data analysis methods thesis

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. 

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?

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 .

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

2. Relevance of Collected 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.

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.

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.

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

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.

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

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. 

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

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. 

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. 

11. Connection with Literature Review

The role of data analytics at the senior management level.

From small and medium-sized businesses to Fortune 500 conglomerates, the success of a modern business is now increasingly tied to how the company implements its data infrastructure and data-based decision-making. According

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Any form of the systematic decision-making process is better enhanced with data. But making sense of big data or even small data analysis when venturing into a decision-making process might

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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.

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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How do I write a dissertation data analysis plan?

How do I do dissertation data analysis?

Data Analysis Plan Overview

Dissertation methodologies require a data analysis plan . Your dissertation data analysis plan should clearly state the statistical tests and assumptions of these tests to examine each of the research questions, how scores are cleaned and created, and the desired sample size for that test. The selection of statistical tests depend on two factors: (1) how the research questions and hypotheses are phrased and (2) the level of measurement of the variables. For example, if the question examines the impact of variable x on variable y, we are talking about regressions, if the question seeks associations or relationships, we are into correlation and chi-square tests, if differences are examined, then t-tests and ANOVA’s are likely the correct test.

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Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.

• Bring dissertation editing expertise to chapters 1-5 in timely manner.
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Level of Measurement

The level of measurement is the second factor used in selecting the correct statistical test. If the research question will examine the impact of X on Y variable, and that outcome variable Y is scale, a linear regression is the correct test. For example, what is the impact of Income on Savings (as a scale variable), the linear regression is the test.  If that outcome variable Y is ordinal, then an ordinal regression is the correct test (e.g., what is the impact of Income on Savings (with Savings as an ordinal $0-$100, $101-$1000, $1001-$10,000, variable), then an ordinal regression is the correct test. If the research question examines relationships, and the X and Y variable are categorical, then chi-square is the appropriate test. The main point is that both the phasing of the research question and the level of measurement of the variables dictate the selection of the test. This video on decision trees may be useful.

Statistical Assumptions in Data Analysis Plan

Part of the data analysis plan is to document the assumptions of a particular statistical test. Most assumptions fall into the normality, homogeneity of variance, and outlier bucket of assumptions. Other tests have additional assumptions. For example, in a linear regression with several predictors, the variance inflation factor needs to be assessed to determine that the predictors are not too highly correlated. This data analysis plan video may be helpful.

Composite Scores and Data Cleaning

Data analysis plans should discuss any reverse coding of the variables and the creation of composite or subscale scores. Before creating composite scores, alpha reliability should be planned to be examined. Data cleaning procedure should be documented.  For example, the removal of outliers, transforming variables to meet normality assumption, etc. 

Sample Size and Power Analysis

After selecting the appropriate statistical tests, data analysis plans should follow-up with a power analysis. The power analysis determines the sample size for a statistical test, given an alpha of .05, a given effect size (small, medium, or large) at a power of .80 (that is, an 80% chance of detecting differences or relationships if in fact difference are present in the data. This power analysis video may be helpful.

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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Have a question ?

Below we address some of the most popular questions we receive regarding our data analysis support, but feel free to get in touch if you have any other questions.

Dissertation Coaching

I have no idea where to start. can you help.

Absolutely. We regularly work with students who are completely new to data analysis (both qualitative and quantitative) and need step-by-step guidance to understand and interpret their data.

Can you analyse my data for me?

The short answer – no. 

The longer answer:

If you’re undertaking qualitative research , we can fast-track your project with our Qualitative Coding Service. With this service, we take care of the initial coding of your dataset (e.g., interview transcripts), providing a firm foundation on which you can build your qualitative analysis (e.g., thematic analysis, content analysis, etc.).

If you’re undertaking quantitative research , we can fast-track your project with our Statistical Testing Service . With this service, we run the relevant statistical tests using SPSS or R, and provide you with the raw outputs. You can then use these outputs/reports to interpret your results and develop your analysis.

Importantly, in both cases, we are not analysing the data for you or providing an interpretation or write-up for you. If you’d like coaching-based support with that aspect of the project, we can certainly assist you with this (i.e., provide guidance and feedback, review your writing, etc.). But it’s important to understand that you, as the researcher, need to engage with the data and write up your own findings. 

Can you help me choose the right data analysis methods?

Yes, we can assist you in selecting appropriate data analysis methods, based on your research aims and research questions, as well as the characteristics of your data.

Which data analysis methods can you assist with?

We can assist with most qualitative and quantitative analysis methods that are commonplace within the social sciences.

Qualitative methods:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Grounded theory

Quantitative methods:

• Descriptive statistics
• Inferential statistics

Can you provide data sets for me to analyse?

If you are undertaking secondary research , we can potentially assist you in finding suitable data sets for your analysis.

If you are undertaking primary research , we can help you plan and develop data collection instruments (e.g., surveys, questionnaires, etc.), but we cannot source the data on your behalf. 

Can you write the analysis/results/discussion chapter/section for me?

No. We can provide you with hands-on guidance through each step of the analysis process, but the writing needs to be your own. Writing anything for you would constitute academic misconduct .

Can you help me organise and structure my results/discussion chapter/section?

Yes, we can assist in structuring your chapter to ensure that you have a clear, logical structure and flow that delivers a clear and convincing narrative.

Can you review my writing and give me feedback?

Absolutely. Our Content Review service is designed exactly for this purpose and is one of the most popular services here at Grad Coach. In a Content Review, we carefully read through your research methodology chapter (or any other chapter) and provide detailed comments regarding the key issues/problem areas, why they’re problematic and what you can do to resolve the issues. You can learn more about Content Review here .

Do you provide software support (e.g., SPSS, R, etc.)?

It depends on the software package you’re planning to use, as well as the analysis techniques/tests you plan to undertake. We can typically provide support for the more popular analysis packages, but it’s best to discuss this in an initial consultation.

Can you help me with other aspects of my research project?

Yes. Data analysis support is only one aspect of our offering at Grad Coach, and we typically assist students throughout their entire dissertation/thesis/research project. You can learn more about our full service offering here .

Can I get a coach that specialises in my topic area?

It’s important to clarify that our expertise lies in the research process itself , rather than specific research areas/topics (e.g., psychology, management, etc.).

In other words, the support we provide is topic-agnostic, which allows us to support students across a very broad range of research topics. That said, if there is a coach on our team who has experience in your area of research, as well as your chosen methodology, we can allocate them to your project (dependent on their availability, of course).

If you’re unsure about whether we’re the right fit, feel free to drop us an email or book a free initial consultation.

What qualifications do your coaches have?

All of our coaches hold a doctoral-level degree (for example, a PhD, DBA, etc.). Moreover, they all have experience working within academia, in many cases as dissertation/thesis supervisors. In other words, they understand what markers are looking for when reviewing a student’s work.

Is my data/topic/study kept confidential?

Yes, we prioritise confidentiality and data security. Your written work and personal information are treated as strictly confidential. We can also sign a non-disclosure agreement, should you wish.

I still have questions…

No problem. Feel free to email us or book an initial consultation to discuss.

What our clients say

We've worked 1:1 with 3000+ students . Here's what some of them have to say:

David's depth of knowledge in research methodology was truly impressive. He demonstrated a profound understanding of the nuances and complexities of my research area, offering insights that I hadn't even considered. His ability to synthesize information, identify key research gaps, and suggest research topics was truly inspiring. I felt like I had a true expert by my side, guiding me through the complexities of the proposal.

Cyntia Sacani (US)

I had been struggling with the first 3 chapters of my dissertation for over a year. I finally decided to give GradCoach a try and it made a huge difference. Alexandra provided helpful suggestions along with edits that transformed my paper. My advisor was very impressed.

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Working with Kerryn has been brilliant. She has guided me through that pesky academic language that makes us all scratch our heads. I can't recommend Grad Coach highly enough; they are very professional, humble, and fun to work with. If like me, you know your subject matter but you're getting lost in the academic language, look no further, give them a go.

