in other academic papers, or by performing a search to see which papers have cited certain works. Bibliometric data can be used by researchers to identify important scholars in a field and determine how influential their research is.

Bibliometric are also useful for measuring productivity because they allow you to quantify how much work has been done within your field. If you’re interested in seeing how much work has been done on a particular topic, it may be helpful to use bibliometric as part of your research process: not only will this give an idea of what needs further study but also help identify gaps where there isn’t enough literature available yet!

Types of Bibliometric analysis:

There are two main types of bibliometric analysis.

Bibliographic coupling

Bibliographic coupling is a measure of the strength of the relationship between two papers. It is defined as the number of documents that are cited together, divided by their total number of citations.

Co-citation analysis

 Co-citation analysis is a technique used to detect relationships between publications in a field and can also be used to identify groups within the literature, called communities or clusters.

Getting started with bibliometric analysis in PhD dissertation:

Bibliometric analysis is the science of analyzing a researcher’s academic output, including publications and citations. It’s useful for measuring the impact of research, providing metrics that can be used to evaluate scholarly performance.

A researcher’s bibliographic record will include all of their published work—papers, books, conference proceedings and so on—as well as any other items they have been involved in publishing (e.g., book chapters). These are known as “outputs” or “output units” and can include things like journal articles or books.

Each piece of research will also have a list of references—a list which includes other works that have influenced or inspired your own research project; these are called “citations” or “cited-by relationships”. The number of times each reference is cited by others gives an indication as to how influential it was in shaping your own work; if one person cites another 12 times but only vice versa once then the latter has clearly been more influential in shaping their thinking!

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Steps of using Bibliometric Analysis in PhD Dissertation

Bibliometric is the use of quantitative measures to evaluate academic research, including journal articles, books, book chapters and other publications. It can be used to measure the impact of researchers, journals and institutions on particular disciplines. Following are the steps of conducting bibliometric analysis:

Step 1: Defining your search strategy

We will now specify the search strategy. This is the most important part of our study and requires careful consideration so that you limit its scope to avoid a large number of irrelevant results. You can also choose different parameters, depending on what you need for your research purposes. To begin, we will define a problem and choose a data source for bibliometric analysis.

In general terms, it is important to have clear objectives when conducting any type of research because this helps determine how we will perform our analyses (e.g., what kind of statistical tests we should use). In particular, defining the problem is critical in bibliometric analysis because it helps us determine which studies are relevant or not; this makes it easier to narrow down our search by selecting specific keywords and/or time periods with more relevance than others would have had otherwise!

Step 2: Collecting your data

There are three main ways to collect data for your bibliometric analysis:

• The first, and most straightforward, is to use the bibliography in your chosen research. If you have access to it, this is generally going to be preferable as your results will be based on a large body of literature. Furthermore, if you want to analyze a particular author’s work (or even just certain topics), you can limit yourself only to those articles that they have contributed too.
• The second method involves using citation indexes such as Web of Science or Scopus. These resources allow you to search across thousands of journals and identify which ones have cited another article or book in their references list. Again, this gives more specific results than simply looking through an author’s complete bibliography because it allows us look at how many times, they were cited by others rather than merely when they themselves were writing about another topic within their field(s). However, note that citation indexes are limited in scope since they only include articles from academic journals (which may not necessarily overlap with other types like newspapers or magazines).
• Additionally, there is also some bias here due do different publication styles between countries – for example some countries tend publish both books/chapters within journals whereas others don’t do so regularly enough for these databases yet still remain active researchers! Finally keep in mind that if using these two methods together then please make sure none overlap each other!

Step 3: Preparing your data

The next step is to prepare your data for analysis. This means that you need to ensure that the data is in the right format and clean it up if necessary. If you are unsure about what this means, read this section carefully.

If you have a large dataset (i.e., more than 10 million records), then it’s unlikely that it will all fit into memory on your computer at once (RAM). Therefore, you will have to use some sort of storage system for the parts of your data that aren’t currently active in memory—a process known as swapping or swapping out—and swap them back when needed again later on.

The most common way for us researchers who don’t do much programming ourselves (yet) is through one of these two tools: RStudio or Jupyter Notebooks

Step 4: Analyzing your data

Once you have your data, it’s time to analyze it. This is an important step in the process that you must not skip—it’s what gives your dissertation its credibility and makes it a useful tool for others. There are many different ways to analyze your data, but one of the most common methods is bibliometric analysis, which uses metrics like citation counts and publication patterns to determine how well-known someone or something is within their field.

If you’re new to this type of analysis, don’t worry: we’ll walk through each step together so that you can learn more about how it works!

Step 5: Visualizing your data to draw insights

Now that you have coded, cleaned, and analyzed your data, it’s time to visualize it. Visualizing your data allows you to draw insights from the patterns of connections between papers in a way that is easy for others to understand. While there are many ways you can do this (for example through word clouds and maps), don’t worry too much about the technical details of how you go about visualizing your data. Instead, focus on generating effective visualizations that will help readers understand what they’re seeing.

Conclusion

A bibliometric analysis is a great way to get started with data, especially if you’re not sure where to start or what questions to ask. It can help you make sense of large amounts of information quickly, and it’s an essential tool for getting your research noticed by other scholars in your field.