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Qualitative Data Analysis Methods for Your Dissertation
At this point in your doctoral degree program, you have likely already designed the qualitative research study that is the backbone of your dissertation. The next step, after obtaining Institutional Review Board (IRB) approval, is to collect and analyze the data to find patterns, connections and relationships relevant to the study’s objectives.
Data analysis is perhaps the most important part of the research process. Once you have collected your high-quality data, as a researcher you must take all the information and properly analyze it to gain relevant insights.
Collecting Qualitative Data
Qualitative data collection is exploratory and focused on discovery. It involves collecting a robust data set so that one can then do an in-depth analysis focused on discovering insights, reasoning and motivations. Since qualitative data focuses on searching for understanding, meaning and social patterns, researchers prefer to use data collection methods that can reveal those patterns. Some of the various qualitative data collection methods include:
- One-on-one interviews
- Focus groups
- Record-keeping
- Observations
- Visual records
Qualitative Data Analysis
Qualitative data is collected through in-person interactions and recorded in the form of words, observations and images. Compared to quantitative research, which collects clear numerical data to draw conclusions, qualitative research is most often used to explore the how and why of people’s emotions, behaviors and perceptions.
Interpreting and analyzing qualitative data can be challenging and time-consuming. It is a process that usually involves reading through many pages of text-based and visual data and notes, and sometimes listening to hours of audio. Therefore, the analysis process in qualitative research typically begins as soon as the data becomes available.
Analyzing your data is vital to the research process, especially since you have likely spent a lot of time and money collecting it. In an effort to conduct the most beneficial analysis, researchers should first understand the two main approaches to qualitative data analysis: 1
1. Inductive Approach
This is a thorough and time-consuming approach to qualitative data analysis with no predetermined rules or structure. Researchers may use this approach in order to identify emerging patterns to reflect what can be discovered.
2. Deductive Approach
In this approach, qualitative data is analyzed based on a structure that is predetermined by the researcher, who can then develop and use questions as a guide for analyzing the data. This approach is preferred when the researcher wants to examine particular categories of information in relation to previous studies or theory. It can be combined with an inductive approach.
Preparing Data for Analysis
Because analysis in qualitative research begins as soon as the data is received, data preparation and analysis occur at the same time, following these steps:
1. Become Acquainted With the Data
Most qualitative data is in a written narrative format. Thus, the researcher will read the data multiple times to become familiar with it and begin identifying similarities and patterns.
2. Review Research Objectives
In a qualitative study, the researcher reviews the study’s objectives and gleans questions or hypotheses that can be answered through the collected data. This is the opposite of quantitative research, which collects data to answer pre-determined questions or hypotheses.
3. Creating Data Structure
Variation is common in qualitative data because this form of research elicits a range of information that is typical in discovery-based research. As data is collected, qualitative researchers will often identify and develop codes to the data that helps categorize and structure the mass of information.
4. Discover Patterns and Connections
Once the qualitative data is collected and coded, researchers will begin identifying themes. They can do this by looking for patterns in the responses to questions and analyze how these answer the core questions driving the study.
Qualitative Data Analysis Methods
There are several methods available for analyzing qualitative research data. The method you choose will depend on your research objectives and questions. These are the most common qualitative data analysis methods to help you complete your dissertation: 2
- Content analysis: This method is used to analyze documented information from texts, email, media and tangible items. Researchers use this method to analyze responses from large datasets, often thousands of pages of publicly available data, as well as from interviewees.
- Thematic analysis: This method focuses on a ground-up or inductive approach to discovering patterns in the data though a series of coding exercises to develop themes.
- Narrative analysis: This method analyzes story-based content from sources such as interviews. Researchers use this method to find stories and create master narratives based on the data to answer their research questions.
- Phenomenological analysis: This method analyzes the way that participants in the study describe their “lived experiences” using a specific approach that focuses on what is meaningful to the participant.
- Grounded theory: This method uses qualitative data to describe a particular phenomenon and to develop a theory based on extensive observations, interviews and other data collection techniques.
There are many qualitative data analysis methods to choose from, but these are the most common methods that will help you as you finalize your dissertation.
