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Getting to the main article
Choosing your route
Setting research questions/ hypotheses
Building the theoretical case
Setting your research strategy
Data analysis techniques
In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.
The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.
We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).
At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:
REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process
This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.
REASON B It takes time to get your head around data analysis
When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.
Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .
11 Tips For Writing a Dissertation Data Analysis
Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation.
Considering the complexity of many data analysis projects, it becomes challenging to get precise results if analysts are not familiar with data analysis tools and tests properly. The analysis is a time-taking process that starts with collecting valid and relevant data and ends with the demonstration of error-free results.
So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!
What is Data Analysis in Dissertation?
Dissertation Data Analysis is the process of understanding, gathering, compiling, and processing a large amount of data. Then identifying common patterns in responses and critically examining facts and figures to find the rationale behind those outcomes.
Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.
Data Analysis Tools
There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests used to perform analysis of data leading to a scientific conclusion:
11 Most Useful Tips for Dissertation Data Analysis
Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.
1. Dissertation Data Analysis Services
The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.
Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.
One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .
For a proper dissertation data analysis, the student should have a clear understanding and statistical knowledge. Through this knowledge and experience, a student can perform dissertation analysis on their own.
Following are some helpful tips for writing a splendid dissertation data analysis:
2. Relevance of Collected Data
It involves data collection of your related topic for research. Carefully analyze the data that tends to be suitable for your analysis. Do not just go with irrelevant data leading to complications in the results. Your data must be relevant and fit with your objectives. You must be aware of how the data is going to help in analysis.
If the data is irrelevant and not appropriate, you might get distracted from the point of focus. To show the reader that you can critically solve the problem, make sure that you write a theoretical proposition regarding the selection and analysis of data.
3. Data Analysis
For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.
Data analysis involves two approaches – Qualitative Data Analysis and Quantitative Data Analysis. Qualitative data analysis comprises research through experiments, focus groups, and interviews. This approach helps to achieve the objectives by identifying and analyzing common patterns obtained from responses.
On the other hand, quantitative analysis refers to the analysis and interpretation of facts and figures – to build reasoning behind the advent of primary findings. An assessment of the main results and the literature review plays a pivotal role in qualitative and quantitative analysis.
The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly. It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal.
4. Qualitative Data Analysis
Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.
Presenting qualitative data analysis in a dissertation can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.
Qualitative Data Analysis Methods
Following are the methods used to perform quantitative data analysis.
- Deductive Method
This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.
- Inductive Method
In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.
5. Quantitative Data Analysis
Quantitative data contains facts and figures obtained from scientific research and requires extensive statistical analysis. After collection and analysis, you will be able to conclude. Generic outcomes can be accepted beyond the sample by assuming that it is representative – one of the preliminary checkpoints to carry out in your analysis to a larger group. This method is also referred to as the “scientific method”, gaining its roots from natural sciences.
The Presentation of quantitative data depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for hard sciences might require numeric inputs and statistics. As for natural sciences , such comprehensive analysis is not required.
Quantitative Analysis Methods
Following are some of the methods used to perform quantitative data analysis.
- Trend analysis: This corresponds to a statistical analysis approach to look at the trend of quantitative data collected over a considerable period.
- Cross-tabulation: This method uses a tabula way to draw readings among data sets in research.
- Conjoint analysis : Quantitative data analysis method that can collect and analyze advanced measures. These measures provide a thorough vision about purchasing decisions and the most importantly, marked parameters.
- TURF analysis: This approach assesses the total market reach of a service or product or a mix of both.
- Gap analysis: It utilizes the side-by-side matrix to portray quantitative data, which captures the difference between the actual and expected performance.
- Text analysis: In this method, innovative tools enumerate open-ended data into easily understandable data.
6. Data Presentation Tools
Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs.
Tables help in presenting both qualitative and quantitative data concisely. While presenting data, always keep your reader in mind. Anything clear to you may not be apparent to your reader. So, constantly rethink whether your data presentation method is understandable to someone less conversant with your research and findings. If the answer is “No”, you may need to rethink your Presentation.
7. Include Appendix or Addendum
After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part.
