data analysis in dissertation

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• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation. 

Considering the complexity of many data analysis projects, it becomes challenging to get precise results if analysts are not familiar with data analysis tools and tests properly. The analysis is a time-taking process that starts with collecting valid and relevant data and ends with the demonstration of error-free results.

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Dissertation Data Analysis  is the process of understanding, gathering, compiling, and processing a large amount of data. Then identifying common patterns in responses and critically examining facts and figures to find the rationale behind those outcomes.

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests  used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

For a proper dissertation data analysis, the student should have a clear understanding and statistical knowledge. Through this knowledge and experience, a student can perform dissertation analysis on their own. 

Following are some helpful tips for writing a splendid dissertation data analysis:

2. Relevance of Collected Data

If the data is irrelevant and not appropriate, you might get distracted from the point of focus. To show the reader that you can critically solve the problem, make sure that you write a theoretical proposition regarding the selection  and analysis of data.

3. Data Analysis

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

On the other hand,  quantitative analysis  refers to the analysis and interpretation of facts and figures – to build reasoning behind the advent of primary findings. An assessment of the main results and the literature review plays a pivotal role in qualitative and quantitative analysis.

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly.  It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal. 

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation  can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

          Qualitative Data Analysis Methods

Following are the methods used to perform quantitative data analysis. 

•   Deductive Method

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

•  Inductive Method

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

5. Quantitative Data Analysis

Quantitative data contains facts and figures obtained from scientific research and requires extensive statistical analysis. After collection and analysis, you will be able to conclude. Generic outcomes can be accepted beyond the sample by assuming that it is representative – one of the preliminary checkpoints to carry out in your analysis to a larger group. This method is also referred to as the “scientific method”, gaining its roots from natural sciences.

The Presentation of quantitative data  depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for  hard sciences  might require numeric inputs and statistics. As for  natural sciences , such comprehensive analysis is not required.

                Quantitative Analysis Methods

Following are some of the methods used to perform quantitative data analysis. 

• Trend analysis:  This corresponds to a statistical analysis approach to look at the trend of quantitative data collected over a considerable period.
• Cross-tabulation:  This method uses a tabula way to draw readings among data sets in research.  
• Conjoint analysis :   Quantitative data analysis method that can collect and analyze advanced measures. These measures provide a thorough vision about purchasing decisions and the most importantly, marked parameters.
• TURF analysis:  This approach assesses the total market reach of a service or product or a mix of both. 
• Gap analysis:  It utilizes the  side-by-side matrix  to portray quantitative data, which captures the difference between the actual and expected performance. 
• Text analysis:  In this method, innovative tools enumerate  open-ended data  into easily understandable data. 

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs. 

Tables help in presenting both qualitative and quantitative data concisely. While presenting data, always keep your reader in mind. Anything clear to you may not be apparent to your reader. So, constantly rethink whether your data presentation method is understandable to someone less conversant with your research and findings. If the answer is “No”, you may need to rethink your Presentation. 

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part. 

The data you find hard to arrange within the text, include that in the  appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation. 

8. Thoroughness of Data

It is a common misconception that the data presented is self-explanatory. Most of the students provide the data and quotes and think that it is enough and explaining everything. It is not sufficient. Rather than just quoting everything, you should analyze and identify which data you will use to approve or disapprove your standpoints. 

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale. 

It also involves answering what you are trying to do with the data and how you have structured your findings. Once you have presented the results, the reader will be looking for interpretation. Hence, it is essential to deliver the understanding as soon as you have submitted your data.

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation. 

In the finding part, you should tell the reader what they are looking for. There should be no suspense for the reader as it would divert their attention. State your findings clearly and concisely so that they can get the idea of what is more to come in your dissertation.

11. Connection with Literature Review

At the ending of your data analysis in the dissertation, make sure to compare your data with other published research. In this way, you can identify the points of differences and agreements. Check the consistency of your findings if they meet your expectations—lookup for bottleneck position. Analyze and discuss the reasons behind it. Identify the key themes, gaps, and the relation of your findings with the literature review. In short, you should link your data with your research question, and the questions should form a basis for literature.

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Wrapping Up

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

In this article, we thoroughly looked at the tips that prove valuable for writing a data analysis in a dissertation. Make sure to give this article a thorough read before you write data analysis in the dissertation leading to the successful future of your research.

Oxbridge Essays. Top 10 Tips for Writing a Dissertation Data Analysis.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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A data analysis dissertation is a complex and challenging project requiring significant time, effort, and expertise. Fortunately, it is possible to successfully complete a data analysis dissertation with careful planning and execution.

As a student, you must know how important it is to have a strong and well-written dissertation, especially regarding data analysis. Proper data analysis is crucial to the success of your research and can often make or break your dissertation.

To get a better understanding, you may review the data analysis dissertation examples listed below;

• Impact of Leadership Style on the Job Satisfaction of Nurses
• Effect of Brand Love on Consumer Buying Behaviour in Dietary Supplement Sector
• An Insight Into Alternative Dispute Resolution
• An Investigation of Cyberbullying and its Impact on Adolescent Mental Health in UK

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Types of data analysis for dissertation.

The various types of data Analysis in a Dissertation are as follows;

1.   Qualitative Data Analysis

Qualitative data analysis is a type of data analysis that involves analyzing data that cannot be measured numerically. This data type includes interviews, focus groups, and open-ended surveys. Qualitative data analysis can be used to identify patterns and themes in the data.

2.   Quantitative Data Analysis

Quantitative data analysis is a type of data analysis that involves analyzing data that can be measured numerically. This data type includes test scores, income levels, and crime rates. Quantitative data analysis can be used to test hypotheses and to look for relationships between variables.

3.   Descriptive Data Analysis

Descriptive data analysis is a type of data analysis that involves describing the characteristics of a dataset. This type of data analysis summarizes the main features of a dataset.

4.   Inferential Data Analysis

Inferential data analysis is a type of data analysis that involves making predictions based on a dataset. This type of data analysis can be used to test hypotheses and make predictions about future events.

5.   Exploratory Data Analysis

Exploratory data analysis is a type of data analysis that involves exploring a data set to understand it better. This type of data analysis can identify patterns and relationships in the data.

Time Period to Plan and Complete a Data Analysis Dissertation?

When planning dissertation data analysis, it is important to consider the dissertation methodology structure and time series analysis as they will give you an understanding of how long each stage will take. For example, using a qualitative research method, your data analysis will involve coding and categorizing your data.

This can be time-consuming, so allowing enough time in your schedule is important. Once you have coded and categorized your data, you will need to write up your findings. Again, this can take some time, so factor this into your schedule.

Finally, you will need to proofread and edit your dissertation before submitting it. All told, a data analysis dissertation can take anywhere from several weeks to several months to complete, depending on the project’s complexity. Therefore, starting planning early and allowing enough time in your schedule to complete the task is important.

Essential Strategies for Data Analysis Dissertation

A.   Planning

The first step in any dissertation is planning. You must decide what you want to write about and how you want to structure your argument. This planning will involve deciding what data you want to analyze and what methods you will use for a data analysis dissertation.

B.   Prototyping

Once you have a plan for your dissertation, it’s time to start writing. However, creating a prototype is important before diving head-first into writing your dissertation. A prototype is a rough draft of your argument that allows you to get feedback from your advisor and committee members. This feedback will help you fine-tune your argument before you start writing the final version of your dissertation.

C.   Executing

After you have created a plan and prototype for your data analysis dissertation, it’s time to start writing the final version. This process will involve collecting and analyzing data and writing up your results. You will also need to create a conclusion section that ties everything together.