Applying Data Analysis to Your Dissertation
When done appropriately, data analysis can provide a solid foundation for the results and discussion sections of your doctoral dissertation. Therefore, it is imperative to conduct thorough and careful data analysis in order to derive meaningful and insightful findings.
If you are struggling with any portion of data analysis while working on your dissertation, speak with your university advisor or professor; they will be happy to assist you further.
Start your doctoral journey at Grand Canyon University. The College of Doctoral Studies at GCU offers a variety of qualitative programs to support your research goals. To learn more about GCU’s doctoral programs, click on Request More Info at the top of this page.
Retrieved from:
1 QuestionPro, Qualitative Data – Definitions, Types, Analysis and Examples in May 2021
2 GradCoach, Qualitative Data Analysis Methods 101 in May 2021
The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.
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- GETTING STARTED
- Introduction
- FUNDAMENTALS
Getting to the main article
Choosing your route
Setting research questions/ hypotheses
Assessment point
Building the theoretical case
Setting your research strategy
Data collection
Data analysis
Data analysis techniques
In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.
The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.
We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).
At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:
REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process
This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.
REASON B It takes time to get your head around data analysis
When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.
Final thoughts...
Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .
11 Tips For Writing a Dissertation Data Analysis if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[728,90],'analyticsfordecisions_com-box-3','ezslot_3',142,'0','0'])};__ez_fad_position('div-gpt-ad-analyticsfordecisions_com-box-3-0');
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?
Data analysis tools, 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.
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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 .
2. Relevance of Collected Data
3. data analysis, 4. qualitative data analysis, 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.
6. Data Presentation Tools
7. include appendix or addendum.
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
9. discussing data.
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
11. connection with literature review, the role of data analytics at the senior management level, the decision-making model explained (in plain terms), 13 reasons why data is important in decision making, wrapping up.
As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.
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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.
Final Considerations
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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Research Methods for Dissertation
Published by Carmen Troy at August 13th, 2021 , Revised On January 9, 2023
Introduction
What are the different research methods for the dissertation, and which one should I use?
Choosing the right research method for a dissertation is a grinding and perplexing aspect of the dissertation research process. A well-defined research methodology helps you conduct your research in the right direction, validates the results of your research, and makes sure that the study you’re conducting answers the set research questions .
The research title, research questions, hypothesis , objectives, and study area generally determine the best research method in the dissertation.
This post’s primary purpose is to highlight what these different types of research methods involve and how you should decide which type of research fits the bill. As you read through this article, think about which one of these research methods will be the most appropriate for your research.
The practical, personal, and academic reasons for choosing any particular method of research are also analyzed. You will find our explanation of experimental, descriptive, historical, quantitative, qualitative, and mixed research methods useful regardless of your field of study.
While choosing the right method of research for your own research, you need to:
- Understand the difference between research methods and methodology .
- Think about your research topic, research questions, and research objectives to make an intelligent decision.
- Know about various types of research methods so that you can choose the most suitable and convenient method as per your research requirements.
Research Methodology Vs. Research Methods
A well-defined research methodology helps you conduct your research in the right direction, validates the results of your research, and makes sure that the study you are conducting answers the set research questions .
Research methods are the techniques and procedures used for conducting research. Choosing the right research method for your writing is an important aspect of the research process .
You need to either collect data or talk to the people while conducting any research. The research methods can be classified based on this distinction.
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Types of Research Methods
Research methods are broadly divided into six main categories.
Experimental Research Methods
Experimental research includes the experiments conducted in the laboratory or observation under controlled conditions. Researchers try to study human behavior by performing various experiments. Experiments can vary from personal and informal natural comparisons. It includes three types of variables;
- Independent variable
- Dependent variable
- Controlled variable
Types of Experimental Methods
Laboratory experiments
The experiments were conducted in the laboratory. Researchers have control over the variables of the experiment.
Field experiment
The experiments were conducted in the open field and environment of the participants by incorporating a few artificial changes. Researchers do not have control over variables under measurement. Participants know that they are taking part in the experiment.
Natural experiments
The experiment is conducted in the natural environment of the participants. The participants are generally not informed about the experiment being conducted on them.
Example : Estimating the health condition of the population.
Quasi-experiments
A quasi-experiment is an experiment that takes advantage of natural occurrences. Researchers cannot assign random participants to groups.