The data you find hard to arrange within the text, include that in the appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation.
8. Thoroughness of Data
It is a common misconception that the data presented is self-explanatory. Most of the students provide the data and quotes and think that it is enough and explaining everything. It is not sufficient. Rather than just quoting everything, you should analyze and identify which data you will use to approve or disapprove your standpoints.
Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.
9. Discussing Data
Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale.
It also involves answering what you are trying to do with the data and how you have structured your findings. Once you have presented the results, the reader will be looking for interpretation. Hence, it is essential to deliver the understanding as soon as you have submitted your data.
10. Findings and Results
Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation.
In the finding part, you should tell the reader what they are looking for. There should be no suspense for the reader as it would divert their attention. State your findings clearly and concisely so that they can get the idea of what is more to come in your dissertation.
11. Connection with Literature Review
At the ending of your data analysis in the dissertation, make sure to compare your data with other published research. In this way, you can identify the points of differences and agreements. Check the consistency of your findings if they meet your expectations—lookup for bottleneck position. Analyze and discuss the reasons behind it. Identify the key themes, gaps, and the relation of your findings with the literature review. In short, you should link your data with your research question, and the questions should form a basis for literature.
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Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.
In this article, we thoroughly looked at the tips that prove valuable for writing a data analysis in a dissertation. Make sure to give this article a thorough read before you write data analysis in the dissertation leading to the successful future of your research.
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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 ).
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 .
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.
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 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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Dissertations 4: methodology: methods.
- Introduction & Philosophy
Primary & Secondary Sources, Primary & Secondary Data
When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.
There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:
Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.
Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123).
Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316).
Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).
Comparison between primary and secondary data
Virtually all research will use secondary sources, at least as background information.
Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'.
The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.
Ultimately, you should state in this section of the methodology:
What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis.
If using primary data, why you employed certain strategies to collect them.
What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature).
Quantitative, Qualitative & Mixed Methods
The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages.
Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496).
Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.
Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.
When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138).
Ultimately, your methodology chapter should state:
Whether you used quantitative research, qualitative research or mixed methods.
Why you chose such methods (and refer to research method sources).
Why you rejected other methods.
How well the method served your research.
The problems or limitations you encountered.
Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:
LinkedIn Learning Video on Academic Research Foundations: Quantitative
The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.
Link to quantitative research video
Some Types of Methods
There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis.
Whatever methods you will use, you will need to consider:
why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose?
what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?)
ethical considerations (see also tab...)
procedure of the research (see box procedural method...).
Check Stella Cottrell's book Dissertations and Project Reports: A Step by Step Guide for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.
Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations.
For more information on Scientific Method, click here .
Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.
Questionnaires and surveys
Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements.
Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142).
This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods.
In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views.
This video focuses on strategies for conducting research using focus groups.
Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box.
Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.
Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.
Extra links and resources:
A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection.
Doing your dissertation during the COVID-19 pandemic
Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts;
- Virtual Focus Groups Guidance on managing virtual focus groups
5 Minute Methods Videos
The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication.
Case Study Research
Quantitative Content Analysis
Qualitative Content Analysis
Social Media Research
Mixed Method Research
In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!).
Include specifics about participants, sample, materials, design and methods.
If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.
Describe all materials used for the study, including equipment, written materials and testing instruments.
Identify the study's design and any variables or controls employed.
Write out the steps in the order that they were completed.
Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected.
Specify statistical techniques applied to the data to reach your conclusions.
Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design.
Highlight any drawbacks that may have limited your ability to conduct your research thoroughly.
You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research.
Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.
Lombard, E. (2010). Primary and secondary sources. The Journal of Academic Librarianship , 36(3), 250-253
Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015). Research Methods for Business Students. New York: Pearson Education.
Specht, D. (2019). The Media And Communications Study Skills Student Guide . London: University of Westminster Press.
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Dissertation survival guide: methodology & data analysis.
- Choosing a topic
- Literature searching
- Evaluating your sources
- Methodology & Data Analysis
- Posters and presentations
Decide on your methodology
Writing a dissertation usually involves doing some original research. This may use qualitative methods such as interviews, or quantitative methods such as surveys. What method is most suitable for you will depend on what you need to find out.