D.   Presenting

The final step in acing your data analysis dissertation is presenting it to your committee. This presentation should be well-organized and professionally presented. During the presentation, you’ll also need to be ready to respond to questions concerning your dissertation.

Data Analysis Tools

Numerous suggestive tools are employed to assess the data and deduce pertinent findings for the discussion section. The tools used to analyze data and get a scientific conclusion are as follows:

a.     Excel

Excel is a spreadsheet program part of the Microsoft Office productivity software suite. Excel is a powerful tool that can be used for various data analysis tasks, such as creating charts and graphs, performing mathematical calculations, and sorting and filtering data.

b.     Google Sheets

Google Sheets is a free online spreadsheet application that is part of the Google Drive suite of productivity software. Google Sheets is similar to Excel in terms of functionality, but it also has some unique features, such as the ability to collaborate with other users in real-time.

c.     SPSS

SPSS is a statistical analysis software program commonly used in the social sciences. SPSS can be used for various data analysis tasks, such as hypothesis testing, factor analysis, and regression analysis.

d.     STATA

STATA is a statistical analysis software program commonly used in the sciences and economics. STATA can be used for data management, statistical modelling, descriptive statistics analysis, and data visualization tasks.

SAS is a commercial statistical analysis software program used by businesses and organizations worldwide. SAS can be used for predictive modelling, market research, and fraud detection.

R is a free, open-source statistical programming language popular among statisticians and data scientists. R can be used for tasks such as data wrangling, machine learning, and creating complex visualizations.

g.     Python

A variety of applications may be used using the distinctive programming language Python, including web development, scientific computing, and artificial intelligence. Python also has a number of modules and libraries that can be used for data analysis tasks, such as numerical computing, statistical modelling, and data visualization.

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Tips to Compose a Successful Data Analysis Dissertation

a.   Choose a Topic You’re Passionate About

The first step to writing a successful data analysis dissertation is to choose a topic you’re passionate about. Not only will this make the research and writing process more enjoyable, but it will also ensure that you produce a high-quality paper.

Choose a topic that is particular enough to be covered in your paper’s scope but not so specific that it will be challenging to obtain enough evidence to substantiate your arguments.

b.   Do Your Research

data analysis in research is an important part of academic writing. Once you’ve selected a topic, it’s time to begin your research. Be sure to consult with your advisor or supervisor frequently during this stage to ensure that you are on the right track. In addition to secondary sources such as books, journal articles, and reports, you should also consider conducting primary research through surveys or interviews. This will give you first-hand insights into your topic that can be invaluable when writing your paper.

c.   Develop a Strong Thesis Statement

After you’ve done your research, it’s time to start developing your thesis statement. It is arguably the most crucial part of your entire paper, so take care to craft a clear and concise statement that encapsulates the main argument of your paper.

Remember that your thesis statement should be arguable—that is, it should be capable of being disputed by someone who disagrees with your point of view. If your thesis statement is not arguable, it will be difficult to write a convincing paper.

d.   Write a Detailed Outline

Once you have developed a strong thesis statement, the next step is to write a detailed outline of your paper. This will offer you a direction to write in and guarantee that your paper makes sense from beginning to end.

Your outline should include an introduction, in which you state your thesis statement; several body paragraphs, each devoted to a different aspect of your argument; and a conclusion, in which you restate your thesis and summarize the main points of your paper.

e.   Write Your First Draft

With your outline in hand, it’s finally time to start writing your first draft. At this stage, don’t worry about perfecting your grammar or making sure every sentence is exactly right—focus on getting all of your ideas down on paper (or onto the screen). Once you have completed your first draft, you can revise it for style and clarity.

And there you have it! Following these simple tips can increase your chances of success when writing your data analysis dissertation. Just remember to start early, give yourself plenty of time to research and revise, and consult with your supervisor frequently throughout the process.

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Studying the above examples gives you valuable insight into the structure and content that should be included in your own data analysis dissertation. You can also learn how to effectively analyze and present your data and make a lasting impact on your readers.

In addition to being a useful resource for completing your dissertation, these examples can also serve as a valuable reference for future academic writing projects. By following these examples and understanding their principles, you can improve your data analysis skills and increase your chances of success in your academic career.

You may also contact Premier Dissertations to develop your data analysis dissertation.

For further assistance, some other resources in the dissertation writing section are shared below;

How Do You Select the Right Data Analysis

How to Write Data Analysis For A Dissertation?

How to Develop a Conceptual Framework in Dissertation?

What is a Hypothesis in a Dissertation?

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Raw Data to Excellence: Master Dissertation Analysis

Discover the secrets of successful dissertation data analysis. Get practical advice and useful insights from experienced experts now!

Have you ever found yourself knee-deep in a dissertation, desperately seeking answers from the data you’ve collected? Or have you ever felt clueless with all the data that you’ve collected but don’t know where to start? Fear not, in this article we are going to discuss a method that helps you come out of this situation and that is Dissertation Data Analysis.

Dissertation data analysis is like uncovering hidden treasures within your research findings. It’s where you roll up your sleeves and explore the data you’ve collected, searching for patterns, connections, and those “a-ha!” moments. Whether you’re crunching numbers, dissecting narratives, or diving into qualitative interviews, data analysis is the key that unlocks the potential of your research.

Dissertation Data Analysis

Dissertation data analysis plays a crucial role in conducting rigorous research and drawing meaningful conclusions. It involves the systematic examination, interpretation, and organization of data collected during the research process. The aim is to identify patterns, trends, and relationships that can provide valuable insights into the research topic.

The first step in dissertation data analysis is to carefully prepare and clean the collected data. This may involve removing any irrelevant or incomplete information, addressing missing data, and ensuring data integrity. Once the data is ready, various statistical and analytical techniques can be applied to extract meaningful information.

Descriptive statistics are commonly used to summarize and describe the main characteristics of the data, such as measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). These statistics help researchers gain an initial understanding of the data and identify any outliers or anomalies.

Furthermore, qualitative data analysis techniques can be employed when dealing with non-numerical data, such as textual data or interviews. This involves systematically organizing, coding, and categorizing qualitative data to identify themes and patterns.

Types of Research

When considering research types in the context of dissertation data analysis, several approaches can be employed:

1. Quantitative Research

This type of research involves the collection and analysis of numerical data. It focuses on generating statistical information and making objective interpretations. Quantitative research often utilizes surveys, experiments, or structured observations to gather data that can be quantified and analyzed using statistical techniques.

2. Qualitative Research

In contrast to quantitative research, qualitative research focuses on exploring and understanding complex phenomena in depth. It involves collecting non-numerical data such as interviews, observations, or textual materials. Qualitative data analysis involves identifying themes, patterns, and interpretations, often using techniques like content analysis or thematic analysis.

3. Mixed-Methods Research

This approach combines both quantitative and qualitative research methods. Researchers employing mixed-methods research collect and analyze both numerical and non-numerical data to gain a comprehensive understanding of the research topic. The integration of quantitative and qualitative data can provide a more nuanced and comprehensive analysis, allowing for triangulation and validation of findings.

Primary vs. Secondary Research

Primary research.

Primary research involves the collection of original data specifically for the purpose of the dissertation. This data is directly obtained from the source, often through surveys, interviews, experiments, or observations. Researchers design and implement their data collection methods to gather information that is relevant to their research questions and objectives. Data analysis in primary research typically involves processing and analyzing the raw data collected.

Secondary Research

Secondary research involves the analysis of existing data that has been previously collected by other researchers or organizations. This data can be obtained from various sources such as academic journals, books, reports, government databases, or online repositories. Secondary data can be either quantitative or qualitative, depending on the nature of the source material. Data analysis in secondary research involves reviewing, organizing, and synthesizing the available data.

