In the scholarly landscape, notable transitions are occurring this September as several journals depart from Scopus, a prominent research database. This article aims to provide a comprehensive analysis of these Discontinued Scopus Indexed Journals – September 2023 and their implications for the academic community. Whether you are an academic, researcher, or an individual with a vested interest in scholarly literature, this exploration offers valuable insights into these transformations.

Discontinued Scopus Indexed Journals – September 2023

1. International Journal of Instruction

Scopus Sourcerecord ID: 21100297818

Journal Source Title: International Journal of Instruction

Print-ISSN: 1694609X

E-ISSN: 13081470

Publisher: Gate Association for Teaching and Education

Reason for discontinuation: Publication Concerns

2. International Journal of Instruction

Scopus Sourcerecord ID: 21101023717

Journal Source Title: International Journal of Membrane Science and Technology 

Print-ISSN: NA

E-ISSN: 24101869

Publisher: Cosmos Scholars Publishing House

Reason for discontinuation: Publication Concerns

3. International Journal of Instruction

Scopus Sourcerecord ID: 21101034437

Journal Source Title: Journal for ReAttach Therapy and Developmental Diversities 

Print-ISSN: NA

E-ISSN: 25897799

Publisher: ReAttach Therapy International Foundation

Reason for discontinuation: Publication Concerns

4. International Journal of Instruction

Scopus Sourcerecord ID: 19926

Journal Source Title: Scientific Programming 

Print-ISSN: 10589244

E-ISSN: 1875919X 

Publisher: Hindawi Publishing Corporation

Reason for discontinuation: Radar

5. International Journal of Instruction

Scopus Sourcerecord ID: 18000156707

Journal Source Title: Security and Communication Networks 

Print-ISSN: 19390114

E-ISSN: 19390122 

Publisher: Hindawi Limited

Reason for discontinuation: Publication Concerns

I hope this article will help you to know about the updated Discontinued Scopus Indexed Journals – September 2023.

Source: Scopus

The post Discontinued Scopus Indexed Journals – September 2023 appeared first on iLovePhD.

Data analysis is a critical component of any research project. Whether you are conducting scientific research, market research, social studies, or any other form of investigation, the process of analyzing data is essential to draw meaningful conclusions and make informed decisions. In this comprehensive guide, iLovePhD will present you with the key steps and techniques to effectively analyse data in research.

How to Analyse Data in Research: A Comprehensive Guide

Define Your Research Objectives

• Before diving into data analysis, it’s crucial to clearly define your research objectives.
• What are you trying to discover or prove through your research? A well-defined research question or hypothesis will guide your data analysis and ensure you focus on relevant aspects of your data.

Collect and Organize Your Data

• Collecting and organizing data is the first step in the data analysis process. Ensure that your data is accurate, complete, and properly organized.
• You may use various data collection methods, such as surveys, experiments, observations, or secondary data sources, depending on your research design.

Data Cleaning and Pre-processing

• Raw data often contains errors, missing values, outliers, and inconsistencies.
• Data cleaning and pre-processing involve identifying and rectifying these issues to ensure the data is suitable for analysis. Common tasks include:

a. Removing duplicates

b. Handling missing data (imputation or removal)

c. Standardizing or normalizing variables

d. Detecting and handling outliers

Clean and well-pre-processed data is essential for meaningful analysis.

Choose the Right Data Analysis Method

• The choice of data analysis method depends on your research question and the nature of your data. Common data analysis methods include:

a. Descriptive Statistics: Summarize and describe data using measures such as mean, median, mode, standard deviation, and variance.

b. Inferential Statistics: Make inferences about a population based on a sample, including hypothesis testing and confidence intervals.

c. Regression Analysis: Explore relationships between variables and make predictions.

d. Qualitative Analysis: Analyse textual or non-numeric data to identify patterns or themes.

e. Data Visualization: Create visual representations of data, such as charts and graphs, to gain insights.

f. Machine Learning: Employ algorithms to analyze and predict outcomes based on data.

Choose the method that best suits your research objectives and data type.

Perform Data Analysis – Analyse Data in Research

• Once you have selected the appropriate method, it’s time to conduct the analysis. Follow these steps:

a. Run statistical tests: Perform the necessary statistical tests or calculations based on your chosen method.

b. Visualize data: Create graphs, charts, or plots to visualize patterns and relationships in your data.

c. Interpret results: Analyze the results of your analysis and consider their implications in the context of your research question.

d. Draw conclusions: Make data-driven conclusions based on your analysis.

