Tutors India offers assistance in quantitative studies and data analyses and explains the steps to conduct a bibliometric analysis.
Introduction
The research process has increasingly diversified with the diversity of the study fields and science. Studies conducted in various fields have become repetitive, researchers often face challenges in defining concepts and setting the course of their work, and it is possible that they will lose their way if they are unable to identify reputed studies or significant researchers. This is where bibliometric analysis can be of assistance.
Various definitions of bibliometric analysis exist. Merigó and Yang (2017) gave the recent definition of bibliometric analysis as ‘A quantitative study of bibliographic material (data) and provides a general picture of a research field that research papers, authors, and fields can include categories.’ Bibliometric analysis filters out research by offering an estimate of their impact. In addition, bibliometric analysis offers a different perspective in a field of research; it can examine the relationship between recent studies and their analyses. Also, bibliometric analysis can form a base for a comprehensive review. Thus, bibliometric research allows one to engage in various activities to avoid bias and study choices. However, one must note that bibliometric analysis cannot replace traditional research methods and can only be used as an adjunct (Brika, 2021).
Steps for conducting a bibliometric analysis
There are two broad techniques for conducting a bibliometric analysis- performance analysis and science mapping. While performance analysis gives importance to research contributions to a specific field, science mapping focuses primarily on analysing the relationships between the research constituents. Here are the general steps involved in conducting bibliometric analyses:
1.Defining the research question:
Defining a research question gives clarity on what a scholar wants to research and analyse. During this process, one should identify the study areas, research topics and timeframe they want to analyse.
2. Data collection:
Relevant data is collected from reliable sources like articles, books, patents, and conference proceedings, from online databases or search engines. Various databases like Web of Science, Google Scholar or Scopus can be helpful.
3. Data pre-processing:
Subsequent to the data collection, the data is pre-processed by cleaning and standardising it. Duplicates are removed, author names are standardised, and the data is formatted for analysis.
4. Data analysis:
Bibliometric software is available that helps with the data analysis. Examples include VOSviewer, BibExcel, or CiteSpace. The number of citations, publications and co-authorship networks are analysed, and the frequently used keywords and topics are identified.
5. Result interpretation:
The results are interpreted after data analysis, which answers the research question. Trends, patterns and gaps in the literature are identified, and conclusions are drawn from the analysis.
6. Result communication:
The results of the analysis are presented clearly and concisely. Finally, the section includes a written report explaining the methodology and findings of the analysis, along with graphical representations like tables, charts and graphs (Donthu, 2021).
Conclusion
Bibliometric analysis is a quantitative study that provides a general picture of a field of research. It finds its applications in various disciplines like management, business and medicine. Conducting bibliometric analysis requires a combination of data collection, data processing, and data analysis skills. Therefore, it is vital to use appropriate software and tools to ensure the accuracy and reliability of the results.
Tutors India has a trusted team of experts from reputed universities with experience in research and academic writing. It assists students in their dissertations at various stages, including statistical analysis, ensuring their work is error-free and aligns with the university guidelines.
References:
Brika SKM, Algamdi A, Chergui K, Musa AA and Zouaghi R (2021) Quality of Higher Education: A Bibliometric Review Study. Front. Educ. 6:666087. doi: 10.3389/feduc.2021.666087.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070#dissertation#dissertationwriting#thesis#thesiswriting#assignment#manuscript#academicresearch#academic#researchproposal#assignment#essay #essaywriting #assignmentwriting
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How to conduct a bibliometric analysis?
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Introduction
The research process has increasingly diversified with the diversity of the study fields and science. Studies conducted in various fields have become repetitive, researchers often face challenges in defining concepts and setting the course of their work, and it is possible that they will lose their way if they are unable to identify reputed studies or significant researchers. This is where bibliometric analysis can be of assistance.
Various definitions of bibliometric analysis exist. Merigó and Yang (2017) gave the recent definition of bibliometric analysis as ‘A quantitative study of bibliographic material (data) and provides a general picture of a research field that research papers, authors, and fields can include categories.’ Bibliometric analysis filters out research by offering an estimate of their impact. In addition, bibliometric analysis offers a different perspective in a field of research; it can examine the relationship between recent studies and their analyses. Also, bibliometric analysis can form a base for a comprehensive review. Thus, bibliometric research allows one to engage in various activities to avoid bias and study choices. However, one must note that bibliometric analysis cannot replace traditional research methods and can only be used as an adjunct (Brika, 2021).
Steps for conducting a bibliometric analysis
There are two broad techniques for conducting a bibliometric analysis- performance analysis and science mapping. While performance analysis gives importance to research contributions to a specific field, science mapping focuses primarily on analysing the relationships between the research constituents. Here are the general steps involved in conducting bibliometric analyses:
1.Defining the research question:
Defining a research question gives clarity on what a scholar wants to research and analyse. During this process, one should identify the study areas, research topics and timeframe they want to analyse.