Example: Comparing the academic performance of the two schools.
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- Find out by hiring an expert from Research Prospect today!
- Despite how challenging the subject may be, we are here to help you.
Descriptive Research Methods
Descriptive research aims at collecting the information to answer the current affairs. It follows the Ex post facto research, which predicts the possible reasons behind the situation that has already occurred. It aims to answer questions like how, what, when, where, and what rather than ‘why.’
Historical Research Methods
In historical research , an investigator collects, analyzes the information to understand, describe, and explain the events that occurred in the past. Researchers try to find out what happened exactly during a certain period of time as accurately and as closely as possible. It does not allow any manipulation or control of variables.
Quantitative Research Methods
Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.
Quantitative research isn’t simply based on statistical analysis or quantitative techniques but rather uses a certain approach to theory to address research hypotheses or research questions, establish an appropriate research methodology, and draw findings & conclusions .
Some most commonly employed quantitative research strategies include data-driven dissertations, theory-driven studies, and reflection-driven research. Regardless of the chosen approach, there are some common quantitative research features as listed below.
- Quantitative research is based on testing or building on existing theories proposed by other researchers whilst taking a reflective or extensive route.
- Quantitative research aims to test the research hypothesis or answer established research questions.
- It is primarily justified by positivist or post-positivist research paradigms.
- The research design can be relationship-based, quasi-experimental, experimental, or descriptive.
- It draws on a small sample to make generalizations to a wider population using probability sampling techniques.
- Quantitative data is gathered according to the established research questions and using research vehicles such as structured observation, structured interviews, surveys, questionnaires, and laboratory results.
- The researcher uses statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or members of the dissertation committee might find it easier to verify your study results with numbers and statistical analysis.
- The accuracy of the study results is based on external and internal validity and the authenticity of the data used.
- Quantitative research answers research questions or tests the hypothesis using charts, graphs, tables, data, and statements.
- It underpins research questions or hypotheses and findings to make conclusions.
- The researcher can provide recommendations for future research and expand or test existing theories.
Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?
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Qualitative Research Methods
It is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behaviour from an informative perspective. It aims at obtaining in-depth details of the problem.
As the term suggests, qualitative research is based on qualitative research methods, including participants’ observations, focus groups, and unstructured interviews.
Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the research process , how research questions are set up, how existing theories are built upon, what research methods are employed, and how the findings are unveiled to the readers.
You may adopt conventional methods, including phenomenological research, narrative-based research, grounded theory research, ethnographies , case studies , and auto-ethnographies.
Again, regardless of the chosen approach to qualitative research, your dissertation will have unique key features as listed below.
- The research questions that you aim to answer will expand or even change as the dissertation writing process continues. This aspect of the research is typically known as an emergent design where the research objectives evolve with time.
- Qualitative research may use existing theories to cultivate new theoretical understandings or fall back on existing theories to support the research process. However, the original goal of testing a certain theoretical understanding remains the same.
- It can be based on various research models, such as critical theory, constructivism, and interpretivism.
- The chosen research design largely influences the analysis and discussion of results and the choices you make. Research design depends on the adopted research path: phenomenological research, narrative-based research, grounded theory-based research, ethnography, case study-based research, or auto-ethnography.
- Qualitative research answers research questions with theoretical sampling, where data gathered from an organization or people are studied.
- It involves various research methods to gather qualitative data from participants belonging to the field of study. As indicated previously, some of the most notable qualitative research methods include participant observation, focus groups, and unstructured interviews .
- It incorporates an inductive process where the researcher analyses and understands the data through his own eyes and judgments to identify concepts and themes that comprehensively depict the researched material.
- The key quality characteristics of qualitative research are transferability, conformity, confirmability, and reliability.
- Results and discussions are largely based on narratives, case study and personal experiences, which help detect inconsistencies, observations, processes, and ideas.s
- Qualitative research discusses theoretical concepts obtained from the results whilst taking research questions and/or hypotheses to draw general conclusions .
Now that you know the unique differences between quantitative and qualitative research methods, you may want to learn a bit about primary and secondary research methods.
Here is an article that will help you distinguish between primary and secondary research and decide whether you need to use quantitative and/or qualitative primary research methods in your dissertation.