If you wanna deepen into Methodology in Research, also read: What is Methodology in Research and How Can We Write it?

Types of Analysis 

Various types of analysis techniques can be employed to examine and interpret the collected data. Of all those types, the ones that are most important and used are:

• Descriptive Analysis: Descriptive analysis focuses on summarizing and describing the main characteristics of the data. It involves calculating measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). Descriptive analysis provides an overview of the data, allowing researchers to understand its distribution, variability, and general patterns.
• Inferential Analysis: Inferential analysis aims to draw conclusions or make inferences about a larger population based on the collected sample data. This type of analysis involves applying statistical techniques, such as hypothesis testing, confidence intervals, and regression analysis, to analyze the data and assess the significance of the findings. Inferential analysis helps researchers make generalizations and draw meaningful conclusions beyond the specific sample under investigation.
• Qualitative Analysis: Qualitative analysis is used to interpret non-numerical data, such as interviews, focus groups, or textual materials. It involves coding, categorizing, and analyzing the data to identify themes, patterns, and relationships. Techniques like content analysis, thematic analysis, or discourse analysis are commonly employed to derive meaningful insights from qualitative data.
• Correlation Analysis: Correlation analysis is used to examine the relationship between two or more variables. It determines the strength and direction of the association between variables. Common correlation techniques include Pearson’s correlation coefficient, Spearman’s rank correlation, or point-biserial correlation, depending on the nature of the variables being analyzed.

Basic Statistical Analysis

When conducting dissertation data analysis, researchers often utilize basic statistical analysis techniques to gain insights and draw conclusions from their data. These techniques involve the application of statistical measures to summarize and examine the data. Here are some common types of basic statistical analysis used in dissertation research:

• Descriptive Statistics
• Frequency Analysis
• Cross-tabulation
• Chi-Square Test
• Correlation Analysis

Advanced Statistical Analysis

In dissertation data analysis, researchers may employ advanced statistical analysis techniques to gain deeper insights and address complex research questions. These techniques go beyond basic statistical measures and involve more sophisticated methods. Here are some examples of advanced statistical analysis commonly used in dissertation research:

Regression Analysis

• Analysis of Variance (ANOVA)
• Factor Analysis
• Cluster Analysis
• Structural Equation Modeling (SEM)
• Time Series Analysis

Examples of Methods of Analysis

Regression analysis is a powerful tool for examining relationships between variables and making predictions. It allows researchers to assess the impact of one or more independent variables on a dependent variable. Different types of regression analysis, such as linear regression, logistic regression, or multiple regression, can be used based on the nature of the variables and research objectives.

Event Study

An event study is a statistical technique that aims to assess the impact of a specific event or intervention on a particular variable of interest. This method is commonly employed in finance, economics, or management to analyze the effects of events such as policy changes, corporate announcements, or market shocks.

Vector Autoregression

Vector Autoregression is a statistical modeling technique used to analyze the dynamic relationships and interactions among multiple time series variables. It is commonly employed in fields such as economics, finance, and social sciences to understand the interdependencies between variables over time.

Preparing Data for Analysis

1. become acquainted with the data.

It is crucial to become acquainted with the data to gain a comprehensive understanding of its characteristics, limitations, and potential insights. This step involves thoroughly exploring and familiarizing oneself with the dataset before conducting any formal analysis by reviewing the dataset to understand its structure and content. Identify the variables included, their definitions, and the overall organization of the data. Gain an understanding of the data collection methods, sampling techniques, and any potential biases or limitations associated with the dataset.

2. Review Research Objectives

This step involves assessing the alignment between the research objectives and the data at hand to ensure that the analysis can effectively address the research questions. Evaluate how well the research objectives and questions align with the variables and data collected. Determine if the available data provides the necessary information to answer the research questions adequately. Identify any gaps or limitations in the data that may hinder the achievement of the research objectives.

3. Creating a Data Structure

This step involves organizing the data into a well-defined structure that aligns with the research objectives and analysis techniques. Organize the data in a tabular format where each row represents an individual case or observation, and each column represents a variable. Ensure that each case has complete and accurate data for all relevant variables. Use consistent units of measurement across variables to facilitate meaningful comparisons.

4. Discover Patterns and Connections

In preparing data for dissertation data analysis, one of the key objectives is to discover patterns and connections within the data. This step involves exploring the dataset to identify relationships, trends, and associations that can provide valuable insights. Visual representations can often reveal patterns that are not immediately apparent in tabular data. 

Qualitative Data Analysis

Qualitative data analysis methods are employed to analyze and interpret non-numerical or textual data. These methods are particularly useful in fields such as social sciences, humanities, and qualitative research studies where the focus is on understanding meaning, context, and subjective experiences. Here are some common qualitative data analysis methods:

Thematic Analysis

The thematic analysis involves identifying and analyzing recurring themes, patterns, or concepts within the qualitative data. Researchers immerse themselves in the data, categorize information into meaningful themes, and explore the relationships between them. This method helps in capturing the underlying meanings and interpretations within the data.

Content Analysis

Content analysis involves systematically coding and categorizing qualitative data based on predefined categories or emerging themes. Researchers examine the content of the data, identify relevant codes, and analyze their frequency or distribution. This method allows for a quantitative summary of qualitative data and helps in identifying patterns or trends across different sources.

Grounded Theory

Grounded theory is an inductive approach to qualitative data analysis that aims to generate theories or concepts from the data itself. Researchers iteratively analyze the data, identify concepts, and develop theoretical explanations based on emerging patterns or relationships. This method focuses on building theory from the ground up and is particularly useful when exploring new or understudied phenomena.

Discourse Analysis

Discourse analysis examines how language and communication shape social interactions, power dynamics, and meaning construction. Researchers analyze the structure, content, and context of language in qualitative data to uncover underlying ideologies, social representations, or discursive practices. This method helps in understanding how individuals or groups make sense of the world through language.

Narrative Analysis

Narrative analysis focuses on the study of stories, personal narratives, or accounts shared by individuals. Researchers analyze the structure, content, and themes within the narratives to identify recurring patterns, plot arcs, or narrative devices. This method provides insights into individuals’ live experiences, identity construction, or sense-making processes.

Applying Data Analysis to Your Dissertation

Applying data analysis to your dissertation is a critical step in deriving meaningful insights and drawing valid conclusions from your research. It involves employing appropriate data analysis techniques to explore, interpret, and present your findings. Here are some key considerations when applying data analysis to your dissertation:

Selecting Analysis Techniques

Choose analysis techniques that align with your research questions, objectives, and the nature of your data. Whether quantitative or qualitative, identify the most suitable statistical tests, modeling approaches, or qualitative analysis methods that can effectively address your research goals. Consider factors such as data type, sample size, measurement scales, and the assumptions associated with the chosen techniques.

Data Preparation

Ensure that your data is properly prepared for analysis. Cleanse and validate your dataset, addressing any missing values, outliers, or data inconsistencies. Code variables, transform data if necessary, and format it appropriately to facilitate accurate and efficient analysis. Pay attention to ethical considerations, data privacy, and confidentiality throughout the data preparation process.

Execution of Analysis

Execute the selected analysis techniques systematically and accurately. Utilize statistical software, programming languages, or qualitative analysis tools to carry out the required computations, calculations, or interpretations. Adhere to established guidelines, protocols, or best practices specific to your chosen analysis techniques to ensure reliability and validity.