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Everything about my experience was great, from Dr. Shaeffer’s expertise, to her patience and flexibility. I reached out to GradCoach after receiving a 78 on a midterm paper. Not only did I get a 100 on my final paper in the same class, but I haven’t received a mark less than A+ since. I recommend GradCoach for everyone who needs help with academic research.

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I started using Grad Coach for my dissertation and I can honestly say that if it wasn’t for them, I would have really struggled. I would strongly recommend them – worth every penny!

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• Cookies & Privacy
• 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

CONSIDERATION ONE

The data analysis process.

The data analysis process involves three steps : (STEP ONE) select the correct statistical tests to run on your data; (STEP TWO) prepare and analyse the data you have collected using a relevant statistics package; and (STEP THREE) interpret the findings properly so that you can write up your results (i.e., usually in Chapter Four: Results ). The basic idea behind each of these steps is relatively straightforward, but the act of analysing your data (i.e., by selecting statistical tests, preparing your data and analysing it, and interpreting the findings from these tests) can be time consuming and challenging. We have tried to make this process as easy as possible by providing comprehensive, step-by-step guides in the Data Analysis part of Lærd Dissertation, but you should leave time at least one week to analyse your data.

STEP ONE Select the correct statistical tests to run on your data

It is common that dissertation students collect good data, but then report the wrong findings because of selecting the incorrect statistical tests to run in the first place. Selecting the correct statistical tests to perform on the data that you have collected will depend on (a) the research questions/hypotheses you have set, together with the research design you have adopted, and (b) the type and nature of your data:

The research questions/hypotheses you have set, together with the research design you have adopted

Your research questions/hypotheses and research design explain what variables you are measuring and how you plan to measure these variables. These highlight whether you want to (a) predict a score or a membership of a group, (b) find out differences between groups or treatments, or (c) explore associations/relationships between variables. These different aims determine the statistical tests that may be appropriate to run on your data. We highlight the word may because the most appropriate test that is identified based on your research questions/hypotheses and research design can change depending on the type and nature of the data you collect; something we discuss next.

The type and nature of the data you collected

Data is not all the same. As you will have identified by now, not all variables are measured in the same way; variables can be dichotomous, ordinal, or continuous. In addition, not all data is normal , as term we explain the Data Analysis section, nor is the data you have collected when comparing groups necessarily equal for each group. As a result, you might think that running a particular statistical test is correct (e.g., a dependent t-test), based on the research questions/hypotheses you have set, but the data you have collected fails certain assumptions that are important to this statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Mann-Whitney U instead of a dependent t-test).

To select the correct statistical tests to run on the data in your dissertation, we have created a Statistical Test Selector to help guide you through the various options.

STEP TWO Prepare and analyse your data using a relevant statistics package

The preparation and analysis of your data is actually a much more practical step than many students realise. Most of the time required to get the results that you will present in your write up (i.e., usually in Chapter Four: Results ) comes from knowing (a) how to enter data into a statistics package (e.g., SPSS) so that it can be analysed correctly, and (b) what buttons to press in the statistics package to correctly run the statistical tests you need:

Entering data is not just about knowing what buttons to press, but: (a) how to code your data correctly to recognise the types of variables that you have, as well as issues such as reverse coding ; (b) how to filter your dataset to take into account missing data and outliers ; (c) how to split files (i.e., in SPSS) when analysing the data for separate subgroups (e.g., males and females) using the same statistical tests; (d) how to weight and unweight data you have collected; and (e) other things you need to consider when entering data. What you have to do when it comes to entering data (i.e., in terms of coding, filtering, splitting files, and weighting/unweighting data) will depend on the statistical tests you plan to run. Therefore, entering data starts with using the Statistical Test Selector to help guide you through the various options. In the Data Analysis section, we help you to understand what you need to know about entering data in the context of your dissertation.

Running statistical tests

Statistics packages do the hard work of statistically analysing your data, but they rely on you making a number of choices. This is not simply about selecting the correct statistical test, but knowing, when you have selected a given test to run on your data, what buttons to press to: (a) test for the assumptions underlying the statistical test; (b) test whether corrections can be made when assumptions are violated ; (c) take into account outliers and missing data ; (d) choose between the different numerical and graphical ways to approach your analysis; and (e) other standard and more advanced tips. In the Data Analysis section, we explain what these considerations are (i.e., assumptions, corrections, outliers and missing data, numerical and graphical analysis) so that you can apply them to your own dissertation. We also provide comprehensive , step-by-step instructions with screenshots that show you how to enter data and run a wide range of statistical tests using the statistics package, SPSS. We do this on the basis that you probably have little or no knowledge of SPSS.

STEP THREE Interpret the findings properly

SPSS produces many tables of output for the typical tests you will run. In addition, SPSS has many new methods of presenting data using its Model viewer. You need to know which of these tables is important for your analysis and what the different figures/numbers mean. Interpreting these findings properly and communicating your results is one of the most important aspects of your dissertation. In the Data Analysis section, we show you how to understand these tables of output, what part of this output you need to look at, and how to write up the results in an appropriate format (i.e., so that you can answer you research hypotheses).

ON YOUR 1ST ORDER

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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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Data Analysis

Methodology chapter of your dissertation should include discussions about the methods of data analysis. You have to explain in a brief manner how you are going to analyze the primary data you will collect employing the methods explained in this chapter.

There are differences between qualitative data analysis and quantitative data analysis . In qualitative researches using interviews, focus groups, experiments etc. data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives.

Data analysis for quantitative studies, on the other hand, involves critical analysis and interpretation of figures and numbers, and attempts to find rationale behind the emergence of main findings. Comparisons of primary research findings to the findings of the literature review are critically important for both types of studies – qualitative and quantitative.

Data analysis methods in the absence of primary data collection can involve discussing common patterns, as well as, controversies within secondary data directly related to the research area.

John Dudovskiy

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

Decoding data is daunting and you might end up in confusion. Here’s where infographics come into the picture. With visuals, you can turn your data into easy-to-understand and dynamic stories that your audience can relate to. Mind the Graph is one such platform that helps scientists to explore a library of visuals and use them to amplify their research work. Sign up now to make your presentation simpler. 

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Data Analysis Techniques in Research – Methods, Tools & Examples

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Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

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

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Unlocking PhD Success: Quantitative Data Analysis Strategies for Your Dissertation Journey Dissertation in 90 Days

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The impact of evidence-based nursing leadership in healthcare settings: a mixed methods systematic review

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• Kirsi Hipp 1 , 4 ,
• Jaakko Varpula 1 ,
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The central component in impactful healthcare decisions is evidence. Understanding how nurse leaders use evidence in their own managerial decision making is still limited. This mixed methods systematic review aimed to examine how evidence is used to solve leadership problems and to describe the measured and perceived effects of evidence-based leadership on nurse leaders and their performance, organizational, and clinical outcomes.

We included articles using any type of research design. We referred nurses, nurse managers or other nursing staff working in a healthcare context when they attempt to influence the behavior of individuals or a group in an organization using an evidence-based approach. Seven databases were searched until 11 November 2021. JBI Critical Appraisal Checklist for Quasi-experimental studies, JBI Critical Appraisal Checklist for Case Series, Mixed Methods Appraisal Tool were used to evaluate the Risk of bias in quasi-experimental studies, case series, mixed methods studies, respectively. The JBI approach to mixed methods systematic reviews was followed, and a parallel-results convergent approach to synthesis and integration was adopted.

Thirty-one publications were eligible for the analysis: case series ( n  = 27), mixed methods studies ( n  = 3) and quasi-experimental studies ( n  = 1). All studies were included regardless of methodological quality. Leadership problems were related to the implementation of knowledge into practice, the quality of nursing care and the resource availability. Organizational data was used in 27 studies to understand leadership problems, scientific evidence from literature was sought in 26 studies, and stakeholders’ views were explored in 24 studies. Perceived and measured effects of evidence-based leadership focused on nurses’ performance, organizational outcomes, and clinical outcomes. Economic data were not available.