2. Data collection:
Relevant data is collected from reliable sources like articles, books, patents, and conference proceedings, from online databases or search engines. Various databases like Web of Science, Google Scholar or Scopus can be helpful.
3. Data pre-processing:
Subsequent to the data collection, the data is pre-processed by cleaning and standardising it. Duplicates are removed, author names are standardised, and the data is formatted for analysis.
4. Data analysis:
Bibliometric software is available that helps with the data analysis. Examples include VOSviewer, BibExcel, or CiteSpace. The number of citations, publications and co-authorship networks are analysed, and the frequently used keywords and topics are identified.
5. Result interpretation:
The results are interpreted after data analysis, which answers the research question. Trends, patterns and gaps in the literature are identified, and conclusions are drawn from the analysis.
6. Result communication:
The results of the analysis are presented clearly and concisely. Finally, the section includes a written report explaining the methodology and findings of the analysis, along with graphical representations like tables, charts and graphs (Donthu, 2021).
Conclusion
Bibliometric analysis is a quantitative study that provides a general picture of a field of research. It finds its applications in various disciplines like management, business and medicine. Conducting bibliometric analysis requires a combination of data collection, data processing, and data analysis skills. Therefore, it is vital to use appropriate software and tools to ensure the accuracy and reliability of the results.
Tutors India has a trusted team of experts from reputed universities with experience in research and academic writing. It assists students in their dissertations at various stages, including statistical analysis, ensuring their work is error-free and aligns with the university guidelines.
References:
Brika SKM, Algamdi A, Chergui K, Musa AA and Zouaghi R (2021) Quality of Higher Education: A Bibliometric Review Study. Front. Educ. 6:666087. doi: 10.3389/feduc.2021.666087.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070#dissertation#dissertationwriting#thesis#thesiswriting#assignment#manuscript#academicresearch#academic#researchproposal#assignment#essay #essaywriting #assignmentwriting #research #researchguidance #phd #masters #peerreview #publication #journals
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Sample sizes for saturation in qualitative research
Dissertation services and help are offered in Tutors India, which critically reviews a paper on sample size in qualitative research.
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Sample sizes for saturation in qualitative research
Dissertation services and help are offered in Tutors India, which critically reviews a paper on sample size in qualitative research.
Introduction
The paper “Sample sizes for saturation in qualitative research: A Systematic review of empirical tests”, published in Social Science & Medicine, Volume 292, January 22, 114523, is a comprehensive systematic review of empirical studies that have tested the concept of saturation in qualitative research.
Critique
The paper examines a range of studies across different fields and methodologies to identify the optimal sample size for achieving data saturation. One strength of the paper is its rigorous methodology. The authors searched several databases and screened studies based on strict inclusion and exclusion criteria. They also assessed the quality of each study using a standardised tool, which added to the reliability of their findings.
The paper concludes that this can be achieved with relatively small sample sizes in some studies, while larger ones may be necessary for others. The authors also identified several factors that can influence the sample size needed for saturation, such as the complexity of the research question, the heterogeneity of the population under study, and the richness and diversity of the data.
One potential limitation of the paper is that it does not provide clear guidance on determining the adequate sample size for qualitative research. While the authors highlight the importance of considering contextual factors, they do not offer a definitive answer on balancing them to arrive at an appropriate sample size. This could leave some researchers unsure of how to apply the findings of this review to their research.
Another limitation is that the review is focused exclusively on studies that have tested saturation in qualitative research. While saturation is a fundamental concept in qualitative research, other factors beyond sample size may influence the quality and validity of qualitative data.
What is an adequate sample size according to the paper?
The concept of sample size in qualitative research differs from that of quantitative research, where the focus is on statistical power and representativeness. The sample size in qualitative research is based on data saturation, where the researcher collects data until no new information or themes emerge.
The paper “Sample Sizes for Saturation in qualitative research: A systematic review of empirical tests” by Guest, Bunce, and Johnson (2022) aims to guide how to determine an adequate sample size for achieving data saturation in qualitative research. The authors conducted a systematic review of 88 studies that tested the concept of saturation across various disciplines.
Based on their review, the authors concluded that no fixed sample size guarantees saturation. Instead, the sample size should be determined based on the complexity of the research question, the richness of the data, the diversity of the sample, and the analytical approach used. They suggest that the sample size should be determined iteratively, with the researcher continuously assessing the quality and depth of the data until saturation is reached.
Conclusion
To conclude, the paper is a valuable contribution to the literature on sample sizes for saturation in qualitative research. It emphasises the need for the researchers to carefully consider the contextual factors when determining the appropriate sample size for their study. However, the paper’s practical applicability may be constrained for some researchers due to the lack of specific guidelines on how to do this. In addition, the paper suggests that the sample size should be determined based on the research question, data richness, sample diversity, and analytical approach. The sample size in qualitative data is based on data saturation, wherein the data is collected until no new information can be gathered.