Interpretation of Results

Thoroughly interpret the results derived from your analysis. Examine statistical outputs, visual representations, or qualitative findings to understand the implications and significance of the results. Relate the outcomes back to your research questions, objectives, and existing literature. Identify key patterns, relationships, or trends that support or challenge your hypotheses.

Drawing Conclusions

Based on your analysis and interpretation, draw well-supported conclusions that directly address your research objectives. Present the key findings in a clear, concise, and logical manner, emphasizing their relevance and contributions to the research field. Discuss any limitations, potential biases, or alternative explanations that may impact the validity of your conclusions.

Validation and Reliability

Evaluate the validity and reliability of your data analysis by considering the rigor of your methods, the consistency of results, and the triangulation of multiple data sources or perspectives if applicable. Engage in critical self-reflection and seek feedback from peers, mentors, or experts to ensure the robustness of your data analysis and conclusions.

In conclusion, dissertation data analysis is an essential component of the research process, allowing researchers to extract meaningful insights and draw valid conclusions from their data. By employing a range of analysis techniques, researchers can explore relationships, identify patterns, and uncover valuable information to address their research objectives.

Turn Your Data Into Easy-To-Understand And Dynamic Stories

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Mastering Dissertation Data Analysis: A Comprehensive Guide

By Laura Brown on 29th December 2023

To craft an effective dissertation data analysis chapter, you need to follow some simple steps:

• Start by planning the structure and objectives of the chapter.
• Clearly set the stage by providing a concise overview of your research design and methodology.
• Proceed to thorough data preparation, ensuring accuracy and organisation.
• Justify your methods and present the results using visual aids for clarity.
• Discuss the findings within the context of your research questions.
• Finally, review and edit your chapter to ensure coherence.

This approach will ensure a well-crafted and impactful analysis section.

Before delving into details on how you can come up with an engaging data analysis show in your dissertation, we first need to understand what it is and why it is required.

What Is Data Analysis In A Dissertation?

The data analysis chapter is a crucial section of a research dissertation that involves the examination, interpretation, and synthesis of collected data. In this chapter, researchers employ statistical techniques, qualitative methods, or a combination of both to make sense of the data gathered during the research process.

Why Is The Data Analysis Chapter So Important?

The primary objectives of the data analysis chapter are to identify patterns, trends, relationships, and insights within the data set. Researchers use various tools and software to conduct a thorough analysis, ensuring that the results are both accurate and relevant to the research questions or hypotheses. Ultimately, the findings derived from this chapter contribute to the overall conclusions of the dissertation, providing a basis for drawing meaningful and well-supported insights.

Steps Required To Craft Data Analysis Chapter To Perfection

Now that we have an idea of what a dissertation analysis chapter is and why it is necessary to put it in the dissertation, let’s move towards how we can create one that has a significant impact. Our guide will move around the bulleted points that have been discussed initially in the beginning. So, it’s time to begin.

Step 1: Planning Your Data Analysis Chapter

Planning your data analysis chapter is a critical precursor to its successful execution.

• Begin by outlining the chapter structure to provide a roadmap for your analysis.
• Start with an introduction that succinctly introduces the purpose and significance of the data analysis in the context of your research.
• Following this, delineate the chapter into sections such as Data Preparation, where you detail the steps taken to organise and clean your data.
• Plan on to clearly define the Data Analysis Techniques employed, justifying their relevance to your research objectives.
• As you progress, plan for the Results Presentation, incorporating visual aids for clarity. Lastly, earmark a section for the Discussion of Findings, where you will interpret results within the broader context of your research questions.

This structured approach ensures a comprehensive and cohesive data analysis chapter, setting the stage for a compelling narrative that contributes significantly to your dissertation. You can always seek our dissertation data analysis help to plan your chapter.

Step 2: Setting The Stage – Introduction to Data Analysis

Your primary objective is to establish a solid foundation for the analytical journey. You need to skillfully link your data analysis to your research questions, elucidating the direct relevance and purpose of the upcoming analysis.

Simultaneously, define key concepts to provide clarity and ensure a shared understanding of the terms integral to your study. Following this, offer a concise overview of your data set characteristics, outlining its source, nature, and any noteworthy features.

This meticulous groundwork alongside our help with dissertation data analysis lays the base for a coherent and purposeful chapter, guiding readers seamlessly into the subsequent stages of your dissertation.

Step 3: Data Preparation

Now this is another pivotal phase in the data analysis process, ensuring the integrity and reliability of your findings. You should start with an insightful overview of the data cleaning and preprocessing procedures, highlighting the steps taken to refine and organise your dataset. Then, discuss any challenges encountered during the process and the strategies employed to address them.

Moving forward, delve into the specifics of data transformation procedures, elucidating any alterations made to the raw data for analysis. Clearly describe the methods employed for normalisation, scaling, or any other transformations deemed necessary. It will not only enhance the quality of your analysis but also foster transparency in your research methodology, reinforcing the robustness of your data-driven insights.

Step 4: Data Analysis Techniques

The data analysis section of a dissertation is akin to choosing the right tools for an artistic masterpiece. Carefully weigh the quantitative and qualitative approaches, ensuring a tailored fit for the nature of your data.

Quantitative Analysis

• Descriptive Statistics: Paint a vivid picture of your data through measures like mean, median, and mode. It’s like capturing the essence of your data’s personality.
• Inferential Statistics:Take a leap into the unknown, making educated guesses and inferences about your larger population based on a sample. It’s statistical magic in action.

Qualitative Analysis

• Thematic Analysis: Imagine your data as a novel, and thematic analysis as the tool to uncover its hidden chapters. Dissect the narrative, revealing recurring themes and patterns.
• Content Analysis: Scrutinise your data’s content like detectives, identifying key elements and meanings. It’s a deep dive into the substance of your qualitative data.

Providing Rationale for Chosen Methods

You should also articulate the why behind the chosen methods. It’s not just about numbers or themes; it’s about the story you want your data to tell. Through transparent rationale, you should ensure that your chosen techniques align seamlessly with your research goals, adding depth and credibility to the analysis.

Step 5: Presentation Of Your Results

You can simply break this process into two parts.

a.    Creating Clear and Concise Visualisations

Effectively communicate your findings through meticulously crafted visualisations. Use tables that offer a structured presentation, summarising key data points for quick comprehension. Graphs, on the other hand, visually depict trends and patterns, enhancing overall clarity. Thoughtfully design these visual aids to align with the nature of your data, ensuring they serve as impactful tools for conveying information.

b.    Interpreting and Explaining Results

Go beyond mere presentation by providing insightful interpretation by taking data analysis services for dissertation. Show the significance of your findings within the broader research context. Moreover, articulates the implications of observed patterns or relationships. By weaving a narrative around your results, you guide readers through the relevance and impact of your data analysis, enriching the overall understanding of your dissertation’s key contributions.

Step 6: Discussion of Findings

While discussing your findings and dissertation discussion chapter , it’s like putting together puzzle pieces to understand what your data is saying. You can always take dissertation data analysis help to explain what it all means, connecting back to why you started in the first place.

Be honest about any limitations or possible biases in your study; it’s like showing your cards to make your research more trustworthy. Comparing your results to what other smart people have found before you adds to the conversation, showing where your work fits in.

Looking ahead, you suggest ideas for what future researchers could explore, keeping the conversation going. So, it’s not just about what you found, but also about what comes next and how it all fits into the big picture of what we know.