Conclusions

This is the first systematic review to examine how evidence is used to solve leadership problems and to describe its measured and perceived effects from different sites. Although a variety of perceptions and effects were identified on nurses’ performance as well as on organizational and clinical outcomes, available knowledge concerning evidence-based leadership is currently insufficient. Therefore, more high-quality research and clinical trial designs are still needed.

Trail registration

The study was registered (PROSPERO CRD42021259624).

Peer Review reports

Global health demands have set new roles for nurse leaders [ 1 ].Nurse leaders are referred to as nurses, nurse managers, or other nursing staff working in a healthcare context who attempt to influence the behavior of individuals or a group based on goals that are congruent with organizational goals [ 2 ]. They are seen as professionals “armed with data and evidence, and a commitment to mentorship and education”, and as a group in which “leaders innovate, transform, and achieve quality outcomes for patients, health care professionals, organizations, and communities” [ 3 ]. Effective leadership occurs when team members critically follow leaders and are motivated by a leader’s decisions based on the organization’s requests and targets [ 4 ]. On the other hand, problems caused by poor leadership may also occur, regarding staff relations, stress, sickness, or retention [ 5 ]. Therefore, leadership requires an understanding of different problems to be solved using synthesizing evidence from research, clinical expertise, and stakeholders’ preferences [ 6 , 7 ]. If based on evidence, leadership decisions, also referred as leadership decision making [ 8 ], could ensure adequate staffing [ 7 , 9 ] and to produce sufficient and cost-effective care [ 10 ]. However, nurse leaders still rely on their decision making on their personal [ 11 ] and professional experience [ 10 ] over research evidence, which can lead to deficiencies in the quality and safety of care delivery [ 12 , 13 , 14 ]. As all nurses should demonstrate leadership in their profession, their leadership competencies should be strengthened [ 15 ].

Evidence-informed decision-making, referred to as evidence appraisal and application, and evaluation of decisions [ 16 ], has been recognized as one of the core competencies for leaders [ 17 , 18 ]. The role of evidence in nurse leaders’ managerial decision making has been promoted by public authorities [ 19 , 20 , 21 ]. Evidence-based management, another concept related to evidence-based leadership, has been used as the potential to improve healthcare services [ 22 ]. It can guide nursing leaders, in developing working conditions, staff retention, implementation practices, strategic planning, patient care, and success of leadership [ 13 ]. Collins and Holton [ 23 ] in their systematic review and meta-analysis examined 83 studies regarding leadership development interventions. They found that leadership training can result in significant improvement in participants’ skills, especially in knowledge level, although the training effects varied across studies. Cummings et al. [ 24 ] reviewed 100 papers (93 studies) and concluded that participation in leadership interventions had a positive impact on the development of a variety of leadership styles. Clavijo-Chamorro et al. [ 25 ] in their review of 11 studies focused on leadership-related factors that facilitate evidence implementation: teamwork, organizational structures, and transformational leadership. The role of nurse managers was to facilitate evidence-based practices by transforming contexts to motivate the staff and move toward a shared vision of change.

As far as we are aware, however, only a few systematic reviews have focused on evidence-based leadership or related concepts in the healthcare context aiming to analyse how nurse leaders themselves uses evidence in the decision-making process. Young [ 26 ] targeted definitions and acceptance of evidence-based management (EBMgt) in healthcare while Hasanpoor et al. [ 22 ] identified facilitators and barriers, sources of evidence used, and the role of evidence in the process of decision making. Both these reviews concluded that EBMgt was of great importance but used limitedly in healthcare settings due to a lack of time, a lack of research management activities, and policy constraints. A review by Williams [ 27 ] showed that the usage of evidence to support management in decision making is marginal due to a shortage of relevant evidence. Fraser [ 28 ] in their review further indicated that the potential evidence-based knowledge is not used in decision making by leaders as effectively as it could be. Non-use of evidence occurs and leaders base their decisions mainly on single studies, real-world evidence, and experts’ opinions [ 29 ]. Systematic reviews and meta-analyses rarely provide evidence of management-related interventions [ 30 ]. Tate et al. [ 31 ] concluded based on their systematic review and meta-analysis that the ability of nurse leaders to use and critically appraise research evidence may influence the way policy is enacted and how resources and staff are used to meet certain objectives set by policy. This can further influence staff and workforce outcomes. It is therefore important that nurse leaders have the capacity and motivation to use the strongest evidence available to effect change and guide their decision making [ 27 ].

Despite of a growing body of evidence, we found only one review focusing on the impact of evidence-based knowledge. Geert et al. [ 32 ] reviewed literature from 2007 to 2016 to understand the elements of design, delivery, and evaluation of leadership development interventions that are the most reliably linked to outcomes at the level of the individual and the organization, and that are of most benefit to patients. The authors concluded that it is possible to improve individual-level outcomes among leaders, such as knowledge, motivation, skills, and behavior change using evidence-based approaches. Some of the most effective interventions included, for example, interactive workshops, coaching, action learning, and mentoring. However, these authors found limited research evidence describing how nurse leaders themselves use evidence to support their managerial decisions in nursing and what the outcomes are.

To fill the knowledge gap and compliment to existing knowledgebase, in this mixed methods review we aimed to (1) examine what leadership problems nurse leaders solve using an evidence-based approach and (2) how they use evidence to solve these problems. We also explored (3) the measured and (4) perceived effects of the evidence-based leadership approach in healthcare settings. Both qualitative and quantitative components of the effects of evidence-based leadership were examined to provide greater insights into the available literature [ 33 ]. Together with the evidence-based leadership approach, and its impact on nursing [ 34 , 35 ], this knowledge gained in this review can be used to inform clinical policy or organizational decisions [ 33 ]. The study is registered (PROSPERO CRD42021259624). The methods used in this review were specified in advance and documented in a priori in a published protocol [ 36 ]. Key terms of the review and the search terms are defined in Table  1 (population, intervention, comparison, outcomes, context, other).

In this review, we used a mixed methods approach [ 37 ]. A mixed methods systematic review was selected as this approach has the potential to produce direct relevance to policy makers and practitioners [ 38 ]. Johnson and Onwuegbuzie [ 39 ] have defined mixed methods research as “the class of research in which the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts or language into a single study.” Therefore, we combined quantitative and narrative analysis to appraise and synthesize empirical evidence, and we held them as equally important in informing clinical policy or organizational decisions [ 34 ]. In this review, a comprehensive synthesis of quantitative and qualitative data was performed first and then discussed in discussion part (parallel-results convergent design) [ 40 ]. We hoped that different type of analysis approaches could complement each other and deeper picture of the topic in line with our research questions could be gained [ 34 ].

Inclusion and exclusion criteria

Inclusion and exclusion criteria of the study are described in Table  1 .

Search strategy

A three-step search strategy was utilized. First, an initial limited search with #MEDLINE was undertaken, followed by analysis of the words used in the title, abstract, and the article’s key index terms. Second, the search strategy, including identified keywords and index terms, was adapted for each included data base and a second search was undertaken on 11 November 2021. The full search strategy for each database is described in Additional file 1 . Third, the reference list of all studies included in the review were screened for additional studies. No year limits or language restrictions were used.

Information sources

The database search included the following: CINAHL (EBSCO), Cochrane Library (academic database for medicine and health science and nursing), Embase (Elsevier), PsycINFO (EBSCO), PubMed (MEDLINE), Scopus (Elsevier) and Web of Science (academic database across all scientific and technical disciplines, ranging from medicine and social sciences to arts and humanities). These databases were selected as they represent typical databases in health care context. Subject headings from each of the databases were included in the search strategies. Boolean operators ‘AND’ and ‘OR’ were used to combine the search terms. An information specialist from the University of Turku Library was consulted in the formation of the search strategies.