Validate and Review Your Analysis

• It’s crucial to review and validate your analysis to ensure its accuracy and reliability. This can be done by:

a. Checking for errors: Recheck your calculations, code, and assumptions for any errors or oversights.

b. Peer review: Have a colleague or mentor review your analysis to provide feedback and identify potential issues.

c. Sensitivity analysis: Test the robustness of your findings by varying parameters or assumptions.

Communicate Your Results

• Effective communication of your research findings is essential to convey the significance of your work. Consider the following:

a. Research reports or papers: Write a clear and concise research report or paper that includes an introduction, methodology, results, and discussion.

b. Visual aids: Use visual aids, such as tables, charts, and graphs, to present your findings in an accessible manner.

c. Presentations: Prepare a presentation to deliver your results to colleagues, peers, or stakeholders.

d. Data transparency: Ensure that you share your data and code with others for transparency and reproducibility.

Data Analysis Methods

let’s dive deeper into each of the mentioned data analysis methods: descriptive statistics, regression analysis, and machine learning.

1. Descriptive Statistics:

• Definition: Descriptive statistics is the process of summarizing and organizing data to provide a clear understanding of its main features. It helps in simplifying and presenting data in a meaningful way.
• Methods: Common descriptive statistics include measures such as mean, median, mode, standard deviation, and variance. These statistics provide insights into the central tendency and variability of your data.
• Use Cases: Descriptive statistics are often used to summarize and describe the characteristics of a dataset before further analysis. They help researchers understand the distribution and key features of their data.

2. Regression Analysis:

• Definition: Regression analysis is a statistical method used to examine the relationship between a dependent variable. Also, one or more independent variables. It helps in understanding how changes in one variable affect another.
• Methods: There are various types of regression analysis, such as linear regression, logistic regression, and multiple regression. Linear regression, for example, aims to fit a linear equation to the data points to predict the dependent variable based on the independent variables.
• Use Cases: Researchers employ regression analysis to understand and quantify relationships between variables. For instance, they can use it to predict sales based on advertising expenditure.

3. Machine Learning:

• Definition: Machine learning is a broader field of data analysis that involves the use of algorithms and models to make predictions or decisions without explicit programming. It allows computers to learn from data and improve their performance over time.
• Methods: There are various machine learning algorithms, including decision trees, support vector machines, neural networks, and clustering algorithms. Researchers select and train these algorithms based on their specific research goals and datasets.
• Use Cases: Researchers apply machine learning in research when they deal with large datasets and complex patterns. They use it for tasks such as image recognition, natural language processing, recommendation systems, and predictive modeling in diverse scientific fields.

In data analysis, it’s essential to maintain a clear understanding of the research objectives, the type of data under analysis, and the suitable tools or software needed to perform the analysis for each of these data analysis methods. Researchers often rely on statistical software like R, Python, or specialized software packages for machine learning, depending on their specific needs.

Data Analysis Softwares

Here’s some information on popular data analysis software and tools commonly used in research:

1. Microsoft Excel:

• Description: Microsoft Excel is a widely used spreadsheet application that offers basic data analysis capabilities. It’s suitable for performing simple calculations, creating charts, and conducting basic statistical analyses.
• Use Cases: Excel is often used for preliminary data exploration, data cleaning, and generating simple descriptive statistics.

2. R:

• Description: R is a powerful and open-source statistical programming language and software environment. It provides extensive capabilities for data analysis, visualization, and statistical modeling.
• Use Cases: Researchers use R for a wide range of tasks, including data visualization with packages like ggplot2, statistical analysis with packages like dplyr, and advanced modeling with packages like glm and randomForest.

3. Python:

• Description: Python is a versatile programming language with numerous libraries and packages for data analysis and machine learning. Libraries like NumPy, pandas, and Matplotlib are commonly used in data analysis projects.
• Use Cases: Python is employed for tasks such as data cleaning, manipulation, visualization, and building machine learning models using libraries like sci-kit-learn and TensorFlow.

4. SPSS (Statistical Package for the Social Sciences):

• Description: SPSS is a user-friendly statistical software package widely used in social sciences and business research. It provides a graphical interface for data analysis.
• Use Cases: Researchers often choose SPSS for its ease of use in conducting various statistical analyses, including regression, ANOVA, and factor analysis.

5. SAS (Statistical Analysis System):

• Description: SAS is a software suite used for advanced statistical analysis, data management, and predictive modeling. It’s commonly used in industries such as healthcare, finance, and research.
• Use Cases: SAS is chosen for complex data analysis tasks, particularly in fields that require adherence to strict data management standards.