Step 7: Writing Style and Tone

In order to perfectly come up with this chapter, follow the below points in your writing and adjust the tone accordingly,

• Use clear and concise language to ensure your audience easily understands complex concepts.
• Avoid unnecessary jargon in data analysis for thesis, and if specialised terms are necessary, provide brief explanations.
• Keep your writing style formal and objective, maintaining an academic tone throughout.
• Avoid overly casual language or slang, as the data analysis chapter is a serious academic document.
• Clearly define terms and concepts, providing specific details about your data preparation and analysis procedures.
• Use precise language to convey your ideas, minimising ambiguity.
• Follow a consistent formatting style for headings, subheadings, and citations to enhance readability.
• Ensure that tables, graphs, and visual aids are labelled and formatted uniformly for a polished presentation.
• Connect your analysis to the broader context of your research by explaining the relevance of your chosen methods and the importance of your findings.
• Offer a balance between detail and context, helping readers understand the significance of your data analysis within the larger study.
• Present enough detail to support your findings but avoid overwhelming readers with excessive information.
• Use a balance of text and visual aids to convey information efficiently.
• Maintain reader engagement by incorporating transitions between sections and effectively linking concepts.
• Use a mix of sentence structures to add variety and keep the writing engaging.
• Eliminate grammatical errors, typos, and inconsistencies through thorough proofreading.
• Consider seeking feedback from peers or mentors to ensure the clarity and coherence of your writing.

You can seek a data analysis dissertation example or sample from CrowdWriter to better understand how we write it while following the above-mentioned points.

Step 8: Reviewing and Editing

Reviewing and editing your data analysis chapter is crucial for ensuring its effectiveness and impact. By revising your work, you refine the clarity and coherence of your analysis, enhancing its overall quality.

Seeking feedback from peers, advisors or dissertation data analysis services provides valuable perspectives, helping identify blind spots and areas for improvement. Addressing common writing pitfalls, such as grammatical errors or unclear expressions, ensures your chapter is polished and professional.

Taking the time to review and edit not only strengthens the academic integrity of your work but also contributes to a final product that is clear, compelling, and ready for scholarly scrutiny.

Concluding On This Data Analysis Help

Be it master thesis data analysis, an undergraduate one or for PhD scholars, the steps remain almost the same as we have discussed in this guide. The primary focus is to be connected with your research questions and objectives while writing your data analysis chapter.

Do not lose your focus and choose the right analysis methods and design. Make sure to present your data through various visuals to better explain your data and engage the reader as well. At last, give it a detailed read and seek assistance from experts and your supervisor for further improvement.

Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.

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• Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
• Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

• If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
• You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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Skills for Learning : Research Skills

Data analysis is an ongoing process that should occur throughout your research project. Suitable data-analysis methods must be selected when you write your research proposal. The nature of your data (i.e. quantitative or qualitative) will be influenced by your research design and purpose. The data will also influence the analysis methods selected.

We run interactive workshops to help you develop skills related to doing research, such as data analysis, writing literature reviews and preparing for dissertations. Find out more on the Skills for Learning Workshops page.

We have online academic skills modules within MyBeckett for all levels of university study. These modules will help your academic development and support your success at LBU. You can work through the modules at your own pace, revisiting them as required. Find out more from our FAQ What academic skills modules are available?

Quantitative data analysis

Broadly speaking, 'statistics' refers to methods, tools and techniques used to collect, organise and interpret data. The goal of statistics is to gain understanding from data. Therefore, you need to know how to:

• Produce data – for example, by handing out a questionnaire or doing an experiment.
• Organise, summarise, present and analyse data.
• Draw valid conclusions from findings.

There are a number of statistical methods you can use to analyse data. Choosing an appropriate statistical method should follow naturally, however, from your research design. Therefore, you should think about data analysis at the early stages of your study design. You may need to consult a statistician for help with this.

Tips for working with statistical data

• Plan so that the data you get has a good chance of successfully tackling the research problem. This will involve reading literature on your subject, as well as on what makes a good study.
• To reach useful conclusions, you need to reduce uncertainties or 'noise'. Thus, you will need a sufficiently large data sample. A large sample will improve precision. However, this must be balanced against the 'costs' (time and money) of collection.
• Consider the logistics. Will there be problems in obtaining sufficient high-quality data? Think about accuracy, trustworthiness and completeness.
• Statistics are based on random samples. Consider whether your sample will be suited to this sort of analysis. Might there be biases to think about?
• How will you deal with missing values (any data that is not recorded for some reason)? These can result from gaps in a record or whole records being missed out.
• When analysing data, start by looking at each variable separately. Conduct initial/exploratory data analysis using graphical displays. Do this before looking at variables in conjunction or anything more complicated. This process can help locate errors in the data and also gives you a 'feel' for the data.
• Look out for patterns of 'missingness'. They are likely to alert you if there’s a problem. If the 'missingness' is not random, then it will have an impact on the results.
• Be vigilant and think through what you are doing at all times. Think critically. Statistics are not just mathematical tricks that a computer sorts out. Rather, analysing statistical data is a process that the human mind must interpret!

Top tips! Try inventing or generating the sort of data you might get and see if you can analyse it. Make sure that your process works before gathering actual data. Think what the output of an analytic procedure will look like before doing it for real.

(Note: it is actually difficult to generate realistic data. There are fraud-detection methods in place to identify data that has been fabricated. So, remember to get rid of your practice data before analysing the real stuff!)

Statistical software packages

Software packages can be used to analyse and present data. The most widely used ones are SPSS and NVivo.

SPSS is a statistical-analysis and data-management package for quantitative data analysis. Click on ‘ How do I install SPSS? ’ to learn how to download SPSS to your personal device. SPSS can perform a wide variety of statistical procedures. Some examples are:

• Data management (i.e. creating subsets of data or transforming data).
• Summarising, describing or presenting data (i.e. mean, median and frequency).
• Looking at the distribution of data (i.e. standard deviation).
• Comparing groups for significant differences using parametric (i.e. t-test) and non-parametric (i.e. Chi-square) tests.
• Identifying significant relationships between variables (i.e. correlation).

NVivo can be used for qualitative data analysis. It is suitable for use with a wide range of methodologies. Click on ‘ How do I access NVivo ’ to learn how to download NVivo to your personal device. NVivo supports grounded theory, survey data, case studies, focus groups, phenomenology, field research and action research.

• Process data such as interview transcripts, literature or media extracts, and historical documents.
• Code data on screen and explore all coding and documents interactively.
• Rearrange, restructure, extend and edit text, coding and coding relationships.
• Search imported text for words, phrases or patterns, and automatically code the results.

Qualitative data analysis

Miles and Huberman (1994) point out that there are diverse approaches to qualitative research and analysis. They suggest, however, that it is possible to identify 'a fairly classic set of analytic moves arranged in sequence'. This involves:

• Affixing codes to a set of field notes drawn from observation or interviews.
• Noting reflections or other remarks in the margins.
• Sorting/sifting through these materials to identify: a) similar phrases, relationships between variables, patterns and themes and b) distinct differences between subgroups and common sequences.
• Isolating these patterns/processes and commonalties/differences. Then, taking them out to the field in the next wave of data collection.
• Highlighting generalisations and relating them to your original research themes.
• Taking the generalisations and analysing them in relation to theoretical perspectives.

        (Miles and Huberman, 1994.)

Patterns and generalisations are usually arrived at through a process of analytic induction (see above points 5 and 6). Qualitative analysis rarely involves statistical analysis of relationships between variables. Qualitative analysis aims to gain in-depth understanding of concepts, opinions or experiences.

Presenting information

There are a number of different ways of presenting and communicating information. The particular format you use is dependent upon the type of data generated from the methods you have employed.

Here are some appropriate ways of presenting information for different types of data:

Bar charts: These   may be useful for comparing relative sizes. However, they tend to use a large amount of ink to display a relatively small amount of information. Consider a simple line chart as an alternative.