Study selection

All identified citations were collated and uploaded into Covidence software (Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia www.covidence.org ), and duplicates were removed by the software. Titles and abstracts were screened and assessed against the inclusion criteria independently by two reviewers out of four, and any discrepancies were resolved by the third reviewer (MV, KH, TL, WC). Studies meeting the inclusion criteria were retrieved in full and archived in Covidence. Access to one full-text article was lacking: the authors for one study were contacted about the missing full text, but no full text was received. All remaining hits of the included studies were retrieved and assessed independently against the inclusion criteria by two independent reviewers of four (MV, KH, TL, WC). Studies that did not meet the inclusion criteria were excluded, and the reasons for exclusion were recorded in Covidence. Any disagreements that arose between the reviewers were resolved through discussions with XL.

Assessment of methodological quality

Eligible studies were critically appraised by two independent reviewers (YT, SH). Standardized critical appraisal instruments based on the study design were used. First, quasi-experimental studies were assessed using the JBI Critical Appraisal Checklist for Quasi-experimental studies [ 44 ]. Second, case series were assessed using the JBI Critical Appraisal Checklist for Case Series [ 45 ]. Third, mixed methods studies were appraised using the Mixed Methods Appraisal Tool [ 46 ].

To increase inter-reviewer reliability, the review agreement was calculated (SH) [ 47 ]. A kappa greater than 0.8 was considered to represent a high level of agreement (0–0.1). In our data, the agreement was 0.75. Discrepancies raised between two reviewers were resolved through discussion and modifications and confirmed by XL. As an outcome, studies that met the inclusion criteria were proceeded to critical appraisal and assessed as suitable for inclusion in the review. The scores for each item and overall critical appraisal scores were presented.

Data extraction

For data extraction, specific tables were created. First, study characteristics (author(s), year, country, design, number of participants, setting) were extracted by two authors independently (JC, MV) and reviewed by TL. Second, descriptions of the interventions were extracted by two reviewers (JV, JC) using the structure of the TIDIeR (Template for Intervention Description and Replication) checklist (brief name, the goal of the intervention, material and procedure, models of delivery and location, dose, modification, adherence and fidelity) [ 48 ]. The extractions were confirmed (MV).

Third, due to a lack of effectiveness data and a wide heterogeneity between study designs and presentation of outcomes, no attempt was made to pool the quantitative data statistically; the findings of the quantitative data were presented in narrative form only [ 44 ]. The separate data extraction tables for each research question were designed specifically for this study. For both qualitative (and a qualitative component of mixed-method studies) and quantitative studies, the data were extracted and tabulated into text format according to preplanned research questions [ 36 ]. To test the quality of the tables and the data extraction process, three authors independently extracted the data from the first five studies (in alphabetical order). After that, the authors came together to share and determine whether their approaches of the data extraction were consistent with each other’s output and whether the content of each table was in line with research question. No reason was found to modify the data extraction tables or planned process. After a consensus of the data extraction process was reached, the data were extracted in pairs by independent reviewers (WC, TY, SH, GL). Any disagreements that arose between the reviewers were resolved through discussion and with a third reviewer (MV).

Data analysis

We were not able to conduct a meta-analysis due to a lack of effectiveness data based on clinical trials. Instead, we used inductive thematic analysis with constant comparison to answer the research question [ 46 , 49 ] using tabulated primary data from qualitative and quantitative studies as reported by the original authors in narrative form only [ 47 ]. In addition, the qualitizing process was used to transform quantitative data to qualitative data; this helped us to convert the whole data into themes and categories. After that we used the thematic analysis for the narrative data as follows. First, the text was carefully read, line by line, to reveal topics answering each specific review question (MV). Second, the data coding was conducted, and the themes in the data were formed by data categorization. The process of deriving the themes was inductive based on constant comparison [ 49 ]. The results of thematic analysis and data categorization was first described in narrative format and then the total number of studies was calculated where the specific category was identified (%).

Stakeholder involvement

The method of reporting stakeholders’ involvement follows the key components by [ 50 ]: (1) people involved, (2) geographical location, (3) how people were recruited, (4) format of involvement, (5) amount of involvement, (6) ethical approval, (7) financial compensation, and (8) methods for reporting involvement.

In our review, stakeholder involvement targeted nurses and nurse leader in China. Nurse Directors of two hospitals recommended potential participants who received a personal invitation letter from researchers to participate in a discussion meeting. Stakeholders’ participation was based on their own free will. Due to COVID-19, one online meeting (1 h) was organized (25 May 2022). Eleven participants joined the meeting. Ethical approval was not applied and no financial compensation was offered. At the end of the meeting, experiences of stakeholders’ involvement were explored.

The meeting started with an introductory presentation with power points. The rationale, methods, and preliminary review results were shared with the participants [ 51 ].The meeting continued with general questions for the participants: (1) Are you aware of the concepts of evidence-based practice or evidence-based leadership?; (2) How important is it to use evidence to support decisions among nurse leaders?; (3) How is the evidence-based approach used in hospital settings?; and (4) What type of evidence is currently used to support nurse leaders’ decision making (e.g. scientific literature, organizational data, stakeholder views)?

Two people took notes on the course and content of the conversation. The notes were later transcripted in verbatim, and the key points of the discussions were summarised. Although answers offered by the stakeholders were very short, the information was useful to validate the preliminary content of the results, add the rigorousness of the review, and obtain additional perspectives. A recommendation of the stakeholders was combined in the Discussion part of this review increasing the applicability of the review in the real world [ 50 ]. At the end of the discussion, the value of stakeholders’ involvement was asked. Participants shared that the experience of participating was unique and the topic of discussion was challenging. Two authors of the review group further represented stakeholders by working together with the research team throughout the review study.

Search results

From seven different electronic databases, 6053 citations were identified as being potentially relevant to the review. Then, 3133 duplicates were removed by an automation tool (Covidence: www.covidence.org ), and one was removed manually. The titles and abstracts of 3040 of citations were reviewed, and a total of 110 full texts were included (one extra citation was found on the reference list but later excluded). Based on the eligibility criteria, 31 studies (32 hits) were critically appraised and deemed suitable for inclusion in the review. The search results and selection process are presented in the PRISMA [ 52 ] flow diagram Fig.  1 . The full list of references for included studies can be find in Additional file 2 . To avoid confusion between articles of the reference list and studies included in the analysis, the studies included in the review are referred inside the article using the reference number of each study (e.g. ref 1, ref 2).

Search results and study selection and inclusion process [ 52 ]

Characteristics of included studies

The studies had multiple purposes, aiming to develop practice, implement a new approach, improve quality, or to develop a model. The 31 studies (across 32 hits) were case series studies ( n  = 27), mixed methods studies ( n  = 3) and a quasi-experimental study ( n  = 1). All studies were published between the years 2004 and 2021. The highest number of papers was published in year 2020.

Table  2 describes the characteristics of included studies and Additional file 3 offers a narrative description of the studies.

Methodological quality assessment

Quasi-experimental studies.

We had one quasi-experimental study (ref 31). All questions in the critical appraisal tool were applicable. The total score of the study was 8 (out of a possible 9). Only one response of the tool was ‘no’ because no control group was used in the study (see Additional file 4 for the critical appraisal of included studies).

Case series studies . A case series study is typically defined as a collection of subjects with common characteristics. The studies do not include a comparison group and are often based on prevalent cases and on a sample of convenience [ 53 ]. Munn et al. [ 45 ] further claim that case series are best described as observational studies, lacking experimental and randomized characteristics, being descriptive studies, without a control or comparator group. Out of 27 case series studies included in our review, the critical appraisal scores varied from 1 to 9. Five references were conference abstracts with empirical study results, which were scored from 1 to 3. Full reports of these studies were searched in electronic databases but not found. Critical appraisal scores for the remaining 22 studies ranged from 1 to 9 out of a possible score of 10. One question (Q3) was not applicable to 13 studies: “Were valid methods used for identification of the condition for all participants included in the case series?” Only two studies had clearly reported the demographic of the participants in the study (Q6). Twenty studies met Criteria 8 (“Were the outcomes or follow-up results of cases clearly reported?”) and 18 studies met Criteria 7 (“Q7: Was there clear reporting of clinical information of the participants?”) (see Additional file 4 for the critical appraisal of included studies).

