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Researchers using qualitative methods tend to:
Image from https://www.editage.com/insights/qualitative-quantitative-or-mixed-methods-a-quick-guide-to-choose-the-right-design-for-your-research?refer-type=infographics
Qualitative Research: an operational description
Purpose : explain; gain insight and understanding of phenomena through intensive collection and study of narrative data
Approach: inductive; value-laden/subjective; holistic, process-oriented
Hypotheses: tentative, evolving; based on the particular study
Lit. Review: limited; may not be exhaustive
Setting: naturalistic, when and as much as possible
Sampling : for the purpose; not necessarily representative; for in-depth understanding
Measurement: narrative; ongoing
Design and Method: flexible, specified only generally; based on non-intervention, minimal disturbance, such as historical, ethnographic, or case studies
Data Collection: document collection, participant observation, informal interviews, field notes
Data Analysis: raw data is words/ ongoing; involves synthesis
Data Interpretation: tentative, reviewed on ongoing basis, speculative
Researchers using quantitative methods tend to:
Quantitative research: an operational description
Purpose: explain, predict or control phenomena through focused collection and analysis of numberical data
Approach: deductive; tries to be value-free/has objectives/ is outcome-oriented
Hypotheses : Specific, testable, and stated prior to study
Lit. Review: extensive; may significantly influence a particular study
Setting: controlled to the degree possible
Sampling: uses largest manageable random/randomized sample, to allow generalization of results to larger populations
Measurement: standardized, numberical; "at the end"
Design and Method: Strongly structured, specified in detail in advance; involves intervention, manipulation and control groups; descriptive, correlational, experimental
Data Collection: via instruments, surveys, experiments, semi-structured formal interviews, tests or questionnaires
Data Analysis: raw data is numbers; at end of study, usually statistical
Data Interpretation: formulated at end of study; stated as a degree of certainty
This page on qualitative and quantitative research has been adapted and expanded from a handout by Suzy Westenkirchner. Used with permission.
Images from https://www.editage.com/insights/qualitative-quantitative-or-mixed-methods-a-quick-guide-to-choose-the-right-design-for-your-research?refer-type=infographics.
Preparing for your doctoral dissertation takes serious perseverance. You’ve endured years of studies and professional development to get to this point. After sleepless nights and labor-intensive research, you’re ready to present the culmination of all of your hard work. Even with a strong base knowledge, it can be difficult — even daunting — to decide how you will begin writing.
By taking a wide-lens view of the dissertation research process , you can best assess the work you have ahead of you and any gaps in your current research strategy. Subsequently, you’ll begin to develop a timeline so you can work efficiently and cross that finish line with your degree in hand.
A dissertation is a published piece of research on a novel topic in your chosen field. Students complete a dissertation as part of a doctoral or PhD program. For most students, a dissertation is the first substantive piece of academic research they will write.
Because a dissertation becomes a published piece of academic literature that other academics may cite, students must defend it in front of a board of experts consisting of peers in their field, including professors, their advisor, and other industry experts.
For many students, a dissertation is the first piece of research in a long career full of research. As such, it’s important to choose a topic that’s interesting and engaging.
Dissertations can take on many forms, based on research and methods of presentation in front of a committee board of academics and experts in the field. Here, we’ll focus on the three main types of dissertation research to get you one step closer to earning your doctoral degree.
The first type of dissertation is known as a qualitative dissertation . A qualitative dissertation mirrors the qualitative research that a doctoral candidate would conduct throughout their studies. This type of research relies on non-numbers-based data collected through things like interviews, focus groups and participant observation.
The decision to model your dissertation research according to the qualitative method will depend largely on the data itself that you are collecting. For example, dissertation research in the field of education or psychology may lend itself to a qualitative approach, depending on the essence of research. Within a qualitative dissertation research model, a candidate may pursue one or more of the following:
Although individual approaches may vary, qualitative dissertations usually include certain foundational characteristics. For example, the type of research conducted to develop a qualitative dissertation often follows an emergent design, meaning that the content and research strategy changes over time. Candidates also rely on research paradigms to further strategize how best to collect and relay their findings. These include critical theory, constructivism and interpretivism, to name a few.