We have lots of books (print and online) on research methods, so don’t just stick to the items on your reading lists. See below for some selected titles that are available from the library. You could also talk to your supervisor or academic skills tutors about suitable methodology in your subject area.
Design your research tools
Next, you need to design your research tools before collecting your data.
Try searching Summon for topics such as "qualitative research methods", "quantitative research methods", "survey design", and more for ideas of how you could collect data for your research. Or, see some recommended books below.
There are also lots of videos and courses on LinkedIn Learning to help you learn about research methods. Try this for example: Quantitative vs. qualitative research .
- Qualtrics If you plan to use a survey to collect data for your dissertation, all staff and students have access to Qualtrics, an online survey service. When accessing Qualtrics for the first time, select "No, I don't have a preexisting account here". You can then create an account using your University of Huddersfield email address.
Recommended books on research methodology
Start analysing your results.
So, you have your data, but what does it mean? This is where you put YOUR data into the context of the literature you’ve already found. There are various tools available from the University that can help you analyse what you have found.
- Data analysis software There are three software packages available to download for home use from the link above: - Nvivo: qualitative data analysis tool. Used for transcribing and analysing interviews and other qualitative data. - SPSS and Minitab, both statistical analysis packages that are most helpful for analysing quantitative data.
- LinkedIn Learning If you need help using any of these software packages, search for courses and videos in LinkedIn Learning that can help you get started.
- Digital Skills training For help, either in a group or one-to-one training, contact the Digital Skills team.
If you've used a qualitative method such as interviewing, you will need to transcribe these to analyse them. Manually transcribing interviews can be a long and laborious process. There are three tools available to you as Huddersfield students that can help speed up the process.
Remember, they are not perfect and the accuracy will depend on the quality of your audio recordings. Recent feedback from students suggests that they are between 85% - 95% accurate so some editing will be required. Unfortunately no automatic transcription tool is 100% accurate.
- Word on the Web Available as part of your Office 365 package, Word Online's transcribe feature converts speech (recorded directly in Word or from an uploaded audio file) to a text transcript. After your conversation, interview, or meeting, you can revisit parts of the recording by playing back the timestamped audio and edit the transcription to make corrections. You can save the full transcript as a Word document or insert snippets of it into existing documents.
- Otter.ai: Otter Voice Notes This tool can be accessed online with your University email account. There is also an app available for apple and android devices. Transcripts can be downloaded as a text file and opened in Word. The standard account allows for up to 600 minutes a month of transcription for free.
- MS Stream This is also available as part of your Office 365 package. You can upload as many videos as you like. Captions can be automatically created, edited within Streams and then downloaded as a transcript and opened in Word. Stream is idea for producing transcripts of video recordings.
For more information, please see the Transcribe Audio and Subtitle Video pages on Brightspace. For help and advice, please ask [email protected] .
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- How to Write a Results Section | Tips & Examples
How to Write a Results Section | Tips & Examples
Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.
A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .
Table of contents
How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.
When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.
Here are a few best practices:
- Your results should always be written in the past tense.
- While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
- Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
- If you have other results you’d like to include, consider adding them to an appendix or footnotes.
- Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.
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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .
Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.
The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:
- A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
- A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
- A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion and conclusion.
A note on tables and figures
In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .
As a rule of thumb:
- Tables are used to communicate exact values, giving a concise overview of various results
- Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings
Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.
A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.
Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.
In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.
For each theme, start with general observations about what the data showed. You can mention:
- Recurring points of agreement or disagreement
- Patterns and trends
- Particularly significant snippets from individual responses
Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .
When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:
“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”
Responses suggest that video game consumers consider some types of games to have more artistic potential than others.
Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.
It should not speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .
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I have completed my data collection and analyzed the results.
I have included all results that are relevant to my research questions.
I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .
I have stated whether each hypothesis was supported or refuted.
I have used tables and figures to illustrate my results where appropriate.
All tables and figures are correctly labelled and referred to in the text.
There is no subjective interpretation or speculation on the meaning of the results.