Mixed-methods studies

Mixed-methods studies involve a combination of qualitative and quantitative methods. This is a common design and includes convergent design, sequential explanatory design, and sequential exploratory design [ 46 ]. There were three mixed-methods studies. The critical appraisal scores for the three studies ranged from 60 to 100% out of a possible 100%. Two studies met all the criteria, while one study fulfilled 60% of the scored criteria due to a lack of information to understand the relevance of the sampling strategy well enough to address the research question (Q4.1) or to determine whether the risk of nonresponse bias was low (Q4.4) (see Additional file 4 for the critical appraisal of included studies).

Intervention or program components

The intervention of program components were categorized and described using the TiDier checklist: name and goal, theory or background, material, procedure, provider, models of delivery, location, dose, modification, and adherence and fidelity [ 48 ]. A description of intervention in each study is described in Additional file 5 and a narrative description in Additional file 6 .

Leadership problems

In line with the inclusion criteria, data for the leadership problems were categorized in all 31 included studies (see Additional file 7 for leadership problems). Three types of leadership problems were identified: implementation of knowledge into practice, the quality of clinical care, and resources in nursing care. A narrative summary of the results is reported below.

Implementing knowledge into practice

Eleven studies (35%) aimed to solve leadership problems related to implementation of knowledge into practice. Studies showed how to support nurses in evidence-based implementation (EBP) (ref 3, ref 5), how to engage nurses in using evidence in practice (ref 4), how to convey the importance of EBP (ref 22) or how to change practice (ref 4). Other problems were how to facilitate nurses to use guideline recommendations (ref 7) and how nurses can make evidence-informed decisions (ref 8). General concerns also included the linkage between theory and practice (ref 1) as well as how to implement the EBP model in practice (ref 6). In addition, studies were motivated by the need for revisions or updates of protocols to improve clinical practice (ref 10) as well as the need to standardize nursing activities (ref 11, ref 14).

The quality of the care

Thirteen (42%) focused on solving problems related to the quality of clinical care. In these studies, a high number of catheter infections led a lack of achievement of organizational goals (ref 2, ref 9). A need to reduce patient symptoms in stem cell transplant patients undergoing high-dose chemotherapy (ref 24) was also one of the problems to be solved. In addition, the projects focused on how to prevent pressure ulcers (ref 26, ref 29), how to enhance the quality of cancer treatment (ref 25) and how to reduce the need for invasive constipation treatment (ref 30). Concerns about patient safety (ref 15), high fall rates (ref 16, ref 19), dissatisfaction of patients (ref 16, ref 18) and nurses (ref 16, ref 30) were also problems that had initiated the projects. Studies addressed concerns about how to promote good contingency care in residential aged care homes (ref 20) and about how to increase recognition of human trafficking problems in healthcare (ref 21).

Resources in nursing care

Nurse leaders identified problems in their resources, especially in staffing problems. These problems were identified in seven studies (23%), which involved concerns about how to prevent nurses from leaving the job (ref 31), how to ensure appropriate recruitment, staffing and retaining of nurses (ref 13) and how to decrease nurses’ burden and time spent on nursing activities (ref 12). Leadership turnover was also reported as a source of dissatisfaction (ref 17); studies addressed a lack of structured transition and training programs, which led to turnover (ref 23), as well as how to improve intershift handoff among nurses (ref 28). Optimal design for new hospitals was also examined (ref 27).

Main features of evidence-based leadership

Out of 31 studies, 17 (55%) included all four domains of an evidence-based leadership approach, and four studies (13%) included evidence of critical appraisal of the results (see Additional file 8 for the main features of evidence-based Leadership) (ref 11, ref 14, ref 23, ref 27).

Organizational evidence

Twenty-seven studies (87%) reported how organizational evidence was collected and used to solve leadership problems (ref 2). Retrospective chart reviews (ref 5), a review of the extent of specific incidents (ref 19), and chart auditing (ref 7, ref 25) were conducted. A gap between guideline recommendations and actual care was identified using organizational data (ref 7) while the percentage of nurses’ working time spent on patient care was analyzed using an electronic charting system (ref 12). Internal data (ref 22), institutional data, and programming metrics were also analyzed to understand the development of the nurse workforce (ref 13).

Surveys (ref 3, ref 25), interviews (ref 3, ref 25) and group reviews (ref 18) were used to better understand the leadership problem to be solved. Employee opinion surveys on leadership (ref 17), a nurse satisfaction survey (ref 30) and a variety of reporting templates were used for the data collection (ref 28) reported. Sometimes, leadership problems were identified by evidence facilitators or a PI’s team who worked with staff members (ref 15, ref 17). Problems in clinical practice were also identified by the Nursing Professional Council (ref 14), managers (ref 26) or nurses themselves (ref 24). Current practices were reviewed (ref 29) and a gap analysis was conducted (ref 4, ref 16, ref 23) together with SWOT analysis (ref 16). In addition, hospital mission and vision statements, research culture established and the proportion of nursing alumni with formal EBP training were analyzed (ref 5). On the other hand, it was stated that no systematic hospital-specific sources of data regarding job satisfaction or organizational commitment were used (ref 31). In addition, statements of organizational analysis were used on a general level only (ref 1).

Scientific evidence identified

Twenty-six studies (84%) reported the use of scientific evidence in their evidence-based leadership processes. A literature search was conducted (ref 21) and questions, PICO, and keywords were identified (ref 4) in collaboration with a librarian. Electronic databases, including PubMed (ref 14, ref 31), Cochrane, and EMBASE (ref 31) were searched. Galiano (ref 6) used Wiley Online Library, Elsevier, CINAHL, Health Source: Nursing/Academic Edition, PubMed, and the Cochrane Library while Hoke (ref 11) conducted an electronic search using CINAHL and PubMed to retrieve articles.

Identified journals were reviewed manually (ref 31). The findings were summarized using ‘elevator speech’ (ref 4). In a study by Gifford et al. (ref 9) evidence facilitators worked with participants to access, appraise, and adapt the research evidence to the organizational context. Ostaszkiewicz (ref 20) conducted a scoping review of literature and identified and reviewed frameworks and policy documents about the topic and the quality standards. Further, a team of nursing administrators, directors, staff nurses, and a patient representative reviewed the literature and made recommendations for practice changes.

Clinical practice guidelines were also used to offer scientific evidence (ref 7, ref 19). Evidence was further retrieved from a combination of nursing policies, guidelines, journal articles, and textbooks (ref 12) as well as from published guidelines and literature (ref 13). Internal evidence, professional practice knowledge, relevant theories and models were synthesized (ref 24) while other study (ref 25) reviewed individual studies, synthesized with systematic reviews or clinical practice guidelines. The team reviewed the research evidence (ref 3, ref 15) or conducted a literature review (ref 22, ref 28, ref 29), a literature search (ref 27), a systematic review (ref 23), a review of the literature (ref 30) or ‘the scholarly literature was reviewed’ (ref 18). In addition, ‘an extensive literature review of evidence-based best practices was carried out’ (ref 10). However, detailed description how the review was conducted was lacking.

Views of stakeholders

A total of 24 studies (77%) reported methods for how the views of stakeholders, i.e., professionals or experts, were considered. Support to run this study was received from nursing leadership and multidisciplinary teams (ref 29). Experts and stakeholders joined the study team in some cases (ref 25, ref 30), and in other studies, their opinions were sought to facilitate project success (ref 3). Sometimes a steering committee was formed by a Chief Nursing Officer and Clinical Practice Specialists (ref 2). More specifically, stakeholders’ views were considered using interviews, workshops and follow-up teleconferences (ref 7). The literature review was discussed with colleagues (ref 11), and feedback and support from physicians as well as the consensus of staff were sought (ref 16).