Because qualitative researchers integrate non-numerical data, their methods of collection often include unstructured interview, focus groups and participant observations. Of course, researchers still need rubrics from which to assess the quality of their findings, even though they won’t be numbers-based. To do so, they subject the data collected to the following criteria: dependability, transferability and validity.
When it comes time to present their findings, doctoral candidates who produce qualitative dissertation research have several options. Some choose to include case studies, personal findings, narratives, observations and abstracts. Their presentation focuses on theoretical insights based on relevant data points.
Quantitative dissertation research, on the other hand, focuses on the numbers. Candidates employ quantitative research methods to aggregate data that can be easily categorized and analyzed. In addition to traditional statistical analysis, quantitative research also hones specific research strategy based on the type of research questions. Quantitative candidates may also employ theory-driven research, replication-based studies and data-driven dissertations.
When conducting research, some candidates who rely on quantitative measures focus their work on testing existing theories, while others create an original approach. To refine their approach, quantitative researchers focus on positivist or post-positivist research paradigms. Quantitative research designs focus on descriptive, experimental or relationship-based designs, to name a few.
To collect the data itself, researchers focus on questionnaires and surveys, structured interviews and observations, data sets and laboratory-based methods. Then, once it’s time to assess the quality of the data, quantitative researchers measure their results against a set of criteria, including: reliability, internal/external validity and construct validity. Quantitative researchers have options when presenting their findings. Candidates convey their results using graphs, data, tables and analytical statements.
Many PhD candidates also use a hybrid model in which they employ both qualitative and quantitative methods of research. Mixed dissertation research models are fairly new and gaining traction. For a variety of reasons, a mixed-method approach offers candidates both versatility and credibility. It’s a more comprehensive strategy that allows for a wider capture of data with a wide range of presentation optimization.
In the most common cases, candidates will first use quantitative methods to collect and categorize their data. Then, they’ll rely on qualitative methods to analyze that data and draw meaningful conclusions to relay to their committee panel.
With a mixed-method approach, although you’re able to collect and analyze a more broad range of data, you run the risk of widening the scope of your dissertation research so much that you’re not able to reach succinct, sustainable conclusions. This is where it becomes critical to outline your research goals and strategy early on in the dissertation process so that the techniques you use to capture data have been thoroughly examined.
After this overview of application and function, you may still be wondering how to go about choosing a dissertation type that’s right for you and your research proposition. In doing so, you’ll have a couple of things to consider:
It’s important to discern exactly what you hope to get out of your doctoral program . Of course, the presentation of your dissertation is, formally speaking, the pinnacle of your research. However, doctoral candidates must also consider:
To discern which type of dissertation research to choose, you have to take a closer look at your learning style, work ethic and even your personality.
Quantitative research tends to be sequential and patterned-oriented. Steps move in a logical order, so it becomes clear what the next step should be at all times. For most candidates, this makes it easier to devise a timeline and stay on track. It also keeps you from getting overwhelmed by the magnitude of research involved. You’ll be able to assess your progress and make simple adjustments to stay on target.
On the other hand, maybe you know that your research will involve many interviews and focus groups. You anticipate that you’ll have to coordinate participants’ schedules, and this will require some flexibility. Instead of creating a rigid schedule from the get-go, allowing your research to flow in a non-linear fashion may actually help you accomplish tasks more efficiently, albeit out of order. This also allows you the personal versatility of rerouting research strategy as you collect new data that leads you down other paths.
After examining the research you need to conduct, consider more broadly: What type of student and researcher are you? In other words, What motivates you to do your best work?
You’ll need to make sure that your methodology is conducive to the data you’re collecting, and you also need to make sure that it aligns with your work ethic so you set yourself up for success. If jumping from one task to another will cause you extra stress, but planning ahead puts you at ease, a quantitative research method may be best, assuming the type of research allows for this.