You've finished writing up your results! Use the other checklists to further improve your thesis.
If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!
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The results chapter of a thesis or dissertation presents your research results concisely and objectively.
In quantitative research , for each question or hypothesis , state:
- The type of analysis used
- Relevant results in the form of descriptive and inferential statistics
- Whether or not the alternative hypothesis was supported
In qualitative research , for each question or theme, describe:
- Recurring patterns
- Significant or representative individual responses
- Relevant quotations from the data
Don’t interpret or speculate in the results chapter.
Results are usually written in the past tense , because they are describing the outcome of completed actions.
The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.
In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.
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What is Data Analysis?
Types of Data Analysis
Data analysis methods.
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs
Descriptive analysis involves summarizing and describing the main features of a dataset. It focuses on organizing and presenting the data in a meaningful way, often using measures such as mean, median, mode, and standard deviation. It provides an overview of the data and helps identify patterns or trends.
Inferential analysis aims to make inferences or predictions about a larger population based on sample data. It involves applying statistical techniques such as hypothesis testing, confidence intervals, and regression analysis. It helps generalize findings from a sample to a larger population.
Exploratory Data Analysis (EDA)
EDA focuses on exploring and understanding the data without preconceived hypotheses. It involves visualizations, summary statistics, and data profiling techniques to uncover patterns, relationships, and interesting features. It helps generate hypotheses for further analysis.
Diagnostic analysis aims to understand the cause-and-effect relationships within the data. It investigates the factors or variables that contribute to specific outcomes or behaviors. Techniques such as regression analysis, ANOVA (Analysis of Variance), or correlation analysis are commonly used in diagnostic analysis.
Predictive analysis involves using historical data to make predictions or forecasts about future outcomes. It utilizes statistical modeling techniques, machine learning algorithms, and time series analysis to identify patterns and build predictive models. It is often used for forecasting sales, predicting customer behavior, or estimating risk.
Prescriptive analysis goes beyond predictive analysis by recommending actions or decisions based on the predictions. It combines historical data, optimization algorithms, and business rules to provide actionable insights and optimize outcomes. It helps in decision-making and resource allocation.
Qualitative Data Analysis
The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:
- Content Analysis, for analyzing behavioral and verbal data.
- Narrative Analysis, for working with data culled from interviews, diaries, and surveys.
- Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.
Quantitative Data Analysis
Also known as statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:
- Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
- Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
- Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body.
- What is data visualization
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Data visualization is commonly used for idea generation, and illustration, so as to help teams and individuals convey data more effectively to colleagues and decision-makers. Frequently used types of data visualizations are listed in the following tabs.
A bar chart is one of the most commonly used forms to present quantitative data. It is simple to create and to understand. It is best used when comparing data from different categories. A bar chart is simple: We usually have a few values – ordered as categories on the x or y axis. Then we have the values expressed as bars (horizontal) or columns (vertical). The extent of the bars is the value.
Bar Chart of Race & Ethnicity in New York (2015)
Datawheel, CC0, via Wikimedia Commons
A pie chart is used to display the proportions of a whole. These charts are useful for percentages. When making a pie chart, please note:
- All portions should add up to a total of 100%.
- Sizes of the portions should represent their value.
- Not too many variables
A line chart is a type of chart used to show information that changes over time. We plot line charts using several points connected by straight lines. The line chart comprises two axes known as the 'x' axis and 'y' axis. The horizontal axis is known as the x-axis.
A scatter plot is a type of plot or diagram to display values for typically two variables for a set of data.
Scatter plots show whether there is a relationship between two variables. The trend line shows the central tendency of the data.
Yi, M. (2019). A complete guide to scatter plots. Retrieved February , 25 , 2021.
A box plot or boxplot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending from the boxes indicating variability outside the upper and lower quartiles.
Michelson experiment (1881)
Data Analysis and Visualization Resources and Tools
The Research Data Services department assists with:
- Open-access tools in analyzing and visualizing data.
- Identify what types of visualization best fit your needs
- Provide tips on creating your visualization
- Offer guidance on interpreting data analysis results
Other Resources on Campus
- Department of Sociology Statistics Tutoring Lab
- The Data Analytics and Research Methodology (DARM) service in the Division of Research also provides faculty with up to six hours of free consulting and may access additional hours through contract consulting: see more information here .