Tutors India has been offering dissertation services since 2001 and has a full-fledged team of academic writers who ensure the work complies with university standards. In addition, it provides educational content from reliable sources, which assists the students in the dissertation process. It makes sure that the work is error- and plagiarism-free after multiple editing so that only the best work is provided to the students.
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What is Hypotheses testing and what are its types?
Tutors India offers statistical services for Masters dissertation and discusses the meaning, types and examples of hypothesis testing.
Introduction
Data has become indispensable to research; presently, researchers deal with big data. Analytical methods are needed to handle the challenges posed by extensive data. The earliest and the most commonly used statistical analysis is hypothesis testing, whose first use dates back to 1710 by John Arbuthnot. However, the modern form of statistical testing is from the combined work of R. A. Fisher, Jerzy Neyman and Egon Pearson. Hypothesis testing finds its application in business, marketing, finance, biology, medicine, social science and psychology. For example, in marketing, it can be used to identify changing consumer preferences and determine a drug’s effectiveness in medicine.
Types and examples of hypothesis testing
The principal underlying hypothesis testing is to determine whether a data sample is typical or atypical compared to the population, assuming that the population’s underlying hypothesis is correct. A statistical technique called hypothesis testing is used to assess a claim or hypothesis about a population parameter using sample data. It involves setting up a null hypothesis, the claim being tested, and an alternative hypothesis, which is the opposite of the null hypothesis. The next step is to calculate the test statistic and compare its p-value to the significance level to decide whether to accept or reject the null hypothesis (Emmert-Streib F, 2019).
There are several types of hypothesis testing, which are as follows:
Types
1.One-sample hypothesis testing: This type of test compares the mean of a sample to a known value or a hypothesised value. In ideal circumstances, this strategy has the advantage of controlling the Type I error rate (the likelihood of rejecting a true null hypothesis) (Francis, G., 2023).
Example: A researcher wants to test whether the average height of students in a school is 6 feet. The null hypothesis is that the average height is 6 feet, and the alternative hypothesis is that it is not equal to 6 feet.
2. Two-sample hypothesis testing: The most common type of hypothesis testing, this test compares the means of two different samples to determine whether they are significantly different (Hedgier S., 2022).
Example: The average salary for male and female employees will be compared to see if there is any discernible difference. While the null hypothesis states that there is no difference in the average salary, the alternative hypothesis is that there is a difference.
3.Chi-square test: If there is a significant correlation between two categorical variables, it will be revealed by this kind of test. The Chi-square test has two specific goals: to determine whether there is no correlation between two or more groups and populations and to determine how closely the observed data distribution matches the predicted distribution (Turhan N.S, 2020).
Example: A researcher wants to test whether a significant association between smoking status and lung cancer exists. For this research null hypothesis states that there is no association between smoking status and lung cancer, and the alternative hypothesis states that there is an association.
4.ANOVA (analysis of variance): This kind of test is used to examine whether the means of three or more groups differ significantly from one another (Lakens D., 2021).
Example: A researcher wants to ascertain whether a statistically significant difference exists in the mean test scores from three teaching approaches. There is no difference in the mean score, according to the null hypothesis. On the contrary, the alternative hypothesis is that at least one of the average scores is different from the others.
Conclusion
Hypothesis testing is a crucial aspect of statistical analysis that allows researchers to draw valid conclusions about populations based on sample data. Although the earliest, hypothesis testing is still widely used in various disciplines. One-sample, two-sample, chi-square and ANOVA are the types of hypothesis testing that find their applications for analysing quantitative studies.
Tutors India offers statistical services for research in various fields, for statistical analysis is a critical component of quantitative research. In addition, its team of experts assists in complete dissertation writing and offers publication support and editing services enabling students to publish their manuscripts in reputed journals.
References
Emmert-Streib F, Dehmer M. Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference. Machine Learning and Knowledge Extraction. 2019; 1(3):945-961. https://doi.org/10.3390/make1030054
Francis, G., Jakicic, V. Equivalent statistics for a one-sample t-test. Behav Res 55, 77–84 (2023). https://doi.org/10.3758/s13428-021-01775-3
Simon Hediger, Loris Michel, Jeffrey Näf, On the use of random forest for two-sample testing, Computational Statistics & Data Analysis, 10.1016/j.csda.2022.107435, 170, (107435), (2022).
Turhan, N. S. (2020). Karl Pearsons Chi-square tests. Educational Research and Reviews, 15(9), 575–580. https://doi.org/10.5897/err2019.3817
Lakens, D., & Caldwell, A. R. (2021). Simulation-based power analysis for factorial analysis of variance designs. Advances in Methods and Practices in Psychological Science, 4(1), 1–14. https://doi.org/10.1177/2515245920951503#dissertation#dissertationwriting#thesis#thesiswriting#assignment#manuscript#academicresearch#academic#researchproposal#assignment#essay #essaywriting #assignmentwriting #research
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