The skills you master while working on your dissertation will serve you well beyond the day you earn your degree. Take into account the skills you’d like to develop for your academic and professional future. In addition to the hard skills you will develop in your area of expertise, you’ll also develop soft skills that are transferable to nearly any professional or academic setting. Perhaps you want to hone your ability to strategize a timeline, gather data efficiently or draw clear conclusions about the significance of your data collection.
If you have considerable experience with quantitative analysis, but lack an extensive qualitative research portfolio, now may be your opportunity to explore — as long as you’re willing to put in the legwork to refine your skills or work closely with your mentor to develop a strategy together.
For many doctoral candidates who hope to pursue a professional career in the world of academia, writing your dissertation is a practice in developing general research strategies that can be applied to any academic project.
Candidates who are unsure which dissertation type best suits their research should consider whether they will take a philosophical or theoretical approach or come up with a thesis that addresses a specific problem or idea. Narrowing down this approach can sometimes happen even before the research begins. Other times, candidates begin to refine their methods once the data begins to tell a more concrete story.
Once you’ve chosen which type of dissertation research you’ll pursue, you’ve already crossed the first hurdle. The next hurdle becomes when and where to fit dedicated research time and visits with your mentor into your schedule. The busyness of day-to-day life shouldn’t prevent you from making your academic dream a reality. In fact, search for programs that assist, not impede, your path to higher levels of academic success.
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For quantitative studies (dissertations & theses).
By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021
So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .
The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.
But how’s that different from the discussion chapter?
Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.
Let’s look at an example.
In your results chapter, you may have a plot that shows how respondents to a survey responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.
It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.
Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.
This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.
How do I decide what’s relevant?
At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study . So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.
As a general guide, your results chapter will typically include the following:
We’ll discuss each of these points in more detail in the next section.
Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.
For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.
There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.
The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.
At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point.
Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).
As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.
This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.
The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.
For example:
The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.
Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.
But what if I’m not interested in generalisability?
Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.
Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.
Most commonly, there are two areas you need to pay attention to:
#1: Composite measures
The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure . For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.
Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.
#2: Data shape
The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.
To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.
Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.
For scaled data, this usually includes statistics such as:
A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.
For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.
When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .
Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .
Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .
First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.
There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .
In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.
If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.
The basic process for hypothesis testing is as follows:
Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.
To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.
Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:
If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.
This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...
Thank you. I will try my best to write my results.
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this was great explaination
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31st July 2021
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Imperial County Office of Education. “Qualitative and Quantitative Research.” Accessed April 12, 2014. http://www.icoe.org/webfm_send/1936 .
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There are a number of reasons why you may choose one type of dissertation over another. Some are more academic in nature, whilst others tend to be more personal or practical . Academic justifications are important because the person marking your dissertation will expect to see such academic justifications in your final product. Personal and practical justifications are similarly important, not because these are something that a marker is looking for, but because the dissertation process can be tough. As a result, many of the decisions you make throughout the dissertation process (e.g., the choice of sampling strategy or data analysis techniques) will be influenced by factors such as cost, ease, convenience, and what skills you have or can learn in time. We briefly discuss these considerations below, and explain how they may influence the particular choice of dissertation type; after all, the academic, personal and practical justifications for a quantitative dissertation are different for qualitative or mixed methods dissertations.
You'll almost always been able to find an academic justification for your choice of dissertation, whether qualitative, quantitative or mixed methods. These academic justifications include factors that are generally philosophical or theoretical , or which refer to a particular research problem or idea .
The reasons that act as a justification for your dissertation will often become clear when you decide on the route you will follow within one of these three types of dissertation (i.e., a qualitative, quantitative or mixed methods dissertation). We have chosen not to go into any more detail about such academic justifications now because they are so specific to the route that you choose. However, you'll learn about these justifications in detail in the Quantitative Dissertations part of Lærd Dissertation, where you can choose between one of three routes (i.e., Route #1: Replication-based dissertations , Route #2: Data-driven dissertations , and Route #3: Theory-driven dissertations ).