Pie charts: These have the benefit of indicating that the data must add up to 100%. However, they make it difficult for viewers to distinguish relative sizes, especially if two slices have a difference of less than 10%.

Other examples of presenting data in graphical form include line charts and  scatter plots .

Qualitative data is more likely to be presented in text form. For example, using quotations from interviews or field diaries.

• Plan ahead, thinking carefully about how you will analyse and present your data.
• Think through possible restrictions to resources you may encounter and plan accordingly.
• Find out about the different IT packages available for analysing your data and select the most appropriate.
• If necessary, allow time to attend an introductory course on a particular computer package. You can book SPSS and NVivo workshops via MyHub .
• Code your data appropriately, assigning conceptual or numerical codes as suitable.
• Organise your data so it can be analysed and presented easily.
• Choose the most suitable way of presenting your information, according to the type of data collected. This will allow your information to be understood and interpreted better.

Primary, secondary and tertiary sources

Information sources are sometimes categorised as primary, secondary or tertiary sources depending on whether or not they are ‘original’ materials or data. For some research projects, you may need to use primary sources as well as secondary or tertiary sources. However the distinction between primary and secondary sources is not always clear and depends on the context. For example, a newspaper article might usually be categorised as a secondary source. But it could also be regarded as a primary source if it were an article giving a first-hand account of a historical event written close to the time it occurred.

• Primary sources
• Secondary sources
• Tertiary sources
• Grey literature

Primary sources are original sources of information that provide first-hand accounts of what is being experienced or researched. They enable you to get as close to the actual event or research as possible. They are useful for getting the most contemporary information about a topic.

Examples include diary entries, newspaper articles, census data, journal articles with original reports of research, letters, email or other correspondence, original manuscripts and archives, interviews, research data and reports, statistics, autobiographies, exhibitions, films, and artists' writings.

Some information will be available on an Open Access basis, freely accessible online. However, many academic sources are paywalled, and you may need to login as a Leeds Beckett student to access them. Where Leeds Beckett does not have access to a source, you can use our  Request It! Service .

Secondary sources interpret, evaluate or analyse primary sources. They're useful for providing background information on a topic, or for looking back at an event from a current perspective. The majority of your literature searching will probably be done to find secondary sources on your topic.

Examples include journal articles which review or interpret original findings, popular magazine articles commenting on more serious research, textbooks and biographies.

The term tertiary sources isn't used a great deal. There's overlap between what might be considered a secondary source and a tertiary source. One definition is that a tertiary source brings together secondary sources.

Examples include almanacs, fact books, bibliographies, dictionaries and encyclopaedias, directories, indexes and abstracts. They can be useful for introductory information or an overview of a topic in the early stages of research.

Depending on your subject of study, grey literature may be another source you need to use. Grey literature includes technical or research reports, theses and dissertations, conference papers, government documents, white papers, and so on.

Artificial intelligence tools

Before using any generative artificial intelligence or paraphrasing tools in your assessments, you should check if this is permitted on your course.

If their use is permitted on your course, you must  acknowledge any use of generative artificial intelligence tools  such as ChatGPT or paraphrasing tools (e.g., Grammarly, Quillbot, etc.), even if you have only used them to generate ideas for your assessments or for proofreading.

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• Published: 08 May 2024

Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework

• Mariyamma Philip 1  

BMC Medical Education volume  24 , Article number:  512 ( 2024 ) Cite this article

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Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2–3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.

One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2–3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.

Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2–3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.

The study concluded that the coursework program was beneficial for improving research scholars’ knowledge and attitudes toward statistics. A refresher program 2–3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars’ anxiety and attitudes toward statistics and its influence on learning outcomes.

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A PhD degree is a research degree, and research scholars submit a thesis based on original research in their chosen field. Doctor of Philosophy (PhD) degrees are awarded in a wide range of academic disciplines, and the PhD students are usually referred as research scholars. A comprehensive understanding of statistics allows research scholars to add rigour to their research. This approach helps them evaluate the current practices and draw informed conclusions from studies that were undertaken to generate their own hypotheses and to design, analyse and interpret complex clinical decisions. Therefore, intensive training at the beginning of the PhD journey is essential, as intensive training in research methodology and statistics in the early stages of research helps scholars design and plan their studies efficiently.

The University Grants Commission of India has taken various initiatives to introduce academic reforms to higher education institutions in India and mandated in 2009 that coursework be treated as a prerequisite for PhD preparation and that a minimum of four credits be assigned to one or more courses on research methodology, which could cover areas such as quantitative methods, computer applications, and research ethics. UGC also clearly states that all candidates admitted to PhD programmes shall be required to complete the prescribed coursework during the initial two semesters [ 1 ]. National Institute of Mental Health and Neurosciences (NIMHANS) at Bangalore, a tertiary care hospital and medical higher education institute in South India, that trains students in higher education in clinical fields, also introduced coursework in the PhD program for research scholars from various backgrounds, such as basic, behavioral and neurosciences, as per the UGC mandate. Research scholars undertake coursework programs soon after admission, which consist of several modules that include research methodology and statistical software training, among others.

Most scholars approach a course in statistics with the prejudice that statistics is uninteresting, demanding, complex or involve much mathematics and, most importantly, it is not relevant to their career goals. They approach statistics with considerable apprehension and negative attitudes, probably because of their inability to grasp the relevance of the application of the methods in their fields of study. This could be resolved by providing sufficient and relevant examples of the application of statistical techniques from various fields of medical research and by providing hands-on experience to learn how these techniques are applied and interpreted on real data. Hence, research methodology and statistical methods and the application of statistical methods using software have been given much importance and are taught as two modules, named Research Methodology and Statistics and Statistical Software Training, at this institute of medical higher education that trains research scholars in fields as diverse as basic, behavioural and neurosciences. Approximately 50% of the coursework curriculum focused on these two modules. Research scholars were thus given an opportunity to understand the theoretical aspects of the research methodology and statistical methods. They were also given hands-on training on statistical software to analyse the data using these methods and to interpret the findings. The coursework program was designed in this specific manner, as this intensive training would enable the research scholars to design their research studies more effectively and analyse their data in a better manner.

It is important to study attitudes toward statistics because attitudes are known to impact the learning process. Also, most importantly, these scholars are expected to utilize the skills in statistics and research methods to design research projects or guide postgraduate students and research scholars in the near future. Several authors have assessed attitudes toward statistics among various students and examined how attitudes affect academic achievement, how attitudes are correlated with knowledge in statistics and how attitudes change after a training program. There are studies on attitudes toward statistics among graduate [ 2 , 3 , 4 ] and postgraduate [ 5 ] medical students, politics, sociology, ( 6 – 7 ) psychology [ 8 , 9 , 10 ], social work [ 11 ], and management students [ 12 ]. However, there is a dearth of related literature on research scholars, and there are only two studies on the attitudes of research scholars. In their study of doctoral students in education-related fields, Cook & Catanzaro (2022) investigated the factors that contribute to statistics anxiety and attitudes toward statistics and how anxiety, attitudes and plans for future research use are connected among doctoral students [ 13 ]. Another study by Sohrabi et al. (2018) on research scholars assessed the change in knowledge and attitude towards teaching and educational design of basic science PhD students at a Medical University after a two-day workshop on empowerment and familiarity with the teaching and learning principles [ 14 ]. There were no studies that assessed changes in the attitudes or knowledge of research scholars across the PhD training period or after intensive training programmes such as PhD coursework. Even though PhD coursework has been established in institutes of higher education in India for more than a decade, there are no published research on the effectiveness of coursework from Indian universities or institutes of higher education.