- T he Collaborative Learning Center (CLC) : please check the math & statistics tutoring schedule here .
- Data Visualization : Schwabish, J. (2021). Better data visualizations: A guide for scholars, researchers, and wonks . Columbia University Press. https://www.jstor.org/stable/10.7312/schw19310
- Data Analysis : Ross, S. M. (2017). Introductory statistics . Academic Press. The course textbook is available for download free of charge from OpenStax at https://openstax.org/details/introductory-statistics .
What is R? What is RStudio?
R is more of a programming language than just a statistics program. it is “a language for data analysis and graphics.” You can use R to create, import, and scrape data from the web; clean and reshape it; visualize it; run statistical analysis and modeling operations on it; text and data mine it; and much more.
RStudio is a user interface for working with R. It is called an Integrated Development Environment (IDE): a piece of software that provides tools to make programming easier. RStudio acts as a sort of wrapper around the R language.
Install R and RStudio
R and RStudio are two separate pieces of software:
- R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis
- RStudio is an integrated development environment (IDE) that makes using R easier. In this course we use RStudio to interact with R.
- Download R from the CRAN website .
- Run the .exe file that was just downloaded
- Go to the RStudio download page
- Under Installers select RStudio x.yy.zzz - Windows Vista/7/8/10 (where x, y, and z represent version numbers)
- Double click the file to install it
- Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.
- Select the .pkg file for the latest R version
- Double click on the downloaded file to install R
- It is also a good idea to install XQuartz (needed by some packages)
- Under Installers select RStudio x.yy.zzz - Mac OS X 10.6+ (64-bit) (where x, y, and z represent version numbers)
- Double click the file to install RStudio
Open Access e-book: Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations . " O'Reilly Media, Inc.".
Python is a high-level , general-purpose programming language . Its design philosophy emphasizes code readability with the use of significant indentation . Python is dynamically typed and garbage-collected . It supports multiple programming paradigms , including structured (particularly procedural ), object-oriented and functional programming . It is often described as a "batteries included" language due to its comprehensive standard library .
- from WIKIPedia
NVivo is a software program used for qualitative and mixed-methods research . Specifically, it is used for the analysis of unstructured text, audio, video, and image data, including (but not limited to) interviews, focus groups, surveys, social media, and journal articles
With Tableau, users can upload data from spreadsheets, cloud-based data management software, and online databases and merge them to identify trends, filter databases, and forecast outcomes. Users also can drag and drop information to transform data and instantly create charts and visualizations. Start your free trial of Tableau here .
Microsoft’s Power BI software provides business intelligence and data analytics tools to clean and transform data, merge data from different sources, and perform grouping, clustering, and forecasting to find patterns in the data. Start Power BI for free here .
Google’s free Charts software creates customizable charts, maps, and diagrams from imported datasets.
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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.
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Make sense of the data
If you’ve collected your data, but are feeling confused about what to do and how to make sense of it all, we can help. One of our friendly coaches will hold your hand through each step and help you interpret your dataset .
Alternatively, if you’re still planning your data collection and analysis strategy, we can help you craft a rock-solid methodology that sets you up for success.
Get your thinking onto paper
If you’ve analysed your data, but are struggling to get your thoughts onto paper, one of our friendly Grad Coaches can help you structure your results and/or discussion chapter to kickstart your writing.
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If you’ve already written up your results but need a second set of eyes, our popular Content Review service can help you identify and address key issues within your writing, before you submit it for grading .
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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.
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 content analysis
- Thematic analysis
- Discourse analysis
- Narrative analysis
- Grounded theory
- 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.
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Writing a Dissertation Data Analysis the Right Way
Do you want to be a college professor? Most teaching positions at four-year universities and colleges require the applicants to have at least a doctoral degree in the field they wish to teach in. If you are looking for information about the dissertation data analysis, it means you have already started working on yours. Congratulations!