A summary of the project findings and suggestions for the studies were discussed at 90-minute weekly meetings by 11 charge nurses. Nurse executive directors were consulted over a 10-week period (ref 31). An implementation team (nurse, dietician, physiotherapist, occupational therapist) was formed to support the implementation of evidence-based prevention measures (ref 26). Stakeholders volunteered to join in the pilot implementation (ref 28) or a stakeholder team met to determine the best strategy for change management, shortcomings in evidence-based criteria were discussed, and strategies to address those areas were planned (ref 5). Nursing leaders, staff members (ref 22), ‘process owners (ref 18) and program team members (ref 18, ref 19, ref 24) met regularly to discuss the problems. Critical input was sought from clinical educators, physicians, nutritionists, pharmacists, and nurse managers (ref 24). The unit director and senior nursing staff reviewed the contents of the product, and the final version of clinical pathways were reviewed and approved by the Quality Control Commission of the Nursing Department (ref 12). In addition, two co-design workshops with 18 residential aged care stakeholders were organized to explore their perspectives about factors to include in a model prototype (ref 20). Further, an agreement of stakeholders in implementing continuous quality services within an open relationship was conducted (ref 1).

Critical appraisal

In five studies (16%), a critical appraisal targeting the literature search was carried out. The appraisals were conducted by interns and teams who critiqued the evidence (ref 4). In Hoke’s study, four areas that had emerged in the literature were critically reviewed (ref 11). Other methods were to ‘critically appraise the search results’ (ref 14). Journal club team meetings (ref 23) were organized to grade the level and quality of evidence and the team ‘critically appraised relevant evidence’ (ref 27). On the other hand, the studies lacked details of how the appraisals were done in each study.

The perceived effects of evidence-based leadership

Perceived effects of evidence-based leadership on nurses’ performance.

Eleven studies (35%) described perceived effects of evidence-based leadership on nurses’ performance (see Additional file 9 for perceived effects of evidence-based leadership), which were categorized in four groups: awareness and knowledge, competence, ability to understand patients’ needs, and engagement. First, regarding ‘awareness and knowledge’, different projects provided nurses with new learning opportunities (ref 3). Staff’s knowledge (ref 20, ref 28), skills, and education levels improved (ref 20), as did nurses’ knowledge comprehension (ref 21). Second, interventions and approaches focusing on management and leadership positively influenced participants’ competence level to improve the quality of services. Their confidence level (ref 1) and motivation to change practice increased, self-esteem improved, and they were more positive and enthusiastic in their work (ref 22). Third, some nurses were relieved that they had learned to better handle patients’ needs (ref 25). For example, a systematic work approach increased nurses’ awareness of the patients who were at risk of developing health problems (ref 26). And last, nurse leaders were more engaged with staff, encouraging them to adopt the new practices and recognizing their efforts to change (ref 8).

Perceived effects on organizational outcomes

Nine studies (29%) described the perceived effects of evidence-based leadership on organizational outcomes (see Additional file 9 for perceived effects of evidence-based leadership). These were categorized into three groups: use of resources, staff commitment, and team effort. First, more appropriate use of resources was reported (ref 15, ref 20), and working time was more efficiently used (ref 16). In generally, a structured approach made implementing change more manageable (ref 1). On the other hand, in the beginning of the change process, the feedback from nurses was unfavorable, and they experienced discomfort in the new work style (ref 29). New approaches were also perceived as time consuming (ref 3). Second, nurse leaders believed that fewer nursing staff than expected left the organization over the course of the study (ref 31). Third, the project helped staff in their efforts to make changes, and it validated the importance of working as a team (ref 7). Collaboration and support between the nurses increased (ref 26). On the other hand, new work style caused challenges in teamwork (ref 3).

Perceived effects on clinical outcomes

Five studies (16%) reported the perceived effects of evidence-based leadership on clinical outcomes (see Additional file 9 for perceived effects of evidence-based leadership), which were categorized in two groups: general patient outcomes and specific clinical outcomes. First, in general, the project assisted in connecting the guideline recommendations and patient outcomes (ref 7). The project was good for the patients in general, and especially to improve patient safety (ref 16). On the other hand, some nurses thought that the new working style did not work at all for patients (ref 28). Second, the new approach used assisted in optimizing patients’ clinical problems and person-centered care (ref 20). Bowel management, for example, received very good feedback (ref 30).

The measured effects of evidence-based leadership

The measured effects on nurses’ performance.

Data were obtained from 20 studies (65%) (see Additional file 10 for measured effects of evidence-based leadership) and categorized nurse performance outcomes for three groups: awareness and knowledge, engagement, and satisfaction. First, six studies (19%) measured the awareness and knowledge levels of participants. Internship for staff nurses was beneficial to help participants to understand the process for using evidence-based practice and to grow professionally, to stimulate for innovative thinking, to give knowledge needed to use evidence-based practice to answer clinical questions, and to make possible to complete an evidence-based practice project (ref 3). Regarding implementation program of evidence-based practice, those with formal EBP training showed an improvement in knowledge, attitude, confidence, awareness and application after intervention (ref 3, ref 11, ref 20, ref 23, ref 25). On the contrary, in other study, attitude towards EBP remained stable ( p  = 0.543). and those who applied EBP decreased although no significant differences over the years ( p  = 0.879) (ref 6).

Second, 10 studies (35%) described nurses’ engagement to new practices (ref 5, ref 6, ref 7, ref 10, ref 16, ref 17, ref 18, ref 21, ref 25, ref 27). 9 studies (29%) studies reported that there was an improvement of compliance level of participants (ref 6, ref 7, ref 10, ref 16, ref 17, ref 18, ref 21, ref 25, ref 27). On the contrary, in DeLeskey’s (ref 5) study, although improvement was found in post-operative nausea and vomiting’s (PONV) risk factors documented’ (2.5–63%), and ’risk factors communicated among anaesthesia and surgical staff’ (0–62%), the improvement did not achieve the goal. The reason was a limited improvement was analysed. It was noted that only those patients who had been seen by the pre-admission testing nurse had risk assessments completed. Appropriate treatment/prophylaxis increased from 69 to 77%, and from 30 to 49%; routine assessment for PONV/rescue treatment 97% and 100% was both at 100% following the project. The results were discussed with staff but further reasons for a lack of engagement in nursing care was not reported.

And third, six studies (19%) reported nurses’ satisfaction with project outcomes. The study results showed that using evidence in managerial decisions improved nurses’ satisfaction and attitudes toward their organization ( P  < 0.05) (ref 31). Nurses’ overall job satisfaction improved as well (ref 17). Nurses’ satisfaction with usability of the electronic charting system significantly improved after introduction of the intervention (ref 12). In handoff project in seven hospitals, improvement was reported in all satisfaction indicators used in the study although improvement level varied in different units (ref 28). In addition, positive changes were reported in nurses’ ability to autonomously perform their job (“How satisfied are you with the tools and resources available for you treat and prevent patient constipation?” (54%, n  = 17 vs. 92%, n  = 35, p  < 0.001) (ref 30).

The measured effects on organizational outcomes

Thirteen studies (42%) described the effects of a project on organizational outcomes (see Additional file 10 for measured effects of evidence-based leadership), which were categorized in two groups: staff compliance, and changes in practices. First, studies reported improved organizational outcomes due to staff better compliance in care (ref 4, ref 13, ref 17, ref 23, ref 27, ref 31). Second, changes in organization practices were also described (ref 11) like changes in patient documentation (ref 12, ref 21). Van Orne (ref 30) found a statistically significant reduction in the average rate of invasive medication administration between pre-intervention and post-intervention ( p  = 0.01). Salvador (ref 24) also reported an improvement in a proactive approach to mucositis prevention with an evidence-based oral care guide. On the contrary, concerns were also raised such as not enough time for new bedside report (ref 16) or a lack of improvement of assessment of diabetic ulcer (ref 8).