This study aimed to determine the effectiveness of PhD coursework and whether intensive training programs such as PhD coursework can influence the knowledge and attitudes toward statistics of research scholars. Additionally, it would be interesting to know if the acquired knowledge could be retained longer, especially 2–3 years after the coursework, the crucial time of PhD data analysis. Hence, this study compares the scores of knowledge in statistics and attitude toward statistics of the research scholars at different time points of their PhD training, i.e., before, soon after and 2–3 years after the coursework.

Participants

This is an observational study of single group with repeated assessments. The institute offers a three-month coursework program consisting of seven modules, the first module is ethics; the fifth is research methodology and statistics; and the last is neurosciences. The study was conducted in January 2020. All research scholars of the institute who had completed PhD coursework in the last three years were considered for this study ( n  = 130). Knowledge and attitudes toward statistics before and soon after the coursework module were assessed as part of the coursework program. They were collected on the first and last day of the program respectively. The author who was also the coordinator of the research methodology and statistics module of the coursework have obtained the necessary permission to use the data for this study. The scholars invited to be part of the study by e-mail. Knowledge and attitude towards statistics 2–3 years after the coursework were assessed online using Google forms. They were also administered a semi structured questionnaire to elicit details about the usefulness of coursework. Participation was voluntary, and consent was also sought online. The confidentiality of the data was assured. Data were not collected from research scholars of Biostatistics or from research scholars who had more than a decade of experience or who had been working in the institute as faculty, assuming that their scores could be higher and could bias the findings. This non funded study was reviewed and approved by the Institute Ethics Committee.

Instruments

Knowledge in Statistics was assessed by a questionnaire prepared by the author and was used as part of the coursework evaluation. The survey included 25 questions that assessed the knowledge of statistics on areas such as descriptive statistics, sampling methods, study design, parametric and nonparametric tests and multivariate analyses. Right answers were assigned a score of 1, and wrong answers were assigned a score of 0. Total scores ranged from 0 to 25. Statistics attitudes were assessed by the Survey of Attitudes toward Statistics (SATS) scale. The SATS is a 36-item scale that measures 6 domains of attitudes towards statistics. The possible range of scores for each item is between 1 and 7. The total score was calculated by dividing the summed score by the number of items. Higher scores indicate more positive attitudes. The SAT-36 is a copyrighted scale, and researchers are allowed to use it only with prior permission. ( 15 – 16 ) The author obtained permission for use in the coursework evaluation and this study. A semi structured questionnaire was also used to elicit details about the usefulness of coursework.

Statistical analysis

Descriptive statistics such as mean, standard deviation, number and percentages were used to describe the socio-demographic data. General Linear Model Repeated Measures of Analysis of variance was used to compare knowledge and attitude scores across assessments. Categorical data from the semi structured questionnaire are presented as percentages. All the statistical tests were two-tailed, and a p value < 0.05 was set a priori as the threshold for statistical significance. IBM SPSS (28.0) was used to analyse the data.

One hundred and thirty research scholars who had completed coursework (CW) in the last 2–3 years were considered for the study. These scholars were sent Google forms to assess their knowledge and attitudes 2–3 years after coursework. 81 scholars responded (62%), and 4 scholars did not consent to participate in the study. The data of 77 scholars were merged with the data obtained during the coursework program (before and soon after CW). Socio-demographic characteristics of the scholars are presented in Table  1 .

The age of the respondents ranged from 23 to 36 years, with an average of 28.7 years (3.01), and the majority of the respondents were females (65%). Years of experience (i.e., after masters) before joining a PhD programme ranged from 0.5 to 9 years, and half of them had less than three years of experience before joining the PhD programme (median-3). More than half of those who responded were research scholars from the behavioural sciences (55%), while approximately 30% were from the basic sciences (29%).

General Linear Model Repeated Measures of Analysis of variance was used to compare the knowledge and attitude scores of scholars before, soon after and 2–3 after the coursework (will now be referred as “later the CW”), and the results are presented below (Table  2 ; Fig.  1 ).

Comparison of knowledge and attitude scores across the assessments. Later the CW – 2–3 years after the coursework

The scores for knowledge and attitude differed significantly across time. Scores of knowledge and attitude increased soon after the coursework; the percentage of change was 77% and 43% respectively. However, significant reductions in knowledge and attitude scores were observed 2–3 years after the coursework compared to scores soon after the coursework. The reduction was higher for attitude scores; knowledge and attitude scores have decreased by 10% and 37% respectively. The change in scores across assessments is evident from the graph, and clearly the effect size is higher for attitude than knowledge.

The scores of knowledge or attitude before the coursework did not significantly differ with respect to gender or age or were not correlated with years of experience. Hence, they were not considered as covariates in the above analysis.

A semi structured questionnaire with open ended questions was also administered to elicit in-depth information about the usefulness of the coursework programme, in which they were also asked to self- rate their knowledge. The data were mostly categorical or narratives. Research scholars’ self-rated knowledge scores (on a scale of 0–10) also showed similar changes; knowledge improved significantly and was retained even after the training (Fig.  2 ).

Self-rated knowledge scores of research scholars over time. Later the CW – 2–3 years after the coursework

The response to the question “ How has coursework changed your attitude toward statistics?”, is presented in Fig.  3 . The responses were Yes, positively, Yes - Negatively, No change – still apprehensive, No change – still appreciate, No change – still hate statistics. The majority of the scholars (70%) reported a positive change in their attitude toward statistics. Moreover, none of the scholars reported negative changes. Approximately 9% of the scholars reported that they were still apprehensive about statistics or hate statistics after the coursework.

How has coursework changed your attitude toward statistics?

Those scholars who reported that they were apprehensive about statistics or hate statistics noted the complexity of the subject, lack of clarity, improper instructions and fear of mathematics as major reasons for their attitude. Some responses are listed below.

“The statistical concepts were not taught in an understandable manner from the UG level” , “I am weak in mathematical concepts. The equations and formulae in statistics scare me”. “Lack of knowledge about the importance of statistics and fear of mathematical equations”. “The preconceived notion that Statistics is difficult to learn” . “In most of the places, it is not taught properly and conceptual clarity is not focused on, and because of this an avoidance builds up, which might be a reason for the negative attitude”.

Majority of the scholars (92%) felt that coursework has helped them in their PhD, and they were happy to recommend it for other research scholars (97%). The responses of the scholars to the question “ How was coursework helpful in your PhD journey ?”, are listed below.

“Course work gave a fair idea on various things related to research as well as statistics” . “Creating the best design while planning methodology, which is learnt form course work, will increase efficiency in completing the thesis, thereby making it faster”. “Course work give better idea of how to proceed in many areas like literature search, referencing, choosing statistical methods, and learning about research procedures”. “Course work gave a good idea of research methodology, biostatistics and ethics. This would help in writing a better protocol and a better thesis”. “It helps us to plan our research well and to formulate, collect and plan for analysis”. “It makes people to plan their statistical analysis well in advance” .

This study evaluated the effectiveness of the existing coursework programme in an institution of higher medical education, and investigated whether the coursework programme benefits research scholars by improving their knowledge of statistics and attitudes towards statistics. The study concluded that the coursework program was beneficial for improving scholars’ knowledge about statistics and attitudes toward statistics.

Unlike other studies that have assessed attitudes toward statistics, the study participants in this study were research scholars. Research scholars need extensive training in statistics, as they need to apply statistical tests and use statistical reasoning in their research thesis, and in their profession to design research projects or their future student dissertations. Notably, no studies have assessed the attitudes or knowledge of research scholars in statistics either across the PhD training period or after intensive statistics training programs. However, the findings of this study are consistent with the findings of a study that compared the knowledge and attitudes toward teaching and education design of PhD students after a two-day educational course and instructional design workshop [ 14 ].