6. MATLAB:

• Description: MATLAB is a programming environment and language commonly used in engineering and scientific research. It offers extensive tools for data analysis, simulation, and modeling.
• Use Cases: MATLAB is popular in research involving signal processing, image analysis, and complex mathematical modeling.

7. Tableau:

• Description: Tableau is a powerful data visualization tool that allows researchers to create interactive and shareable data dashboards. It’s often used for visualizing research findings.
• Use Cases: Tableau is ideal for creating compelling visualizations that help researchers communicate their data effectively.

Also Read: 5 Free Data Analysis and Graph Plotting Software for Thesis

Conclusion

Data analysis is a fundamental step in the research process, allowing researchers to make informed decisions and draw meaningful conclusions.

Data analysis is an iterative process, and continuous refinement and validation are important to produce reliable results.

I hope this article will help you to learn more about how to perform Analyse Data in Research.

The post How to Analyse Data in Research? appeared first on iLovePhD.

When it comes to writing a research paper, one of the most important parts is the abstract. An abstract is a concise summary of the main points of your research, and it’s typically the first thing that readers will see. Therefore, it’s essential to make sure that your abstract is well-written and informative.

To help you write an effective abstract, we’ve put together a template that you can use as a guide. This template will ensure that you include all the necessary information in your abstract and that you present it in a clear and organized manner.

Writing an Effective Abstract for Your Research Paper: Tips and Format

Here’s the template:

1. Background: Start by providing some context for your research. This could include a brief overview of the problem you’re trying to solve or the question you’re trying to answer.
2. Objectives: Next, state the objectives of your research. What did you hope to accomplish with your study?
3. Methods: Describe the methods you used to conduct your research. This should include information on the participants, materials, and procedures.
4. Results: Present the results of your research. What did you find? Be sure to include any statistical analyses that you performed.
5. Conclusions: Finally, conclude your abstract with a summary of your main findings and their implications. What are the broader implications of your research? What are the next steps that need to be taken?

Abstract Writing Template: Research Methodology

here is an example of an abstract format with the recommended number of words and some tips:

Title: [Insert the title of your research paper here]

Abstract:

[The abstract should be a concise summary of your research paper. It should be written in one paragraph and should not exceed 150 to 250 words. Here is a possible format you can use:]

• Background: [1-2 sentences] Provide some context for your research. Explain why this topic is important and what gap in the literature you are trying to address.
• Objectives: [1-2 sentences] State the objectives of your research. What did you hope to accomplish with your study? What research questions did you try to answer?
• Methods: [2-3 sentences] Describe the methods you used to conduct your research. This should include information on the participants, materials, and procedures. Be specific and concise.
• Results: [2-3 sentences] Present the results of your research. What did you find? Highlight the key findings of your study and explain their significance.
• Conclusions: [1-2 sentences] Finally, conclude your abstract with a summary of your main findings and their implications. What are the broader implications of your research? What are the next steps that need to be taken?

Download the PDF to Write an Abstract for Your Research Paper

Tips to Write an impressive abstract

• Use clear and concise language. Avoid using technical terms or jargon that may be unfamiliar to readers.
• Focus on the key findings and their implications, rather than providing a detailed description of every aspect of your study.
• Make sure to proofread your abstract carefully to ensure that it is free of errors and typos.
• Use active voice and present tense to make the abstract more engaging and dynamic.
• Avoid using abbreviations or acronyms unless they are widely recognized in your field.

Keywords Should be Avoided while Writing Abstract

When writing your abstract, it’s important to keep in mind that it should be concise and to the point. Aim for a length of around 150-250 words, and avoid including unnecessary details or jargon. You want your abstract to be easy to understand for readers who may not be familiar with your specific field of study.

In addition to following the template above, there are a few other tips you should keep in mind when writing your abstract. First, make sure to use clear and concise language. Avoid using overly technical terms or jargon that may be unfamiliar to readers.

Second, be sure to highlight the most important aspects of your research. Focus on the key findings and their implications, rather than providing a detailed description of every aspect of your study.

Finally, proofread your abstract carefully to ensure that it is free of errors and typos. A well-written and error-free abstract will help to make a good first impression on readers and increase the chances that they will continue reading your paper.

In conclusion, writing an abstract for a research paper is an important task that requires careful attention to detail. By following the template and tips outlined above, you can create a clear and informative abstract that will engage readers and draw them into your research.

Also Read: How to Create Graphical Abstract

The post Template for Writing Abstract in Research Paper appeared first on iLovePhD.