The measured effects on clinical outcomes

A variety of improvements in clinical outcomes were reported (see Additional file 10 for measured effects of evidence-based leadership): improvement in patient clinical status and satisfaction level. First, a variety of improvement in patient clinical status was reported. improvement in Incidence of CAUTI decreased 27.8% between 2015 and 2019 (ref 2) while a patient-centered quality improvement project reduced CAUTI rates to 0 (ref 10). A significant decrease in transmission rate of MRSA transmission was also reported (ref 27) and in other study incidences of CLABSIs dropped following of CHG bathing (ref 14). Further, it was possible to decrease patient nausea from 18 to 5% and vomiting to 0% (ref 5) while the percentage of patients who left the hospital without being seen was below 2% after the project (ref 17). In addition, a significant reduction in the prevalence of pressure ulcers was found (ref 26, ref 29) and a significant reduction of mucositis severity/distress was achieved (ref 24). Patient falls rate decreased (ref 15, ref 16, ref 19, ref 27).

Second, patient satisfaction level after project implementation improved (ref 28). The scale assessing healthcare providers by consumers showed improvement, but the changes were not statistically significant. Improvement in an emergency department leadership model and in methods of communication with patients improved patient satisfaction scores by 600% (ref 17). In addition, new evidence-based unit improved patient experiences about the unit although not all items improved significantly (ref 18).

Stakeholder involvement in the mixed-method review

To ensure stakeholders’ involvement in the review, the real-world relevance of our research [ 53 ], achieve a higher level of meaning in our review results, and gain new perspectives on our preliminary findings [ 50 ], a meeting with 11 stakeholders was organized. First, we asked if participants were aware of the concepts of evidence-based practice or evidence-based leadership. Responses revealed that participants were familiar with the concept of evidence-based practice, but the topic of evidence-based leadership was totally new. Examples of nurses and nurse leaders’ responses are as follows: “I have heard a concept of evidence-based practice but never a concept of evidence-based leadership.” Another participant described: “I have heard it [evidence-based leadership] but I do not understand what it means.”

Second, as stakeholder involvement is beneficial to the relevance and impact of health research [ 54 ], we asked how important evidence is to them in supporting decisions in health care services. One participant described as follows: “Using evidence in decisions is crucial to the wards and also to the entire hospital.” Third, we asked how the evidence-based approach is used in hospital settings. Participants expressed that literature is commonly used to solve clinical problems in patient care but not to solve leadership problems. “In [patient] medication and care, clinical guidelines are regularly used. However, I am aware only a few cases where evidence has been sought to solve leadership problems.”

And last, we asked what type of evidence is currently used to support nurse leaders’ decision making (e.g. scientific literature, organizational data, stakeholder views)? The participants were aware that different types of information were collected in their organization on a daily basis (e.g. patient satisfaction surveys). However, the information was seldom used to support decision making because nurse leaders did not know how to access this information. Even so, the participants agreed that the use of evidence from different sources was important in approaching any leadership or managerial problems in the organization. Participants also suggested that all nurse leaders should receive systematic training related to the topic; this could support the daily use of the evidence-based approach.

To our knowledge, this article represents the first mixed-methods systematic review to examine leadership problems, how evidence is used to solve these problems and what the perceived and measured effects of evidence-based leadership are on nurse leaders and their performance, organizational, and clinical outcomes. This review has two key findings. First, the available research data suggests that evidence-based leadership has potential in the healthcare context, not only to improve knowledge and skills among nurses, but also to improve organizational outcomes and the quality of patient care. Second, remarkably little published research was found to explore the effects of evidence-based leadership with an efficient trial design. We validated the preliminary results with nurse stakeholders, and confirmed that nursing staff, especially nurse leaders, were not familiar with the concept of evidence-based leadership, nor were they used to implementing evidence into their leadership decisions. Our data was based on many databases, and we screened a large number of studies. We also checked existing registers and databases and found no registered or ongoing similar reviews being conducted. Therefore, our results may not change in the near future.

We found that after identifying the leadership problems, 26 (84%) studies out of 31 used organizational data, 25 (81%) studies used scientific evidence from the literature, and 21 (68%) studies considered the views of stakeholders in attempting to understand specific leadership problems more deeply. However, only four studies critically appraised any of these findings. Considering previous critical statements of nurse leaders’ use of evidence in their decision making [ 14 , 30 , 31 , 34 , 55 ], our results are still quite promising.

Our results support a previous systematic review by Geert et al. [ 32 ], which concluded that it is possible to improve leaders’ individual-level outcomes, such as knowledge, motivation, skills, and behavior change using evidence-based approaches. Collins and Holton [ 23 ] particularly found that leadership training resulted in significant knowledge and skill improvements, although the effects varied widely across studies. In our study, evidence-based leadership was seen to enable changes in clinical practice, especially in patient care. On the other hand, we understand that not all efforts to changes were successful [ 56 , 57 , 58 ]. An evidence-based approach causes negative attitudes and feelings. Negative emotions in participants have also been reported due to changes, such as discomfort with a new working style [ 59 ]. Another study reported inconvenience in using a new intervention and its potential risks for patient confidentiality. Sometimes making changes is more time consuming than continuing with current practice [ 60 ]. These findings may partially explain why new interventions or program do not always fully achieve their goals. On the other hand, Dubose et al. [ 61 ] state that, if prepared with knowledge of resistance, nurse leaders could minimize the potential negative consequences and capitalize on a powerful impact of change adaptation.

We found that only six studies used a specific model or theory to understand the mechanism of change that could guide leadership practices. Participants’ reactions to new approaches may be an important factor in predicting how a new intervention will be implemented into clinical practice. Therefore, stronger effort should be put to better understanding the use of evidence, how participants’ reactions and emotions or practice changes could be predicted or supported using appropriate models or theories, and how using these models are linked with leadership outcomes. In this task, nurse leaders have an important role. At the same time, more responsibilities in developing health services have been put on the shoulders of nurse leaders who may already be suffering under pressure and increased burden at work. Working in a leadership position may also lead to role conflict. A study by Lalleman et al. [ 62 ] found that nurses were used to helping other people, often in ad hoc situations. The helping attitude of nurses combined with structured managerial role may cause dilemmas, which may lead to stress. Many nurse leaders opt to leave their positions less than 5 years [ 63 ].To better fulfill the requirements of health services in the future, the role of nurse leaders in evidence-based leadership needs to be developed further to avoid ethical and practical dilemmas in their leadership practices.

It is worth noting that the perceived and measured effects did not offer strong support to each other but rather opened a new venue to understand the evidence-based leadership. Specifically, the perceived effects did not support to measured effects (competence, ability to understand patients’ needs, use of resources, team effort, and specific clinical outcomes) while the measured effects could not support to perceived effects (nurse’s performance satisfaction, changes in practices, and clinical outcomes satisfaction). These findings may indicate that different outcomes appear if the effects of evidence-based leadership are looked at using different methodological approach. Future study is encouraged using well-designed study method including mixed-method study to examine the consistency between perceived and measured effects of evidence-based leadership in health care.

There is a potential in nursing to support change by demonstrating conceptual and operational commitment to research-based practices [ 64 ]. Nurse leaders are well positioned to influence and lead professional governance, quality improvement, service transformation, change and shared governance [ 65 ]. In this task, evidence-based leadership could be a key in solving deficiencies in the quality, safety of care [ 14 ] and inefficiencies in healthcare delivery [ 12 , 13 ]. As WHO has revealed, there are about 28 million nurses worldwide, and the demand of nurses will put nurse resources into the specific spotlight [ 1 ]. Indeed, evidence could be used to find solutions for how to solve economic deficits or other problems using leadership skills. This is important as, when nurses are able to show leadership and control in their own work, they are less likely to leave their jobs [ 66 ]. On the other hand, based on our discussions with stakeholders, nurse leaders are not used to using evidence in their own work. Further, evidence-based leadership is not possible if nurse leaders do not have access to a relevant, robust body of evidence, adequate funding, resources, and organizational support, and evidence-informed decision making may only offer short-term solutions [ 55 ]. We still believe that implementing evidence-based strategies into the work of nurse leaders may create opportunities to protect this critical workforce from burnout or leaving the field [ 67 ]. However, the role of the evidence-based approach for nurse leaders in solving these problems is still a key question.