Statistics educators need not only impart knowledge but they should also motivate the learners to appreciate the role of statistics and to continue to learn the quantitative skills that is needed in their professional lives. Therefore, the role of learners’ attitudes toward statistics requires special attention. Since PhD coursework is possibly a major contributor to creating a statistically literate research community, scholars’ attitudes toward statistics need to be considered important and given special attention. Passionate and engaging statistics educators who have adequate experience in illustrating relatable examples could help scholars feel less anxious and build competence and better attitudes toward statistics. Statistics educators should be aware of scholars’ anxiety, fears and attitudes toward statistics and about its influence on learning outcomes and further interest in the subject.

Strengths and limitations

Analysis of changes in knowledge and attitudes scores across various time points of PhD training is the major strength of the study. Additionally, this study evaluates the effectiveness of intensive statistical courses for research scholars in terms of changes in knowledge and attitudes. This study has its own limitations: the data were collected through online platforms, and the nonresponse rate was about 38%. Ability in mathematics or prior learning experience in statistics, interest in the subject, statistics anxiety or performance in coursework were not assessed; hence, their influence could not be studied. The reliability and validity of the knowledge questionnaire have not been established at the time of this study. However, author who had prepared the questionnaire had ensured questions from different areas of statistics that were covered during the coursework, it has also been used as part of the coursework evaluation. Despite these limitations, this study highlights the changes in attitudes and knowledge following an intensive training program. Future research could investigate the roles of age, sex, mathematical ability, achievement or performance outcomes and statistics anxiety.

The study concluded that a rigorous and intensive training program such as PhD coursework was beneficial for improving knowledge about statistics and attitudes toward statistics. However, the significant reduction in attitude and knowledge scores after 2–3 years of coursework indicates that a refresher program might be helpful for research scholars as they approach the analysis stage of their thesis. Statistics educators must develop innovative methods to teach research scholars from nonstatistical backgrounds. They also must be empathetic to understanding scholars’ anxiety, fears and attitudes toward statistics and to understand its influence on learning outcomes and further interest in the subject.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The author would like to thank the participants of the study and peers and experts who examined the content of the questionnaire for their time and effort.

This research did not receive any grants from funding agencies in the public, commercial, or not-for-profit sectors.

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This study used data already collected data (before and soon after coursework). The data pertaining to knowledge and attitude towards statistics 2–3 years after coursework were collected from research scholars through the online survey platform Google forms. The participants were invited to participate in the survey through e-mail. The study was explained in detail, and participation in the study was completely voluntary. Informed consent was obtained online in the form of a statement of consent. The confidentiality of the data was assured, even though identifiable personal information was not collected. This non-funded study was reviewed and approved by NIMHANS Institute Ethics Committee (No. NIMHANS/21st IEC (BS&NS Div.)

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Philip, M. Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework. BMC Med Educ 24 , 512 (2024). https://doi.org/10.1186/s12909-024-05487-y

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Transparent and Scalable Knowledge-based Geospatial Mapping Systems for Trustworthy Urban Studies

This dissertation explores the integration of remote sensing and artificial intelligence (AI) in geospatial mapping, specifically through the development of knowledge-based mapping systems. Remote sensing has revolutionized Earth observation by providing data that far surpasses traditional in-situ measurements. Over the last decade, significant advancements in inferential capabilities have been achieved through the fusion of geospatial sciences and AI (GeoAI), particularly with the application of deep learning. Despite its benefits, the reliance on data-driven AI has introduced challenges, including unpredictable errors and biases due to imperfect labeling and the opaque nature of the processes involved.

The research highlights the limitations of solely using data-driven AI methods for geospatial mapping, which tend to produce spatially heterogeneous errors and lack transparency, thus compromising the trustworthiness of the outputs. In response, it proposes novel knowledge-based mapping systems that prioritize transparency and scalability. This research has developed comprehensive techniques to extract key Earth and urban features and has introduced a 3D urban land cover mapping system, including a 3D Landscape Clustering framework aimed at enhancing urban climate studies. The developed systems utilize universally applicable physical knowledge of targets, captured through remote sensing, to enhance mapping accuracy and reliability without the typical drawbacks of data-driven approaches.

The dissertation emphasizes the importance of moving beyond mere accuracy to consider the broader implications of error patterns in geospatial mappings. It demonstrates the value of integrating generalizable target knowledge, explicitly represented in remote sensing data, into geospatial mapping to address the trustworthiness challenges in AI mapping systems. By developing mapping systems that are open, transparent, and scalable, this work aims to mitigate the effects of spatially heterogeneous errors, thereby improving the trustworthiness of geospatial mapping and analysis across various fields. Additionally, the dissertation introduces methodologies to support urban pathway accessibility and flood management studies through dependable geospatial systems. These efforts aim to establish a robust foundation for informed urban planning, efficient resource allocation, and enriched environmental insights, contributing to the development of more sustainable, resilient, and smart cities.

HM157522D0009/HM157523F0135

Degree type.

• Doctor of Philosophy
• Civil Engineering

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Civil engineering not elsewhere classified
• Geoinformatics not elsewhere classified
• Urban and regional planning not elsewhere classified
• Other environmental sciences not elsewhere classified
• Artificial intelligence not elsewhere classified
• Image processing
• Computer vision

Research on Economic Operation Analysis Method of Aerospace Enterprises Based on Big Data

• Conference paper
• First Online: 11 May 2024
• Cite this conference paper

• Jia Yuxin 40 ,
• Zuo Bo 40 ,
• Zhang Hanjia 40 &
• Sun Yan 40  

Part of the book series: Lecture Notes in Electrical Engineering ((volume 1188))

Included in the following conference series:

• International Conference On Signal And Information Processing, Networking And Computers

This thesis summarizes the construction of a digital financial management platform in the operational management process of aerospace enterprises, covering comprehensive budgeting, contract management, and full life-cycle funding for projects, among other financial management tasks. By establishing a digital financial platform based on unified data sources, conducting in-depth data governance, and achieving interconnection and sharing of financial and business information, a management dashboard based on real-time data is created. Cost data is accurately delivered to different users through cost models, ensuring timely dissemination of financial information. By assetizing financial data and modeling business processes, standard tools and methods are provided for financial management in aerospace enterprises, ensuring that costs are known, visible, and controllable, and effectively improving the level of financial meticulous management.

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Ding, G., Song, D., Wang, Q.: Research on the construction of financial and economic information system based on business-finance integration-a case study of a certain institution. Manag. Adm. 1 (01), 106–110 (2021)

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China Academy of Space Technology, Beijing, 100094, China

Jia Yuxin, Zuo Bo, Zhang Hanjia & Sun Yan

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China Academy of Space Technology, Beijing, China

Beijing University of Posts and Telecommunications, Beijing, Beijing, China

China Unicom Research Institute, Beijing, Beijing, China

Zhilei Ling

Xinzhou Cheng

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Yuxin, J., Bo, Z., Hanjia, Z., Yan, S. (2024). Research on Economic Operation Analysis Method of Aerospace Enterprises Based on Big Data. In: Wang, Y., Zou, J., Xu, L., Ling, Z., Cheng, X. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2023. Lecture Notes in Electrical Engineering, vol 1188. Springer, Singapore. https://doi.org/10.1007/978-981-97-2124-5_11

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DOI : https://doi.org/10.1007/978-981-97-2124-5_11

Published : 11 May 2024

Publisher Name : Springer, Singapore

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Online ISBN : 978-981-97-2124-5

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