One of the major challenges of doing a dissertation, especially if you are an undergraduate, is uncertainty : Can I plan out the dissertation process from the start? Will I be able to finish on time? Can I get my head around the research paradigms and research designs that guide my choice of dissertation (i.e., qualitative, quantitative or mixed methods)? Do I have the right skills to analyse qualitative or quantitative data? What software packages will I have to learn to do this, if any?
Dissertations are often worth a good proportion of your final year mark, if not the grade of your entire degree, so how tolerant you are to uncertainty matters. On this basis, think about the following:
Am I a bit of a planning freak?
If you are, you may prefer to take on a quantitative dissertation rather than qualitative dissertation . One of the broad advantages of quantitative dissertations is that they tend to be more sequential in nature, such that you can often set out, right from the start of the dissertation process, the various stages you will need to go through in order to answer your research questions or hypotheses. This is because in quantitative dissertations, it is far less common to change major components of the research process (e.g., your research questions or hypotheses, or research design), after you've decided what these are going to be, which you typically do at the very start of the dissertation process. Not only does this make it possible to plan what you will be doing from month-to-month, but it also reduces the uncertainty through the dissertation process. You'll see in the Quantitative Dissertations section how we have been able to provide comprehensive, step-by-step guides to walk you through the dissertation process, as well as chapter-by-chapter guides to show you how to write up.
By contrast, qualitative dissertations are not sequential, but reflexive and emergent in nature, which means that what you planned to do at the start of the dissertation process is more likely to have to be modified. Such modification takes place because one of the tenets of qualitative research is flexibility to allow for things that are learnt during the research process to be integrated (e.g., initial interviews may suggest that you need to add or omit a particular research question). Whilst such changes may only happen a few times, and may be minor in many cases, they do add an element of uncertainty. At a basic level, imagine the difference between knowing how many participants you need to have to fill in your questionnaire, and therefore, roughly how long this will take (i.e., a quantitative dissertation ), as opposed to being quite uncertain how many interviews you need to arrange to collect sufficient data to answer your research questions (i.e., a qualitative dissertation ). Whilst these might sound like small points, it can mean having to put aside another month to collect sufficient interview data in a qualitative dissertation compared with a quantitative one.
What are my strong points?
Whilst qualitative and quantitative dissertations are more than just the use of qualitative or qualitative research methods and data, there is no escaping the fact that qualitative dissertations use qualitative research methods and collect qualitative data (i.e., from unstructured interviews, focus groups, participant observation, etc.), and quantitative dissertations use quantitative research methods, collecting quantitative data (i.e., from data sets, surveys, structured interviews, structured observation, etc.). If you've spent your degree working with quantitative research designs (e.g., randomized control trials, pre- and post-test designs, relationship-based designs, etc.), as well as quantitative research methods and data, the logical choice might be to take on a quantitative dissertation . The same can be said for qualitative dissertations , since in both cases, the learning curve will be a lot higher if you're completely unaccustomed to the components that make up these different types of dissertation.
What am I interested in?
At the end of the day, the dissertation process is a long one, lasting around 6 months (in most cases). If you're not interested in experimental research, you prefer working with more unstructured research methods (e.g., depth interviews, unstructured observation, etc.), or you hate quantitative data analysis (i.e., any form of statistics), taking on a quantitative dissertation may not be a good idea. The same can be said for qualitative dissertations , which require a lot of perseverance and dedication, especially during the data collection process, which can be time consuming and requires a lot of toeing-and-froing. Choose a type of dissertation that is going to keep you interested, and which you will not find boring or demoralizing.
If you're taking on a qualitative dissertation , we wish you good luck (although you will still be able to learn a little about appropriate research methods and sampling techniques in the Fundamentals section of Lærd Dissertation). However, if you're taking on a quantitative dissertation (or a mixed methods dissertation that is mainly quantitative in its focus), go to the Quantitative Dissertations part of Lærd Dissertation now. We have extensive guides to help you through the process.