Limitations

This study aimed to use a broad search strategy to ensure a comprehensive review but, nevertheless, limitations exist: we may have missed studies not included in the major international databases. To keep search results manageable, we did not use specific databases to systematically search grey literature although it is a rich source of evidence used in systematic reviews and meta-analysis [ 68 ]. We still included published conference abstract/proceedings, which appeared in our scientific databases. It has been stated that conference abstracts and proceedings with empirical study results make up a great part of studies cited in systematic reviews [ 69 ]. At the same time, a limited space reserved for published conference publications can lead to methodological issues reducing the validity of the review results [ 68 ]. We also found that the great number of studies were carried out in western countries, restricting the generalizability of the results outside of English language countries. The study interventions and outcomes were too different across studies to be meaningfully pooled using statistical methods. Thus, our narrative synthesis could hypothetically be biased. To increase transparency of the data and all decisions made, the data, its categorization and conclusions are based on original studies and presented in separate tables and can be found in Additional files. Regarding a methodological approach [ 34 ], we used a mixed methods systematic review, with the core intention of combining quantitative and qualitative data from primary studies. The aim was to create a breadth and depth of understanding that could confirm to or dispute evidence and ultimately answer the review question posed [ 34 , 70 ]. Although the method is gaining traction due to its usefulness and practicality, guidance in combining quantitative and qualitative data in mixed methods systematic reviews is still limited at the theoretical stage [ 40 ]. As an outcome, it could be argued that other methodologies, for example, an integrative review, could have been used in our review to combine diverse methodologies [ 71 ]. We still believe that the results of this mixed method review may have an added value when compared with previous systematic reviews concerning leadership and an evidence-based approach.

Our mixed methods review fills the gap regarding how nurse leaders themselves use evidence to guide their leadership role and what the measured and perceived impact of evidence-based leadership is in nursing. Although the scarcity of controlled studies on this topic is concerning, the available research data suggest that evidence-based leadership intervention can improve nurse performance, organizational outcomes, and patient outcomes. Leadership problems are also well recognized in healthcare settings. More knowledge and a deeper understanding of the role of nurse leaders, and how they can use evidence in their own managerial leadership decisions, is still needed. Despite the limited number of studies, we assume that this narrative synthesis can provide a good foundation for how to develop evidence-based leadership in the future.

Implications

Based on our review results, several implications can be recommended. First, the future of nursing success depends on knowledgeable, capable, and strong leaders. Therefore, nurse leaders worldwide need to be educated about the best ways to manage challenging situations in healthcare contexts using an evidence-based approach in their decisions. This recommendation was also proposed by nurses and nurse leaders during our discussion meeting with stakeholders.

Second, curriculums in educational organizations and on-the-job training for nurse leaders should be updated to support general understanding how to use evidence in leadership decisions. And third, patients and family members should be more involved in the evidence-based approach. It is therefore important that nurse leaders learn how patients’ and family members’ views as stakeholders are better considered as part of the evidence-based leadership approach.

Future studies should be prioritized as follows: establishment of clear parameters for what constitutes and measures evidence-based leadership; use of theories or models in research to inform mechanisms how to effectively change the practice; conducting robust effectiveness studies using trial designs to evaluate the impact of evidence-based leadership; studying the role of patient and family members in improving the quality of clinical care; and investigating the financial impact of the use of evidence-based leadership approach within respective healthcare systems.

Data availability

The authors obtained all data for this review from published manuscripts.

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Acknowledgements

We want to thank the funding bodies, the Finnish National Agency of Education, Asia Programme, the Department of Nursing Science at the University of Turku, and Xiangya School of Nursing at the Central South University. We also would like to thank the nurses and nurse leaders for their valuable opinions on the topic.

The work was supported by the Finnish National Agency of Education, Asia Programme (grant number 26/270/2020) and the University of Turku (internal fund 26003424). The funders had no role in the study design and will not have any role during its execution, analysis, interpretation of the data, decision to publish, or preparation of the manuscript.

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Maritta Välimäki, Tella Lantta, Kirsi Hipp & Jaakko Varpula

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Xiangya Nursing, School of Central South University, Changsha, 410013, China

Shuang Hu, Jiarui Chen, Yao Tang, Wenjun Chen & Xianhong Li

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Contributions

Study design: MV, XL. Literature search and study selection: MV, KH, TL, WC, XL. Quality assessment: YT, SH, XL. Data extraction: JC, MV, JV, WC, YT, SH, GL. Analysis and interpretation: MV, SH. Manuscript writing: MV. Critical revisions for important intellectual content: MV, XL. All authors read and approved the final manuscript.

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Differences between the original protocol

We modified criteria for the included studies: we included published conference abstracts/proceedings, which form a relatively broad knowledge base in scientific knowledge. We originally planned to conduct a survey with open-ended questions followed by a face-to-face meeting to discuss the preliminary results of the review. However, to avoid extra burden in nurses due to COVID-19, we decided to limit the validation process to the online discussion only.

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Välimäki, M., Hu, S., Lantta, T. et al. The impact of evidence-based nursing leadership in healthcare settings: a mixed methods systematic review. BMC Nurs 23 , 452 (2024). https://doi.org/10.1186/s12912-024-02096-4

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DOI : https://doi.org/10.1186/s12912-024-02096-4

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A ship energy consumption prediction method based on tgma model and feature selection.

1. Introduction

2.1. temporal convolutional network, 2.1.1. causal convolution, 2.1.2. dilated convolution, 2.1.3. residual connection, 2.2. gated recurrent unit, 2.3. multi-head self-attention mechanism, 2.4. maximum information coefficient, 3. data analysis and feature selection, 3.1. data acquisition and processing, 3.2. autocorrelation analysis of energy consumption data, 3.3. correlation analysis and feature selection, 4. energy consumption prediction model establishment and analysis, 4.1. prediction model establishment.

• Acquisition of general parameters and fuel consumption data during the operation of the ship and the surrounding environmental parameters during the voyage;
• Examine the distribution and spatial distribution characteristics of the data and perform a correlation analysis between the input features and the ship energy consumption data in order to select the input features for the model;
• The pre-processed features and ship energy consumption data are input into the TGMA model, and the ship energy consumption data at future moments are obtained by learning the features from the training set.

4.2. Validation of the TGMA model

4.3. comparison of different prediction models, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Liu, Y.; Wang, K.; Lu, Y.; Zhang, Y.; Li, Z.; Ma, R.; Huang, L. A Ship Energy Consumption Prediction Method Based on TGMA Model and Feature Selection. J. Mar. Sci. Eng. 2024 , 12 , 1098. https://doi.org/10.3390/jmse12071098

Liu Y, Wang K, Lu Y, Zhang Y, Li Z, Ma R, Huang L. A Ship Energy Consumption Prediction Method Based on TGMA Model and Feature Selection. Journal of Marine Science and Engineering . 2024; 12(7):1098. https://doi.org/10.3390/jmse12071098

Liu, Yuhang, Kai Wang, Yong Lu, Yongfeng Zhang, Zhongwei Li, Ranqi Ma, and Lianzhong Huang. 2024. "A Ship Energy Consumption Prediction Method Based on TGMA Model and Feature Selection" Journal of Marine Science and Engineering 12, no. 7: 1098. https://doi.org/10.3390/jmse12071098

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