Truth be told, learning how to write a data analysis the right way can be tricky. This is, after all, one of the most important chapters of your paper. It is also the most difficult to write, unfortunately. The good news is that we will help you with all the information you need to write a good data analysis chapter right now. And remember, if you need an original dissertation data analysis example, our PhD experts can write one for you in record time. You’ll be amazed how much you can learn from a well-written example.
OK, But What Is the Data Analysis Section?
Don’t know what the data analysis section is or what it is used for? No problem, we’ll explain it to you. Understanding the data analysis meaning is crucial to understanding the next sections of this blog post.
Basically, the data analysis section is the part where you analyze and discuss the data you’ve uncovered. In a typical dissertation, you will present your findings (the data) in the Results section. You will explain how you obtained the data in the Methodology chapter.
The data analysis section should be reserved just for discussing your findings. This means you should refrain from introducing any new data in there. This is extremely important because it can get your paper penalized quite harshly. Remember, the evaluation committee will look at your data analysis section very closely. It’s extremely important to get this chapter done right.
Learn What to Include in Data Analysis
Don’t know what to include in data analysis? Whether you need to do a quantitative data analysis or analyze qualitative data, you need to get it right. Learning how to analyze research data is extremely important, and so is learning what you need to include in your analysis. Here are the basic parts that should mandatorily be in your dissertation data analysis structure:
- The chapter should start with a brief overview of the problem. You will need to explain the importance of your research and its purpose. Also, you will need to provide a brief explanation of the various types of data and the methods you’ve used to collect said data. In case you’ve made any assumptions, you should list them as well.
- The next part will include detailed descriptions of each and every one of your hypotheses. Alternatively, you can describe the research questions. In any case, this part of the data analysis chapter will make it clear to your readers what you aim to demonstrate.
- Then, you will introduce and discuss each and every piece of important data. Your aim is to demonstrate that your data supports your thesis (or answers an important research question). Go in as much detail as possible when analyzing the data. Each question should be discussed in a single paragraph and the paragraph should contain a conclusion at the end.
- The very last part of the data analysis chapter that an undergraduate must write is the conclusion of the entire chapter. It is basically a short summary of the entire chapter. Make it clear that you know what you’ve been talking about and how your data helps answer the research questions you’ve been meaning to cover.
Dissertation Data Analysis Methods
If you are reading this, it means you need some data analysis help. Fortunately, our writers are experts when it comes to the discussion chapter of a dissertation, the most important part of your paper. To make sure you write it correctly, you need to first ensure you learn about the various data analysis methods that are available to you. Here is what you can – and should – do during the data analysis phase of the paper:
- Validate the data. This means you need to check for fraud (were all the respondents really interviewed?), screen the respondents to make sure they meet the research criteria, check that the data collection procedures were properly followed, and then verify that the data is complete (did each respondent receive all the questions or not?). Validating the data is no as difficult as you imagine. Just pick several respondents at random and call them or email them to find out if the data is valid.
For example, an outlier can be identified using a scatter plot or a box plot. Points (values) that are beyond an inner fence on either side are mild outliers, while points that are beyond an outer fence are called extreme outliers.
- If you have a large amount of data, you should code it. Group similar data into sets and code them. This will significantly simplify the process of analyzing the data later.
For example, the median is almost always used to separate the lower half from the upper half of a data set, while the percentage can be used to make a graph that emphasizes a small group of values in a large set o data.
ANOVA, for example, is perfect for testing how much two groups differ from one another in the experiment. You can safely use it to find a relationship between the number of smartphones in a family and the size of the family’s savings.
Analyzing qualitative data is a bit different from analyzing quantitative data. However, the process is not entirely different. Here are some methods to analyze qualitative data:
You should first get familiar with the data, carefully review each research question to see which one can be answered by the data you have collected, code or index the resulting data, and then identify all the patterns. The most popular methods of conducting a qualitative data analysis are the grounded theory, the narrative analysis, the content analysis, and the discourse analysis. Each has its strengths and weaknesses, so be very careful which one you choose.