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When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
A Guide to Quantitative and Qualitative Dissertation Research (Second Edition) March 24, 2017. James P. Sampson, Jr., Ph.D. 1114 West Call Street, Suite 1100 College of Education Florida State University Tallahassee, FL 32306-4450. [email protected].
Published on 4 April 2022 by Raimo Streefkerk. Revised on 8 May 2023. When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research is expressed in numbers and graphs.
Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes.2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ...
This is an important cornerstone of the scientific method. Quantitative research can be pretty fast. The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average.
How to choose a research methodology. To choose the right research methodology for your dissertation or thesis, you need to consider three important factors. Based on these three factors, you can decide on your overarching approach - qualitative, quantitative or mixed methods. Once you've made that decision, you can flesh out the finer ...
When to Use Qualitative and Quantitative Research Model? The research title, research questions, hypothesis, objectives, and study area generally determine the dissertation's best research method. If the primary aim of your research is to test a hypothesis, validate an existing theory or perhaps measure some variables, then the quantitative research model will be the more appropriate choice ...
Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. ... 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.
The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language. Quantitative research collects numerical ...
Revised on September 5, 2024. Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which ...
Dissertation word counts vary widely across different fields, institutions, and levels of education: An undergraduate dissertation is typically 8,000-15,000 words; ... Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw ...
Quantitative Research (an operational definition) Quantitative research: an operational description. Purpose: explain, predict or control phenomena through focused collection and analysis of numberical data. Approach: deductive; tries to be value-free/has objectives/ is outcome-oriented. Hypotheses: Specific, testable, and stated prior to study.
Here, we'll focus on the three main types of dissertation research to get you one step closer to earning your doctoral degree. 1. Qualitative. The first type of dissertation is known as a qualitative dissertation. A qualitative dissertation mirrors the qualitative research that a doctoral candidate would conduct throughout their studies.
Types of dissertation. Whilst we describe the main characteristics of qualitative, quantitative and mixed methods dissertations, the Lærd Dissertation site currently focuses on helping guide you through quantitative dissertations, whether you are a student of the social sciences, psychology, education or business, or are studying medical or biological sciences, sports science, or another ...
Qualitative research. This method is used to understand thoughts, concepts, or experiences of people via interviews, focus groups, case studies, discourse analysis, and literature review. It is basically a survey done to gather people thoughts and experience. Let us look at the techniques in qualitative research.
How To Analyze Qualitative vs. Quantitative Data. Another of the similarities of qualitative and quantitative research is that both look for patterns in the data they collect that point to a relationship between elements. Both qualitative and quantitative data are instrumental in supporting existing theories and developing new ones.
This is thanks in large part to your strategic research design. As you prepare for your quantitative dissertation research, you'll need to think about structuring your research design. There are several types of quantitative research designs, such as the experimental, comparative or predictive correlational designs.
The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you've found in terms of the quantitative data you've collected. It presents the data using a clear text narrative, supported by tables, graphs and charts.
Whether you need help determining which methodology will work best with your study or looking for in-depth review and analysis of your methods section, our dissertation experts are happy to assist you. Give us a call at any time to discuss your methodology in detail. Call 857 600 2241. [email protected].
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Next steps. If you're taking on a qualitative dissertation, we wish you good luck (although you will still be able to learn a little about appropriate research methods and sampling techniques in the Fundamentals section of Lærd Dissertation). However, if you're taking on a quantitative dissertation (or a mixed methods dissertation that is mainly quantitative in its focus), go to the ...
qualitative research professor. I was positive that I would design a quantitative research study but the qualitative courses in the program highlighted the merits of qualitative research. Dr. Cozza and Ms. Rosaria Cimino, thanks for the advisement support. To all the Ed.D. candidates that I encountered on my academic journey, especially my