Alternatively, you can base your dissertation on secondary research, which is descriptive and explanatory in essence.
Types of Qualitative Research Methods
Action research
Action research aims at finding an immediate solution to a problem. The researchers can also act as the participants of the research. It is used in the educational field.
A case study includes data collection from multiple sources over time. It is widely used in social sciences to study the underlying information, organization, community, or event. It does not provide any solution to the problem. Researchers cannot act as the participants of the research.
Ethnography
In this type of research, the researcher examines the people in their natural environment. Ethnographers spend time with people to study people and their culture closely. They can consult the literature before conducting the study.
Mixed Methods of Research
When you combine quantitative and qualitative methods of research, the resulting approach becomes mixed methods of research.
Over the last few decades, much of the research in academia has been conducted using mixed methods because of the greater legitimacy this particular technique has gained for several reasons including the feeling that combining the two types of research can provide holistic and more dependable results.
Here is what mixed methods of research involve:
- Interpreting and investigating the information gathered through quantitative and qualitative techniques.
- There could be more than one stage of research. Depending on the research topic, occasionally it would be more appropriate to perform qualitative research in the first stage to figure out and investigate a problem to unveil key themes; and conduct quantitative research in stage two of the process for measuring relationships between the themes.
Note: However, this method has one prominent limitation, which is, as previously mentioned, combining qualitative and quantitative research can be difficult because they both are different in terms of design and approach. In many ways, they are contrasting styles of research, and so care must be exercised when basing your dissertation on mixed methods of research.
When choosing a research method for your own dissertation, it would make sense to carefully think about your research topic , research questions , and research objectives to make an intelligent decision in terms of the philosophy of research design .
Dissertations based on mixed methods of research can be the hardest to tackle even for PhD students.
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FAQs About Research Methods for Dissertations
What is the difference between research methodology and research methods.
Research methodology helps you conduct your research in the right direction, validates the results of your research and makes sure that the study you are conducting answers the set research questions.
Research methods are the techniques and procedures used for conducting research. Choosing the right research method for your writing is an important aspect of the research process.
What are the types of research methods?
The types of research methods include:
- Experimental research methods.
- Descriptive research methods
- Historical Research methods
What is a quantitative research method?
Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.
What is a qualitative research method?
It is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.
What is meant by mixed methods research?
Mixed methods of research involve:
- There could be more than one stage of research. Depending on the research topic, occasionally, it would be more appropriate to perform qualitative research in the first stage to figure out and investigate a problem to unveil key themes; and conduct quantitative research in stage two of the process for measuring relationships between the themes.
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Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication.
Ethnography is a type of research where a researcher observes the people in their natural environment. Here is all you need to know about ethnography.
What are the different types of research you can use in your dissertation? Here are some guidelines to help you choose a research strategy that would make your research more credible.
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Data Analysis
Methodology chapter of your dissertation should include discussions about the methods of data analysis. You have to explain in a brief manner how you are going to analyze the primary data you will collect employing the methods explained in this chapter.
There are differences between qualitative data analysis and quantitative data analysis . In qualitative researches using interviews, focus groups, experiments etc. data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives.
Data analysis for quantitative studies, on the other hand, involves critical analysis and interpretation of figures and numbers, and attempts to find rationale behind the emergence of main findings. Comparisons of primary research findings to the findings of the literature review are critically important for both types of studies – qualitative and quantitative.
Data analysis methods in the absence of primary data collection can involve discussing common patterns, as well as, controversies within secondary data directly related to the research area.
John Dudovskiy
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These are the most common qualitative data analysis methods to help you complete your dissertation: 2 Content analysis: This method is used to analyze documented information from texts, email, media and tangible items. Thematic analysis: This method focuses on a ground-up or inductive approach to ...
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.
The data analysis methods described here are based on basic content analysis as described by Elo and Kyngäs 4 and Graneheim and Lundman, 5 and the integrative review as described by Whittemore and Knafl, 6 but modified to be applicable to analysing the results of published studies, rather than empirical data. The methods described here are inductive, that is, they do not describe how to use a pre-existing model or theory to analyse data, but instead describe how to find patterns and answers ...
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.
The researcher uses statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or members of the dissertation committee might find it easier to verify your study results with numbers and statistical 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.