Of course, it goes without saying that you need to become familiar with each of the different methods used to analyze various types of data. Going into detail for each method is not possible in a single blog post. After all, there are entire books written about these methods. However, if you are having any trouble with analyzing the data – or if you don’t know which dissertation data analysis methods suits your data best – you can always ask our dissertation experts. Our customer support department is online 24 hours a day, 7 days a week – even during holidays. We are always here for you!
Tips and Tricks to Write the Analysis Chapter
Did you know that the best way to learn how to write a data analysis chapter is to get a great example of data analysis in research paper? In case you don’t have access to such an example and don’t want to get assistance from our experts, we can still help you. Here are a few very useful tips that should make writing the analysis chapter a lot easier:
- Always start the chapter with a short introductory paragraph that explains the purpose of the chapter. Don’t just assume that your audience knows what a discussion chapter is. Provide them with a brief overview of what you are about to demonstrate.
- When you analyze and discuss the data, keep the literature review in mind. Make as many cross references as possible between your analysis and the literature review. This way, you will demonstrate to the evaluation committee that you know what you’re talking about.
- Never be afraid to provide your point of view on the data you are analyzing. This is why it’s called a data analysis and not a results chapter. Be as critical as possible and make sure you discuss every set of data in detail.
- If you notice any patterns or themes in the data, make sure you acknowledge them and explain them adequately. You should also take note of these patterns in the conclusion at the end of the chapter.
- Do not assume your readers are familiar with jargon. Always provide a clear definition of the terms you are using in your paper. Not doing so can get you penalized. Why risk it?
- Don’t be afraid to discuss both the advantage and the disadvantages you can get from the data. Being biased and trying to ignore the drawbacks of the results will not get you far.
- Always remember to discuss the significance of each set of data. Also, try to explain to your audience how the various elements connect to each other.
- Be as balanced as possible and make sure your judgments are reasonable. Only strong evidence should be used to support your claims and arguments. Weak evidence just shows that you did not do your best to uncover enough information to answer the research question.
- Get dissertation data analysis help whenever you feel like you need it. Don’t leave anything to chance because the outcome of your dissertation depends in large part on the data analysis chapter.
Finally, don’t be afraid to make effective use of any quantitative data analysis software you can get your hands on. We know that many of these tools can be quite expensive, but we can assure you that the investment is a good idea. Many of these tools are of real help when it comes to analyzing huge amounts of data.
Finally, you need to be aware that the data analysis chapter should not be rushed in any way. We do agree that the Results chapter is extremely important, but we consider that the Discussion chapter is equally as important. Why? Because you will be explaining your findings and not just presenting some results. You will have the option to talk about your personal opinions. You are free to unleash your critical thinking and impress the evaluation committee. The data analysis section is where you can really shine.
Also, you need to make sure that this chapter is as interesting as it can be for the reader. Make sure you discuss all the interesting results of your research. Explain peculiar findings. Make correlations and reference other works by established authors in your field. Show your readers that you know that subject extremely well and that you are perfectly capable of conducting a proper analysis no matter how complex the data may be. This way, you can ensure that you get maximum points for the data analysis chapter. If you can’t do a great job, get help ASAP!
Need Some Assistance With Data Analysis?
If you are a university student or a graduate, you may need some cheap help with writing the analysis chapter of your dissertation. Remember, time saving is extremely important because finishing the dissertation on time is mandatory. You should consider our amazing services the moment you notice you are not on track with your dissertation. Also, you should get help from our dissertation writing service in case you can’t do a terrific job writing the data analysis chapter. This is one of the most important chapters of your paper and the supervisor will look closely at it.
Why risk getting penalized when you can get high quality academic writing services from our team of experts? All our writers are PhD degree holders, so they know exactly how to write any chapter of a dissertation the right way. This also means that our professionals work fast. They can get the analysis chapter done for you in no time and bring you back on track. It’s also worth noting that we have access to the best software tools for data analysis. We will bring our knowledge and technical know-how to your project and ensure you get a top grade on your paper. Get in touch with us and let’s discuss the specifics of your project right now!
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Raw Data to Excellence: Master Dissertation Analysis
Discover the secrets of successful dissertation data analysis. Get practical advice and useful insights from experienced experts now!
Have you ever found yourself knee-deep in a dissertation, desperately seeking answers from the data you’ve collected? Or have you ever felt clueless with all the data that you’ve collected but don’t know where to start? Fear not, in this article we are going to discuss a method that helps you come out of this situation and that is Dissertation Data Analysis.
Dissertation data analysis is like uncovering hidden treasures within your research findings. It’s where you roll up your sleeves and explore the data you’ve collected, searching for patterns, connections, and those “a-ha!” moments. Whether you’re crunching numbers, dissecting narratives, or diving into qualitative interviews, data analysis is the key that unlocks the potential of your research.
Dissertation Data Analysis
Dissertation data analysis plays a crucial role in conducting rigorous research and drawing meaningful conclusions. It involves the systematic examination, interpretation, and organization of data collected during the research process. The aim is to identify patterns, trends, and relationships that can provide valuable insights into the research topic.
The first step in dissertation data analysis is to carefully prepare and clean the collected data. This may involve removing any irrelevant or incomplete information, addressing missing data, and ensuring data integrity. Once the data is ready, various statistical and analytical techniques can be applied to extract meaningful information.
Descriptive statistics are commonly used to summarize and describe the main characteristics of the data, such as measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). These statistics help researchers gain an initial understanding of the data and identify any outliers or anomalies.
Furthermore, qualitative data analysis techniques can be employed when dealing with non-numerical data, such as textual data or interviews. This involves systematically organizing, coding, and categorizing qualitative data to identify themes and patterns.
Types of Research
When considering research types in the context of dissertation data analysis, several approaches can be employed:
1. Quantitative Research
This type of research involves the collection and analysis of numerical data. It focuses on generating statistical information and making objective interpretations. Quantitative research often utilizes surveys, experiments, or structured observations to gather data that can be quantified and analyzed using statistical techniques.
2. Qualitative Research
In contrast to quantitative research, qualitative research focuses on exploring and understanding complex phenomena in depth. It involves collecting non-numerical data such as interviews, observations, or textual materials. Qualitative data analysis involves identifying themes, patterns, and interpretations, often using techniques like content analysis or thematic analysis.
3. Mixed-Methods Research
This approach combines both quantitative and qualitative research methods. Researchers employing mixed-methods research collect and analyze both numerical and non-numerical data to gain a comprehensive understanding of the research topic. The integration of quantitative and qualitative data can provide a more nuanced and comprehensive analysis, allowing for triangulation and validation of findings.
Primary vs. Secondary Research
Primary research 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 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
- 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:
- 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.
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 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:
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 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 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 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 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.
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.
Based on your analysis and interpretation, draw well-supported conclusions that directly address your research objectives. Present the key findings in a clear, concise, and logical manner, emphasizing their relevance and contributions to the research field. Discuss any limitations, potential biases, or alternative explanations that may impact the validity of your conclusions.
Validation and Reliability
Evaluate the validity and reliability of your data analysis by considering the rigor of your methods, the consistency of results, and the triangulation of multiple data sources or perspectives if applicable. Engage in critical self-reflection and seek feedback from peers, mentors, or experts to ensure the robustness of your data analysis and conclusions.
In conclusion, dissertation data analysis is an essential component of the research process, allowing researchers to extract meaningful insights and draw valid conclusions from their data. By employing a range of analysis techniques, researchers can explore relationships, identify patterns, and uncover valuable information to address their research objectives.
Turn Your Data Into Easy-To-Understand And Dynamic Stories
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About Sowjanya Pedada
Sowjanya is a passionate writer and an avid reader. She holds MBA in Agribusiness Management and now is working as a content writer. She loves to play with words and hopes to make a difference in the world through her writings. Apart from writing, she is interested in reading fiction novels and doing craftwork. She also loves to travel and explore different cuisines and spend time with her family and friends.
Home » Data Analysis – Process, Methods and Types
Data Analysis – Process, Methods and Types
Table of Contents
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.
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:
This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.
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.
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.
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.
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.
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:
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.
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.
This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.
This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.
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.
This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial 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.
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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Data Analysis in Research: Types & Methods
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 .
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 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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